1 00:00:02,520 --> 00:00:24,400 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. This is Wall Street Week. 2 00:00:24,640 --> 00:00:28,880 Speaker 1: I'm David Weston bringing you stories of capitalism this week. 3 00:00:28,960 --> 00:00:32,640 Speaker 1: As President Trump takes drastic action to curtail federal spending, 4 00:00:33,040 --> 00:00:36,000 Speaker 1: Ray Dallio of Bridgewater lays out his plan for what 5 00:00:36,200 --> 00:00:41,240 Speaker 1: needs to be done. Plus, Pfizer CEO Albert Borla announces 6 00:00:41,280 --> 00:00:47,239 Speaker 1: earnings that include his huge investment in tackling cancer. But 7 00:00:47,360 --> 00:00:51,680 Speaker 1: we start with a story about lost opportunity, the opportunity 8 00:00:51,880 --> 00:00:54,880 Speaker 1: to make real progress fighting one of the most devastating 9 00:00:54,920 --> 00:00:59,520 Speaker 1: diseases we know, Alzheimer's, with questions now being raised about 10 00:00:59,600 --> 00:01:02,520 Speaker 1: much of the work that's gone into finding a cure. 11 00:01:06,280 --> 00:01:10,080 Speaker 2: Alzheimer's disease effects millions and millions of Americans, but not 12 00:01:10,240 --> 00:01:13,520 Speaker 2: just millions and millions of individual patients, but also orders 13 00:01:13,520 --> 00:01:15,880 Speaker 2: of magnitude larger than that because of the effects on 14 00:01:15,920 --> 00:01:19,680 Speaker 2: their families and their effects on larger society. This is 15 00:01:19,720 --> 00:01:20,920 Speaker 2: a massive problem. 16 00:01:21,400 --> 00:01:26,000 Speaker 1: Nearly seven million Americans over sixty five, about one inh nine, 17 00:01:26,319 --> 00:01:30,680 Speaker 1: suffer from Alzheimer's. Costs of care are soaring three hundred 18 00:01:30,680 --> 00:01:34,200 Speaker 1: and sixty billion dollars in twenty twenty four, projected to 19 00:01:34,280 --> 00:01:38,360 Speaker 1: hit one trillion dollars by twenty fifty, four hundred thousand 20 00:01:38,440 --> 00:01:42,160 Speaker 1: dollars per person, with seventy percent born by the family. 21 00:01:43,319 --> 00:01:46,399 Speaker 1: The race for a cure is on, but not without 22 00:01:46,440 --> 00:01:51,880 Speaker 1: its setbacks. As Vanderbilt neurologist Matthew Shragg explains, so in 23 00:01:51,880 --> 00:01:55,120 Speaker 1: recent years we've had some candidates for treatment to come up, 24 00:01:55,480 --> 00:01:58,720 Speaker 1: even been approved to the FDA and been sold ultimately, 25 00:01:58,760 --> 00:02:00,000 Speaker 1: although with some controversy. 26 00:02:00,240 --> 00:02:02,560 Speaker 2: This family of drugs that we're talking about are a 27 00:02:02,560 --> 00:02:06,000 Speaker 2: group of antibodies, and antibodies are a molecule that your 28 00:02:06,040 --> 00:02:09,480 Speaker 2: body produces naturally. These ones are synthetic ones that have 29 00:02:09,560 --> 00:02:13,200 Speaker 2: been engineered, and they're designed to bind up a little 30 00:02:13,200 --> 00:02:16,280 Speaker 2: tiny protein called beta amyloid, and we think that it's 31 00:02:16,320 --> 00:02:19,320 Speaker 2: poisonous to the brain, and that's thought to be the 32 00:02:19,480 --> 00:02:24,440 Speaker 2: fundamental driver of Alzheimer's disease. The antibodies administered to patients 33 00:02:24,800 --> 00:02:27,400 Speaker 2: bind up to that beta amyloid and help to remove 34 00:02:27,440 --> 00:02:30,080 Speaker 2: it from the brain, and the idea is hopefully that 35 00:02:30,120 --> 00:02:32,840 Speaker 2: will interfere with the progression of Alzheimer's disease. 36 00:02:33,360 --> 00:02:34,280 Speaker 3: Hopefully it will. 37 00:02:34,720 --> 00:02:36,880 Speaker 1: But in fact, there have been some treatments that appear 38 00:02:36,919 --> 00:02:39,600 Speaker 1: to make some progress on the science but don't have 39 00:02:39,639 --> 00:02:40,960 Speaker 1: clinical results. 40 00:02:40,680 --> 00:02:44,320 Speaker 2: Well, so fundamentally what patients care about is not is 41 00:02:44,320 --> 00:02:46,960 Speaker 2: to beta amyloid removed from the brain right. Patients are 42 00:02:47,000 --> 00:02:50,200 Speaker 2: concerned about can they think clearly? Can they remember things? 43 00:02:50,760 --> 00:02:54,760 Speaker 2: And these drugs didn't robustly show in effect, you had 44 00:02:55,360 --> 00:02:59,800 Speaker 2: a reasonably good biochemical outcome, but that really didn't help people. 45 00:03:00,760 --> 00:03:03,800 Speaker 4: The problem I think that we've seen over thirty years 46 00:03:03,800 --> 00:03:07,400 Speaker 4: of study of the amyloid hypothesis and the development of 47 00:03:08,040 --> 00:03:14,920 Speaker 4: dozens of anti amyloid drugs and vaccines, is that it 48 00:03:14,960 --> 00:03:15,840 Speaker 4: hasn't panned out. 49 00:03:16,520 --> 00:03:20,600 Speaker 1: Charles Pillar is an investigative reporter for Science magazine. 50 00:03:20,840 --> 00:03:27,280 Speaker 4: Many many neurologists and other doctors whose patients have asked 51 00:03:27,320 --> 00:03:32,120 Speaker 4: about these drugs have the opinion that this statistical effect 52 00:03:32,240 --> 00:03:35,480 Speaker 4: is not really a clinical effect. In other words, the 53 00:03:35,720 --> 00:03:40,000 Speaker 4: impact is so subtle as to be imperceptible to patients 54 00:03:40,000 --> 00:03:44,040 Speaker 4: of their families. However, these drugs also come with great risks, 55 00:03:44,080 --> 00:03:47,640 Speaker 4: including the risk of death or brain damage associated with 56 00:03:47,960 --> 00:03:50,800 Speaker 4: brain swelling or bleeding that they can cause. 57 00:03:51,440 --> 00:03:56,040 Speaker 1: Biogen received FDA approval for the first anti amyloid drug 58 00:03:56,120 --> 00:04:00,000 Speaker 1: in twenty twenty one, leading to hope for Alzheimer's patients. 59 00:04:00,640 --> 00:04:05,240 Speaker 1: This and other similar approvals, Pillar says ignited a market frenzy, 60 00:04:05,760 --> 00:04:09,200 Speaker 1: but Shrag calls it a dead end, a view echoed 61 00:04:09,240 --> 00:04:13,520 Speaker 1: in Pillar's new book Doctored Fraud, Arrogance, and Tragedy In 62 00:04:13,560 --> 00:04:17,279 Speaker 1: the Quest to cure Alzheimer's. There's been a fair amount 63 00:04:17,320 --> 00:04:20,960 Speaker 1: of research work, money put into it. Why is it 64 00:04:21,000 --> 00:04:22,520 Speaker 1: that we haven't made more progress? 65 00:04:22,800 --> 00:04:25,760 Speaker 2: The simplest answer is that it's a complicated disease. I've 66 00:04:25,800 --> 00:04:29,279 Speaker 2: become more concerned as time has gone on that research 67 00:04:29,320 --> 00:04:31,160 Speaker 2: integrity may be part of that equation. 68 00:04:32,080 --> 00:04:35,839 Speaker 1: After Shreg criticized the FDA's approval of the biogen drug, 69 00:04:36,200 --> 00:04:41,160 Speaker 1: two physicians skeptical of another Alzheimer's treatment, this one from Cassava, 70 00:04:41,520 --> 00:04:45,560 Speaker 1: hired him to do an independent investigation. Cassava stock had 71 00:04:45,640 --> 00:04:49,400 Speaker 1: soared nine hundred percent, leading to a five point four 72 00:04:49,560 --> 00:04:53,440 Speaker 1: billion dollar valuation, and the physicians ended up shortening the 73 00:04:53,480 --> 00:04:56,599 Speaker 1: stock and filing a whistleblower complaint with the SEC. 74 00:04:57,480 --> 00:05:00,800 Speaker 2: A number of individuals were concerned that something fishy might 75 00:05:00,839 --> 00:05:03,760 Speaker 2: be going on and started to examine the basic science 76 00:05:03,800 --> 00:05:07,000 Speaker 2: and some of the other supporting information around this drug 77 00:05:07,560 --> 00:05:10,520 Speaker 2: and started to develop concerns and their attorney reached out 78 00:05:10,600 --> 00:05:13,440 Speaker 2: to me to be part of the process to evaluate 79 00:05:13,520 --> 00:05:17,000 Speaker 2: the science behind this drug, Sima Philem and Cassava sciences. 80 00:05:17,279 --> 00:05:19,360 Speaker 1: What did you find when you started looking into it? 81 00:05:19,880 --> 00:05:24,240 Speaker 2: So a lot of the basic science that supported the 82 00:05:24,320 --> 00:05:27,200 Speaker 2: use of this drug came from a single laboratory, a 83 00:05:27,279 --> 00:05:30,279 Speaker 2: laboratory run by a gentleman named how Yangwong that city, 84 00:05:30,360 --> 00:05:33,359 Speaker 2: University of New York. When we looked at his work, 85 00:05:33,520 --> 00:05:38,200 Speaker 2: we found an extended pattern of alterations in the data 86 00:05:38,480 --> 00:05:41,200 Speaker 2: coming out of this laboratory. A lot of the data, 87 00:05:41,680 --> 00:05:44,240 Speaker 2: a lot of these sorts of experiments, often the output 88 00:05:44,279 --> 00:05:47,640 Speaker 2: is an image, and sometimes you can see when you 89 00:05:47,680 --> 00:05:49,560 Speaker 2: look at an image things that make you think it's 90 00:05:49,560 --> 00:05:52,039 Speaker 2: been altered in the same way you could walk through 91 00:05:52,080 --> 00:05:54,680 Speaker 2: a magazine island, look at a photo and say, maybe 92 00:05:54,720 --> 00:05:58,520 Speaker 2: that's been touched up. Something strikes you as something unnatural 93 00:05:58,560 --> 00:06:01,640 Speaker 2: about that image. Went through his work, we found lots 94 00:06:01,680 --> 00:06:03,960 Speaker 2: of that sort of problems. 95 00:06:04,040 --> 00:06:07,000 Speaker 1: But just getting it wrong is something different that can 96 00:06:07,080 --> 00:06:08,120 Speaker 1: put into any scientist. 97 00:06:08,279 --> 00:06:12,200 Speaker 2: We all get it wrong. Yeah, absolutely, this is not 98 00:06:12,360 --> 00:06:16,640 Speaker 2: about making mistakes. We're looking at a very coherent pattern 99 00:06:17,160 --> 00:06:22,120 Speaker 2: of altering data to fit certain hypotheses. When you collect 100 00:06:22,200 --> 00:06:25,279 Speaker 2: your data right, there should be very little reason to 101 00:06:25,560 --> 00:06:29,360 Speaker 2: make cut and pace type changes within an image. You know, 102 00:06:29,520 --> 00:06:32,719 Speaker 2: I think there are things that you can see that 103 00:06:32,800 --> 00:06:34,760 Speaker 2: are universally inappropriate. 104 00:06:35,440 --> 00:06:39,279 Speaker 1: Shrag's findings supported a citizens petition urging the FDA to 105 00:06:39,320 --> 00:06:40,760 Speaker 1: halt clinical trials. 106 00:06:41,400 --> 00:06:47,000 Speaker 5: I was tagged on Twitter in a post where somebody said, hey, Elizabeth, 107 00:06:47,120 --> 00:06:49,760 Speaker 5: you need to look at this, and it was a 108 00:06:49,800 --> 00:06:51,400 Speaker 5: link to a citizen petition. 109 00:06:52,080 --> 00:06:56,599 Speaker 1: Dutch microbiologist Elizabeth Bick is a forensic image expert and 110 00:06:56,800 --> 00:07:01,960 Speaker 1: pioneer of crowdsourcing to uncover flawed studies. Using such platforms 111 00:07:01,960 --> 00:07:06,080 Speaker 1: as pub Peer, she and other sleuths publicly share evidence 112 00:07:06,120 --> 00:07:07,880 Speaker 1: of apparent data manipulation. 113 00:07:08,680 --> 00:07:12,480 Speaker 5: These were photos of these scientific papers that looked at 114 00:07:12,560 --> 00:07:16,440 Speaker 5: Western blots, which are protein blots, and it showed all 115 00:07:16,520 --> 00:07:19,440 Speaker 5: kinds of problems in these scientific papers, and it looked 116 00:07:19,480 --> 00:07:24,400 Speaker 5: like a lot of that body of work contained potential 117 00:07:24,520 --> 00:07:28,560 Speaker 5: fraudulent results. And so I completely agree that it would 118 00:07:28,600 --> 00:07:32,240 Speaker 5: make sense to ask the FDA to hold the trials 119 00:07:32,400 --> 00:07:35,680 Speaker 5: on humans, because if the work done on animals already 120 00:07:35,680 --> 00:07:39,239 Speaker 5: looks fraudulent, it's very unlikely that a book work. 121 00:07:39,320 --> 00:07:42,800 Speaker 1: Do you know what ultimately happened the Cassava and its medication. 122 00:07:42,960 --> 00:07:46,280 Speaker 2: There have been a number of investigations. The Department of 123 00:07:46,480 --> 00:07:49,480 Speaker 2: Justice appears to have pursued a case against how Yongwang. 124 00:07:49,480 --> 00:07:52,840 Speaker 2: He was actually arrested for this type of data manipulation, 125 00:07:52,920 --> 00:07:56,920 Speaker 2: accusations of data manipulation and fraud, and so I know 126 00:07:57,000 --> 00:07:58,320 Speaker 2: that case is proceeding. 127 00:07:58,640 --> 00:08:01,600 Speaker 4: The stock plummeted eighty five percent in a matter of 128 00:08:01,800 --> 00:08:05,239 Speaker 4: basically minutes when it was announced that the drug had failed, 129 00:08:05,800 --> 00:08:07,680 Speaker 4: and so what happened is a lot of people in 130 00:08:07,720 --> 00:08:11,040 Speaker 4: the market lost money on this drug, enormous amounts of money. 131 00:08:11,520 --> 00:08:15,400 Speaker 4: The question I have is why the FDA, which is 132 00:08:15,440 --> 00:08:18,080 Speaker 4: the agency that had the power to intervene, didn't do 133 00:08:18,160 --> 00:08:21,160 Speaker 4: anything for literally years knowing what they knew. 134 00:08:22,000 --> 00:08:25,080 Speaker 1: Cassava says it is cooperating with the DOJ and has 135 00:08:25,120 --> 00:08:28,960 Speaker 1: made changes to the leadership team. Wang pleaded not guilty, 136 00:08:29,160 --> 00:08:32,040 Speaker 1: But what Pillar and Shrag had found led them to 137 00:08:32,120 --> 00:08:36,920 Speaker 1: look beyond Cassava, enlisting a team of private citizens, including Bick, 138 00:08:37,280 --> 00:08:41,120 Speaker 1: to investigate whether there might be other problems with Alzheimer's research. 139 00:08:41,400 --> 00:08:45,600 Speaker 1: They say they uncovered numerous instances of data manipulation going 140 00:08:45,640 --> 00:08:49,280 Speaker 1: all the way back to one particularly influential paper published 141 00:08:49,360 --> 00:08:50,960 Speaker 1: nearly twenty years ago. 142 00:08:51,520 --> 00:08:54,800 Speaker 4: We were both kind of stunned when we together discussed 143 00:08:55,200 --> 00:09:00,960 Speaker 4: this experiment by Karen Ashen Sylvan Lesnay that was so 144 00:09:01,120 --> 00:09:04,600 Speaker 4: instrumental to the field and yet seemed to be very suspect. 145 00:09:05,080 --> 00:09:08,640 Speaker 1: Ash and Lesnie's two thousand and six Nature publication shaped 146 00:09:08,640 --> 00:09:11,160 Speaker 1: the field's understanding of Alzheimer's. 147 00:09:11,520 --> 00:09:15,040 Speaker 2: The anilinkistgate hypothesis took a bit of a stumble in 148 00:09:15,080 --> 00:09:18,800 Speaker 2: the early two thousands, and this group of investigators Karen 149 00:09:18,880 --> 00:09:21,640 Speaker 2: ash and Sylvain Lesnie were one of those who came 150 00:09:21,679 --> 00:09:25,360 Speaker 2: in and said, Aha, we think we've identified the silver bullet. 151 00:09:25,559 --> 00:09:28,440 Speaker 4: And so what you had is, for the first time, 152 00:09:28,840 --> 00:09:32,480 Speaker 4: ostensibly an experiment that showed a kind of cause and effect, 153 00:09:32,600 --> 00:09:35,520 Speaker 4: direct cause and effect of a substance that seemed to 154 00:09:35,559 --> 00:09:38,520 Speaker 4: be causing Almozheimer's disease. And lo and behold, it was 155 00:09:38,840 --> 00:09:40,760 Speaker 4: an amyloid beta protein. 156 00:09:40,880 --> 00:09:43,600 Speaker 2: And it sort of rescued the amyloid hypothesis. It was 157 00:09:43,679 --> 00:09:47,439 Speaker 2: cited thousands and thousands of times, and variations of this 158 00:09:47,559 --> 00:09:50,160 Speaker 2: theme were developed. But what we found in the course 159 00:09:50,600 --> 00:09:54,080 Speaker 2: of doing this research integrity work, I bumped into a 160 00:09:54,160 --> 00:09:57,640 Speaker 2: number of papers from Sylvain Leslie, the first author, and 161 00:09:57,720 --> 00:10:01,400 Speaker 2: we found that there seemed to be artifact vitual changes 162 00:10:01,440 --> 00:10:04,800 Speaker 2: in his work as well, including in this famous Nature paper. 163 00:10:05,400 --> 00:10:07,840 Speaker 2: And as we analyze that paper, we found that the 164 00:10:07,920 --> 00:10:11,520 Speaker 2: majority of the figures seemed to have evidence of tampering 165 00:10:11,559 --> 00:10:12,280 Speaker 2: in them. 166 00:10:12,559 --> 00:10:15,560 Speaker 1: And I understand doctor Ash actually has withdrawn the paper 167 00:10:15,600 --> 00:10:16,320 Speaker 1: now she has. 168 00:10:16,400 --> 00:10:18,880 Speaker 2: It was retracted in the last six months or so. 169 00:10:19,480 --> 00:10:23,480 Speaker 1: The University of Minnesota conducted an investigation and found no 170 00:10:23,800 --> 00:10:27,719 Speaker 1: research misconduct, but the authors, with the exception of Lesnie, 171 00:10:28,040 --> 00:10:32,040 Speaker 1: agreed to retract the paper, and Ash took responsibility, though 172 00:10:32,120 --> 00:10:35,800 Speaker 1: she said she had no knowledge of any image manipulations 173 00:10:36,080 --> 00:10:39,200 Speaker 1: until it was brought to my attention. She continues to 174 00:10:39,240 --> 00:10:43,320 Speaker 1: back the amyloid hypothesis and her conclusions. Lesnie has not 175 00:10:43,480 --> 00:10:47,480 Speaker 1: commented and remains in good standing with the NIH. According 176 00:10:47,480 --> 00:10:52,600 Speaker 1: to Ash, Lesnie denies fabricating the data. How did NIH react? 177 00:10:53,120 --> 00:10:55,640 Speaker 2: What I can tell you is that we saw very 178 00:10:55,679 --> 00:11:02,280 Speaker 2: slow progress in dealing with these things. We saw Sylvane 179 00:11:02,360 --> 00:11:06,840 Speaker 2: Lesni continue to get funding, even new funding after these 180 00:11:06,880 --> 00:11:10,760 Speaker 2: reports came out and were widely accepted that these were 181 00:11:10,880 --> 00:11:14,240 Speaker 2: serious problems, And initially you wonder how could that be 182 00:11:15,960 --> 00:11:18,720 Speaker 2: until you take a larger look at the field and 183 00:11:18,760 --> 00:11:21,600 Speaker 2: you realize that the leadership of the NIH has not 184 00:11:21,720 --> 00:11:24,120 Speaker 2: been completely immune to these types of problems. 185 00:11:24,920 --> 00:11:29,160 Speaker 1: The administrator overseeing Leslie's NIH grant co authored the now 186 00:11:29,240 --> 00:11:33,000 Speaker 1: retracted Nature paper. The team also found issues with the 187 00:11:33,040 --> 00:11:35,320 Speaker 1: work of the man who led the division at the 188 00:11:35,440 --> 00:11:38,400 Speaker 1: NIH National Institute on Aging. 189 00:11:38,679 --> 00:11:42,320 Speaker 2: A gentleman named Eliashemus Leah, who was the neuroscience director 190 00:11:42,320 --> 00:11:45,400 Speaker 2: at the NIA. And this was probably one of the 191 00:11:45,440 --> 00:11:51,440 Speaker 2: most prolific cases of apparently doctor data that I'd ever seen. 192 00:11:51,600 --> 00:11:54,800 Speaker 2: We stopped analyzing when we got to something like one 193 00:11:54,880 --> 00:11:58,920 Speaker 2: hundred and thirty papers and just said that's enough. We 194 00:11:59,400 --> 00:12:01,480 Speaker 2: can't spend the rest of our lives looking at this. 195 00:12:02,200 --> 00:12:04,720 Speaker 2: But this is somebody who has been able to ascend 196 00:12:04,760 --> 00:12:07,480 Speaker 2: to the very highest echelons at the NIAH. 197 00:12:08,080 --> 00:12:11,480 Speaker 1: The day Pillars Report in Science was published, Masleah was 198 00:12:11,520 --> 00:12:15,040 Speaker 1: removed from his post. So it appears that there is 199 00:12:15,080 --> 00:12:18,080 Speaker 1: a broader problem than you first understood. I wonder what 200 00:12:18,160 --> 00:12:20,360 Speaker 1: thoughts you have about where it comes from. 201 00:12:20,880 --> 00:12:24,120 Speaker 2: Science is a human endeavor, right, Scientists are people. The 202 00:12:24,160 --> 00:12:26,800 Speaker 2: Garden of Eden happened to all of us, Right, we 203 00:12:26,920 --> 00:12:30,480 Speaker 2: are all flawed people, and scientists are going to be 204 00:12:31,200 --> 00:12:34,360 Speaker 2: subject to the same temptations as everybody else, and so 205 00:12:34,400 --> 00:12:36,320 Speaker 2: I think part of what needs to happen as a 206 00:12:36,400 --> 00:12:38,800 Speaker 2: discipline is that we need to be a little bit 207 00:12:38,920 --> 00:12:39,600 Speaker 2: less naive. 208 00:12:40,080 --> 00:12:42,800 Speaker 1: What advice do you have for an investor, given what 209 00:12:42,840 --> 00:12:45,080 Speaker 1: you've said about some of the problems with science, It 210 00:12:45,120 --> 00:12:48,680 Speaker 1: really is quite unsettling if we think the science we 211 00:12:48,760 --> 00:12:50,559 Speaker 1: think we know, we don't know. 212 00:12:50,760 --> 00:12:53,439 Speaker 2: If you're going to make an investment of not only 213 00:12:53,600 --> 00:12:56,800 Speaker 2: millions or tens of millions or sometimes hundreds of millions 214 00:12:56,840 --> 00:12:59,400 Speaker 2: of dollars in clinical trials, but also ask hundreds or 215 00:12:59,440 --> 00:13:03,240 Speaker 2: thousands of patients to accept the risk of exposing themselves 216 00:13:03,320 --> 00:13:06,439 Speaker 2: to experimental drugs, we should start with do we think 217 00:13:06,480 --> 00:13:09,800 Speaker 2: we're starting from a trustworthy foundation. That's a fair question, 218 00:13:09,960 --> 00:13:12,680 Speaker 2: and it should be asked consistently. And I think that 219 00:13:12,760 --> 00:13:16,559 Speaker 2: sometimes it's time to ask the field to move on, right. 220 00:13:16,600 --> 00:13:20,440 Speaker 2: I think that even a non scientist can listen to 221 00:13:20,559 --> 00:13:24,280 Speaker 2: Alzheimer's research over the last thirty years and say, I've 222 00:13:24,320 --> 00:13:25,160 Speaker 2: been hearing. 223 00:13:24,880 --> 00:13:27,480 Speaker 3: Ameloid and ameloid and ameloid. 224 00:13:27,480 --> 00:13:31,240 Speaker 2: But what's not changing is what's happening for our family members, Right, 225 00:13:31,280 --> 00:13:33,960 Speaker 2: And then it's time for something else before we do 226 00:13:34,000 --> 00:13:36,480 Speaker 2: this again. Tell me why it's going to be different 227 00:13:36,559 --> 00:13:41,440 Speaker 2: this time, and I think that the investors can motivate. 228 00:13:41,120 --> 00:13:49,520 Speaker 1: Change coming up. When Pfizer announced its earnings, many were 229 00:13:49,520 --> 00:13:52,400 Speaker 1: focused on its huge commitments to finding a cure for 230 00:13:52,480 --> 00:13:56,520 Speaker 1: another dread of disease, the disease of cancer. We hear 231 00:13:56,559 --> 00:14:00,120 Speaker 1: from Pfizer's CEO, Albert Borla about his plans to do 232 00:14:00,000 --> 00:14:03,600 Speaker 1: do for cancer what Pfizer did for COVID. That's next 233 00:14:03,679 --> 00:14:17,240 Speaker 1: on Wall Street Week. This is a story about hope, 234 00:14:17,600 --> 00:14:20,480 Speaker 1: hope for those who need it desperately when they learn 235 00:14:20,560 --> 00:14:24,440 Speaker 1: they have that disease. All of us dread the diagnosis 236 00:14:24,480 --> 00:14:31,080 Speaker 1: of cancer. The surgery was successful. However, tests after the 237 00:14:31,120 --> 00:14:33,600 Speaker 1: operation un cancer had been present. 238 00:14:34,160 --> 00:14:36,280 Speaker 6: I know a little bit more people who've been told 239 00:14:36,320 --> 00:14:38,000 Speaker 6: they have cancer know a little bit more on other people. 240 00:14:38,080 --> 00:14:39,840 Speaker 3: That's steering you right in the face, and it may 241 00:14:39,920 --> 00:14:40,600 Speaker 3: actually happen. 242 00:14:41,080 --> 00:14:44,520 Speaker 1: In twenty twenty four, the United States hit a new milestone, 243 00:14:45,000 --> 00:14:49,360 Speaker 1: estimated to pass two million newly diagnosed cases of cancer. 244 00:14:50,400 --> 00:14:53,520 Speaker 3: One it is that the cancer incidence is increasing. Right now. 245 00:14:53,520 --> 00:14:56,960 Speaker 7: We have fifteen million approximately in the world people that 246 00:14:57,000 --> 00:14:59,680 Speaker 7: they are suffering from cancer every year. Probably the number 247 00:14:59,680 --> 00:15:03,520 Speaker 7: will go to thirty plus in twenty fifty. And that 248 00:15:03,600 --> 00:15:06,080 Speaker 7: is happening because first of all, people live longer lives, 249 00:15:06,200 --> 00:15:10,880 Speaker 7: and that's the AIDS favors unfortunately cancer, but also there 250 00:15:10,880 --> 00:15:14,560 Speaker 7: are risk fock stores and who have better ways of 251 00:15:14,680 --> 00:15:15,880 Speaker 7: detecting now guns. 252 00:15:15,960 --> 00:15:17,720 Speaker 3: So that's one they're living longer. 253 00:15:17,800 --> 00:15:20,000 Speaker 1: But actually we're seeing more instance, are we not in 254 00:15:20,040 --> 00:15:21,560 Speaker 1: younger people, which is surprising. 255 00:15:21,760 --> 00:15:25,440 Speaker 7: We are, and this is kind of emerging right now. 256 00:15:25,880 --> 00:15:30,200 Speaker 7: And there are risk factors starting from environment, starting from 257 00:15:30,400 --> 00:15:36,400 Speaker 7: multiple alcohol smoking, but also it is the fact that 258 00:15:36,560 --> 00:15:42,520 Speaker 7: we can detect cancers way more efficiently today than we 259 00:15:42,600 --> 00:15:43,960 Speaker 7: could do it years back. 260 00:15:45,480 --> 00:15:48,960 Speaker 1: Doctor Albert Borla is the chairman and CEO of Vizer. 261 00:15:49,560 --> 00:15:53,680 Speaker 1: Under his leadership, the pharmaceutical company partnered with BioNTech during 262 00:15:53,720 --> 00:15:59,120 Speaker 1: the COVID nineteen pandemic to roll out life saving mRNA vaccines. 263 00:16:00,160 --> 00:16:05,440 Speaker 7: How to operate with speed. The vaccine or the operational 264 00:16:05,440 --> 00:16:09,880 Speaker 7: war speed that the President Traub actually had initiated was 265 00:16:10,200 --> 00:16:14,040 Speaker 7: an operation that removed obstacles, but also within the companies 266 00:16:14,400 --> 00:16:16,720 Speaker 7: was an operation that we were suiting for the impossible, 267 00:16:17,560 --> 00:16:21,840 Speaker 7: and we came with the mindset nothing is impossible. We 268 00:16:21,880 --> 00:16:25,440 Speaker 7: can make it possible. That's a mindset that we can 269 00:16:25,560 --> 00:16:29,880 Speaker 7: use again, not to find the solution for COVID, but 270 00:16:29,960 --> 00:16:32,640 Speaker 7: to find the solution for cancer or for Alzheimers. 271 00:16:34,120 --> 00:16:37,360 Speaker 1: Cancer is doctor world's next big challenge. At Pfizer. In 272 00:16:37,400 --> 00:16:40,720 Speaker 1: twenty twenty three, Pfizer completed it's forty three billion dollar 273 00:16:40,800 --> 00:16:45,240 Speaker 1: acquisition of Siegen, a company basing a particular cancer treatment 274 00:16:45,280 --> 00:16:49,840 Speaker 1: on monoclonal antibodies. Just this week, Peizer reported earnings that 275 00:16:49,880 --> 00:16:53,080 Speaker 1: included an increase in spending on oncology to about twenty 276 00:16:53,120 --> 00:16:57,920 Speaker 1: five percent of its revenue. You have made cancer a 277 00:16:57,960 --> 00:17:01,760 Speaker 1: real priority for Pfizer, in your investments, in your R 278 00:17:01,800 --> 00:17:03,800 Speaker 1: and D, in your development, even your acquisitions. 279 00:17:04,080 --> 00:17:09,000 Speaker 7: Why because we think that we are ready to provide 280 00:17:09,000 --> 00:17:11,720 Speaker 7: a solution. We want to save the world again, and 281 00:17:11,760 --> 00:17:14,920 Speaker 7: I think our best chance this time it is with cancer. 282 00:17:15,200 --> 00:17:16,919 Speaker 3: The science is there. 283 00:17:17,240 --> 00:17:21,119 Speaker 7: We invest probably double then all the profits that we 284 00:17:21,280 --> 00:17:24,879 Speaker 7: made with COVID to acquire a new technology which is 285 00:17:25,280 --> 00:17:26,120 Speaker 7: promising a lot. 286 00:17:27,680 --> 00:17:30,720 Speaker 1: The number of US cancer cases may be going up, 287 00:17:31,160 --> 00:17:35,159 Speaker 1: but the mortality rate is actually going down. It's fallen 288 00:17:35,320 --> 00:17:38,760 Speaker 1: thirty four percent in the last thirty years, and the 289 00:17:38,840 --> 00:17:43,040 Speaker 1: national Institutes of Health project that if this trend continues, 290 00:17:43,320 --> 00:17:46,679 Speaker 1: the United States will have twenty six million cancer survivors 291 00:17:46,800 --> 00:17:50,760 Speaker 1: by twenty forty, a forty four percent increase over twenty 292 00:17:50,880 --> 00:17:54,880 Speaker 1: twenty two. Adfiser hopes to keep this trend going through 293 00:17:54,920 --> 00:17:59,760 Speaker 1: one particular oncology treatment turbocharged by its acquisition of Cgen. 294 00:18:00,800 --> 00:18:06,320 Speaker 7: It's called Antibody drug conjugate or ADC technology, and this 295 00:18:06,520 --> 00:18:10,840 Speaker 7: is like a missile that it is GPS guided, so 296 00:18:10,880 --> 00:18:15,000 Speaker 7: it doesn't go anywhere in the body, is going straight 297 00:18:15,520 --> 00:18:18,040 Speaker 7: to the target, which is the cancer cell. And what 298 00:18:18,240 --> 00:18:22,800 Speaker 7: is on top of this missile warhead, which we call payload, 299 00:18:23,080 --> 00:18:27,200 Speaker 7: a chemotherapy that is going to be targeting those cells. 300 00:18:27,320 --> 00:18:31,480 Speaker 7: We know that some cancer cells express some proteins. Then 301 00:18:31,680 --> 00:18:35,920 Speaker 7: we create an antibody, but it is attracted by these proteins. 302 00:18:36,160 --> 00:18:40,200 Speaker 7: The antibody is the GPS system. Then you have a payload, 303 00:18:40,440 --> 00:18:44,520 Speaker 7: which is the warkhead. It can be chemical or nuclear 304 00:18:44,640 --> 00:18:47,840 Speaker 7: or tactical, depends on what you want to attack. And 305 00:18:47,880 --> 00:18:50,719 Speaker 7: then you need to link the two together and that 306 00:18:50,840 --> 00:18:55,119 Speaker 7: is called the conjugation. That means that we believe that 307 00:18:55,200 --> 00:18:58,640 Speaker 7: in the next ten years, most of the general chemotherapies 308 00:18:58,920 --> 00:19:02,320 Speaker 7: could be replaced with targeted therapies like that that have 309 00:19:02,760 --> 00:19:06,520 Speaker 7: way less side effects because the chemotherapy attacks also the 310 00:19:06,560 --> 00:19:10,720 Speaker 7: healthy cells. Right now we can minimize the impact on 311 00:19:10,760 --> 00:19:14,160 Speaker 7: the healthy cells and maximize the impact on the cancer cells. 312 00:19:14,600 --> 00:19:16,639 Speaker 1: How far along are we in the process with this 313 00:19:16,760 --> 00:19:20,520 Speaker 1: new technology of really having something that will start curing people. 314 00:19:20,640 --> 00:19:25,520 Speaker 7: I think already we start having traumatic results results that, 315 00:19:25,600 --> 00:19:29,960 Speaker 7: for example, they can triple the life expectancy of the 316 00:19:30,040 --> 00:19:34,120 Speaker 7: current standards of care. But I don't think it will 317 00:19:34,200 --> 00:19:37,399 Speaker 7: be a miracle that will come from one day to another. 318 00:19:37,760 --> 00:19:41,640 Speaker 7: It will be though constant improvement. Every quarter. We will 319 00:19:41,680 --> 00:19:45,280 Speaker 7: have not only us, but the others as well, releases 320 00:19:45,440 --> 00:19:47,840 Speaker 7: of new data of new medicines. 321 00:19:48,200 --> 00:19:50,600 Speaker 3: Eventually, some of these cancers will. 322 00:19:50,480 --> 00:19:52,760 Speaker 7: Be cured, which means that they will disappear and will 323 00:19:52,760 --> 00:19:53,840 Speaker 7: not come back back again. 324 00:19:54,400 --> 00:19:57,639 Speaker 3: But many of them will become chronic disease. 325 00:19:57,960 --> 00:20:01,720 Speaker 7: You can learn to leave with your concert with medicines 326 00:20:01,760 --> 00:20:04,640 Speaker 7: that are not affecting the quality of your life. 327 00:20:04,800 --> 00:20:07,320 Speaker 1: The Pfizer is not alone in looking for ways to 328 00:20:07,359 --> 00:20:11,280 Speaker 1: make cancer more manageable. Research has a key ally in 329 00:20:11,440 --> 00:20:12,720 Speaker 1: artificial intelligence. 330 00:20:13,560 --> 00:20:18,040 Speaker 8: I think allows several things. First is it makes diagnosis 331 00:20:18,080 --> 00:20:21,919 Speaker 8: and treatment planning more accurate. You know, we're moving away 332 00:20:22,040 --> 00:20:26,119 Speaker 8: from one size fits all in terms of treatment of 333 00:20:26,160 --> 00:20:29,080 Speaker 8: cancer or many other conditions. We know that each of 334 00:20:29,160 --> 00:20:32,600 Speaker 8: us has different manifestations of the way a tumor is 335 00:20:32,640 --> 00:20:36,120 Speaker 8: going to grow or spread, or and also how it's 336 00:20:36,119 --> 00:20:38,199 Speaker 8: going to respond to treatment, and we need to be 337 00:20:38,240 --> 00:20:41,720 Speaker 8: able to bring the best treatment to each patient at 338 00:20:41,720 --> 00:20:44,920 Speaker 8: that particular moment in time, and AI is enabling us. 339 00:20:44,840 --> 00:20:45,280 Speaker 5: To do that. 340 00:20:47,600 --> 00:20:50,879 Speaker 1: Doctor Lloyd Minor is the dean of the Stanford University's 341 00:20:50,920 --> 00:20:53,800 Speaker 1: School of Medicine, where he has pushed the institution to 342 00:20:53,800 --> 00:20:58,240 Speaker 1: focus on precision health, intended to tailor care to patient's 343 00:20:58,359 --> 00:20:59,600 Speaker 1: individual needs. 344 00:21:00,280 --> 00:21:03,240 Speaker 8: I think, really we're finding things otherwise we would have 345 00:21:03,600 --> 00:21:06,760 Speaker 8: not found because the human brain can only store so 346 00:21:06,840 --> 00:21:11,840 Speaker 8: much information and traditional analytic methods have their limitations. Generative 347 00:21:11,840 --> 00:21:17,040 Speaker 8: AI is bringing out relationships that we would have perhaps 348 00:21:17,160 --> 00:21:21,440 Speaker 8: never been able to garner without the advent of foundation 349 00:21:21,600 --> 00:21:25,520 Speaker 8: models and their application to the interpretation of medical data. 350 00:21:25,560 --> 00:21:28,760 Speaker 8: I think the deployment of generative AI, the pace of 351 00:21:28,760 --> 00:21:33,360 Speaker 8: that deployment and its impact is greater and faster than 352 00:21:33,359 --> 00:21:36,480 Speaker 8: any the pace of any technological innovation that I've seen 353 00:21:36,520 --> 00:21:38,760 Speaker 8: in my life. And I think we're still at the 354 00:21:38,840 --> 00:21:40,400 Speaker 8: relatively early stages. 355 00:21:40,920 --> 00:21:44,000 Speaker 1: Deep minds Alpha fold is proof of how much AI 356 00:21:44,200 --> 00:21:47,560 Speaker 1: can do in the medical field. It uses its vast 357 00:21:47,600 --> 00:21:51,720 Speaker 1: computational power, trained by one hundred and seventy thousand proteins, 358 00:21:52,040 --> 00:21:53,800 Speaker 1: to predict protein structures. 359 00:21:54,160 --> 00:21:57,320 Speaker 9: I remember when I was when I was starting in college, 360 00:21:57,760 --> 00:22:02,119 Speaker 9: I joined this lab and were crystallizing proteins and putting 361 00:22:02,119 --> 00:22:05,600 Speaker 9: the coordinates of their atoms into a database along with 362 00:22:05,720 --> 00:22:09,720 Speaker 9: many others, And I remember Professor Steve Harrison, who was 363 00:22:09,760 --> 00:22:12,960 Speaker 9: the leader of our lab, telling us it might take 364 00:22:13,280 --> 00:22:17,920 Speaker 9: fifty years before we're able to predict how a protein 365 00:22:18,000 --> 00:22:20,480 Speaker 9: folds given its amino acid sequence. 366 00:22:21,520 --> 00:22:24,280 Speaker 1: Marty Chavez has been a fixture on Wall Street since 367 00:22:24,359 --> 00:22:27,919 Speaker 1: nineteen ninety three, when he joined Golden Sachs, but his 368 00:22:28,080 --> 00:22:32,320 Speaker 1: biochemistry roots go back to his undergraduate days at Harvard University. 369 00:22:32,720 --> 00:22:35,800 Speaker 1: Today he serves as a board member of Alphabet, whose 370 00:22:35,800 --> 00:22:39,879 Speaker 1: subsidiary Deep Mind won the Nobel Prize in Chemistry for 371 00:22:40,040 --> 00:22:41,720 Speaker 1: Alpha fold last year. 372 00:22:43,480 --> 00:22:48,280 Speaker 9: They gave it the fifty thousand amino acid sequences for 373 00:22:48,400 --> 00:22:52,399 Speaker 9: proteins that had been sequencedruck and the structure was known 374 00:22:52,720 --> 00:22:57,200 Speaker 9: through crystallography. They trained the neural network on that database, 375 00:22:57,280 --> 00:23:00,640 Speaker 9: the Protein Data Bank, and then they did something kind 376 00:23:00,640 --> 00:23:05,400 Speaker 9: of amazing, which is they then guessed seventy five thousand 377 00:23:05,840 --> 00:23:09,600 Speaker 9: protein sequences for which the structure was not known. So 378 00:23:09,680 --> 00:23:13,280 Speaker 9: here's the amino acid sequence, guessed the structure. They fed 379 00:23:13,280 --> 00:23:19,080 Speaker 9: those guesses back into the model, and then I'm oversimplifying, 380 00:23:19,080 --> 00:23:23,240 Speaker 9: of course, but now the model seems to predict with 381 00:23:23,800 --> 00:23:29,720 Speaker 9: uncanny accuracy the shape in space of proteins that have 382 00:23:29,840 --> 00:23:34,600 Speaker 9: never been crystallized. It seems that the model discovered some 383 00:23:34,800 --> 00:23:38,600 Speaker 9: deep patterns in the way proteins fold in space, and 384 00:23:38,640 --> 00:23:42,000 Speaker 9: somehow compressed and captured that knowledge into the model. 385 00:23:45,720 --> 00:23:48,600 Speaker 1: We hear a lot about general of AI, and there 386 00:23:48,640 --> 00:23:50,760 Speaker 1: were just a Nobel Prize given out for the alpha 387 00:23:50,760 --> 00:23:54,320 Speaker 1: fold technology. What does that mean, if anything, for cancer? 388 00:23:54,359 --> 00:23:55,800 Speaker 1: How does those two fit together? 389 00:23:56,119 --> 00:23:58,879 Speaker 7: Well, it's a I believe not only for cancer, but 390 00:23:58,920 --> 00:24:02,720 Speaker 7: also for cancer is going to propel the biological research. 391 00:24:02,960 --> 00:24:05,680 Speaker 3: AI in unprecedented ways. 392 00:24:05,920 --> 00:24:09,320 Speaker 7: We are going to sort them the time that we 393 00:24:09,600 --> 00:24:12,359 Speaker 7: need to be able to develop molecules. 394 00:24:12,520 --> 00:24:15,280 Speaker 1: What's the time horizon as far as you understand it 395 00:24:15,560 --> 00:24:17,880 Speaker 1: for generally AI really making a big difference. 396 00:24:18,400 --> 00:24:21,479 Speaker 7: I think quick, I think already we are deploying it. 397 00:24:21,520 --> 00:24:23,520 Speaker 7: So it made very big difference in the example of 398 00:24:23,520 --> 00:24:26,400 Speaker 7: what I gave you. But as we are deploying any 399 00:24:26,480 --> 00:24:31,199 Speaker 7: different aspects of the process from discovery to development, I 400 00:24:31,240 --> 00:24:34,240 Speaker 7: think with the next two three years will have phenomenal results. 401 00:24:36,000 --> 00:24:38,800 Speaker 1: Borler points out that Pfizer is investing twice what it 402 00:24:38,880 --> 00:24:41,720 Speaker 1: made off of COVID treatments to address the scourge of cancer, 403 00:24:42,200 --> 00:24:44,720 Speaker 1: and it will need to replicate some version of that 404 00:24:44,840 --> 00:24:48,920 Speaker 1: COVID success in redirecting its efforts. After its stock price 405 00:24:49,080 --> 00:24:52,560 Speaker 1: surged from that success, shares are down nearly fifty percent 406 00:24:52,640 --> 00:24:56,679 Speaker 1: since December twenty twenty one, leading activist investor Starboard to 407 00:24:56,720 --> 00:24:59,879 Speaker 1: take a one billion dollar stake, Advisor turning up pre 408 00:25:00,000 --> 00:25:02,440 Speaker 1: I'm suround the company to prove the value of its 409 00:25:02,440 --> 00:25:06,720 Speaker 1: cgen acquisition and investment in its drug line. I know 410 00:25:06,800 --> 00:25:09,239 Speaker 1: that you're committed to this fight against cancer as an 411 00:25:09,240 --> 00:25:12,439 Speaker 1: ad vizor to save lives first and foremost, but it's 412 00:25:12,440 --> 00:25:16,000 Speaker 1: also a business. How big a business opportunity is it? Potentially, 413 00:25:16,359 --> 00:25:18,280 Speaker 1: if you really have breakthroughs, it. 414 00:25:18,320 --> 00:25:20,800 Speaker 7: Is very big business opportunity. I will tell you something. 415 00:25:20,880 --> 00:25:26,359 Speaker 7: There is a myth about the pharmaceutical business model and 416 00:25:26,480 --> 00:25:29,240 Speaker 7: the myth says that whatever is good for patients and 417 00:25:29,320 --> 00:25:32,280 Speaker 7: whatever is good for storeholders fundamentally at odds. 418 00:25:32,480 --> 00:25:36,800 Speaker 3: In fact, the reverse is true. You can't make any money. 419 00:25:36,800 --> 00:25:39,119 Speaker 7: Actually, you're going to lose all your money in this 420 00:25:39,200 --> 00:25:43,000 Speaker 7: business unless you are able to discover very meaningful, not 421 00:25:43,280 --> 00:25:47,800 Speaker 7: meet too solutions, very meaningful solutions for the patients. So 422 00:25:48,320 --> 00:25:52,560 Speaker 7: if we discover medicines that really double and triple the survival, 423 00:25:52,960 --> 00:25:55,840 Speaker 7: then is very good business. If we fail because we 424 00:25:55,880 --> 00:25:57,959 Speaker 7: take a lot of risk, is not good business. 425 00:25:58,080 --> 00:26:02,320 Speaker 1: You always have new drugs in the pipeline. Where are 426 00:26:02,400 --> 00:26:04,480 Speaker 1: you on cancer drugs in the pipeline right now? 427 00:26:05,400 --> 00:26:06,320 Speaker 3: It is the crowns. 428 00:26:06,760 --> 00:26:09,920 Speaker 7: We are having most of our pipeline drugs and the 429 00:26:10,040 --> 00:26:13,680 Speaker 7: most important are coming from cancer. Of course, we have vaccines, 430 00:26:13,960 --> 00:26:17,000 Speaker 7: we have internal medicines, we have work in obesity, we 431 00:26:17,080 --> 00:26:20,200 Speaker 7: have work in authrieties and the inflammation. 432 00:26:20,680 --> 00:26:22,320 Speaker 3: But cancer is where we have most of them. 433 00:26:22,400 --> 00:26:26,480 Speaker 7: And I would say that three of the most prominent 434 00:26:28,040 --> 00:26:32,320 Speaker 7: candidates are entering, or have entered, or are about to enter, 435 00:26:32,359 --> 00:26:33,320 Speaker 7: face three studies. 436 00:26:33,800 --> 00:26:36,800 Speaker 1: For doctor Borla, the mission to make life more livable 437 00:26:36,800 --> 00:26:39,960 Speaker 1: with cancer is worthwhile because it is a disease that 438 00:26:40,000 --> 00:26:43,159 Speaker 1: affects us all, no matter where we come from or 439 00:26:43,200 --> 00:26:43,840 Speaker 1: who we are. 440 00:26:44,840 --> 00:26:47,320 Speaker 3: It is a global problem and affects all of us. 441 00:26:47,680 --> 00:26:51,520 Speaker 7: Maybe not all of us as patients, thankfully, but all 442 00:26:51,560 --> 00:26:57,400 Speaker 7: of us as sons and daughters, as fathers and mothers, unfortunately, 443 00:26:57,800 --> 00:27:01,600 Speaker 7: as friends, as neighbors. There is something that is scaring 444 00:27:01,640 --> 00:27:04,320 Speaker 7: people right now, and it is not only in the US, 445 00:27:04,400 --> 00:27:09,320 Speaker 7: or in Europe or in the most advanced economic recounts. 446 00:27:10,720 --> 00:27:11,119 Speaker 3: Coming up. 447 00:27:11,320 --> 00:27:14,439 Speaker 1: President Trump says he wants to get federal spending down. 448 00:27:15,000 --> 00:27:18,439 Speaker 1: Bridgewater founder Ray Dalio gives us his diagnosis for what 449 00:27:18,600 --> 00:27:21,840 Speaker 1: could turn into a debt crisis and what the President 450 00:27:22,080 --> 00:27:40,960 Speaker 1: and Congress need to do to avoid it. This is 451 00:27:41,000 --> 00:27:44,960 Speaker 1: a story about going broke. Whether you're a person, a company, 452 00:27:45,080 --> 00:27:47,719 Speaker 1: or an entire nation. It all comes down to the 453 00:27:47,760 --> 00:27:51,120 Speaker 1: same thing, not being able to pay your debts as 454 00:27:51,119 --> 00:27:51,679 Speaker 1: they come do. 455 00:27:53,680 --> 00:27:59,119 Speaker 6: The US fiscal federal government's fiscal path fiscal policy is 456 00:27:59,119 --> 00:28:02,480 Speaker 6: on an unsustainable path. The level of our debt relativity 457 00:28:02,480 --> 00:28:05,560 Speaker 6: economy is not unsustainable. The path is unsustainable. 458 00:28:06,040 --> 00:28:08,760 Speaker 1: The United States has been running up a large tab 459 00:28:08,880 --> 00:28:12,080 Speaker 1: for years now, going from a debt of five point 460 00:28:12,119 --> 00:28:15,560 Speaker 1: seven trillion dollars in two thousand to over thirty five 461 00:28:15,720 --> 00:28:19,320 Speaker 1: trillion dollars in twenty twenty four, and so far it 462 00:28:19,359 --> 00:28:22,840 Speaker 1: doesn't show any signs of slowing down, with the Congressional 463 00:28:22,840 --> 00:28:26,000 Speaker 1: Budget Office projecting that at the rate it's going, the 464 00:28:26,119 --> 00:28:30,080 Speaker 1: US will owe over fifty trillion dollars by twenty thirty five, 465 00:28:30,640 --> 00:28:34,280 Speaker 1: or about one hundred and eighteen percent of its annual GDP. 466 00:28:34,760 --> 00:28:36,760 Speaker 3: The debt ceiling was the Trump. 467 00:28:36,560 --> 00:28:40,160 Speaker 1: Administration comes to office aware of the challenge posed by 468 00:28:40,160 --> 00:28:43,080 Speaker 1: the federal debt and is taking steps it says will 469 00:28:43,120 --> 00:28:47,040 Speaker 1: help address the problem. Bridgewater founder Ray Daalio has written 470 00:28:47,080 --> 00:28:50,840 Speaker 1: a new book available at no charge online, addressing the 471 00:28:51,000 --> 00:28:55,160 Speaker 1: US debt problem. It's called How Countries Go Broke, and 472 00:28:55,200 --> 00:28:57,400 Speaker 1: he starts with how much the US is going to 473 00:28:57,440 --> 00:29:00,000 Speaker 1: have to borrow and who will lend it the money 474 00:29:00,000 --> 00:29:00,840 Speaker 1: money it needs. 475 00:29:01,920 --> 00:29:05,920 Speaker 10: The difference is they can print money, and that's basically it. 476 00:29:06,360 --> 00:29:09,480 Speaker 11: So what you have is a situation where there's a 477 00:29:09,560 --> 00:29:13,640 Speaker 11: supply and a demand for debt. As we have a 478 00:29:13,680 --> 00:29:17,000 Speaker 11: new supply that will equal in the United States. 479 00:29:17,080 --> 00:29:18,920 Speaker 10: Now, by the way, this is a problem. 480 00:29:18,680 --> 00:29:22,160 Speaker 11: Elsewhere too, but it's about seven and a half percent 481 00:29:22,360 --> 00:29:23,120 Speaker 11: of GDP. 482 00:29:23,480 --> 00:29:25,880 Speaker 10: So the supply of debt is going to be large. 483 00:29:26,240 --> 00:29:27,040 Speaker 10: That's new debt. 484 00:29:27,320 --> 00:29:30,920 Speaker 11: If I calculate also who are the buyers of that debt, 485 00:29:31,680 --> 00:29:34,680 Speaker 11: and that there's not going to be enough demand for 486 00:29:34,760 --> 00:29:37,840 Speaker 11: that debt doesn't look like there'll be enough demand. And 487 00:29:37,880 --> 00:29:39,960 Speaker 11: the way that it works, so there's two things to 488 00:29:40,040 --> 00:29:43,040 Speaker 11: keep in mind. There's supply and demand, and then what 489 00:29:43,080 --> 00:29:45,800 Speaker 11: do central banks do or what do the governments do 490 00:29:45,880 --> 00:29:50,280 Speaker 11: when they don't have enough demand. Central banks come in 491 00:29:50,360 --> 00:29:54,720 Speaker 11: and make up that demand. They essentially print money, and 492 00:29:54,760 --> 00:29:57,720 Speaker 11: then they buy that and then that has a consequence. 493 00:29:58,240 --> 00:30:00,960 Speaker 11: And so just like in two thousand, twenty twenty one 494 00:30:01,320 --> 00:30:06,680 Speaker 11: we went through they needed to give money away and 495 00:30:07,000 --> 00:30:11,560 Speaker 11: the government needed to borrow, and the central bank produced 496 00:30:11,560 --> 00:30:15,640 Speaker 11: the money and bought that. That devalues money, raises inflation, 497 00:30:15,760 --> 00:30:16,080 Speaker 11: and so on. 498 00:30:17,160 --> 00:30:19,720 Speaker 1: Thus far, the United States has been able to support 499 00:30:19,800 --> 00:30:22,960 Speaker 1: ever growing debt and deficits because of that ability to 500 00:30:23,040 --> 00:30:27,560 Speaker 1: print money. But Dahlio warns that throughout history countries have 501 00:30:27,640 --> 00:30:30,480 Speaker 1: run up against the demand of creditors sooner or later 502 00:30:31,000 --> 00:30:31,760 Speaker 1: to get paid. 503 00:30:32,440 --> 00:30:35,720 Speaker 11: It is when the owners of that debt we have 504 00:30:36,640 --> 00:30:42,800 Speaker 11: over thirty six probian dollars essentially of government debt, when 505 00:30:42,800 --> 00:30:46,440 Speaker 11: they start to also fear that it may be monetized 506 00:30:46,520 --> 00:30:51,200 Speaker 11: like the Japanese. Japanese is a very good example Japanese. 507 00:30:51,400 --> 00:30:54,720 Speaker 11: If you have terrible investment to own bonds, they didn't 508 00:30:54,720 --> 00:30:57,400 Speaker 11: go down in value, but you got an interest rate 509 00:30:57,400 --> 00:31:00,600 Speaker 11: that was three percent below another interest rate US interest 510 00:31:00,680 --> 00:31:03,680 Speaker 11: rate by way example, and the currency depreciated by three 511 00:31:03,680 --> 00:31:06,040 Speaker 11: and a half percent. They were losing six and a 512 00:31:06,080 --> 00:31:08,600 Speaker 11: half percent a year for many, many years. 513 00:31:09,280 --> 00:31:10,720 Speaker 10: That's what it can be like. 514 00:31:10,840 --> 00:31:14,480 Speaker 11: Of course, there's an inflation component that enters into the depreciation. 515 00:31:14,960 --> 00:31:17,400 Speaker 11: One of the things I also want to emphasize is 516 00:31:17,480 --> 00:31:23,040 Speaker 11: people pay too much attention to depreciation against another exchange rate. 517 00:31:23,520 --> 00:31:28,080 Speaker 11: Normally at such times, many countries have this issue and 518 00:31:28,120 --> 00:31:31,480 Speaker 11: they don't want their currency to depreciate. So what you 519 00:31:31,600 --> 00:31:36,840 Speaker 11: see is the depreciation of all currencies in relationship to 520 00:31:36,960 --> 00:31:39,560 Speaker 11: things like gold or other assets. 521 00:31:39,800 --> 00:31:42,320 Speaker 10: And that's where you have to pay attention to it. 522 00:31:42,920 --> 00:31:45,840 Speaker 1: So if there is a debt wall out there somewhere 523 00:31:45,960 --> 00:31:48,360 Speaker 1: that we will run into, how close are we to 524 00:31:48,480 --> 00:31:53,040 Speaker 1: it and how can we tell? As with our personal finances, 525 00:31:53,320 --> 00:31:56,040 Speaker 1: Daliu says, it's when we end up borrowing not to 526 00:31:56,120 --> 00:31:59,160 Speaker 1: invest in our future, but to pay the interests on 527 00:31:59,200 --> 00:32:01,000 Speaker 1: what we've already borrowed. 528 00:32:00,880 --> 00:32:04,800 Speaker 11: Debt isn't the problem. If the debt is used to 529 00:32:04,880 --> 00:32:08,160 Speaker 11: produce an income that is large enough or larger than 530 00:32:08,280 --> 00:32:11,640 Speaker 11: enough to service the debt. It's like a company if 531 00:32:11,680 --> 00:32:15,960 Speaker 11: you more like your finances. But what happens is it accumulates, 532 00:32:16,000 --> 00:32:19,680 Speaker 11: tends to accumulate. Think of it like a disease that 533 00:32:19,800 --> 00:32:23,280 Speaker 11: has a progression, and it goes through these various stages, 534 00:32:24,560 --> 00:32:27,000 Speaker 11: and so you can kind of see where the stages are. 535 00:32:27,160 --> 00:32:31,000 Speaker 11: We're relatively late in the cycle because we're also having 536 00:32:31,080 --> 00:32:35,240 Speaker 11: central banks lose money. That's a marker when they're balance 537 00:32:35,280 --> 00:32:38,680 Speaker 11: sheet deteriorates, when they have a negative networth. For example, 538 00:32:38,960 --> 00:32:41,920 Speaker 11: in the UK, can the central bank have a negative 539 00:32:41,920 --> 00:32:46,160 Speaker 11: net worth according to their laws, the central government has 540 00:32:46,160 --> 00:32:50,600 Speaker 11: got to recapitalize the bank. That becomes a budget item. 541 00:32:51,000 --> 00:32:55,040 Speaker 11: So there are certain red flags. There are a bunch 542 00:32:55,080 --> 00:32:56,920 Speaker 11: in this study that I've put out so that you 543 00:32:56,960 --> 00:33:01,120 Speaker 11: could see them debt service payments, borrowing to borrow. So 544 00:33:01,240 --> 00:33:04,120 Speaker 11: we are in the later stages of this. This is 545 00:33:04,160 --> 00:33:08,600 Speaker 11: something that will be I think the most important issue 546 00:33:08,800 --> 00:33:11,320 Speaker 11: that we'll be talking about. We're not talking about it now, 547 00:33:11,560 --> 00:33:13,600 Speaker 11: but we will be talking about it. Over the next 548 00:33:13,600 --> 00:33:14,120 Speaker 11: few months. 549 00:33:14,160 --> 00:33:19,520 Speaker 1: Because the budget is the issue, Dio sees signs in 550 00:33:19,560 --> 00:33:22,720 Speaker 1: the market that now point toward the end of the cycle. 551 00:33:23,200 --> 00:33:28,200 Speaker 11: What happens is that you see interest rates rise, led 552 00:33:28,240 --> 00:33:32,400 Speaker 11: by the long end when there's an easing of monetary 553 00:33:32,520 --> 00:33:38,240 Speaker 11: policy and the currency depreciating. Well, you think, wait, isn't 554 00:33:38,320 --> 00:33:41,720 Speaker 11: Fed lowering interest rates. Isn't the Bank of England lowering 555 00:33:41,800 --> 00:33:47,480 Speaker 11: interest rates? Why are long term interest rates going up? Okay, 556 00:33:47,920 --> 00:33:50,880 Speaker 11: they're not intervening. There must be something with the supply 557 00:33:51,160 --> 00:33:54,480 Speaker 11: demand of those bonds, because it's not their transactions that 558 00:33:54,520 --> 00:33:56,080 Speaker 11: are driving those interest rates up. 559 00:33:56,840 --> 00:34:00,320 Speaker 10: And the currency is falling at the same time. 560 00:34:01,000 --> 00:34:05,560 Speaker 11: Okay, that is reflecting leaving, in other words, selling the 561 00:34:05,600 --> 00:34:07,440 Speaker 11: bonds and leaving that instrument. 562 00:34:07,480 --> 00:34:10,720 Speaker 10: So when you have that dynamic, it's a red flag. 563 00:34:11,360 --> 00:34:15,440 Speaker 11: So we're having elements of that dynamic. So not only 564 00:34:15,600 --> 00:34:18,520 Speaker 11: is the supply demand operating that way, but the market 565 00:34:18,560 --> 00:34:20,200 Speaker 11: action is operating that way. 566 00:34:21,120 --> 00:34:24,560 Speaker 1: If Dalio is right, if the United States is approaching 567 00:34:24,560 --> 00:34:27,120 Speaker 1: the end of what he calls the long debt cycle, 568 00:34:27,760 --> 00:34:30,560 Speaker 1: what should it do to make sure it doesn't hit 569 00:34:30,719 --> 00:34:31,320 Speaker 1: that wall. 570 00:34:31,920 --> 00:34:38,400 Speaker 11: There are three factors that drive the budget deficit, spending, cut, spending, 571 00:34:38,640 --> 00:34:44,400 Speaker 11: it'll be reduced taxes. Race tax is not tax rates 572 00:34:45,400 --> 00:34:48,479 Speaker 11: but tax. Sometimes you can cut tag race and raised 573 00:34:48,960 --> 00:34:51,040 Speaker 11: get that more tax revenue. So I just want to 574 00:34:51,080 --> 00:34:56,680 Speaker 11: say taxes, tax revenue, but interest. If the government was 575 00:34:56,760 --> 00:35:00,319 Speaker 11: operating as a unit with the central bank, and there 576 00:35:00,360 --> 00:35:04,560 Speaker 11: was a coordination, if there was a fiscal tightening coordinated 577 00:35:04,600 --> 00:35:10,080 Speaker 11: with monetary easing, both of those things reduce the problem. 578 00:35:10,320 --> 00:35:13,160 Speaker 11: And so an action of getting it a three percent 579 00:35:13,160 --> 00:35:16,520 Speaker 11: of GDP stream out of number the three percent solution. 580 00:35:17,000 --> 00:35:19,759 Speaker 11: I think that the policymakers have to start from the 581 00:35:19,800 --> 00:35:23,880 Speaker 11: top and say three percent of GDP, we can do this, 582 00:35:24,040 --> 00:35:26,920 Speaker 11: We will do this in one way or another. They 583 00:35:26,960 --> 00:35:31,279 Speaker 11: can't let their pet programs. I think they don't have 584 00:35:31,520 --> 00:35:34,680 Speaker 11: that mantra. They don't have what is the amount? And 585 00:35:34,719 --> 00:35:38,720 Speaker 11: then they have to agree at least if we can't agree, 586 00:35:38,719 --> 00:35:39,640 Speaker 11: what do we do? 587 00:35:39,640 --> 00:35:40,880 Speaker 10: Do it proportionately? 588 00:35:41,960 --> 00:35:46,400 Speaker 11: Whatever you do, you must do this because if you don't, 589 00:35:46,640 --> 00:35:49,879 Speaker 11: you are risking what I would call you this, this 590 00:35:50,160 --> 00:35:54,480 Speaker 11: heart attack due to that constriction of debt service payments 591 00:35:54,560 --> 00:35:57,839 Speaker 11: and or a piece of that plaque breaking off sort 592 00:35:57,880 --> 00:36:01,440 Speaker 11: of the sale of those bonds. Your risking that needlessly, 593 00:36:01,600 --> 00:36:04,000 Speaker 11: and so many things are so beautiful you don't need 594 00:36:04,040 --> 00:36:04,560 Speaker 11: to do that. 595 00:36:05,320 --> 00:36:08,840 Speaker 1: In the end, Dahlio's how Countries Go broke isn't simply 596 00:36:08,880 --> 00:36:12,560 Speaker 1: a Jeremiah. It does warrn of some pretty dire consequences 597 00:36:12,560 --> 00:36:15,480 Speaker 1: if we don't act and don't act soon, But he 598 00:36:15,560 --> 00:36:18,400 Speaker 1: also sees a path forward toward what he calls a 599 00:36:18,480 --> 00:36:20,000 Speaker 1: beautiful deleveraging. 600 00:36:20,360 --> 00:36:23,520 Speaker 11: A beautiful deleveraging one way or another is there's ways 601 00:36:23,560 --> 00:36:27,400 Speaker 11: of reducing your debt and debt burdens, some of which 602 00:36:27,600 --> 00:36:32,480 Speaker 11: are deflationary and some of which are inflationary, and if 603 00:36:32,520 --> 00:36:36,560 Speaker 11: you balance those, you will reduce it in a balanced way. 604 00:36:36,960 --> 00:36:38,879 Speaker 10: That's what I mean by beautiful deleveraging. 605 00:36:39,320 --> 00:36:45,160 Speaker 11: So, for example, fiscal spending cuts or tightening raising taxes 606 00:36:45,800 --> 00:36:50,920 Speaker 11: will reduce the debt burden, but it's a deflationary influence. 607 00:36:52,239 --> 00:36:57,200 Speaker 11: There is interest rates, and reducing interest rates is a 608 00:36:57,320 --> 00:37:02,440 Speaker 11: stimulative way because it reduces debts servius payments directly for 609 00:37:02,520 --> 00:37:05,600 Speaker 11: the government, so they have a lower debt bill, and 610 00:37:05,640 --> 00:37:08,880 Speaker 11: it also is stimulative to the economy at the same time, 611 00:37:09,400 --> 00:37:13,360 Speaker 11: which can raise tax revenues and also raise asset prices. 612 00:37:13,719 --> 00:37:17,320 Speaker 11: If we think beyond what our usual constraints are about 613 00:37:17,680 --> 00:37:21,120 Speaker 11: dealing with the budget. In other words, don't just think 614 00:37:21,160 --> 00:37:27,320 Speaker 11: about spending and taxes as vehicles, but also think about 615 00:37:27,600 --> 00:37:31,000 Speaker 11: interest rates and the interest rate component that could be 616 00:37:31,040 --> 00:37:35,279 Speaker 11: a balance dealing with that reduction that can accomplish the 617 00:37:35,360 --> 00:37:40,640 Speaker 11: goal without being depressing and gets it on a better 618 00:37:40,640 --> 00:37:41,480 Speaker 11: physical track. 619 00:37:43,560 --> 00:37:45,680 Speaker 1: That does it for us. Here at Wall Street Week, 620 00:37:45,880 --> 00:37:49,080 Speaker 1: I'm David Weston. This is Bloomberg. See you next week 621 00:37:49,120 --> 00:38:06,080 Speaker 1: for more stories of capitalism.