1 00:00:04,880 --> 00:00:08,400 Speaker 1: On this episode of Each World. Open the Books operates 2 00:00:08,440 --> 00:00:13,600 Speaker 1: the largest private database of public spending ever in history, 3 00:00:13,680 --> 00:00:18,880 Speaker 1: ten billion points of data that includes all federal salaries, 4 00:00:19,440 --> 00:00:23,120 Speaker 1: line by line spending by the Feds, all fifty state 5 00:00:23,280 --> 00:00:26,960 Speaker 1: checkbooks including their own line by line spending each year, 6 00:00:27,640 --> 00:00:32,599 Speaker 1: plus millions of state level salaries and pension funds, and 7 00:00:32,720 --> 00:00:39,640 Speaker 1: spending across more than seventeen thousand municipalities. Their goal is 8 00:00:39,680 --> 00:00:43,160 Speaker 1: to capture every dime and put it online and as 9 00:00:43,159 --> 00:00:47,120 Speaker 1: close to real time as possible. Their database is open 10 00:00:47,159 --> 00:00:54,320 Speaker 1: to journalists, researchers, activists, public officials, and most importantly, every 11 00:00:54,400 --> 00:00:59,520 Speaker 1: American taxpayer. Now Opening the Books as joining forces with 12 00:00:59,600 --> 00:01:03,200 Speaker 1: Citizen Portal to create a first of its kind tool 13 00:01:03,760 --> 00:01:10,080 Speaker 1: combining the largest private database of government spending with artificial intelligence. 14 00:01:10,880 --> 00:01:14,880 Speaker 1: The new database has indexed over a million hours of 15 00:01:15,000 --> 00:01:20,280 Speaker 1: public official remarks and meetings. For the first time, taxpayers 16 00:01:20,280 --> 00:01:25,040 Speaker 1: can compare what politicians say with what they do and spend, 17 00:01:25,600 --> 00:01:29,959 Speaker 1: all in one place. This is an amazing achievement and 18 00:01:30,040 --> 00:01:34,880 Speaker 1: that's why I'm really pleased to welcome my guest, John Hart, 19 00:01:35,240 --> 00:01:53,440 Speaker 1: the CEO of Opening the Books. John, Welcome and thank 20 00:01:53,480 --> 00:01:54,760 Speaker 1: you for joining me in news World. 21 00:01:55,360 --> 00:01:57,480 Speaker 2: Well, mister speaker, it is an honor and a privilege. 22 00:01:57,480 --> 00:01:59,520 Speaker 2: Thank you for taking the time to have this conversation. 23 00:02:00,280 --> 00:02:03,360 Speaker 1: Well, you know, the seems to be a remarkable period 24 00:02:03,800 --> 00:02:08,960 Speaker 1: for talking about fraud and misspending and corruption. You know, 25 00:02:09,000 --> 00:02:12,840 Speaker 1: recent federal investigations and covered over a billion dollars in 26 00:02:12,960 --> 00:02:17,800 Speaker 1: fraud across several Minnesota schemes, with nearly one hundred people 27 00:02:17,840 --> 00:02:22,240 Speaker 1: already charged. From your perspective, how does something of that 28 00:02:22,280 --> 00:02:24,720 Speaker 1: scale actually happen? Man? How do you get to a 29 00:02:24,760 --> 00:02:28,840 Speaker 1: point where a state government manages to spend a billion 30 00:02:28,919 --> 00:02:29,920 Speaker 1: dollars on fraud? 31 00:02:30,880 --> 00:02:33,080 Speaker 2: I think it happens because we've lost touch of our 32 00:02:33,080 --> 00:02:36,240 Speaker 2: founding principles and our first principles, and our founders were 33 00:02:36,280 --> 00:02:39,640 Speaker 2: obsessively focused on what I view is the right question, 34 00:02:40,120 --> 00:02:42,959 Speaker 2: which is what is the optimal relationship between the individual 35 00:02:43,000 --> 00:02:45,600 Speaker 2: and the state. And so when too much power is 36 00:02:45,639 --> 00:02:50,440 Speaker 2: consolidated in a central government, then fraud becomes a feature, 37 00:02:50,520 --> 00:02:53,320 Speaker 2: not a bug, and so the system is really set 38 00:02:53,400 --> 00:02:56,160 Speaker 2: up to be defrauded. I think what we're doing with 39 00:02:56,320 --> 00:02:59,119 Speaker 2: AI and technology is so important because we're looking over 40 00:02:59,160 --> 00:03:02,880 Speaker 2: the horizon and saying, how can we prevent this from happening? 41 00:03:02,880 --> 00:03:03,480 Speaker 1: In the future. 42 00:03:04,120 --> 00:03:05,800 Speaker 2: And the way to do it, in the simplest way 43 00:03:05,800 --> 00:03:08,520 Speaker 2: I can put it, is what AI will enable us 44 00:03:08,520 --> 00:03:11,839 Speaker 2: to do is do pattern recognition on a scale never 45 00:03:11,880 --> 00:03:17,000 Speaker 2: before possible. And then pattern recognition enables pattern prediction. When 46 00:03:17,040 --> 00:03:20,120 Speaker 2: you can predict patterns, you can stop them. The Free 47 00:03:20,120 --> 00:03:23,000 Speaker 2: Press that a piece on our collaboration with Citizen Portal 48 00:03:23,600 --> 00:03:26,600 Speaker 2: and citizen Portal was founded by a GUNNYM. Paul Allen, 49 00:03:26,639 --> 00:03:30,160 Speaker 2: who was a co founder of ancestry dot com. And 50 00:03:30,280 --> 00:03:34,400 Speaker 2: what we're doing is combining data sets. So we have 51 00:03:34,440 --> 00:03:36,840 Speaker 2: the open the books data set of all government spending 52 00:03:37,320 --> 00:03:39,960 Speaker 2: at all levels, again not just federal, but state and local. 53 00:03:40,360 --> 00:03:43,040 Speaker 2: And when you merge data sets, that enables you to 54 00:03:43,080 --> 00:03:47,520 Speaker 2: do correlations that recognize patterns. And so I think where 55 00:03:47,600 --> 00:03:51,080 Speaker 2: to point with AI technology that is like sixteen oh 56 00:03:51,160 --> 00:03:54,760 Speaker 2: nine when Galileo used the telescope to look at patterns 57 00:03:54,800 --> 00:03:56,680 Speaker 2: in the sky, look at all these points of light 58 00:03:57,280 --> 00:04:01,400 Speaker 2: and discovered there are these bodies orbiting this other point 59 00:04:01,400 --> 00:04:06,640 Speaker 2: of light and that changed civilization. So the underlying project 60 00:04:06,640 --> 00:04:09,160 Speaker 2: that we're working on we call it aqueduct, and that's 61 00:04:09,160 --> 00:04:11,960 Speaker 2: an allusion to the Roman technology that brought clean water 62 00:04:12,280 --> 00:04:16,800 Speaker 2: to an urban area transform Western civilization, unleashed incredible amounts 63 00:04:16,800 --> 00:04:20,240 Speaker 2: of human flourishing. If we can bring clean data and 64 00:04:20,279 --> 00:04:24,200 Speaker 2: clear insights to the electorate, it will revitalize and rejuvenate 65 00:04:24,640 --> 00:04:27,880 Speaker 2: citizen engagement like nothing in our lifetime. And so we're 66 00:04:27,920 --> 00:04:29,960 Speaker 2: thrilled to partner with Citizen Portal on this. 67 00:04:30,480 --> 00:04:34,880 Speaker 1: Now, when you talk about citizen engagement everyday, citizens will 68 00:04:34,920 --> 00:04:37,960 Speaker 1: be listening to this conversation. What are you talking about 69 00:04:38,320 --> 00:04:39,320 Speaker 1: and how would it occur? 70 00:04:40,040 --> 00:04:43,920 Speaker 2: Right now, people are rightly concerned about AI technology creating 71 00:04:43,960 --> 00:04:47,200 Speaker 2: what we call a surveillance state, where the state can 72 00:04:47,279 --> 00:04:49,120 Speaker 2: know more about you than you know about the state. 73 00:04:49,120 --> 00:04:51,279 Speaker 2: We're trying to flip the script, and the best way 74 00:04:51,320 --> 00:04:55,160 Speaker 2: to thwart a surveillance state is to create a surveillance citizenry. 75 00:04:55,839 --> 00:04:58,640 Speaker 2: So we're not suggesting let's delegate our authority as we 76 00:04:58,760 --> 00:05:02,240 Speaker 2: the people to this mysteri black box technology called AI. 77 00:05:02,760 --> 00:05:04,839 Speaker 2: What we're trying to do is give you the tools 78 00:05:04,920 --> 00:05:08,080 Speaker 2: you need to become a super intelligent citizen. So you've 79 00:05:08,080 --> 00:05:11,839 Speaker 2: got this AI robot on your flank helping you identify, 80 00:05:11,920 --> 00:05:15,040 Speaker 2: navigate government. So case in point, let's say, as a citizen, 81 00:05:15,120 --> 00:05:17,839 Speaker 2: you're at a town hall meeting people are rightly concerned 82 00:05:17,880 --> 00:05:21,440 Speaker 2: about government doing visual recognition and surveillance on individuals. But 83 00:05:21,480 --> 00:05:23,960 Speaker 2: you could take your phone up and look at the 84 00:05:23,960 --> 00:05:28,080 Speaker 2: politician talking and then immediately have access to all of 85 00:05:28,080 --> 00:05:30,440 Speaker 2: their votes, all of the things they've said, a deep 86 00:05:30,520 --> 00:05:34,920 Speaker 2: analysis of the policy positions they've taken that informs you 87 00:05:35,080 --> 00:05:39,120 Speaker 2: it's not about delegating authority, it's about investing authority with 88 00:05:39,279 --> 00:05:42,279 Speaker 2: we the people. And the applications are mind blowing for 89 00:05:42,360 --> 00:05:45,000 Speaker 2: what could be done in the very near future with this. 90 00:05:45,920 --> 00:05:48,920 Speaker 1: You know, this is a rapidly evolving area because of 91 00:05:49,520 --> 00:05:55,080 Speaker 1: artificial intelligence. You help create USA spending good GOV. Back 92 00:05:55,120 --> 00:05:58,200 Speaker 1: in two thousand and six, did you ever expect that 93 00:05:58,240 --> 00:06:01,240 Speaker 1: you'd get to a point where literally, in real time, 94 00:06:01,720 --> 00:06:05,880 Speaker 1: everyday citizens could look at virtually everything you know? 95 00:06:05,920 --> 00:06:08,560 Speaker 2: Honestly, we did you know back in two thousand and four, 96 00:06:08,680 --> 00:06:11,039 Speaker 2: two thousand and five, And you and I will both 97 00:06:11,120 --> 00:06:13,840 Speaker 2: remember that well world enough that the Google search engine 98 00:06:13,880 --> 00:06:16,680 Speaker 2: was a mind blowing technology. So as a kid, I 99 00:06:16,720 --> 00:06:19,000 Speaker 2: grew up reading the Encyclopedia Britannic. As I pull it 100 00:06:19,000 --> 00:06:20,680 Speaker 2: off if I was bored in the summer and read, 101 00:06:21,200 --> 00:06:23,520 Speaker 2: and then all of a sudden, all of the information 102 00:06:23,640 --> 00:06:27,520 Speaker 2: ever written is available to search online. And you know, 103 00:06:27,600 --> 00:06:29,800 Speaker 2: I work for Coburn, and it's wonderful that we're having 104 00:06:29,800 --> 00:06:32,719 Speaker 2: this conversation because, as you remember very well, Coburn was 105 00:06:32,760 --> 00:06:35,000 Speaker 2: often your best friend but also your nemesis. And so 106 00:06:35,080 --> 00:06:37,920 Speaker 2: it speaks to the power of our system that we're 107 00:06:37,920 --> 00:06:41,040 Speaker 2: having this conversation all these years later and the shared 108 00:06:41,080 --> 00:06:44,120 Speaker 2: passion we have for the future and what we did. 109 00:06:44,160 --> 00:06:45,640 Speaker 2: When Cobra got to the set, I was with him 110 00:06:45,680 --> 00:06:48,080 Speaker 2: in the House. We got to the Senate. He was 111 00:06:48,120 --> 00:06:52,440 Speaker 2: in sense that you would have appropriation committee staffers hide 112 00:06:52,560 --> 00:06:55,960 Speaker 2: basic spending data. So he was one of these legislators 113 00:06:55,960 --> 00:06:58,640 Speaker 2: that actually believed in reading bills before he voted on him. 114 00:06:59,120 --> 00:07:02,200 Speaker 2: And we would ask the committee staff reports, so rather 115 00:07:02,279 --> 00:07:04,640 Speaker 2: than give us a searchable document, they would take an 116 00:07:04,680 --> 00:07:09,640 Speaker 2: extra step and turn it into an unsearchable PDF. And 117 00:07:09,720 --> 00:07:13,000 Speaker 2: in response to that, there was an ambitious colleague from 118 00:07:13,000 --> 00:07:18,000 Speaker 2: Illinois named Barack Obama. And our first plan with Obama, 119 00:07:18,000 --> 00:07:20,720 Speaker 2: who clearly wanted to have a bipartisan accomplishment, was to 120 00:07:20,720 --> 00:07:23,600 Speaker 2: get him to moderate his views on healthcare, which that 121 00:07:23,720 --> 00:07:25,880 Speaker 2: didn't succeed sadly. We could do a couple of the 122 00:07:25,920 --> 00:07:28,840 Speaker 2: podcasts on that But what did click with him was 123 00:07:28,880 --> 00:07:32,160 Speaker 2: this idea of transparency. And transparency is a first principle 124 00:07:32,800 --> 00:07:35,680 Speaker 2: that was written into the Constitution. It precedes the Bill 125 00:07:35,680 --> 00:07:38,880 Speaker 2: of Rights and the First Amendment itself. So the argument 126 00:07:38,920 --> 00:07:41,480 Speaker 2: I make is transparency is like the oxygen in the 127 00:07:41,480 --> 00:07:44,360 Speaker 2: public square. We can't speak and debate if we can't breathe. 128 00:07:45,120 --> 00:07:49,400 Speaker 2: And the founders really were inspired, as you talk about frequently, 129 00:07:49,520 --> 00:07:54,240 Speaker 2: the Classics, the Romans, the Greeks. In fifty BC, Cicero said, 130 00:07:54,360 --> 00:07:57,840 Speaker 2: true law is right reason and agreement with nature. So 131 00:07:58,000 --> 00:08:01,280 Speaker 2: true law, which is natural law, is the synthesis of 132 00:08:01,360 --> 00:08:04,840 Speaker 2: using reason and understanding the world as it is. And 133 00:08:04,880 --> 00:08:08,480 Speaker 2: that informed the founder's focus on transparency. And it's in 134 00:08:08,520 --> 00:08:12,880 Speaker 2: the Constitution, and so we fought, let's merge these first 135 00:08:12,880 --> 00:08:17,360 Speaker 2: principles and these ancient truths with modern technology. And the 136 00:08:17,480 --> 00:08:20,280 Speaker 2: vision of that bill, mister Speaker, wasn't to just change 137 00:08:20,280 --> 00:08:22,240 Speaker 2: a law, but to shape a norm and to create 138 00:08:22,280 --> 00:08:26,119 Speaker 2: an expectation that government spending and all localities should always 139 00:08:26,160 --> 00:08:29,920 Speaker 2: be online. And now the technology has evolved, real time 140 00:08:29,960 --> 00:08:32,360 Speaker 2: transparency should be the norm. So we're working on an 141 00:08:32,440 --> 00:08:36,479 Speaker 2: upgrade to USA spending to really create real time transparency 142 00:08:36,480 --> 00:08:38,880 Speaker 2: to me, that's one of these no brainer policy positions 143 00:08:38,880 --> 00:08:41,719 Speaker 2: that everyone on both parties should be falling over each 144 00:08:41,760 --> 00:08:43,040 Speaker 2: other to get ahead of that one. 145 00:08:43,480 --> 00:08:48,400 Speaker 1: How much has the acceleration of technology changed all this. 146 00:08:49,520 --> 00:08:51,880 Speaker 2: It's changed quite a bit because it's made it very, 147 00:08:51,960 --> 00:08:55,600 Speaker 2: very hard for policy makers to hide the ball. But 148 00:08:55,720 --> 00:08:58,880 Speaker 2: you know, history is an eternal battle between freedom and tyranny. 149 00:08:59,080 --> 00:09:02,720 Speaker 2: So anytime there's an technology state, actors will want to 150 00:09:02,800 --> 00:09:05,920 Speaker 2: use that to keep themselves in power. It's really a 151 00:09:05,960 --> 00:09:08,439 Speaker 2: balance of power question where's the center of gravity and 152 00:09:08,480 --> 00:09:11,360 Speaker 2: a free society is the center of gravity and authority 153 00:09:11,480 --> 00:09:13,360 Speaker 2: with we the people or is it with the state. 154 00:09:14,040 --> 00:09:16,600 Speaker 2: And the founders were obsessed with this question. That's why 155 00:09:16,600 --> 00:09:20,640 Speaker 2: they wrote the Constitution, which is primarily a document about limitations, 156 00:09:20,720 --> 00:09:25,199 Speaker 2: not permissions. Every generation has a responsibility to use technology 157 00:09:25,240 --> 00:09:29,120 Speaker 2: responsibly to advance human freedom. So this fits into our 158 00:09:29,160 --> 00:09:31,840 Speaker 2: broader mission and open the books called the AI and 159 00:09:31,880 --> 00:09:34,720 Speaker 2: the Future of Freedom Initiative, and this partnership with sit 160 00:09:34,800 --> 00:09:36,760 Speaker 2: as sup portal as one of those elements, along with 161 00:09:36,800 --> 00:09:55,840 Speaker 2: the Aqueduct project and some other things. 162 00:09:57,480 --> 00:10:01,360 Speaker 1: Youild a great line you said, instead of creating a 163 00:10:01,400 --> 00:10:06,000 Speaker 1: surveillance state, we believe artificial intelligence, you can create a 164 00:10:06,040 --> 00:10:09,560 Speaker 1: surveillance citizenry sort of taking the Chinese model and putting 165 00:10:09,559 --> 00:10:13,160 Speaker 1: it on its head. Walk uts through what a surveillance 166 00:10:13,280 --> 00:10:14,240 Speaker 1: citizenry will be. 167 00:10:14,360 --> 00:10:18,720 Speaker 2: Like, let's say we merge the open the book's data set, okay, 168 00:10:19,040 --> 00:10:21,080 Speaker 2: and just take a once quick step back. Is the 169 00:10:21,080 --> 00:10:24,600 Speaker 2: way AI really works. To demystify the technology. You need 170 00:10:24,640 --> 00:10:27,559 Speaker 2: three things. You need big data, you need an algorithm, 171 00:10:27,720 --> 00:10:30,000 Speaker 2: and then you need processing power. And you don't need 172 00:10:30,000 --> 00:10:32,920 Speaker 2: as much processing power as people think to build the technology. 173 00:10:33,600 --> 00:10:37,080 Speaker 2: And the aih big data is like enriched uranium. It 174 00:10:37,080 --> 00:10:40,400 Speaker 2: could be used for peaceful purposes or destructive purposes. So 175 00:10:40,440 --> 00:10:42,679 Speaker 2: we're sitting on this very big, rich data set and 176 00:10:42,760 --> 00:10:45,040 Speaker 2: open the books. Now, if you take our data set 177 00:10:45,320 --> 00:10:47,680 Speaker 2: and then you merge it with other data sets, for 178 00:10:47,760 --> 00:10:52,079 Speaker 2: example the Bureau of Labor Stats or economic performance indicators, 179 00:10:52,120 --> 00:10:57,400 Speaker 2: wage growth, education, and you start to identify patterns, then 180 00:10:57,480 --> 00:11:00,840 Speaker 2: that enables you to do pattern prediction and power recognition. 181 00:11:01,280 --> 00:11:04,120 Speaker 2: And that was the genius behind Billy Bean and Moneyball 182 00:11:04,320 --> 00:11:07,120 Speaker 2: is that they didn't invent new metrics. They discovered metrics 183 00:11:07,120 --> 00:11:09,880 Speaker 2: that were always there but never applied. So what we're 184 00:11:09,880 --> 00:11:13,040 Speaker 2: trying to do is give citizens a deeper understanding of 185 00:11:13,080 --> 00:11:18,439 Speaker 2: the relationship between data so that they can make better decisions. 186 00:11:18,800 --> 00:11:20,760 Speaker 2: For example, as you and I know, is the CBO 187 00:11:20,880 --> 00:11:24,520 Speaker 2: scoring model is notoriously incorrect. It's a black box, it's 188 00:11:24,520 --> 00:11:28,560 Speaker 2: hard to understand, it's politicized. So why not devolve that 189 00:11:28,720 --> 00:11:31,440 Speaker 2: power and give it to we the people. So what 190 00:11:31,440 --> 00:11:34,680 Speaker 2: we're describing this we can run circles around CBO in 191 00:11:34,720 --> 00:11:37,880 Speaker 2: a very short period of time by building this technology 192 00:11:37,960 --> 00:11:41,000 Speaker 2: and again invest that power with the electorate so that 193 00:11:41,040 --> 00:11:44,200 Speaker 2: we preserve a free society. A practical example is let's 194 00:11:44,200 --> 00:11:46,920 Speaker 2: look at energy policy. So the project I did before 195 00:11:46,920 --> 00:11:49,920 Speaker 2: Open the Books was on energy and environment. We did 196 00:11:49,920 --> 00:11:53,240 Speaker 2: a report called free Economies or Clean Economies, and we 197 00:11:53,280 --> 00:11:56,600 Speaker 2: looked at the correlation between the index of economic freedom 198 00:11:56,679 --> 00:12:00,079 Speaker 2: and the Yale Environmental Performance Index to show that a 199 00:12:00,240 --> 00:12:03,480 Speaker 2: free economies or twice as clean as less free. So 200 00:12:03,600 --> 00:12:05,720 Speaker 2: the conventional wisdom we hear a lot from kind of 201 00:12:05,720 --> 00:12:08,520 Speaker 2: a progressive fundamental his crowd is that we need a 202 00:12:08,600 --> 00:12:11,520 Speaker 2: top down command and control model to quote have a 203 00:12:11,520 --> 00:12:14,400 Speaker 2: better environment, when actually to have a better environment and 204 00:12:14,440 --> 00:12:18,600 Speaker 2: to have more economic growth, you need abundant energy. That's 205 00:12:18,600 --> 00:12:20,640 Speaker 2: what the data and evidence shows we're a five or 206 00:12:20,679 --> 00:12:23,640 Speaker 2: one C three non partisan organization. But I believe in 207 00:12:23,760 --> 00:12:26,760 Speaker 2: being transparent about worldview and assumption so people can make 208 00:12:26,800 --> 00:12:31,200 Speaker 2: their own judgments. I believe in submitting my worldview to evidence, 209 00:12:31,280 --> 00:12:34,640 Speaker 2: and let's have an evidence based conversation and let's see 210 00:12:34,679 --> 00:12:37,920 Speaker 2: whose policy perspectives are the most effective. 211 00:12:39,040 --> 00:12:41,800 Speaker 1: It ought to be possible to have an artificial intelligence 212 00:12:42,760 --> 00:12:46,239 Speaker 1: check on CBO where you could ask the artificial intelligence, 213 00:12:46,240 --> 00:12:49,640 Speaker 1: given this data, what do you come up with? And 214 00:12:49,720 --> 00:12:53,439 Speaker 1: I suspect it would be dramatically different and more accurate 215 00:12:53,720 --> 00:12:55,280 Speaker 1: than the Congressional Budget Office. 216 00:12:55,440 --> 00:12:59,840 Speaker 2: Absolutely, this technology has been proven to work in the 217 00:13:00,480 --> 00:13:02,880 Speaker 2: Let me give you an example. When most people think 218 00:13:02,920 --> 00:13:06,120 Speaker 2: about AI, they think CHAD, GPT, and those are all 219 00:13:06,120 --> 00:13:09,960 Speaker 2: the large language models, and it's very powerful technology. It 220 00:13:10,080 --> 00:13:13,440 Speaker 2: hallucinates their problems with it. But the better example is 221 00:13:13,480 --> 00:13:17,600 Speaker 2: something called alpha fold and alpha fold, if your listeners 222 00:13:17,600 --> 00:13:20,120 Speaker 2: are familiar with it, spend fifteen minutes reading about this, 223 00:13:20,160 --> 00:13:23,600 Speaker 2: because what alpha fold is is the chemistry field figured 224 00:13:23,600 --> 00:13:27,520 Speaker 2: out how to use AI to map proteins, and protein 225 00:13:27,559 --> 00:13:30,959 Speaker 2: mapping is critical for drug development, so they used AI 226 00:13:31,160 --> 00:13:35,160 Speaker 2: to do mapping prediction and they did fifty thousand years 227 00:13:35,160 --> 00:13:38,240 Speaker 2: of research in one year, and that's going to revolutionize 228 00:13:38,240 --> 00:13:41,400 Speaker 2: pharmaceutical development. So what we're talking about with aqueduct is 229 00:13:41,440 --> 00:13:44,280 Speaker 2: really the better analogy is it's like alpha fold for 230 00:13:44,400 --> 00:13:47,760 Speaker 2: public policy, where we're looking at all the relationships between 231 00:13:47,800 --> 00:13:50,840 Speaker 2: the data to enable you to do pattern recognition and 232 00:13:50,880 --> 00:13:54,160 Speaker 2: pattern prediction. Like a movie that I refer to a 233 00:13:54,200 --> 00:13:57,320 Speaker 2: lot too is called Minority Report. Okay, this is the 234 00:13:57,320 --> 00:14:00,600 Speaker 2: Tom Cruise film. It was all about stopping few your crime, 235 00:14:00,760 --> 00:14:02,880 Speaker 2: where this idea that you could figure out who's going 236 00:14:02,920 --> 00:14:05,560 Speaker 2: to commit a crime and stop it before it happened. Well, 237 00:14:06,000 --> 00:14:08,920 Speaker 2: we can prevent future spending crime, you can prevent future 238 00:14:08,960 --> 00:14:13,360 Speaker 2: waste by understanding the relationship between all these data sets. 239 00:14:13,760 --> 00:14:17,000 Speaker 2: And again it's about investing that authority within individuals so 240 00:14:17,080 --> 00:14:19,480 Speaker 2: they can make the decision. It's not about handing it 241 00:14:19,520 --> 00:14:21,640 Speaker 2: off to some mysterious technology. 242 00:14:23,120 --> 00:14:26,600 Speaker 1: You know. I just was looking today at Steve Moore's 243 00:14:26,640 --> 00:14:31,720 Speaker 1: newsletter and he has a report that the more we 244 00:14:31,800 --> 00:14:36,480 Speaker 1: invest in mass transit, the fewer people write it. The 245 00:14:36,600 --> 00:14:39,520 Speaker 1: number of people on mass transit going down every year, 246 00:14:39,880 --> 00:14:41,560 Speaker 1: or the amount of money the federal government puts in 247 00:14:41,640 --> 00:14:44,880 Speaker 1: goes up every year. It's astounding. 248 00:14:45,640 --> 00:14:48,200 Speaker 2: The best way to make something expensive is for government 249 00:14:48,240 --> 00:14:51,600 Speaker 2: to make it affordable because the central conceit of government 250 00:14:51,680 --> 00:14:53,920 Speaker 2: is that we know how to allocate scarce resources better 251 00:14:53,960 --> 00:14:57,040 Speaker 2: than the market. I'll give you a really good practical 252 00:14:57,040 --> 00:14:59,520 Speaker 2: example of how this works. So I talked about understanding 253 00:14:59,600 --> 00:15:03,240 Speaker 2: energy policy. At Open the Books. We have the biggest 254 00:15:03,320 --> 00:15:06,280 Speaker 2: data set ever put together. So there are thirteen thousand, 255 00:15:06,360 --> 00:15:08,800 Speaker 2: five hundred school districts in the United States. We have 256 00:15:08,920 --> 00:15:13,240 Speaker 2: coverage of about ninety percent. We did a comparison of 257 00:15:13,360 --> 00:15:16,640 Speaker 2: twelve thousand, five hundred school districts and did a correlation 258 00:15:16,760 --> 00:15:22,320 Speaker 2: study between expenditures on overhead versus student achievement. So here 259 00:15:22,360 --> 00:15:25,640 Speaker 2: we took two data sets. We compared student achievement with 260 00:15:25,720 --> 00:15:29,360 Speaker 2: overhead spending and guess what we found a negative correlation. 261 00:15:29,480 --> 00:15:31,800 Speaker 2: Now there is a slight negative correlation that the schools 262 00:15:31,800 --> 00:15:35,720 Speaker 2: that spent more on overhead had worse student achievement. So 263 00:15:35,800 --> 00:15:38,400 Speaker 2: that should peak people's interests because you know, as limited 264 00:15:38,440 --> 00:15:40,920 Speaker 2: government people, we know that more spending and education is 265 00:15:40,920 --> 00:15:43,640 Speaker 2: not the answer that's been studied, but no one has 266 00:15:43,720 --> 00:15:47,160 Speaker 2: looked at twelve thousand, five hundred school districts. So as 267 00:15:47,200 --> 00:15:49,720 Speaker 2: we build these arguments and data sets it's going to 268 00:15:49,800 --> 00:15:54,160 Speaker 2: again create super intelligent citizens. And every generation has these 269 00:15:54,560 --> 00:15:56,680 Speaker 2: conclusions they make that we look back and think, I 270 00:15:56,720 --> 00:16:00,480 Speaker 2: can't believe we believed XYZ was a sound way to 271 00:16:00,480 --> 00:16:04,080 Speaker 2: approach problem, like, for example, the use of leeches in medicine. 272 00:16:04,240 --> 00:16:07,080 Speaker 2: You know, for centuries we use leeches, and there was 273 00:16:07,160 --> 00:16:10,280 Speaker 2: no magic moment when people realized that was a dumb idea. 274 00:16:10,920 --> 00:16:14,760 Speaker 2: It took decades. Leeches peaked in the eighteen thirties eighteen fifties, 275 00:16:15,400 --> 00:16:17,080 Speaker 2: and then you had germ theory and other sort of 276 00:16:17,120 --> 00:16:20,520 Speaker 2: advances in technology and medicine, and it took decades to 277 00:16:20,520 --> 00:16:22,800 Speaker 2: move away from that. So there are things that we're 278 00:16:22,840 --> 00:16:26,000 Speaker 2: doing with the relationship between again the individual and the 279 00:16:26,000 --> 00:16:29,760 Speaker 2: state that make no sense. That is going to become 280 00:16:29,800 --> 00:16:31,920 Speaker 2: more and more clear the more we submit to an 281 00:16:31,920 --> 00:16:35,000 Speaker 2: evidence based conversation. That's what we're calling for and open 282 00:16:35,000 --> 00:16:38,240 Speaker 2: the books is let's open the books together and have 283 00:16:38,320 --> 00:16:40,240 Speaker 2: a fair fight on the left and right and see 284 00:16:40,280 --> 00:16:42,560 Speaker 2: who's right. Let's submit to the evidence. 285 00:16:43,560 --> 00:16:46,160 Speaker 1: One of the things you did. It's fascinating. I got 286 00:16:46,200 --> 00:16:50,080 Speaker 1: to know Paul Allen, the co creator of ancestry dot com, 287 00:16:50,160 --> 00:16:52,960 Speaker 1: when he was working at Gallup and he's very, very 288 00:16:52,960 --> 00:16:58,320 Speaker 1: smart and thinks neatly. And you've partnered with citizen portal ai, 289 00:16:58,400 --> 00:17:04,120 Speaker 1: which he helped create, and you now have this enormous 290 00:17:04,359 --> 00:17:08,480 Speaker 1: database that's searchable. It's a big jump for you to 291 00:17:08,600 --> 00:17:10,760 Speaker 1: go to work with suits in portalai and greet this 292 00:17:11,080 --> 00:17:12,080 Speaker 1: what led you to do that. 293 00:17:13,040 --> 00:17:16,280 Speaker 2: Paul and I are both I think we're unreasonably persistent 294 00:17:16,320 --> 00:17:20,400 Speaker 2: and optimistic people. So we were in different fields twenty 295 00:17:20,480 --> 00:17:22,920 Speaker 2: years ago again when the Google search engine came online 296 00:17:22,960 --> 00:17:25,199 Speaker 2: and we looked over the horizon and Cobran's office and 297 00:17:25,240 --> 00:17:27,919 Speaker 2: our team really got that right, like we called it. 298 00:17:27,960 --> 00:17:29,560 Speaker 2: We realized, this is going to be a big deal. 299 00:17:29,600 --> 00:17:31,560 Speaker 2: We need to get government spending out there as fast 300 00:17:31,600 --> 00:17:35,120 Speaker 2: as possible because that's where technology is moving. And then 301 00:17:35,160 --> 00:17:37,679 Speaker 2: Paul was in a similar spot, but in the private sector. 302 00:17:37,720 --> 00:17:41,440 Speaker 2: He realized, wow, people are passionate about their story. Let's 303 00:17:41,440 --> 00:17:43,840 Speaker 2: figure out how to get all these genealogy records online 304 00:17:44,200 --> 00:17:47,439 Speaker 2: and built this incredibly successful site called ancestry dot com. 305 00:17:47,960 --> 00:17:51,240 Speaker 2: So we were both entrepreneurs and pioneers in different fields. 306 00:17:52,040 --> 00:17:55,560 Speaker 2: And then in the interim years, when I left Cobran's office, 307 00:17:55,720 --> 00:17:58,119 Speaker 2: you know, I started my own consulting shop, and I 308 00:17:58,160 --> 00:18:02,000 Speaker 2: worked with not just nonprofits and political candidates, but Silicon 309 00:18:02,080 --> 00:18:06,200 Speaker 2: Valley companies. So I learned directly from CEOs how these 310 00:18:06,240 --> 00:18:08,960 Speaker 2: new technologies and search engines work and how they're built. 311 00:18:09,800 --> 00:18:12,480 Speaker 2: And again, the secret, as I alluded to before, is 312 00:18:12,520 --> 00:18:16,640 Speaker 2: an assembly line of data entry. When Adam Ajievsky unexpectedly 313 00:18:16,680 --> 00:18:18,760 Speaker 2: passed away a year and a half ago, I was 314 00:18:18,840 --> 00:18:22,280 Speaker 2: involved in Opening the Books for years. But that tragedy happened, 315 00:18:22,400 --> 00:18:24,520 Speaker 2: and the board asked me if I would come in 316 00:18:24,560 --> 00:18:28,560 Speaker 2: and lead the organization. And I immediately realized that because 317 00:18:28,600 --> 00:18:30,800 Speaker 2: of all my work in data, Open the Books was 318 00:18:30,840 --> 00:18:34,280 Speaker 2: sitting on an incredibly powerful gold mine. Again, this enriched 319 00:18:34,359 --> 00:18:38,280 Speaker 2: geranium set of data. And so then a few months later, 320 00:18:38,320 --> 00:18:41,639 Speaker 2: this guy, Paul Allen started to contact me, and his 321 00:18:41,840 --> 00:18:45,439 Speaker 2: approach to me was actually not unlike Adamanjievsky's approach to 322 00:18:45,480 --> 00:18:48,320 Speaker 2: me when I worked for Coburn. He just wouldn't stop 323 00:18:48,320 --> 00:18:51,199 Speaker 2: asking to meet and have a conversation. So usually there 324 00:18:51,200 --> 00:18:53,160 Speaker 2: are two things that are true in the situations where 325 00:18:53,440 --> 00:18:55,879 Speaker 2: they're either a crank or they're a genius. Paul Allen 326 00:18:55,960 --> 00:19:01,720 Speaker 2: is a genius, right, very smart, very principal, very fundamentally 327 00:19:01,760 --> 00:19:05,359 Speaker 2: decent person, so it has been an absolute joy to 328 00:19:05,400 --> 00:19:07,960 Speaker 2: collaborate with him. So what we're doing again is we're 329 00:19:08,040 --> 00:19:11,640 Speaker 2: enabling his users to access our data in a way 330 00:19:11,680 --> 00:19:13,840 Speaker 2: that's free of charge. He has a separate for profit 331 00:19:14,520 --> 00:19:18,080 Speaker 2: model that he's using, but to me, it's a natural partnership. 332 00:19:18,560 --> 00:19:21,040 Speaker 2: The reason we've collected all this data again, and the 333 00:19:21,080 --> 00:19:24,960 Speaker 2: whole genesis of the Cobra Obama Bill, was we wanted 334 00:19:24,960 --> 00:19:27,080 Speaker 2: to keep the center of gravity and authority with we 335 00:19:27,160 --> 00:19:31,120 Speaker 2: the people. Again, that's the timeless struggle throughout history, this 336 00:19:31,200 --> 00:19:35,119 Speaker 2: battle between freedom and authoritarianism. So it's really a natural 337 00:19:35,160 --> 00:19:38,199 Speaker 2: outgrowth of that belief system that led us to work together. 338 00:19:38,600 --> 00:19:42,720 Speaker 2: This is one step. Paul has built a fabulous tool 339 00:19:43,000 --> 00:19:46,399 Speaker 2: that is really designed on the user interface. But what 340 00:19:46,480 --> 00:19:49,119 Speaker 2: I think is going to be transformative over time is 341 00:19:49,160 --> 00:19:52,840 Speaker 2: the back end Aqueduct project, which is the merging of 342 00:19:52,880 --> 00:19:55,240 Speaker 2: all the data sets. So it's the merging of these 343 00:19:55,280 --> 00:19:58,760 Speaker 2: different sets that again are like Galileo looking at the 344 00:19:58,800 --> 00:20:02,520 Speaker 2: stars over for undred years ago. We're still doing pattern 345 00:20:03,119 --> 00:20:06,639 Speaker 2: recognition pattern prediction based on that technology. We're in a 346 00:20:06,720 --> 00:20:09,480 Speaker 2: renaissance moment with representative government. 347 00:20:26,160 --> 00:20:31,000 Speaker 1: How much did the emergence of artificial intelligence and very 348 00:20:31,200 --> 00:20:35,320 Speaker 1: large scale computing. How central is that to the kind 349 00:20:35,359 --> 00:20:36,960 Speaker 1: of revolution that you're undertaking? 350 00:20:38,480 --> 00:20:41,439 Speaker 2: Very central, because I think the Alpha fold example proves 351 00:20:41,480 --> 00:20:44,240 Speaker 2: that this technology work. So we don't have time to 352 00:20:44,280 --> 00:20:48,080 Speaker 2: describe quantum computing. But the people that built Alpha fold, 353 00:20:48,119 --> 00:20:51,199 Speaker 2: again we're talking about chemists. They were concerned about molecules 354 00:20:51,200 --> 00:20:54,000 Speaker 2: and you deal with quantum uncertainty at that level, and 355 00:20:54,040 --> 00:20:56,760 Speaker 2: they thought, well, in order to ever do anything meaningfully predicted, 356 00:20:56,800 --> 00:20:59,680 Speaker 2: we'd have to have quantum computing. And that's of course 357 00:20:59,680 --> 00:21:02,399 Speaker 2: the next level of competing technology that exists, but it 358 00:21:02,440 --> 00:21:05,000 Speaker 2: isn't quite at the level of doing the simulations that 359 00:21:05,040 --> 00:21:07,760 Speaker 2: we need, but it could be. But they prove that 360 00:21:07,840 --> 00:21:10,719 Speaker 2: you can just use AI and an algorithm to do 361 00:21:10,760 --> 00:21:13,800 Speaker 2: these pattern predictions, and if they can figure that out 362 00:21:13,840 --> 00:21:16,240 Speaker 2: at the molecular level, we could certainly do a lot 363 00:21:16,280 --> 00:21:19,480 Speaker 2: more than we're doing on the policy level, because in 364 00:21:19,520 --> 00:21:21,720 Speaker 2: the policy space, you're dealing with economics, you're dealing with 365 00:21:21,720 --> 00:21:25,240 Speaker 2: an incredibly complex set of unknowns, where the way the 366 00:21:25,280 --> 00:21:30,680 Speaker 2: technology really works is doing essentially in almost an unlimited 367 00:21:30,720 --> 00:21:33,240 Speaker 2: amount of future simulations. So it's like a time machine 368 00:21:33,280 --> 00:21:36,119 Speaker 2: where you would enter one variable, let's say one hundred 369 00:21:36,119 --> 00:21:39,879 Speaker 2: billion dollars on mass transit to your example earlier. Using 370 00:21:39,920 --> 00:21:44,640 Speaker 2: this tool, you can simulate trillions of future realities or millions, 371 00:21:45,240 --> 00:21:48,960 Speaker 2: and then compare the result in those future simulations, and 372 00:21:49,000 --> 00:21:51,720 Speaker 2: you get a probability curve to figure out what is 373 00:21:51,760 --> 00:21:54,639 Speaker 2: the impact of those things. That's what AI can do 374 00:21:54,720 --> 00:21:57,560 Speaker 2: that we can't do with Excel. So as a staffer, 375 00:21:57,680 --> 00:22:00,119 Speaker 2: we would beat our heads against the Excel wall at 376 00:22:00,160 --> 00:22:02,840 Speaker 2: least some much you could do with traditional pen and 377 00:22:02,920 --> 00:22:07,960 Speaker 2: paper or the Excel spreadsheet, and AI allows next level comparisons. 378 00:22:08,520 --> 00:22:13,280 Speaker 1: We're entering a period right now where even with traditional computing, 379 00:22:13,320 --> 00:22:17,960 Speaker 1: because of scale, you're going to have real time data 380 00:22:18,200 --> 00:22:22,760 Speaker 1: granularity that was unthinkable, and you're going to be able 381 00:22:22,800 --> 00:22:26,879 Speaker 1: to have data that covers virtually everything that happens in 382 00:22:26,920 --> 00:22:31,240 Speaker 1: American government. If we do get to quantum computing, which 383 00:22:31,240 --> 00:22:34,760 Speaker 1: we probably will at some point, that changes everything overnight 384 00:22:35,280 --> 00:22:39,479 Speaker 1: and gives you a totally different level of capabilities. But 385 00:22:39,600 --> 00:22:44,800 Speaker 1: isn't also true that these large models I actually learn 386 00:22:45,240 --> 00:22:47,800 Speaker 1: the more they work with you, the more they understand 387 00:22:47,840 --> 00:22:52,640 Speaker 1: what you are looking for, the more sophisticated their analysis 388 00:22:52,640 --> 00:22:53,159 Speaker 1: will become. 389 00:22:53,680 --> 00:22:56,639 Speaker 2: That's absolutely correct. That brings up, a critically important point 390 00:22:56,720 --> 00:23:00,720 Speaker 2: that I think separates our work from other people talking 391 00:23:00,760 --> 00:23:05,480 Speaker 2: about using economic modeling with AI. Getting the coalition right 392 00:23:05,600 --> 00:23:07,879 Speaker 2: is as important as getting the technology right, because if 393 00:23:07,920 --> 00:23:10,679 Speaker 2: people don't trust the technology, they're never going to use 394 00:23:10,680 --> 00:23:12,160 Speaker 2: it and it's not going to be adopted. So if 395 00:23:12,160 --> 00:23:14,760 Speaker 2: people think, oh, this is a right wing thing or 396 00:23:14,800 --> 00:23:17,399 Speaker 2: this is a Republican thing, they're not going to use it. 397 00:23:17,440 --> 00:23:20,360 Speaker 2: So with the Aquidup project, we're building a steering committee 398 00:23:20,400 --> 00:23:24,320 Speaker 2: of people from all over the spectrum, but we're all 399 00:23:24,400 --> 00:23:26,840 Speaker 2: united in a belief that we want to have an 400 00:23:26,840 --> 00:23:30,040 Speaker 2: evidence based conversation. So you can be transparent about your 401 00:23:30,080 --> 00:23:33,560 Speaker 2: world view, your assumptions. But let's submit our assumptions to 402 00:23:33,600 --> 00:23:36,359 Speaker 2: the evidence, and let's bring a group of people together 403 00:23:36,560 --> 00:23:39,760 Speaker 2: and explain to the public step by step what data 404 00:23:39,760 --> 00:23:43,159 Speaker 2: sets we're including, how it's being shaped so that the 405 00:23:43,240 --> 00:23:46,440 Speaker 2: technology can be used and trusted. There aren't these bright 406 00:23:46,480 --> 00:23:49,600 Speaker 2: lines where we woke up one day realize leeches were 407 00:23:49,640 --> 00:23:53,280 Speaker 2: really dumb to use for medicine. It takes time, and 408 00:23:53,320 --> 00:23:55,800 Speaker 2: so it may take ten or twenty years, but hopefully 409 00:23:55,840 --> 00:23:59,359 Speaker 2: faster for this technology to be adopted and used in 410 00:23:59,400 --> 00:24:00,520 Speaker 2: a very practice way. 411 00:24:01,119 --> 00:24:04,960 Speaker 1: If I understand it. The more information you generate, the 412 00:24:04,960 --> 00:24:11,080 Speaker 1: more it's analyzed, the clearer it becomes, the more potential 413 00:24:11,119 --> 00:24:14,920 Speaker 1: control you're giving the citizenship to actually check in balance 414 00:24:15,440 --> 00:24:18,480 Speaker 1: what's being done with the politicians and what's being done 415 00:24:18,520 --> 00:24:22,320 Speaker 1: with the bureaucracy. And in that sense, it all becomes 416 00:24:22,320 --> 00:24:27,160 Speaker 1: a kind of partnership between the technology and the citizen. 417 00:24:27,680 --> 00:24:31,080 Speaker 2: Exactly. It's a partnership led by the citizen. That's the 418 00:24:31,080 --> 00:24:34,000 Speaker 2: critically important point, because that's so much the anxiety around 419 00:24:34,040 --> 00:24:36,960 Speaker 2: AI is quote, losing control of the technology. And I'm not 420 00:24:37,000 --> 00:24:39,520 Speaker 2: dismissive of those fears, but I think we have a 421 00:24:39,560 --> 00:24:43,200 Speaker 2: responsibility to shape it. Because technology doesn't have a reverse gear. 422 00:24:43,760 --> 00:24:45,560 Speaker 2: That doesn't work. We're not going to put this gen 423 00:24:45,720 --> 00:24:48,520 Speaker 2: back in the bottle, So let's direct the genie. And 424 00:24:48,600 --> 00:24:51,200 Speaker 2: I think one of the most important policy things that 425 00:24:51,280 --> 00:24:55,560 Speaker 2: Congress the administration can do is to do everything possible 426 00:24:55,600 --> 00:24:59,399 Speaker 2: to push for real time transparency, maximum transparency at all levels, 427 00:24:59,480 --> 00:25:02,919 Speaker 2: in all way, because that will make citizens more intelligent. 428 00:25:03,520 --> 00:25:06,720 Speaker 2: And one of the great breakthroughs of DOGE when Elon 429 00:25:06,800 --> 00:25:09,840 Speaker 2: and DOGE got access to the Treasury payment system. For 430 00:25:09,880 --> 00:25:11,600 Speaker 2: people that don't know what that is, the Treasury payment 431 00:25:11,640 --> 00:25:13,360 Speaker 2: system is really where the money goes out the door. 432 00:25:14,000 --> 00:25:16,400 Speaker 2: And there was all this controversy because Elon got access 433 00:25:16,440 --> 00:25:18,240 Speaker 2: to it. People were afraid I was he going to 434 00:25:18,280 --> 00:25:20,720 Speaker 2: turn off SOERCI security payments for people that voted for 435 00:25:20,760 --> 00:25:24,080 Speaker 2: Kamala Harris. You know, all that was nonsense and didn't 436 00:25:24,119 --> 00:25:26,840 Speaker 2: come to pass. But I had this epiphany that this 437 00:25:26,960 --> 00:25:29,919 Speaker 2: is really the time to push for giving all Americans 438 00:25:29,920 --> 00:25:32,639 Speaker 2: access to that system, because the Treasury payment system is 439 00:25:32,680 --> 00:25:35,560 Speaker 2: like the Administrative States Holy of Holies, you know, only 440 00:25:35,600 --> 00:25:39,080 Speaker 2: the high priests shall enter, when really it should be 441 00:25:39,119 --> 00:25:42,359 Speaker 2: America's checkbook because it's our money, it's not the government's money. 442 00:25:42,400 --> 00:25:43,679 Speaker 2: Just as you and I can go and look at 443 00:25:43,720 --> 00:25:46,720 Speaker 2: our personal checking account whatever we wish, we should have 444 00:25:46,760 --> 00:25:49,199 Speaker 2: that same right to look at the government's checkbook in 445 00:25:49,240 --> 00:25:52,600 Speaker 2: real time. And there are legislative chance things that can 446 00:25:52,640 --> 00:25:54,960 Speaker 2: be done, and we're working on Chip Roy did a 447 00:25:55,000 --> 00:25:57,959 Speaker 2: bill that would give Congress access to the Treasury payment system, 448 00:25:58,400 --> 00:26:00,720 Speaker 2: and we're working with Chip and other members on both 449 00:26:00,760 --> 00:26:04,520 Speaker 2: sides of the aisle to really enable real time transparency, 450 00:26:04,520 --> 00:26:07,000 Speaker 2: which would effectively be an upgrade to the USA spending 451 00:26:07,480 --> 00:26:10,520 Speaker 2: and that is achievable and urgent to do as quickly. Again, 452 00:26:10,560 --> 00:26:13,840 Speaker 2: if you care about fraud, Again, there's no constituency for 453 00:26:13,920 --> 00:26:17,360 Speaker 2: fraud except for the fraudster. They're the only person benefitting 454 00:26:17,400 --> 00:26:19,520 Speaker 2: from it. So if you're on the left or right, 455 00:26:19,640 --> 00:26:22,320 Speaker 2: there's no reason to be against greater transparency. 456 00:26:22,920 --> 00:26:25,359 Speaker 1: You've worked with this a long time. You clearly have 457 00:26:25,400 --> 00:26:30,320 Speaker 1: a general vision. You understand the technology that's emerging. If 458 00:26:30,359 --> 00:26:35,480 Speaker 1: your vision succeeds and the technology continues to evolve, what 459 00:26:35,600 --> 00:26:39,280 Speaker 1: do you see in the long run as the relationship 460 00:26:39,320 --> 00:26:40,720 Speaker 1: between citizens and government. 461 00:26:41,200 --> 00:26:44,480 Speaker 2: I really see we're at the most important renaissance point 462 00:26:44,800 --> 00:26:47,960 Speaker 2: in hundreds, if not thousands of years. Two thousand years ago, 463 00:26:48,000 --> 00:26:51,040 Speaker 2: Cicero said true law is right, reason and agreement with nature. 464 00:26:51,680 --> 00:26:54,480 Speaker 2: He made assumptions about nature that science has proven over 465 00:26:54,520 --> 00:26:59,040 Speaker 2: and over again, that we know that systems flourish when 466 00:26:59,040 --> 00:27:03,040 Speaker 2: there's balance. For example, when you think about astrophysics, the 467 00:27:03,040 --> 00:27:06,280 Speaker 2: Goldilock zone. Okay, for a planet to have liquid water, 468 00:27:06,600 --> 00:27:08,240 Speaker 2: it can't be too close to the sun, it can't 469 00:27:08,240 --> 00:27:11,560 Speaker 2: be too far away for a star to form, you 470 00:27:11,640 --> 00:27:14,080 Speaker 2: can't have too much gravity because then it becomes a 471 00:27:14,119 --> 00:27:16,600 Speaker 2: black hole. Or if you don't have enough, it never 472 00:27:16,680 --> 00:27:21,240 Speaker 2: forms and everything's just flying around chaotically. Progressives. We typically 473 00:27:21,280 --> 00:27:24,399 Speaker 2: talk to Coburner over the years, the accusation may be, well, 474 00:27:24,440 --> 00:27:26,600 Speaker 2: your anti government's like, now, we're not anti government. We 475 00:27:26,680 --> 00:27:30,960 Speaker 2: just believe in the right balance that usually history errs 476 00:27:30,960 --> 00:27:33,320 Speaker 2: in one direction or the other. And so what I 477 00:27:33,359 --> 00:27:36,080 Speaker 2: see is we're in the beginning of one of the 478 00:27:36,119 --> 00:27:41,320 Speaker 2: great advances of citizen engagement, and I see this tool 479 00:27:41,520 --> 00:27:44,000 Speaker 2: as one of the most important ways to address our 480 00:27:44,040 --> 00:27:47,320 Speaker 2: debt burden, to enable economic growth, and again to deal 481 00:27:47,359 --> 00:27:51,560 Speaker 2: with the debt issue. As you've studied for decades, it's 482 00:27:51,720 --> 00:27:55,280 Speaker 2: largely a healthcare entitlement problem. So this transparency theme is 483 00:27:55,320 --> 00:27:58,520 Speaker 2: connected at a very deep level to all of these problems. 484 00:27:58,560 --> 00:28:01,720 Speaker 2: So if you have greater transparency, you have greater competition, 485 00:28:01,840 --> 00:28:06,160 Speaker 2: you have greater accountability. You cannot have accountability without visibility, 486 00:28:06,400 --> 00:28:09,919 Speaker 2: and you can allow markets to work without visibility, and 487 00:28:10,119 --> 00:28:13,240 Speaker 2: scarce resources can't become more abundant resources unless you have 488 00:28:13,320 --> 00:28:17,280 Speaker 2: competition and entrepreneurship. I have a very optimistic vision that 489 00:28:17,320 --> 00:28:19,119 Speaker 2: I think we need to be clear eyed and realistic 490 00:28:19,160 --> 00:28:22,800 Speaker 2: about the dangers. And we're in this era of frothy expectation. 491 00:28:23,080 --> 00:28:26,080 Speaker 2: Like every technology we had the dot com bubble, and 492 00:28:26,119 --> 00:28:28,240 Speaker 2: there's a lot of chatter and talk about AI that's 493 00:28:28,240 --> 00:28:30,520 Speaker 2: not going to go anywhere. But what really separates I 494 00:28:30,560 --> 00:28:33,800 Speaker 2: think what we're doing one is the quality of the coalition. 495 00:28:34,040 --> 00:28:36,760 Speaker 2: We have the ability to bring together people who are 496 00:28:37,040 --> 00:28:40,040 Speaker 2: very serious leaders in the field. But then secondly, we 497 00:28:40,080 --> 00:28:42,160 Speaker 2: have the actual data. If you talk about AI, you 498 00:28:42,200 --> 00:28:44,400 Speaker 2: have no data to bring, you're not really doing much. 499 00:28:45,080 --> 00:28:47,720 Speaker 1: Sort of a marriage made in heaven because you now 500 00:28:47,760 --> 00:28:52,760 Speaker 1: have an ability to bring the material to transform it, 501 00:28:53,160 --> 00:28:55,640 Speaker 1: but you also able to bring the material, and so 502 00:28:55,760 --> 00:28:59,000 Speaker 1: the two coming together. I predict the next three to 503 00:28:59,080 --> 00:29:02,840 Speaker 1: five years open the books we'll have an astonishing impact 504 00:29:03,240 --> 00:29:06,280 Speaker 1: on government at every level, first in the US, but 505 00:29:06,360 --> 00:29:09,480 Speaker 1: then around the world. John, thank you for joining me. 506 00:29:10,000 --> 00:29:13,240 Speaker 1: Our listeners can see the work you're doing on transparency 507 00:29:13,720 --> 00:29:18,440 Speaker 1: by going to openthebooks dot com, and people conscribe to 508 00:29:18,480 --> 00:29:23,959 Speaker 1: your substack at Opendthebooks dot substack dot com. This has 509 00:29:23,960 --> 00:29:26,480 Speaker 1: been a great conversation. I want to check in with 510 00:29:26,520 --> 00:29:28,360 Speaker 1: you again in a couple months because I have a 511 00:29:28,400 --> 00:29:31,280 Speaker 1: hunch you're going to keep evolving and it's going to 512 00:29:31,280 --> 00:29:32,320 Speaker 1: get even more exciting. 513 00:29:33,200 --> 00:29:35,440 Speaker 2: Well, I'm thrilled to have the opportunity. Thank you so much, 514 00:29:35,440 --> 00:29:38,760 Speaker 2: mister speaker, for your vision and your deep understanding of 515 00:29:38,840 --> 00:29:41,000 Speaker 2: history and also the future and the optimism that you're 516 00:29:41,040 --> 00:29:42,400 Speaker 2: bringing to this dialogue. 517 00:29:45,760 --> 00:29:48,600 Speaker 1: Thank you to my guest, John Hart. News World is 518 00:29:48,640 --> 00:29:52,800 Speaker 1: produced by gamers, Sweet sixty and iHeartMedia. Our executive producer 519 00:29:52,840 --> 00:29:57,600 Speaker 1: is Guarnsey Sloan. Our researcher is Rachel Peterson. The artwork 520 00:29:57,640 --> 00:30:01,720 Speaker 1: for the show was created by Steve and special thanks 521 00:30:01,760 --> 00:30:04,520 Speaker 1: to the team at Gingrish three sixty. If you've been 522 00:30:04,560 --> 00:30:07,800 Speaker 1: enjoying Newtsworld, I hope you'll go to Apple Podcast and 523 00:30:07,880 --> 00:30:10,480 Speaker 1: both rate us with five stars and give us a 524 00:30:10,520 --> 00:30:14,600 Speaker 1: review so others can learn what it's all about. Join 525 00:30:14,640 --> 00:30:18,520 Speaker 1: me on substack at gingrish sweet sixty dot net. I'm 526 00:30:18,600 --> 00:30:20,720 Speaker 1: newt Gingrich. This is Newtsworld.