WEBVTT - Bloomberg Businessweek Weekend: December 27th, 2024 0:00:00.080 --> 0:00:06.760 Bloomberg Audio Studios, Podcasts, radio news. 0:00:08.360 --> 0:00:12.680 This is Bloomberg Business Week inside from the reporters and 0:00:12.880 --> 0:00:16.440 editors who bring you America's most trusted business magazine, plus 0:00:16.520 --> 0:00:20.680 global business, finance and tech news. The Bloomberg Business Week 0:00:20.720 --> 0:00:25.640 Podcast with Carol Messer and Tim Stenebeck from Bloomberg Radio. 0:00:26.960 --> 0:00:29.280 Hi, everyone, Welcome to a holiday edition of our Bloomberg 0:00:29.280 --> 0:00:32.559 Business Wee Weekend podcast. We hope you had a wonderful 0:00:32.640 --> 0:00:35.760 Christmas and are having a happy Hanukkah and Kwanza. And 0:00:35.800 --> 0:00:38.400 in the spirit of the holiday season, amid the playing 0:00:38.400 --> 0:00:41.240 in the snow or basket in the sun, spending time 0:00:41.280 --> 0:00:44.680 with loved ones, friends, family, you may also find yourself 0:00:44.720 --> 0:00:46.360 with a little bit of time on your hands. 0:00:46.560 --> 0:00:49.040 With that in mind, here's our team's winter reading list, 0:00:49.120 --> 0:00:51.839 courtesy of authors and books featured on a Bloomberg Business 0:00:51.840 --> 0:00:55.360 Week just over the last six months. Everything from America's 0:00:55.400 --> 0:00:59.200 government teacher to the hidden intricacies of zoning, to the 0:00:59.280 --> 0:01:01.720 journalists who took a journey into the heart of American 0:01:01.760 --> 0:01:04.720 democracy and the Mac and Cheese millionaire. 0:01:04.800 --> 0:01:07.600 Yeah, miyamyelm love that we begin with our team's pick. 0:01:07.680 --> 0:01:09.880 It's something we cannot seem to stop talking about this 0:01:10.000 --> 0:01:14.360 year the AI revolution. We've recently dedicated an entire weekend 0:01:14.440 --> 0:01:18.120 podcast and broadcast to artificial intelligence, covering everything from big 0:01:18.160 --> 0:01:21.200 tech's massive gains this year and Part two investments in 0:01:21.400 --> 0:01:24.720 artificial intelligence, to the large sums of energy needed to 0:01:24.760 --> 0:01:28.759 power the data centers doing all the computing required by AI. 0:01:29.200 --> 0:01:32.360 As investors reward companies use of AI, others are ringing 0:01:32.360 --> 0:01:35.760 alarm bells about getting the tech under control before it 0:01:35.880 --> 0:01:39.840 controls US. Doctor Terrence Sanowski is Francis Crickchair at the 0:01:39.880 --> 0:01:43.760 Salk Institute for Biological Studies and Distinguished Professor at the 0:01:43.800 --> 0:01:47.200 University of California at San Diego. He's also President of 0:01:47.240 --> 0:01:51.400 the Neural Information Processing Systems Foundation. His dual expertise in 0:01:51.480 --> 0:01:54.320 AI and neuroscience led him to write the book Chat, 0:01:54.360 --> 0:01:58.400 GPT and the Future of AI, The Deep Language Revolution. 0:01:58.840 --> 0:02:02.760 I think that what's happening right now is really unbelievable 0:02:02.840 --> 0:02:06.920 in terms of the breath and the depth and the excitement, 0:02:07.040 --> 0:02:10.720 and so I was there at the beginning. Jeffrey Hinton 0:02:10.720 --> 0:02:14.400 and I collaborated in the nineteen eighties all the learning 0:02:14.440 --> 0:02:18.160 algorithms that are being used today for these large language 0:02:18.200 --> 0:02:22.919 models and deep learning were developed by US back in 0:02:22.960 --> 0:02:26.160 that era, and of course what we didn't have back 0:02:26.160 --> 0:02:29.680 then were computers that were fast enough that could scale 0:02:29.760 --> 0:02:34.000 up these models to be be able to solve these 0:02:34.080 --> 0:02:36.360 very difficult problems in artificial intelligence. 0:02:36.680 --> 0:02:38.359 So I want to ask you, how did you think 0:02:38.360 --> 0:02:41.760 about neural networks in large language models in the nineteen eighties, 0:02:41.800 --> 0:02:43.080 how do you think about them today. 0:02:43.680 --> 0:02:48.320 We actually had a premonition that these large language models 0:02:48.560 --> 0:02:51.639 were really good at language, and that was a particular project, 0:02:51.720 --> 0:02:54.600 a summer project for a gratitude in my lap called 0:02:54.720 --> 0:02:58.320 net talk, which was trained on a dictionary to be 0:02:58.320 --> 0:03:00.960 able to pronounce English tesch, you know, if you give 0:03:00.960 --> 0:03:03.760 it an article from the Wall Street Journal and they 0:03:03.760 --> 0:03:06.840 would pronounce it in an understandable way. And this in 0:03:07.040 --> 0:03:11.200 linguistics is a very difficult problem because English is so irregular. 0:03:11.520 --> 0:03:16.239 There are a lot of regularities, but you also have irregularities, 0:03:16.280 --> 0:03:19.600 and then you have rules for the irregularities. But it 0:03:19.680 --> 0:03:22.160 really was amazing that a small, tiny network with just 0:03:22.200 --> 0:03:25.600 a few hundred units and tens of thousands of weights, 0:03:25.680 --> 0:03:29.160 the parameters, the connections between the units could do that. 0:03:29.280 --> 0:03:33.480 It was like an amazing compression of complexity, and now 0:03:33.480 --> 0:03:37.360 we know that these large language models, the deep learning networks, 0:03:37.960 --> 0:03:41.440 they love language and they are capable of things that 0:03:41.440 --> 0:03:42.680 we never could have imagined. 0:03:42.960 --> 0:03:45.480 That's really what I wanted to talk about the idea 0:03:45.480 --> 0:03:50.000 of super intelligent AI. What are we not thinking about? 0:03:50.120 --> 0:03:52.160 What's the threat out there? 0:03:52.480 --> 0:03:55.640 My good friend Jeff is very concerned, and I think 0:03:55.680 --> 0:03:58.960 he's one of the smartest people I've ever met. And 0:03:59.240 --> 0:04:03.640 if he's worried about it, then there's some as a concern. However, 0:04:04.080 --> 0:04:08.360 I think that even if you're concerned, it's very difficult 0:04:08.760 --> 0:04:11.360 to know when that's going to happen, if it ever 0:04:11.440 --> 0:04:15.000 will happen. And there are super forecasters out there, and 0:04:15.000 --> 0:04:18.440 this is from the Economist magazine, who are much better 0:04:18.560 --> 0:04:22.520 at people who are experts at predicting you know, if 0:04:22.560 --> 0:04:26.200 and when there may be a catastrophic or existential threat, 0:04:26.680 --> 0:04:30.039 and it turns out that in fact, they're not as 0:04:30.920 --> 0:04:39.840 concerned as the experts in AI. I'm happy that someone 0:04:39.960 --> 0:04:43.599 is thinking about the worst case outcome, because if not, 0:04:43.800 --> 0:04:45.640 then if it ever happens, we're in trouble. 0:04:46.200 --> 0:04:48.080 But paint that picture for us, because I think a 0:04:48.080 --> 0:04:51.000 lot of people are worried about doomsday scenarios here. And 0:04:51.320 --> 0:04:55.080 if Jeffrey Hinton is worried about that stuff, I mean, 0:04:55.920 --> 0:04:57.640 should we we should be worried about it. 0:04:57.680 --> 0:05:01.640 You're saying, I think that we should could be cautious, 0:05:02.200 --> 0:05:05.400 that is to say, we we should be constantly thinking 0:05:05.520 --> 0:05:08.120 along the lines that Jeff is in terms of what 0:05:08.200 --> 0:05:11.680 could possibly happen, and you know, be cautious and put 0:05:11.680 --> 0:05:14.000 in precautions so that it can't happen. 0:05:14.320 --> 0:05:17.440 What I'm really concerned about are the unintended consequences, things 0:05:17.480 --> 0:05:20.800 that you cannot predict. Something may happen that you know, 0:05:20.880 --> 0:05:22.119 no one thought of, even Jeff. 0:05:22.320 --> 0:05:24.760 Yeah, and like you know, we have learned certainly, right, 0:05:24.800 --> 0:05:29.839 great financial crisis pandemic like the un the unthinkable can 0:05:29.960 --> 0:05:32.320 happen and you throw technology into it and you just 0:05:32.400 --> 0:05:36.479 kind of don't know where it's going to go exactly. Okay, 0:05:36.480 --> 0:05:37.400 So now I'm terrified. 0:05:37.480 --> 0:05:38.760 Okay. 0:05:40.480 --> 0:05:42.680 You know when you talk to people, do you say, wait, 0:05:42.720 --> 0:05:45.000 this is really going to be net a good thing. 0:05:45.440 --> 0:05:50.280 Look, all the technologies have good and bad consequences, and 0:05:50.440 --> 0:05:53.240 you try to mitigate the bad and you have to 0:05:53.480 --> 0:05:56.839 balance them, you know. Yeah, and right now it looks 0:05:56.880 --> 0:05:58.840 like the good is way way ahead of the bad 0:05:59.000 --> 0:06:01.240 in terms of the act that may have on us 0:06:01.520 --> 0:06:05.360 and society and businesses. But you know, like I say, 0:06:05.400 --> 0:06:07.880 we have to be careful because we don't really know 0:06:07.880 --> 0:06:08.559 where it's heading. 0:06:09.200 --> 0:06:11.279 I got to ask you because I am still trying 0:06:11.279 --> 0:06:13.440 to understand, and I get worried that we throw these 0:06:13.480 --> 0:06:16.960 words around, certainly not you, but we as we try 0:06:16.960 --> 0:06:22.040 to understand this, not having full comprehension of what artificial intelligence, 0:06:22.120 --> 0:06:24.560 the large language models that we're talking about today, where 0:06:24.560 --> 0:06:28.160 it takes us. Is it as subtle at evolving in 0:06:28.200 --> 0:06:31.400 life changing as the Internet was for us. 0:06:31.920 --> 0:06:36.800 So this is something that is emerging. And I have 0:06:37.240 --> 0:06:39.760 since the book was sent to press in the summer, 0:06:39.839 --> 0:06:42.800 I have a sub stack where I have tried to 0:06:42.880 --> 0:06:45.080 fill in with, you know, the new things that are happening. 0:06:45.120 --> 0:06:51.960 And I'm preparing something a twelfth version the blog on 0:06:52.680 --> 0:06:57.400 the question of whether AI is overhyped or underhyped, And 0:06:58.400 --> 0:07:01.000 you know, I've thought a lot about this, and you know, 0:07:01.080 --> 0:07:04.200 I think that it depends on the timescale. I think 0:07:04.240 --> 0:07:07.520 that on the short timescale it is overhyped. There's no 0:07:07.560 --> 0:07:09.840 doubt about it. There's just so much out there. I mean, 0:07:09.960 --> 0:07:13.600 every day, the newspapers are filled with AI and your program. 0:07:14.080 --> 0:07:16.680 But I think in the long run it's actually under hyped. 0:07:16.760 --> 0:07:19.800 I think the real change in the Internet, for example, 0:07:20.240 --> 0:07:24.200 didn't occur within the first ten years. It was much later. Again, 0:07:24.320 --> 0:07:29.480 unintended applications that marriage that you know, have enormous impact 0:07:29.600 --> 0:07:32.120 on our lives, like social media. So I think the 0:07:32.160 --> 0:07:33.680 same thing's going to happen with AI. 0:07:34.360 --> 0:07:39.120 But is it is it different like anternet is not 0:07:39.640 --> 0:07:42.120 I wanted to say comfortable, but it's not because there's 0:07:42.160 --> 0:07:45.840 some really bad things that happen and we know that, right, 0:07:45.880 --> 0:07:47.720 and that's the battle we have with social media. And 0:07:47.720 --> 0:07:49.440 we want to talk to you about kind of regulatory 0:07:49.480 --> 0:07:51.960 oversight of AI in a moment. But I just I'm 0:07:52.000 --> 0:07:54.480 just trying to understand. You know, it does feel so 0:07:54.560 --> 0:07:57.400 seamless and just such a part of everything we do. 0:07:58.280 --> 0:08:01.360 But it hasn't necessarily replace a ton of jobs. It's 0:08:01.400 --> 0:08:04.240 created jobs, it's replaced some jobs. I guess you could say, 0:08:05.040 --> 0:08:08.400 I'm just trying to understand, Like on what scale do 0:08:08.440 --> 0:08:11.720 you put it? You mentioned the Internet, so is it 0:08:11.760 --> 0:08:14.000 apples to apples or is it something else? 0:08:15.280 --> 0:08:18.840 No, Well, first of all, it uses the internet, So 0:08:18.920 --> 0:08:24.240 I mean that's like the the machinery that you need 0:08:24.280 --> 0:08:27.280 to reach to scale up and reach a large population. 0:08:28.440 --> 0:08:32.400 But it's more intimate than the Internet in the following 0:08:32.440 --> 0:08:35.400 sense that it talks to us, right. I mean, it's 0:08:35.440 --> 0:08:38.320 as if an alien landed on the Earth and could 0:08:38.320 --> 0:08:42.040 talk to us in English and it knew everything about 0:08:42.040 --> 0:08:45.679 you know, what, human's history, everything, and the only thing 0:08:45.679 --> 0:08:48.720 we can be sure if it's not human. But it's 0:08:48.800 --> 0:08:51.839 really quite remarkable. Let me give you one example of 0:08:51.840 --> 0:08:55.120 something that I was really surprised at when they did 0:08:55.120 --> 0:08:58.720 a study of whether people who needed cognitive therapy preferred 0:08:59.360 --> 0:09:05.240 real humans or AI. They preferred AI, which was really 0:09:05.320 --> 0:09:07.680 quite remarkable. I didn't expect that. And part of the 0:09:07.720 --> 0:09:11.560 reason is that the AI is not judgmental like humans. 0:09:12.000 --> 0:09:14.320 Well wait, but isn't it depend on the data like 0:09:14.400 --> 0:09:14.760 we talk. 0:09:14.679 --> 0:09:16.840 About, it wasn't getting trained on judgmental data. 0:09:17.440 --> 0:09:19.560 Actually, it's a good question. What was it trained on. 0:09:19.720 --> 0:09:23.960 I think that it was fine tuned with you know, 0:09:24.200 --> 0:09:27.720 data from real subjects that we're talking with a doctor. 0:09:28.520 --> 0:09:31.440 But even without that, I'll tell you something again it's 0:09:31.480 --> 0:09:35.880 shocking is that it is actually empathy. These large language 0:09:35.880 --> 0:09:39.560 models can empathize with humans. And why is it. How 0:09:39.640 --> 0:09:41.960 is that? Well, it actually absorbed a lot of text 0:09:42.000 --> 0:09:46.720 out there, novels, letters, and read it and so forth, 0:09:46.800 --> 0:09:52.559 and where empathy was being you know, part of the discussion, 0:09:52.760 --> 0:09:54.320 and so it absorbed that too. 0:09:55.040 --> 0:09:55.199 Well. 0:09:55.240 --> 0:09:58.280 I wanted to hear hear a little bit of your 0:09:58.320 --> 0:10:00.559 thoughts on on what we heard from you Musk. He 0:10:00.640 --> 0:10:04.200 actually participated in a surprise conversation at the Future Investment 0:10:04.200 --> 0:10:06.559 Initiative to discuss the future of AI. 0:10:06.920 --> 0:10:08.839 It's most likely going to be great, and there's this 0:10:09.000 --> 0:10:11.840 some chance which could be ten to twenty percent that 0:10:13.160 --> 0:10:16.000 it goes bad. The chances on. 0:10:16.080 --> 0:10:17.120 Zero that it goes bad. 0:10:17.440 --> 0:10:19.440 But overall, at one point where we said the covers 0:10:19.480 --> 0:10:22.600 eighty percent full is one positive way to look at it. 0:10:22.640 --> 0:10:27.200 Maybe ninety percent, okay, eighty or ninety percent positive. The 0:10:27.280 --> 0:10:31.600 question I have for you, professor, do we need an 0:10:31.679 --> 0:10:35.760 international regulatory body? Do we need the largest, most powerful 0:10:35.760 --> 0:10:39.360 governments around the world to create some sort of standard 0:10:39.400 --> 0:10:41.920 to ensure or help ensure that this goes the right way. 0:10:42.559 --> 0:10:45.920 Well, as you know, in the UK, they have passed 0:10:46.520 --> 0:10:50.240 an AI Act which is like one hundred pages long 0:10:50.400 --> 0:10:56.400 and you know, incredibly detailed, and it's already obsolete. I 0:10:56.640 --> 0:11:00.360 just moving blasting forward and you know, you trying to 0:11:00.360 --> 0:11:04.200 catch up with it. But I do believe that it's 0:11:04.240 --> 0:11:08.080 absolutely essential that it be regulated, and it should be 0:11:08.120 --> 0:11:11.839 regulated by people who are building it. The government, okay, 0:11:12.000 --> 0:11:15.400 is the business of protecting people. And we'll see how 0:11:15.480 --> 0:11:20.240 that plays out. But for example, genetics, this happened, you know, 0:11:20.320 --> 0:11:23.040 back in the sixties seventies. They had a meeting where 0:11:23.080 --> 0:11:26.040 they came together at a soilomar and they came up 0:11:26.080 --> 0:11:30.480 with rules and regulations for doing experiments under the careful 0:11:30.840 --> 0:11:34.320 protection so that nothing leaks out, nothing gets out. And 0:11:34.400 --> 0:11:35.720 I think we need to do the same. 0:11:36.280 --> 0:11:36.760 Okay. 0:11:37.000 --> 0:11:39.320 So when does as you said, ten years for the 0:11:39.320 --> 0:11:43.320 internet to really kind of make its impact and presence 0:11:43.440 --> 0:11:47.200 really known and maybe, you know, integrated into our lives. 0:11:47.320 --> 0:11:52.760 So is it a decade before we see LMS and 0:11:52.920 --> 0:11:55.960 AI at this level integrated into our lives. 0:11:56.520 --> 0:11:59.560 We are at a stage that aviation was at when 0:12:00.120 --> 0:12:04.400 at Kitty Hawk the Wright brothers made the first flight. 0:12:04.600 --> 0:12:07.520 It was ten feet up and one hundred feet long, 0:12:08.120 --> 0:12:12.240 and that really was the you know, something that then 0:12:12.520 --> 0:12:16.360 took decades and decades to build. And the most difficult thing, 0:12:16.440 --> 0:12:19.960 by the way, that airplanes. You know, design of airplanes 0:12:19.960 --> 0:12:23.840 had to solve was control. How do you make a 0:12:23.880 --> 0:12:26.040 go where you want to go without crashing? And that's 0:12:26.040 --> 0:12:29.160 something that again it's like we're going through right now 0:12:29.160 --> 0:12:31.760 with AI. And yes, it will take decades. It's not 0:12:31.800 --> 0:12:32.800 going to happen overnight. 0:12:33.160 --> 0:12:36.160 That was doctor Terrence Sadowski, Francis Crick, Chair at the 0:12:36.280 --> 0:12:39.760 Salk Institute for Biological Studies and Distinguished Professor at the 0:12:39.880 --> 0:12:43.160 University of California at San Diego. His new book chat 0:12:43.200 --> 0:12:46.560 GPT in the Future of AI, The Deep Language Revolution 0:12:47.160 --> 0:12:47.400 is out. 0:12:47.520 --> 0:12:50.440 Now you're listening to Bloomberg Business Week. AI certainly in 0:12:50.480 --> 0:12:53.880 focus this year. Another thing in focus US politics. 0:12:54.080 --> 0:12:56.240 A book that our team liked came from America, so 0:12:56.360 --> 0:12:59.120 called The Government Teacher who wrote about the small and 0:12:59.160 --> 0:13:02.920 the Mighty, twelve unsung Americans who changed the course of history. 0:13:03.120 --> 0:13:05.400 That happens to be my pick more when we come 0:13:05.440 --> 0:13:07.000 back right here on BusinessWeek. 0:13:11.240 --> 0:13:14.800 You're listening to the Bloomberg Business Week podcast. Catch us 0:13:14.840 --> 0:13:18.079 live weekday afternoons from two to five pm. Easter Listen 0:13:18.120 --> 0:13:20.280 on Apple car Play and and Brout Auto with a 0:13:20.280 --> 0:13:25.120 Bloomberg Business app, or watch us live on YouTube all right. 0:13:25.120 --> 0:13:27.480 Everybody, think back to your history classes growing up, you 0:13:27.559 --> 0:13:30.720 probably remember learning about, yeah, of course, the president's wars, 0:13:30.800 --> 0:13:34.440 major wars, and the social issues of our past. But 0:13:34.520 --> 0:13:36.760 some things that may have gone under the radar were 0:13:36.800 --> 0:13:39.840 the lesser known figures in American history who had a 0:13:39.960 --> 0:13:42.640 major impact. And that's what our next author wanted to 0:13:42.640 --> 0:13:43.400 bring to light. 0:13:43.679 --> 0:13:46.480 This next book. It's actually Carol's pick is by number 0:13:46.520 --> 0:13:49.560 one New York Times bestselling author, educator, and podcast host 0:13:49.559 --> 0:13:53.120 Sharon McMahon. Known to many as America's government teacher, she 0:13:53.200 --> 0:13:57.120 informs her million plus audience about history, government and current events. 0:13:57.200 --> 0:13:59.160 Now, we should note we spoke to Sharon weeks before 0:13:59.160 --> 0:14:01.760 the presidential election. We did get her take on whether 0:14:01.760 --> 0:14:04.200 the electoral system in the United States is working or 0:14:04.320 --> 0:14:07.360 if it should change. So here's my pick for your 0:14:07.440 --> 0:14:10.800 winter holiday read from author Sharon McMahon and her book 0:14:11.040 --> 0:14:14.040 The Small and the Mighty Twelve Unsung Americans who changed 0:14:14.080 --> 0:14:15.120 the course of history. 0:14:15.559 --> 0:14:17.760 I do think that it is time to reform the 0:14:17.800 --> 0:14:21.120 electoral system in a number of ways. I don't think 0:14:21.120 --> 0:14:23.000 the system, first of all, works the way that the 0:14:23.040 --> 0:14:25.840 founders or the framers intended it to work. But I 0:14:25.880 --> 0:14:28.480 also think that The Framers were very clear that the 0:14:28.520 --> 0:14:31.840 system should be refined as needed, which is why they 0:14:31.960 --> 0:14:35.080 built two separate ways to amend the Constitution right there 0:14:35.080 --> 0:14:38.800 into the document. This is not holy scripture. This is 0:14:38.840 --> 0:14:40.880 a like, hey, here's a working document with some of 0:14:40.880 --> 0:14:42.760 our ideas of how we might run this country. And 0:14:42.800 --> 0:14:45.040 if it's not working out, here's two ways we can 0:14:45.120 --> 0:14:48.200 change it. So the concept of winner take all in 0:14:48.240 --> 0:14:51.600 the electoral college that's not in the constitution. Winner take 0:14:51.640 --> 0:14:54.800 all actually didn't develop until thirty to forty years after 0:14:55.120 --> 0:14:58.000 the Constitution was ratified. The Framers never intended for a 0:14:58.040 --> 0:15:01.880 winner take all electoral college, and most Americans do not 0:15:02.040 --> 0:15:05.760 feel well represented by the way the current system works. 0:15:06.160 --> 0:15:09.280 If you are a Blue voter in Alabama or a 0:15:09.360 --> 0:15:12.320 Red voter in California, I guarantee that there's an element 0:15:12.360 --> 0:15:15.120 of feeling disenfranchised by the way the system works. But 0:15:15.160 --> 0:15:17.280 I would also advocate for a couple of other changes. 0:15:17.320 --> 0:15:21.000 One of them is, as you mentioned, an abbreviated election period. 0:15:21.080 --> 0:15:23.680 We don't need we don't need to spend one billion 0:15:23.720 --> 0:15:26.920 dollars each. That's what Biden and Trump each spent in 0:15:26.960 --> 0:15:31.960 twenty twenty one billion dollars each Most Americans agree we 0:15:32.000 --> 0:15:34.400 could find better things to spend two billion dollars on 0:15:34.720 --> 0:15:38.120 then flyers that are put in the trash and commercials 0:15:38.120 --> 0:15:41.120 that air for thirty seconds at a time. That's one way. 0:15:41.360 --> 0:15:44.920 I also think so many Americans feel like the way, 0:15:45.040 --> 0:15:50.160 the way the process for choosing nominees is broken, the 0:15:50.960 --> 0:15:53.240 process for how do we get to pick which person 0:15:53.320 --> 0:15:56.240 is going to represent us? The primary process because they 0:15:56.280 --> 0:15:59.160 happen in a you know, sort of like dominoes fall. 0:15:59.360 --> 0:16:01.720 If you're in a state with early primary, you get 0:16:01.760 --> 0:16:05.200 an outsize amount of input. State with a late primary, 0:16:05.480 --> 0:16:08.440 often there's only one person left standing. Why can't all 0:16:08.480 --> 0:16:11.760 Americans have to say on one national primary election day? 0:16:12.320 --> 0:16:14.440 I agree. You know, it's interesting. I'd heard on a 0:16:14.480 --> 0:16:20.000 podcast that I listened to and said that one thing 0:16:20.040 --> 0:16:21.680 to keep in mind, though, when you look at the 0:16:21.720 --> 0:16:25.720 election outcome when Donald Trump was elected. And I'm not 0:16:25.760 --> 0:16:27.960 taking sides here, but just so, you need to understand 0:16:28.000 --> 0:16:31.880 that when you have candidates who are very very different individuals, 0:16:32.360 --> 0:16:34.920 that depending on who wins or loses in the case 0:16:34.960 --> 0:16:36.960 of when Donald Trump was in the White House, that 0:16:37.040 --> 0:16:41.440 there is roughly half of America who agree with or 0:16:41.720 --> 0:16:44.720 like who he is as a leader, and you could 0:16:44.720 --> 0:16:46.880 say for the other side, and when you have such 0:16:46.920 --> 0:16:49.720 a difference, how do we I don't know, how do 0:16:49.800 --> 0:16:52.280 we make it so a government that is much more 0:16:52.320 --> 0:16:56.840 representative of people who feel very different about the way forward, 0:16:57.120 --> 0:16:58.840 not just as leaders but as Americans. 0:16:59.480 --> 0:17:02.200 Yeah, yeah, you know, I think there's this is the 0:17:02.800 --> 0:17:05.560 problem that we're having now, which is that there is 0:17:05.640 --> 0:17:10.520 not an ideological center of the country. We have these 0:17:10.560 --> 0:17:13.440 sort of you know, farther left and farther right. And yes, 0:17:13.480 --> 0:17:15.560 you can make a lot of arguments that what's far 0:17:15.640 --> 0:17:17.760 left in America is not far left overseas. 0:17:17.920 --> 0:17:18.119 I do. 0:17:18.160 --> 0:17:21.760 I understand all of that. But an ideological center is 0:17:21.800 --> 0:17:25.320 a moderating force in politics. It's a moderating force on 0:17:25.359 --> 0:17:28.199 the Supreme Court, it's a moderating force in Congress. You 0:17:28.280 --> 0:17:31.880 have to convince the people in the ideological center that 0:17:32.359 --> 0:17:34.600 the side, that the position that's slightly to the right 0:17:34.680 --> 0:17:37.400 or slightly to the left, is the one worth taking. 0:17:38.000 --> 0:17:43.520 And that ideological center is often persuadable by the best arguments, 0:17:43.560 --> 0:17:47.480 by the best ideas, And so right now, the electoral 0:17:47.560 --> 0:17:54.280 system heavily favors people who are more ideologically extreme, more 0:17:54.320 --> 0:17:56.920 ideological you know, farther to the right or to the left. 0:17:57.240 --> 0:18:01.160 And that ideological center is something that we've eliminated by 0:18:01.200 --> 0:18:05.280 and large by Jerrymangering and by other mechanisms that again 0:18:05.320 --> 0:18:08.679 were never intended by the Framers to be used. And 0:18:08.720 --> 0:18:11.879 that ideological center is something that Americans can choose to 0:18:12.000 --> 0:18:14.680 vote for if they want to. They can choose to 0:18:14.760 --> 0:18:17.800 vote for those candidates who are less extreme. 0:18:18.200 --> 0:18:20.879 So you're optimistic about that because it does seem like 0:18:20.920 --> 0:18:24.240 in recent years we've just become more polarized and we've 0:18:24.240 --> 0:18:26.200 become more extreme. 0:18:27.200 --> 0:18:30.080 Well, I mean, we can't make positive change by having 0:18:30.119 --> 0:18:34.720 a fatalistic viewpoint that nothing good can ever happen. If 0:18:34.720 --> 0:18:36.960 you want positive change to happen, you have to hope 0:18:36.960 --> 0:18:41.040 that it will. And I like to reiterate that hope 0:18:41.119 --> 0:18:43.560 is not a feeling that you wait to experience where 0:18:43.560 --> 0:18:45.080 you're going to wake up one morning and the birds 0:18:45.080 --> 0:18:46.480 are going to be chirping and this guy's going to 0:18:46.480 --> 0:18:49.840 be blue. Our ancestors, the people who built this country, 0:18:50.280 --> 0:18:54.560 operated on this assumption that hope was a choice that 0:18:54.640 --> 0:18:57.720 they could make, and it was that choice from which 0:18:57.760 --> 0:18:59.080 all good things would follow. 0:18:59.320 --> 0:19:04.239 Sharon, who are these unsung heroes, and like, I don't know, 0:19:04.240 --> 0:19:06.480 how did you kind of go about this process, Why 0:19:06.480 --> 0:19:07.800 did you want to write it? Give us a little 0:19:07.800 --> 0:19:08.760 bit of background. 0:19:08.720 --> 0:19:10.679 You know, as you mentioned, I've been a teacher for 0:19:10.680 --> 0:19:12.919 a long time, and it is often the stories of 0:19:12.960 --> 0:19:15.359 the people you ever heard of that are some of 0:19:15.400 --> 0:19:18.960 the most interesting. They're the stories that children and adults 0:19:18.960 --> 0:19:22.520 alike feel like, dang, I wish I had known about 0:19:22.560 --> 0:19:24.919 them before, you know, So those kinds of stories, the 0:19:25.000 --> 0:19:28.080 stories that you just don't see in the bold face 0:19:28.119 --> 0:19:31.639 and the textbooks that are often the most intriguing. You know, 0:19:31.680 --> 0:19:35.360 Americans love like the secret of some kind, the secret 0:19:35.440 --> 0:19:38.639 story behind something that happened, right, that's just sort of 0:19:38.680 --> 0:19:41.800 innate human nature. So I have found them interesting for 0:19:41.840 --> 0:19:44.879 a long time. And when I conceptualized this book and 0:19:44.920 --> 0:19:48.600 wanted to sort of bring some of these these stories 0:19:48.640 --> 0:19:51.439 to bear, bring some of these characters to the table, 0:19:51.960 --> 0:19:53.600 that was one of the things I was thinking about, 0:19:53.680 --> 0:19:58.480 is who has made a significant contribution, a significant impact 0:19:59.040 --> 0:20:03.520 where they were with the resources available to them. I 0:20:03.560 --> 0:20:07.879 think we often mistakenly believe that the levers of power 0:20:07.920 --> 0:20:11.359 that are accessed by government are the only ways to 0:20:11.400 --> 0:20:14.080 make a difference, and the people in this book by 0:20:14.119 --> 0:20:18.040 and large do not access power via the levers of government. 0:20:18.119 --> 0:20:21.040 They most of the time, are not wealthy or famous. 0:20:21.440 --> 0:20:24.480 Most of them had very significant challenges in their own lives, 0:20:24.560 --> 0:20:29.480 and yet they set out, by virtue of circumstance and 0:20:29.720 --> 0:20:34.000 luck in many cases, did something truly extraordinary with their lives. 0:20:34.520 --> 0:20:37.119 The examples in your book end in the nineteen fifties, 0:20:37.520 --> 0:20:40.280 and I'm wondering if there are any examples today of 0:20:40.359 --> 0:20:43.960 people who are alive that wielded power outside of traditional 0:20:43.960 --> 0:20:46.919 government channels that we can look to who are currently alive. 0:20:48.240 --> 0:20:51.080 Well, there's many of them, of course, whether I would 0:20:51.080 --> 0:20:54.920 put them in a book is another story, a good one, 0:20:54.960 --> 0:20:57.400 But of course, like Elon Musk is a great example 0:20:57.440 --> 0:21:00.400 of somebody who doesn't belong in my book, of course, 0:21:00.400 --> 0:21:02.320 but he's a great example of somebody who is using 0:21:02.320 --> 0:21:04.960 a tremendous amount of power outside of the levers of government. 0:21:05.520 --> 0:21:08.720 But most of the people in this book had no 0:21:08.920 --> 0:21:13.879 such large platform from which to jump like Ewon does, 0:21:14.359 --> 0:21:16.760 did not have billions of dollars, did not have to 0:21:16.800 --> 0:21:19.320 have the ability to buy a mouthpiece the size of 0:21:19.800 --> 0:21:26.320 Twitter slash X. But there are absolutely still great Americans. 0:21:26.320 --> 0:21:28.480 I think we often think of Great Americans as being 0:21:28.600 --> 0:21:30.919 like the people in the black and white pictures are 0:21:30.920 --> 0:21:33.960 the oil paintings of the past, and I really wanted 0:21:34.000 --> 0:21:36.960 to dispell that notion that Great Americans were people. 0:21:36.680 --> 0:21:37.240 Of the past. 0:21:37.520 --> 0:21:39.600 There are people today, and I think many would argue 0:21:39.600 --> 0:21:41.640 that some of the poll workers right of the last 0:21:41.680 --> 0:21:46.600 few years elections are those unsung Americans and heroes. Give 0:21:46.680 --> 0:21:48.119 us one or two if you will, that are in 0:21:48.200 --> 0:21:48.600 your book. 0:21:49.880 --> 0:21:51.399 One of the people that I really love in this 0:21:51.440 --> 0:21:53.960 book is a woman named Septa Mclark who had a 0:21:54.440 --> 0:22:00.359 very tumultuous upbringing. She almost commits suicide, is saved the 0:22:00.440 --> 0:22:02.960 last moment from committing suicide. On more than one occasion. 0:22:03.000 --> 0:22:07.480