1 00:00:00,640 --> 00:00:04,160 Speaker 1: Hi, I'm Molly John Fast and this is Fast Politics, 2 00:00:04,360 --> 00:00:07,120 Speaker 1: where we discussed the top political headlines with some of 3 00:00:07,160 --> 00:00:11,479 Speaker 1: today's best minds. We're on vacation, but that doesn't mean 4 00:00:11,520 --> 00:00:14,120 Speaker 1: we don't have a great show for you. Today. Wired's 5 00:00:14,240 --> 00:00:19,159 Speaker 1: own Stephen Levy stops by to tell us about the 6 00:00:19,160 --> 00:00:23,600 Speaker 1: big tech executives who are now working in the US Army. 7 00:00:24,160 --> 00:00:27,800 Speaker 1: Kind of scary. But first we have Pro Publica's own 8 00:00:28,480 --> 00:00:33,360 Speaker 1: Megan Rose about how the FDA is not properly policing 9 00:00:33,520 --> 00:00:38,599 Speaker 1: the generic drugs in your medicine cabinet. Also scary. Welcome 10 00:00:38,680 --> 00:00:42,760 Speaker 1: to Fast Politics, Megan, Thank you for having me. We're 11 00:00:42,840 --> 00:00:48,120 Speaker 1: so excited to have you. You focus on drugs, regulation 12 00:00:48,680 --> 00:00:55,040 Speaker 1: of drugs, medical healthcare, medical stuff, etc. Right, who wrote 13 00:00:55,040 --> 00:00:58,480 Speaker 1: a piece called seven Things to Know about Pro Publica's 14 00:00:58,520 --> 00:01:02,560 Speaker 1: investigation of the FDA secret gamble on generic drugs, So 15 00:01:02,680 --> 00:01:05,720 Speaker 1: explain to us what is happening at the FDI. 16 00:01:06,319 --> 00:01:09,360 Speaker 2: We found that for more than a decade, the FDA 17 00:01:09,520 --> 00:01:14,000 Speaker 2: has banned substandard drug making factories in India other places 18 00:01:14,040 --> 00:01:18,320 Speaker 2: overseas from sending drugs to the US. Essentially, the agency 19 00:01:18,360 --> 00:01:21,680 Speaker 2: said this factory is so bad, we can't accept any 20 00:01:21,760 --> 00:01:24,560 Speaker 2: of the drugs that makes. But then the agency has 21 00:01:24,640 --> 00:01:28,399 Speaker 2: quietly given those same factories like a special path to 22 00:01:28,520 --> 00:01:32,000 Speaker 2: keep sending certain drugs here, and they really told nobody 23 00:01:32,000 --> 00:01:34,320 Speaker 2: about it. We found this happened with more than one 24 00:01:34,400 --> 00:01:37,280 Speaker 2: hundred and fifty drugs or their key ingredients, and it 25 00:01:37,360 --> 00:01:40,280 Speaker 2: affects a ton of Americans because we're talking about drugs 26 00:01:40,400 --> 00:01:44,480 Speaker 2: for cancer, depression, epilepsy, lugarrix disease. It really runs the 27 00:01:44,520 --> 00:01:48,520 Speaker 2: whole gamut of getting these drugs that are from places 28 00:01:48,560 --> 00:01:51,400 Speaker 2: that we ordinarily wouldn't trust to make our medicines. 29 00:01:51,800 --> 00:01:54,960 Speaker 1: So they have decided now to get drugs from there, 30 00:01:55,160 --> 00:01:56,800 Speaker 1: even though they wouldn't before. 31 00:01:57,200 --> 00:02:00,960 Speaker 2: Yeah, so they the FDA goes over to factories that 32 00:02:01,000 --> 00:02:03,080 Speaker 2: are making drugs coming into the US and they do 33 00:02:03,120 --> 00:02:06,400 Speaker 2: inspections to make sure they're up to par. And there 34 00:02:06,400 --> 00:02:09,880 Speaker 2: are times where these factories are really doing a poor job. 35 00:02:10,120 --> 00:02:13,720 Speaker 2: The FDA says, Okay, nope, we're done. We are banning you. 36 00:02:14,000 --> 00:02:16,480 Speaker 2: And normally that would mean they couldn't send anything, but 37 00:02:16,520 --> 00:02:19,920 Speaker 2: then they made these choices to say, well, we need 38 00:02:20,360 --> 00:02:23,880 Speaker 2: XYZ drug, So even though you're so bad we're banning 39 00:02:23,919 --> 00:02:26,440 Speaker 2: everything else, we're going to let you keep making and 40 00:02:26,520 --> 00:02:28,720 Speaker 2: sending these particular drugs to US. 41 00:02:28,840 --> 00:02:32,640 Speaker 1: So secretive group inside the FDA, right, they exempted the 42 00:02:32,720 --> 00:02:35,720 Speaker 1: medications from the import bands. They sort of said it 43 00:02:35,800 --> 00:02:39,680 Speaker 1: was to protect from drug shortages, but really, who the 44 00:02:39,680 --> 00:02:42,160 Speaker 1: fuck knows? And I just want to read this because 45 00:02:42,160 --> 00:02:44,840 Speaker 1: I think this is important. The FDA allowed into United 46 00:02:44,840 --> 00:02:47,600 Speaker 1: States at least one hundred and fifty drugs or their ingredients, 47 00:02:47,639 --> 00:02:51,160 Speaker 1: and these banded factories found to have mold, foul water, 48 00:02:51,360 --> 00:02:55,880 Speaker 1: dirty labs, or fraudulent testing protocols. Nearly all came from 49 00:02:55,880 --> 00:02:57,119 Speaker 1: factories in India. 50 00:02:57,200 --> 00:03:00,000 Speaker 2: Yeah, so the problems that the inspectors find at these 51 00:03:00,120 --> 00:03:05,840 Speaker 2: factories are pretty gross. They ranged from pigeon feces on 52 00:03:06,120 --> 00:03:10,840 Speaker 2: boxes of ingredients going into batches of drugs, or glass 53 00:03:10,880 --> 00:03:13,920 Speaker 2: getting mixed into injectibles like shots they are going to 54 00:03:13,919 --> 00:03:16,280 Speaker 2: go into your body, which that could kill you. Or 55 00:03:16,440 --> 00:03:20,040 Speaker 2: there are lots of problems like they manipulate testing records, 56 00:03:20,080 --> 00:03:21,960 Speaker 2: and there's all kinds of ways in which they do it, 57 00:03:22,000 --> 00:03:24,800 Speaker 2: but the general outcome is that they make it appear 58 00:03:24,840 --> 00:03:27,280 Speaker 2: as if every drug that they have tested for quality 59 00:03:27,320 --> 00:03:30,720 Speaker 2: has passed, when in fact, many, many batches have failed. 60 00:03:30,919 --> 00:03:32,720 Speaker 2: But what they send to the FDA is oh, look, 61 00:03:32,760 --> 00:03:35,200 Speaker 2: this is all good, and here these drugs we're sending. 62 00:03:35,480 --> 00:03:39,040 Speaker 2: So a lot of the drugs that we are getting 63 00:03:39,040 --> 00:03:42,120 Speaker 2: from these factories are called sterile injectibles. I know, that's 64 00:03:42,160 --> 00:03:44,600 Speaker 2: a not a term. Everybody knows that these are shots 65 00:03:44,640 --> 00:03:47,480 Speaker 2: that go directly into a person's veins or their muscles, 66 00:03:47,480 --> 00:03:50,440 Speaker 2: so the drug is in the bloodstream really fast. So 67 00:03:50,600 --> 00:03:52,840 Speaker 2: this type of medication it has to be free of 68 00:03:52,840 --> 00:03:55,040 Speaker 2: contaminants and be really pure. It could harm you, it 69 00:03:55,040 --> 00:03:57,360 Speaker 2: could kill you, like the glass and the injectibles. So 70 00:03:57,400 --> 00:03:59,520 Speaker 2: there are a lot of ruled about factories of how 71 00:03:59,560 --> 00:04:02,320 Speaker 2: they need to make these drugs so they're safe. So 72 00:04:02,440 --> 00:04:06,480 Speaker 2: picture workers on production lines in downs and gloves. You know, 73 00:04:06,520 --> 00:04:08,800 Speaker 2: there are restrictions on how they can touch things and 74 00:04:08,840 --> 00:04:12,240 Speaker 2: when machines need to be cleaned, like very precise instructions 75 00:04:12,280 --> 00:04:17,320 Speaker 2: to ensure sterility. But these rules are also the bare minimum. 76 00:04:17,400 --> 00:04:19,680 Speaker 2: So like when an FDA inspector goes into a factory, 77 00:04:19,680 --> 00:04:21,640 Speaker 2: that's what they're looking for. They're checking to see if 78 00:04:21,720 --> 00:04:24,680 Speaker 2: like the very basic safety standards are met. So that 79 00:04:24,760 --> 00:04:27,520 Speaker 2: means that these factories that have been banned have failed 80 00:04:27,560 --> 00:04:30,599 Speaker 2: to meet like even the lowest far and import bands 81 00:04:30,600 --> 00:04:33,920 Speaker 2: don't happen often. This is something the agency reserves for 82 00:04:34,160 --> 00:04:37,320 Speaker 2: the worst of the worst. So right now patients here 83 00:04:37,320 --> 00:04:39,679 Speaker 2: in this country are going in for surgery, they're getting 84 00:04:39,680 --> 00:04:42,640 Speaker 2: created for cancer, and they're unknowingly be injected with the 85 00:04:42,720 --> 00:04:44,919 Speaker 2: drug that's made at one of those factories. 86 00:04:45,560 --> 00:04:49,520 Speaker 1: Jesus Christ, this is like the good FDI. Like, this 87 00:04:49,720 --> 00:04:53,400 Speaker 1: is probably biden FDI. Imagine what this FDA is going 88 00:04:53,480 --> 00:04:53,800 Speaker 1: to be like. 89 00:04:53,920 --> 00:04:54,320 Speaker 3: Now. 90 00:04:54,640 --> 00:04:55,400 Speaker 1: This group that. 91 00:04:55,320 --> 00:04:58,280 Speaker 2: Makes these choices is the Drug Division inside the FDA, 92 00:04:58,600 --> 00:05:01,600 Speaker 2: and they just had massive c to their staff. They 93 00:05:01,600 --> 00:05:05,120 Speaker 2: were already pretty under resourced, so it'll be interesting to 94 00:05:05,120 --> 00:05:06,960 Speaker 2: see going forward what they were able to do and 95 00:05:07,000 --> 00:05:07,240 Speaker 2: not do. 96 00:05:07,920 --> 00:05:11,599 Speaker 1: So, I mean, it's going to be ten times worse. Right, 97 00:05:12,160 --> 00:05:13,280 Speaker 1: those are the fears. I mean. 98 00:05:13,320 --> 00:05:16,040 Speaker 2: We talked to Janet Woodcock. She was head of the 99 00:05:16,120 --> 00:05:18,800 Speaker 2: Drug Division for FDA for like almost twenty five years, 100 00:05:18,800 --> 00:05:21,479 Speaker 2: and she's very concerned about all the slashes because she 101 00:05:21,520 --> 00:05:24,800 Speaker 2: was constantly asking for more resources throughout her tenure and 102 00:05:24,839 --> 00:05:28,720 Speaker 2: the FDA really got it. She former commissioners, they're really 103 00:05:28,720 --> 00:05:31,920 Speaker 2: concerned about what the FDA if they're going to do 104 00:05:32,000 --> 00:05:36,200 Speaker 2: their job. Beforehand, they had so much approving drugs, reviewing drugs, 105 00:05:36,240 --> 00:05:39,880 Speaker 2: tracking harm, inspecting factories, and now they'd have less staff 106 00:05:39,920 --> 00:05:41,320 Speaker 2: and less money to do it. 107 00:05:41,400 --> 00:05:45,640 Speaker 1: Can you explain to us why you think this has 108 00:05:45,720 --> 00:05:47,080 Speaker 1: not gotten more attention? 109 00:05:47,480 --> 00:05:50,760 Speaker 2: Wouldn't the FDA made the choice to ban these drugs. 110 00:05:51,000 --> 00:05:54,520 Speaker 2: They did so very secretively, and since it was about 111 00:05:54,560 --> 00:05:56,880 Speaker 2: six to eight people in the room who were making 112 00:05:56,920 --> 00:06:00,960 Speaker 2: a decision, they never told Congress, they never told the public. 113 00:06:01,040 --> 00:06:04,360 Speaker 2: It was just kept very under wraps, so people didn't 114 00:06:04,400 --> 00:06:07,520 Speaker 2: know about it. And if you go on the FDA website, 115 00:06:07,520 --> 00:06:09,760 Speaker 2: like if you know enough to say, I'm gonna go 116 00:06:09,839 --> 00:06:13,080 Speaker 2: see like what factories have been banned from making drugs, 117 00:06:13,080 --> 00:06:14,800 Speaker 2: you can go look up an import alert on the 118 00:06:14,839 --> 00:06:18,560 Speaker 2: FDA's website. It's full of jargon and random codes that 119 00:06:18,600 --> 00:06:20,520 Speaker 2: you would then need a dictionary to figure out what 120 00:06:20,520 --> 00:06:24,560 Speaker 2: they mean. And then buried in there is like a 121 00:06:24,600 --> 00:06:29,360 Speaker 2: line that talks about products that are exempt from the band, 122 00:06:29,600 --> 00:06:31,279 Speaker 2: except they don't use that language. You have to like 123 00:06:31,320 --> 00:06:33,159 Speaker 2: decode the language to get there. And then you would 124 00:06:33,200 --> 00:06:35,239 Speaker 2: see the list of the drugs, and then it's buried 125 00:06:35,240 --> 00:06:37,600 Speaker 2: in this like massive document you have to scroll through, 126 00:06:37,640 --> 00:06:40,520 Speaker 2: so you would never find it unless you were really 127 00:06:40,560 --> 00:06:43,719 Speaker 2: looking for it. And they didn't bother to tell anybody, 128 00:06:43,880 --> 00:06:46,720 Speaker 2: you know, it's interesting. They have been doing this since 129 00:06:47,160 --> 00:06:50,240 Speaker 2: at least twenty and twelve, so it's been a long time. 130 00:06:50,560 --> 00:06:53,480 Speaker 1: Yeah, And has no one ever died from these drugs 131 00:06:53,560 --> 00:06:54,760 Speaker 1: or do we just not know? 132 00:06:55,520 --> 00:06:59,360 Speaker 2: A harm is really hard to track because unless you 133 00:06:59,400 --> 00:07:01,880 Speaker 2: are taking a drug, like there's a few proprion a 134 00:07:01,960 --> 00:07:06,359 Speaker 2: depressive antidepression drug. If you are on this antidepressive drug, 135 00:07:06,440 --> 00:07:08,800 Speaker 2: if it has a problem, it'll smell like rotten eggs, 136 00:07:08,880 --> 00:07:11,040 Speaker 2: So that's really like obvious. You open your pill bottle 137 00:07:11,040 --> 00:07:12,640 Speaker 2: and it's like you get hit with the smell. But 138 00:07:12,720 --> 00:07:15,200 Speaker 2: a lot of things they don't have such an obvious flaw, 139 00:07:15,320 --> 00:07:18,120 Speaker 2: so it's hard to know that like I'm having an 140 00:07:18,160 --> 00:07:21,880 Speaker 2: issue because I took this drug. So it's really underreported. 141 00:07:22,000 --> 00:07:25,560 Speaker 2: But the FDA has a database where they collect complaints. 142 00:07:25,560 --> 00:07:27,520 Speaker 2: So if you're a healthcare provider, you're a pharmacist, or 143 00:07:27,560 --> 00:07:30,200 Speaker 2: a patient and you suspect your drug has harmed you 144 00:07:30,240 --> 00:07:33,000 Speaker 2: in some way, you can file a complaint. And there 145 00:07:33,080 --> 00:07:36,440 Speaker 2: are lots of reports about these exempted drugs. So we 146 00:07:36,720 --> 00:07:40,480 Speaker 2: looked at two companies alone who are currently under import ban, 147 00:07:40,600 --> 00:07:43,480 Speaker 2: and they have three factories that are banned with exempted drugs, 148 00:07:43,720 --> 00:07:46,760 Speaker 2: and we found six hundred complaints just from those factories 149 00:07:46,800 --> 00:07:52,200 Speaker 2: alone about the exempted drugs, including like seventy hospitalizations nodding deaths. So, 150 00:07:52,520 --> 00:07:54,960 Speaker 2: even though in your causation is very hard to prove, 151 00:07:55,360 --> 00:07:57,840 Speaker 2: there are patterns there that the FDA could have been 152 00:07:57,880 --> 00:07:59,960 Speaker 2: looking at to decide if these drugs really worse. 153 00:08:00,480 --> 00:08:02,840 Speaker 1: I mean, how do you get to a situation like this. 154 00:08:03,720 --> 00:08:06,920 Speaker 1: Clearly the federal government is not doing its job. 155 00:08:07,360 --> 00:08:11,680 Speaker 2: We have had very serious drug shortages for a long time, 156 00:08:12,160 --> 00:08:15,680 Speaker 2: and nobody has seemed to really dive into wanting to 157 00:08:15,720 --> 00:08:19,520 Speaker 2: fix that problem. And granted it's complicated. There are drug 158 00:08:19,680 --> 00:08:23,360 Speaker 2: supply is So drug shortages really started in the twenty 159 00:08:23,480 --> 00:08:27,680 Speaker 2: ten twenty eleven they peaked why so it's an interesting question. 160 00:08:27,960 --> 00:08:32,439 Speaker 2: There was some closures of some domestic drug making plants. 161 00:08:32,720 --> 00:08:36,000 Speaker 2: There was some critical ingredients that were in short supply, 162 00:08:36,160 --> 00:08:38,439 Speaker 2: like kind of the plethora like this kind of a 163 00:08:38,480 --> 00:08:43,800 Speaker 2: combination of factors that led to these shortages, including FDA enforcement. 164 00:08:43,920 --> 00:08:47,000 Speaker 2: So if an FDA inspector goes to a factory and 165 00:08:47,000 --> 00:08:50,319 Speaker 2: shuts them down, if it's foreign factory, fans them from 166 00:08:50,360 --> 00:08:53,320 Speaker 2: sending their drugs, well, we're out of that supply. So 167 00:08:53,400 --> 00:08:56,200 Speaker 2: it's kind of like, well, FDA does their job to 168 00:08:56,240 --> 00:08:58,720 Speaker 2: say you're not doing these factories can't make drugs, then 169 00:08:58,760 --> 00:09:01,160 Speaker 2: they have fewer drugs, and so it's kind of this 170 00:09:01,280 --> 00:09:05,160 Speaker 2: self defeating cycle. So when these drug shortages happened and 171 00:09:05,200 --> 00:09:08,120 Speaker 2: they Congress was pissed, public was pissed, it got a 172 00:09:08,160 --> 00:09:11,800 Speaker 2: lot of attention. FDA kind of switched what it was 173 00:09:11,840 --> 00:09:14,920 Speaker 2: doing to really focus on drug shortages first, and so 174 00:09:15,480 --> 00:09:18,000 Speaker 2: at that point, when they decided to ban a factory, 175 00:09:18,240 --> 00:09:20,200 Speaker 2: the agency would first look to see if it effects 176 00:09:20,200 --> 00:09:22,400 Speaker 2: a drug shortage. So if we cut off this factory 177 00:09:22,400 --> 00:09:24,800 Speaker 2: from sending drugs here, would there'll still be enough drugs 178 00:09:24,800 --> 00:09:27,120 Speaker 2: for all the patients that need them. For many of 179 00:09:27,120 --> 00:09:29,840 Speaker 2: these factories, the FDA says that the answer was no, 180 00:09:30,040 --> 00:09:33,079 Speaker 2: although we have no way to fact check that because 181 00:09:33,120 --> 00:09:37,000 Speaker 2: they won't release shortage data. They have don't tell us 182 00:09:37,000 --> 00:09:39,360 Speaker 2: what their metrics are for making these decisions, so we 183 00:09:39,440 --> 00:09:41,559 Speaker 2: just kind of have to trust them that this would 184 00:09:41,600 --> 00:09:44,120 Speaker 2: actually have caused a shortage. But you know, the FDA 185 00:09:44,200 --> 00:09:46,640 Speaker 2: told us that nobody wanted to carve these drugs out 186 00:09:46,679 --> 00:09:48,520 Speaker 2: of an import band, but they had to balance the 187 00:09:48,600 --> 00:09:51,280 Speaker 2: risks to a patient having no drugs at all or 188 00:09:51,320 --> 00:09:55,040 Speaker 2: having one that's poorly made. But again, like the agency 189 00:09:55,320 --> 00:09:57,800 Speaker 2: didn't raise the alarms to the public or to Congress 190 00:09:57,800 --> 00:09:59,839 Speaker 2: and everyone said, hey, this is the terrible choice for 191 00:09:59,920 --> 00:10:03,559 Speaker 2: me because of a drug shortage or Since they've been 192 00:10:03,559 --> 00:10:07,280 Speaker 2: making these decisions since at least twenty twelve, that's more 193 00:10:07,320 --> 00:10:10,400 Speaker 2: than a decade of lost opportunity to change the equation 194 00:10:10,720 --> 00:10:14,000 Speaker 2: because the FDA stayed silent, So nobody over these years 195 00:10:14,000 --> 00:10:16,319 Speaker 2: has had a chance to say, Okay, if we've ended 196 00:10:16,400 --> 00:10:18,880 Speaker 2: up here where we're like making this choice between no 197 00:10:18,960 --> 00:10:21,440 Speaker 2: drugs or bad drugs, what we need to do to 198 00:10:21,480 --> 00:10:23,560 Speaker 2: fix the system. And you're right, that's a failure of 199 00:10:23,559 --> 00:10:27,479 Speaker 2: the failure of government all around, because it would take Congress, 200 00:10:27,600 --> 00:10:29,800 Speaker 2: it would you know, take the White House, and it 201 00:10:29,800 --> 00:10:33,199 Speaker 2: would take first the FDA saying hey, we have this problem, 202 00:10:33,320 --> 00:10:34,520 Speaker 2: we need some help dealing with it. 203 00:10:34,920 --> 00:10:37,280 Speaker 1: Another part of the story is that there's a secret 204 00:10:37,600 --> 00:10:42,120 Speaker 1: panel of doctors working in the FDA, right, I mean 205 00:10:42,160 --> 00:10:44,760 Speaker 1: are they doctors? Who are these people and how did 206 00:10:44,760 --> 00:10:45,800 Speaker 1: they get this job? 207 00:10:46,400 --> 00:10:51,680 Speaker 2: So the FDA have always had a drug shortage staff 208 00:10:51,880 --> 00:10:54,480 Speaker 2: and they're not doctors. Sol though some of them are 209 00:10:54,920 --> 00:10:59,640 Speaker 2: healthcare professionals in some way, and I think in the beginning, 210 00:10:59,720 --> 00:11:03,880 Speaker 2: before we're twenty ten, it wasn't a very glamorous job. 211 00:11:04,040 --> 00:11:06,400 Speaker 2: They were kind of on the outskirts of the FDA. 212 00:11:06,679 --> 00:11:09,840 Speaker 2: And then the shortages happened, and Janet Woodcock, who led 213 00:11:09,840 --> 00:11:12,440 Speaker 2: the agency for or led the drug division of the 214 00:11:12,440 --> 00:11:14,960 Speaker 2: agency for a very long time, she elevated them to 215 00:11:15,000 --> 00:11:18,600 Speaker 2: work directly to her office, and so all of a sudden, 216 00:11:18,679 --> 00:11:21,720 Speaker 2: this small crew of people were getting a seat at 217 00:11:21,760 --> 00:11:24,240 Speaker 2: the table and all of these decisions that were normally 218 00:11:24,280 --> 00:11:27,600 Speaker 2: made by specialists in compliance. So if you are inspectors 219 00:11:27,760 --> 00:11:30,439 Speaker 2: and you're reviewing inspection and deciding what should happen to 220 00:11:30,960 --> 00:11:33,840 Speaker 2: know a stroubbled factory, you would make that choice. But now, 221 00:11:33,840 --> 00:11:36,200 Speaker 2: all of a sudden, drug shortage people are at the table, 222 00:11:36,280 --> 00:11:38,840 Speaker 2: and they're also saying, well, maybe we can't do that 223 00:11:38,880 --> 00:11:42,760 Speaker 2: because you're gonna cause a shortage. So let's figure out 224 00:11:42,920 --> 00:11:45,680 Speaker 2: how these products they are supposed to be sterile and 225 00:11:45,800 --> 00:11:48,120 Speaker 2: aren't being made and only that keeps them sterile, can 226 00:11:48,160 --> 00:11:49,319 Speaker 2: still come to this country. 227 00:11:49,480 --> 00:11:53,720 Speaker 1: There's not really accountability, right, there's no congressional oversight to 228 00:11:53,760 --> 00:11:56,839 Speaker 1: the FDA. Is there or is there not and why 229 00:11:56,960 --> 00:11:58,199 Speaker 1: isn't there If there. 230 00:11:58,080 --> 00:12:01,680 Speaker 2: Isn't, There is lots of compression oversight of the FDA, 231 00:12:02,040 --> 00:12:07,480 Speaker 2: but it's a huge agency overseas, you know, food, medical devices, drugs, 232 00:12:07,520 --> 00:12:11,440 Speaker 2: and if the FDA never tells them about something, they 233 00:12:11,480 --> 00:12:15,040 Speaker 2: don't know what's happening. And it's interesting there was when 234 00:12:15,040 --> 00:12:18,800 Speaker 2: the drug shortages first really started to hit crisis level, 235 00:12:19,080 --> 00:12:21,800 Speaker 2: Congress was so mad. The FDA said, listen, you guys 236 00:12:21,800 --> 00:12:24,360 Speaker 2: need to send us a report every year that tells 237 00:12:24,440 --> 00:12:27,640 Speaker 2: us exactly what you're doing to stop drug shortages, including 238 00:12:27,840 --> 00:12:30,400 Speaker 2: the ways in which you're using what the FDA calls 239 00:12:30,520 --> 00:12:34,040 Speaker 2: regulatory flexibility, meaning they can like kind of fudge their 240 00:12:34,160 --> 00:12:37,319 Speaker 2: rules a bit to allow these bad drugs to come in. 241 00:12:37,440 --> 00:12:41,520 Speaker 2: But the FDA never mentioned this practice to Congress. So 242 00:12:41,800 --> 00:12:43,840 Speaker 2: for years they were telling them all these things they 243 00:12:43,840 --> 00:12:46,120 Speaker 2: were doing and never saying, oh, by the way, we're 244 00:12:46,160 --> 00:12:51,000 Speaker 2: bringing in drugs from band factories. Not until their last 245 00:12:51,040 --> 00:12:53,719 Speaker 2: report in twenty twenty four did they tell Congress in 246 00:12:53,760 --> 00:12:57,920 Speaker 2: a tiny little footnote that said making exemptions or what 247 00:12:57,960 --> 00:13:00,600 Speaker 2: they call carve outs import bands is one way in 248 00:13:00,640 --> 00:13:03,040 Speaker 2: which they're dealing with shortages. So this is the first 249 00:13:03,040 --> 00:13:06,319 Speaker 2: time Congress has learned of it, and how many lawmakers 250 00:13:06,360 --> 00:13:07,719 Speaker 2: have time to read their footnotes. 251 00:13:08,120 --> 00:13:12,200 Speaker 1: Is Congress ready to start holding hearings on this or 252 00:13:12,240 --> 00:13:14,480 Speaker 1: do they not know about it still? 253 00:13:14,679 --> 00:13:18,080 Speaker 2: Or do they There is a committee that is very 254 00:13:18,080 --> 00:13:20,880 Speaker 2: interested in the FDA and has been dogging them for 255 00:13:20,960 --> 00:13:24,280 Speaker 2: the last couple of years, particularly about foreign inspections because 256 00:13:24,280 --> 00:13:27,000 Speaker 2: the FDA was like not doing them very often and 257 00:13:27,120 --> 00:13:30,080 Speaker 2: most there's like a huge percentage of our drugs are 258 00:13:30,120 --> 00:13:33,360 Speaker 2: generic meds that come from India, but these factories were 259 00:13:33,360 --> 00:13:36,000 Speaker 2: not getting inspected, so the FDA didn't have any eyes 260 00:13:36,040 --> 00:13:39,079 Speaker 2: on them. That Congress was really upset about that, and 261 00:13:39,559 --> 00:13:42,680 Speaker 2: they have been looking into that issue quite a bit. 262 00:13:42,760 --> 00:13:45,480 Speaker 2: So it'll be interesting to see if that this this 263 00:13:45,600 --> 00:13:48,520 Speaker 2: in court band practice comes up at the next time 264 00:13:49,040 --> 00:13:50,320 Speaker 2: they call FDA to the Hill. 265 00:13:50,679 --> 00:13:54,959 Speaker 1: So this really is a generic drug problem, like an 266 00:13:55,000 --> 00:13:57,960 Speaker 1: on label drug won't have this problem. 267 00:13:57,679 --> 00:14:00,040 Speaker 2: The most part. Now, brands like the big brand and 268 00:14:00,240 --> 00:14:02,679 Speaker 2: drugs like they have their own problems of their factories, 269 00:14:02,720 --> 00:14:05,320 Speaker 2: but they have substantially more money, so it's less of 270 00:14:05,360 --> 00:14:08,400 Speaker 2: an issue. But what we were talking about, these are 271 00:14:08,480 --> 00:14:12,120 Speaker 2: all generic meds, which are about ninety percent of the 272 00:14:12,200 --> 00:14:14,880 Speaker 2: drugs that we all take. It's very rare to be 273 00:14:14,960 --> 00:14:18,320 Speaker 2: on a brand these days unless it's the only like 274 00:14:18,400 --> 00:14:21,680 Speaker 2: they're still underpatent. Otherwise all your insurance companies say, okay, 275 00:14:21,680 --> 00:14:23,800 Speaker 2: now you need to be on the cheaper generic, and 276 00:14:23,840 --> 00:14:26,480 Speaker 2: a lot of the generics, like about a fifth of 277 00:14:26,520 --> 00:14:30,400 Speaker 2: the generics the world's generic factories are in India and 278 00:14:30,440 --> 00:14:32,720 Speaker 2: they get a lot of their ingredients from China. And 279 00:14:32,760 --> 00:14:35,840 Speaker 2: that's the other aspect of this story is we found 280 00:14:36,000 --> 00:14:39,360 Speaker 2: that one hundred and fifty drugs. It's also including the 281 00:14:39,400 --> 00:14:42,880 Speaker 2: key ingredients. So these are factories that they don't make 282 00:14:42,920 --> 00:14:45,000 Speaker 2: a finished drug. You're not going to you don't take 283 00:14:45,160 --> 00:14:47,960 Speaker 2: a pill that they make, but they make like the 284 00:14:48,080 --> 00:14:50,920 Speaker 2: crucial ingredient that other drug makers are going to put 285 00:14:50,920 --> 00:14:54,280 Speaker 2: into their pills. And they're coming from these band factories 286 00:14:54,440 --> 00:14:58,040 Speaker 2: going into untold number of drugs we don't know which ones. 287 00:14:58,560 --> 00:15:01,080 Speaker 2: So we could have a whole list these are exempted drugs, 288 00:15:01,120 --> 00:15:03,400 Speaker 2: but it wouldn't cover everything because you could be taking 289 00:15:03,440 --> 00:15:07,240 Speaker 2: a drug from a manufacturer who bought their ingredient from 290 00:15:07,240 --> 00:15:09,520 Speaker 2: one of these band factories, and so that just like 291 00:15:09,640 --> 00:15:10,920 Speaker 2: multiplies the problem. 292 00:15:11,320 --> 00:15:14,760 Speaker 1: Oh yeah, yeah, how will the FDA change under the 293 00:15:14,800 --> 00:15:15,800 Speaker 1: Trump administration. 294 00:15:16,320 --> 00:15:18,560 Speaker 2: Well, I think we're still seeing how that is going 295 00:15:18,600 --> 00:15:22,240 Speaker 2: to play out. But the Trump administration and Robert Kennedy 296 00:15:22,360 --> 00:15:26,560 Speaker 2: Junior have thoughts about really slashing and burning the agency. 297 00:15:26,920 --> 00:15:30,359 Speaker 2: We've seen so many cuts. They're talking about less oversight 298 00:15:30,600 --> 00:15:34,480 Speaker 2: of drugs, faster review of applications. So if you want 299 00:15:34,520 --> 00:15:37,680 Speaker 2: to make a generic drug for Americans, you have to 300 00:15:37,720 --> 00:15:40,600 Speaker 2: give the FDA an application saying like, here's our formula 301 00:15:40,680 --> 00:15:42,200 Speaker 2: for the drug, here's how we're going to make it, 302 00:15:42,240 --> 00:15:44,000 Speaker 2: here's where we're going to make it, and then the 303 00:15:44,120 --> 00:15:46,880 Speaker 2: FDA has to approve it. And part of that approval 304 00:15:47,560 --> 00:15:50,240 Speaker 2: is making sure that they can make this drug in 305 00:15:50,280 --> 00:15:53,720 Speaker 2: a quality way. And sometimes that foods an inspection and 306 00:15:54,120 --> 00:15:56,800 Speaker 2: you know, sometimes it doesn't risk base. Again, the FDA 307 00:15:56,840 --> 00:15:59,160 Speaker 2: won't really tell us how they make those choices. But 308 00:15:59,560 --> 00:16:02,600 Speaker 2: if you you are speeding up that process, there's going 309 00:16:02,640 --> 00:16:05,160 Speaker 2: to be less review that is happening. So if you 310 00:16:05,240 --> 00:16:08,400 Speaker 2: cut people and you require them to move faster, like 311 00:16:08,560 --> 00:16:11,320 Speaker 2: what is going to go through the cracks? What things 312 00:16:11,320 --> 00:16:13,720 Speaker 2: are going to be approved that maybe shouldn't. And that's 313 00:16:13,720 --> 00:16:18,320 Speaker 2: an important question because we found that the FDA was 314 00:16:18,400 --> 00:16:21,640 Speaker 2: giving it stamp of approval to knowing bad actors. So 315 00:16:21,720 --> 00:16:24,400 Speaker 2: companies that had been in trouble for years, like inspects, 316 00:16:24,440 --> 00:16:27,320 Speaker 2: have been finding violations again and again, and yet all 317 00:16:27,400 --> 00:16:30,320 Speaker 2: the while the FDA is approving these companies to make 318 00:16:30,400 --> 00:16:34,080 Speaker 2: more drugs. So the agency itself, even you know, staffed 319 00:16:34,120 --> 00:16:36,760 Speaker 2: pre Trump, was not like talking to each other in 320 00:16:36,800 --> 00:16:39,280 Speaker 2: a way that they were saying, well, wait, what is 321 00:16:39,320 --> 00:16:41,720 Speaker 2: the history of this company? Can we really trust them 322 00:16:41,760 --> 00:16:44,920 Speaker 2: to make the drugs in a safe quality way? And 323 00:16:44,960 --> 00:16:47,400 Speaker 2: as we've found out, like quite often, the answers no. 324 00:16:47,880 --> 00:16:50,000 Speaker 2: You know, one inspector told us that we let them 325 00:16:50,040 --> 00:16:52,640 Speaker 2: grow to be monsters and now we can't go back. 326 00:16:53,000 --> 00:16:55,880 Speaker 2: So if you have fewer people doing the same amount 327 00:16:55,920 --> 00:16:57,680 Speaker 2: of work, what's going to happen next? 328 00:16:59,280 --> 00:17:03,680 Speaker 1: I wanted to take any drugs ever. Again. Thank you, 329 00:17:03,840 --> 00:17:04,480 Speaker 1: Thank you. 330 00:17:04,400 --> 00:17:06,560 Speaker 2: Megan, Thank you so much for having me. 331 00:17:10,760 --> 00:17:17,040 Speaker 1: Stephen Levy is a columnist for Wired. Welcome to Fast Politics, 332 00:17:17,359 --> 00:17:20,320 Speaker 1: Steven Levy, It's great to be here. We are talking 333 00:17:20,480 --> 00:17:25,160 Speaker 1: about perhaps one of the scariest things that I have 334 00:17:25,320 --> 00:17:30,000 Speaker 1: stumbled on, is a story about tech executives. And so 335 00:17:30,040 --> 00:17:36,440 Speaker 1: we have metas CTO and leaders from open AI and 336 00:17:36,760 --> 00:17:41,240 Speaker 1: Palenteer have joined a detachment intended to make the US 337 00:17:42,000 --> 00:17:46,560 Speaker 1: Armed forces leaner, smarter, and more lethal. 338 00:17:46,960 --> 00:17:53,080 Speaker 3: Discuss Yeah, well, this is Detachment two O one. This 339 00:17:53,160 --> 00:17:56,280 Speaker 3: is something that has been in the works for about 340 00:17:56,320 --> 00:17:59,920 Speaker 3: a year, maybe from someone who is the Chief Talent 341 00:18:00,160 --> 00:18:04,119 Speaker 3: Management Officer of the Pentagon, which I learned is actually 342 00:18:04,160 --> 00:18:09,200 Speaker 3: a post someone who was a combat soldier and officer 343 00:18:09,560 --> 00:18:13,480 Speaker 3: then went to Walmart and helped them with veterans, and 344 00:18:13,520 --> 00:18:17,240 Speaker 3: then he went back to the Pentagon and he came 345 00:18:17,359 --> 00:18:21,119 Speaker 3: up with this idea talking to the Palantiner CTO to 346 00:18:21,240 --> 00:18:24,560 Speaker 3: get executives from Silicon Valley and make them soldiers at 347 00:18:24,600 --> 00:18:28,960 Speaker 3: least in the Army Reserve so they can continue this 348 00:18:29,119 --> 00:18:31,160 Speaker 3: trend that's been going on for a couple of years 349 00:18:31,240 --> 00:18:35,919 Speaker 3: really of Silicon Valley companies being more willing to work 350 00:18:35,960 --> 00:18:39,840 Speaker 3: with the military and for the military in applying their 351 00:18:39,840 --> 00:18:40,880 Speaker 3: advanced technology. 352 00:18:41,040 --> 00:18:46,199 Speaker 1: Advanced technology sounds good, right, I want to you know, 353 00:18:46,440 --> 00:18:49,720 Speaker 1: I was a person who toured the Tesla factory and thought, like, 354 00:18:50,200 --> 00:18:53,360 Speaker 1: this guy's got This was before he became the Elon 355 00:18:53,440 --> 00:18:56,919 Speaker 1: Musk that we all know now, but I thought this 356 00:18:57,000 --> 00:19:00,159 Speaker 1: is pretty good. This looks good many years before he 357 00:19:00,240 --> 00:19:02,520 Speaker 1: became what he is. But I just am curious, like, 358 00:19:02,840 --> 00:19:06,560 Speaker 1: why is this bad, because it clearly is. Can we 359 00:19:07,000 --> 00:19:09,080 Speaker 1: go back and talk about what Palenteer is up to 360 00:19:09,160 --> 00:19:13,399 Speaker 1: because Palenteers is very involved with our military, and I 361 00:19:13,480 --> 00:19:15,440 Speaker 1: wondered if you could just sort of give us a 362 00:19:15,440 --> 00:19:16,479 Speaker 1: little background on that. 363 00:19:17,080 --> 00:19:20,080 Speaker 3: Okay, yeah, well I haven't done, you know, a giant 364 00:19:20,119 --> 00:19:23,159 Speaker 3: investigation into Pallenteer, but it's pretty well known. They're a 365 00:19:23,200 --> 00:19:29,760 Speaker 3: company which has been very effective selling technology. It's data 366 00:19:29,800 --> 00:19:35,920 Speaker 3: mining to get information from huge cash's you know, of data, 367 00:19:35,920 --> 00:19:39,760 Speaker 3: a lot of personal information, you know, to help improve security. 368 00:19:40,000 --> 00:19:43,520 Speaker 3: They're increasingly getting into AI and it's a company that 369 00:19:43,640 --> 00:19:48,119 Speaker 3: thrives on government contracting and is you know, super outfront 370 00:19:48,480 --> 00:19:51,480 Speaker 3: in saying that, you know, we're not going to apologize 371 00:19:51,520 --> 00:19:54,480 Speaker 3: for it. If this is scary to some people, it's 372 00:19:54,520 --> 00:19:58,760 Speaker 3: absolutely necessary for national security. And there's a lot of 373 00:19:58,760 --> 00:20:01,560 Speaker 3: critics of Palenteer who feel, you know, they saw the 374 00:20:01,560 --> 00:20:04,800 Speaker 3: police departments and things like that, it's sort of enabling 375 00:20:04,920 --> 00:20:09,480 Speaker 3: a surveillance state. So that debate goes on, and I 376 00:20:09,520 --> 00:20:13,320 Speaker 3: think the larger debate we're talking about here is, as 377 00:20:13,359 --> 00:20:17,240 Speaker 3: you say, Gee, it sounds important, and it is important 378 00:20:17,600 --> 00:20:21,040 Speaker 3: that our military is up to date, and the fact 379 00:20:21,119 --> 00:20:27,200 Speaker 3: is because of these ancient contractors that you know, they've 380 00:20:27,280 --> 00:20:30,399 Speaker 3: traditionally work with these big defense contractors that do these 381 00:20:30,440 --> 00:20:33,080 Speaker 3: big contracts, and they come up with these elephants that 382 00:20:33,400 --> 00:20:37,040 Speaker 3: cost billions of dollars, hardly work. It's a good idea 383 00:20:37,040 --> 00:20:40,879 Speaker 3: to apply the Silicon Valley methodology to this. You know, 384 00:20:40,920 --> 00:20:45,440 Speaker 3: you see what happened when Ukraine attacked Russia with these 385 00:20:45,520 --> 00:20:48,719 Speaker 3: drones are really an innovative scheme where you know, they 386 00:20:49,000 --> 00:20:51,800 Speaker 3: parked you know, these trucks or whatever and they just 387 00:20:51,840 --> 00:20:54,919 Speaker 3: opened up like trojan horses and have drones out there. 388 00:20:55,119 --> 00:20:57,119 Speaker 3: That's like, you know, almost like a hacker kind of 389 00:20:57,160 --> 00:21:00,719 Speaker 3: scheme and military to be able to do that. I 390 00:21:00,720 --> 00:21:04,000 Speaker 3: think what's different about this case is there's like a 391 00:21:04,080 --> 00:21:08,439 Speaker 3: line to say, okay, we're getting the expertise from Silicon Valley, 392 00:21:08,760 --> 00:21:14,200 Speaker 3: to say we're getting Silicon Valley people in the military, 393 00:21:14,440 --> 00:21:17,640 Speaker 3: you know, of course to join the military, but this 394 00:21:17,760 --> 00:21:21,480 Speaker 3: program lets them into the military and gives them the 395 00:21:21,480 --> 00:21:26,800 Speaker 3: special privileges that most Army reservists and certainly you know, 396 00:21:26,880 --> 00:21:29,440 Speaker 3: full time soldiers don't have. They're not going to get 397 00:21:29,480 --> 00:21:33,080 Speaker 3: sent to wars a battlefield, and they're not gonna uh, 398 00:21:33,400 --> 00:21:37,080 Speaker 3: maybe they visit a war zone who knows. But their 399 00:21:37,160 --> 00:21:38,919 Speaker 3: unit's not going to get called up. They are a 400 00:21:38,920 --> 00:21:41,880 Speaker 3: separate unit and of themselves. So you know, it's not 401 00:21:41,920 --> 00:21:45,480 Speaker 3: like saying I'm in this Army reserve unit. I'm living 402 00:21:45,520 --> 00:21:49,879 Speaker 3: my life in you know, with my wife and my 403 00:21:49,960 --> 00:21:53,200 Speaker 3: kids and my job or my you know, my partner, 404 00:21:53,480 --> 00:21:58,359 Speaker 3: and bang I'm sent out to you know, Fallujah, you know, 405 00:21:57,800 --> 00:22:02,560 Speaker 3: or some war zone. And no, their unit's not going 406 00:22:02,560 --> 00:22:04,159 Speaker 3: to get called up because they say, that's not what 407 00:22:04,200 --> 00:22:07,760 Speaker 3: it's for. They aren't required to spend all one hundred 408 00:22:07,760 --> 00:22:10,679 Speaker 3: and twenty annual hours, you know, working on a base 409 00:22:10,800 --> 00:22:14,680 Speaker 3: like most reservists do. They could work from home, and 410 00:22:14,800 --> 00:22:17,439 Speaker 3: the start off as lieutenant colonels, which is a very 411 00:22:17,520 --> 00:22:21,160 Speaker 3: high rank, and it's very unusual to start out at 412 00:22:21,200 --> 00:22:24,160 Speaker 3: that rank. And the Army told me, well, yeah, here's 413 00:22:24,200 --> 00:22:26,879 Speaker 3: these examples in World War two and World War One, 414 00:22:27,040 --> 00:22:30,480 Speaker 3: but not quite the same. So it's a different thing, 415 00:22:30,800 --> 00:22:35,000 Speaker 3: which I think gets our attention because they're still working 416 00:22:35,080 --> 00:22:37,720 Speaker 3: for these companies, some of them who are working on 417 00:22:37,800 --> 00:22:42,719 Speaker 3: military contracts. So there's this potential conflict of interest that 418 00:22:43,119 --> 00:22:46,199 Speaker 3: the Army says, and they say, yeah, we're doing this 419 00:22:46,240 --> 00:22:48,760 Speaker 3: in a personal capacity, but it's tough to separate that 420 00:22:49,040 --> 00:22:50,640 Speaker 3: from the job they work at every day. 421 00:22:50,880 --> 00:22:55,600 Speaker 1: Can you explain what data mining is just for those 422 00:22:55,640 --> 00:22:58,160 Speaker 1: of us who are not completely because I think that's 423 00:22:58,200 --> 00:23:02,040 Speaker 1: an important just to explain what that exactly looks like. 424 00:23:02,600 --> 00:23:04,919 Speaker 3: Sure, you know, well, you know, look, it's something that 425 00:23:05,119 --> 00:23:10,080 Speaker 3: you know, META certainly does and the security forces, you know, 426 00:23:10,200 --> 00:23:12,480 Speaker 3: national they do it in national defense when you fly. 427 00:23:13,000 --> 00:23:16,520 Speaker 3: It's taking you know, a lot of information. Personal information 428 00:23:16,840 --> 00:23:18,800 Speaker 3: in this case is also using a business sense, but 429 00:23:19,040 --> 00:23:23,280 Speaker 3: what we're talking about is personal information. And because you're 430 00:23:23,320 --> 00:23:26,760 Speaker 3: able to accumulate it, there's things that they that people 431 00:23:26,800 --> 00:23:29,680 Speaker 3: can learn about you because they have a lot of information, 432 00:23:30,080 --> 00:23:34,119 Speaker 3: which isn't necessarily you know, the kind of information you shared. 433 00:23:34,280 --> 00:23:37,119 Speaker 3: There might be something that even people close to you 434 00:23:37,200 --> 00:23:40,800 Speaker 3: don't know. But because they have this kind of database 435 00:23:40,840 --> 00:23:42,720 Speaker 3: and that kind of database, and they know what you buy, 436 00:23:43,160 --> 00:23:46,280 Speaker 3: and they know where you went to school and what 437 00:23:46,400 --> 00:23:50,320 Speaker 3: you lived and what your critical information is, they know 438 00:23:50,440 --> 00:23:53,199 Speaker 3: a lot about you and can make conclusions about you 439 00:23:53,600 --> 00:23:56,440 Speaker 3: that are very very sophisticated. And I mean you could 440 00:23:56,440 --> 00:23:58,960 Speaker 3: see this just the way Meta uses it in using 441 00:23:59,000 --> 00:24:02,600 Speaker 3: the like buttons. A researcher who discovered some years ago. 442 00:24:02,680 --> 00:24:05,040 Speaker 3: I wrote about this. I wrote a book about Facebook 443 00:24:05,200 --> 00:24:07,440 Speaker 3: that with like less than a dozen likes, that can 444 00:24:07,480 --> 00:24:13,360 Speaker 3: know your sexual orientation and political leanings. With like fifty, 445 00:24:13,400 --> 00:24:15,400 Speaker 3: they'll know you as well as a friend. With three 446 00:24:15,480 --> 00:24:17,800 Speaker 3: hundred likes, they'll know you as well as your partners, 447 00:24:17,840 --> 00:24:21,840 Speaker 3: spouse might know you. So that's pretty useful information. And 448 00:24:22,160 --> 00:24:25,159 Speaker 3: Polunteer is an expert at that and using it in 449 00:24:25,280 --> 00:24:29,280 Speaker 3: national security versus intelligence forces use it for the national 450 00:24:29,440 --> 00:24:31,080 Speaker 3: security That is. 451 00:24:31,520 --> 00:24:38,520 Speaker 1: A completely insane and terrifying I mean, yikes. So you 452 00:24:38,560 --> 00:24:44,840 Speaker 1: have these guys Meta Polunteer, other technology companies involved in 453 00:24:44,840 --> 00:24:47,720 Speaker 1: the military. Sort of where is this going wrong? 454 00:24:48,840 --> 00:24:51,520 Speaker 3: Well, again, you know, this is like a new program, 455 00:24:51,600 --> 00:24:55,600 Speaker 3: you know, you know, and we all know what's going 456 00:24:55,640 --> 00:24:58,879 Speaker 3: to happen with it, I guess do. I questioned a 457 00:24:58,920 --> 00:25:01,480 Speaker 3: couple things, and I talked to the army about this, 458 00:25:01,680 --> 00:25:03,879 Speaker 3: you know. One was why do you need to have 459 00:25:03,960 --> 00:25:09,120 Speaker 3: them in the army Army reserves right? Why can't they 460 00:25:09,160 --> 00:25:13,640 Speaker 3: do this as consultants? And I was told that there's 461 00:25:13,680 --> 00:25:16,879 Speaker 3: something special and unique that they would be in a 462 00:25:17,040 --> 00:25:19,280 Speaker 3: uniform and do this. So I talked to one of 463 00:25:19,320 --> 00:25:24,400 Speaker 3: the new lieutenant colonels the fellow from open Ai who 464 00:25:24,400 --> 00:25:26,560 Speaker 3: I've known for years, and you know, and I like 465 00:25:26,640 --> 00:25:29,040 Speaker 3: the guy, and he was saying, well, you know, because 466 00:25:29,040 --> 00:25:31,240 Speaker 3: we're in a uniform, we'd be able to connect on that. 467 00:25:31,440 --> 00:25:33,639 Speaker 3: I don't know if mathe that's necessarily the case. I mean, 468 00:25:33,640 --> 00:25:36,360 Speaker 3: if someone gives you great advice, you're going to take 469 00:25:36,400 --> 00:25:38,720 Speaker 3: that advice. And you know, and it could just as 470 00:25:38,760 --> 00:25:41,800 Speaker 3: well be that the people who serving in the rank 471 00:25:41,800 --> 00:25:44,920 Speaker 3: and file might resent these people who at a high 472 00:25:44,960 --> 00:25:47,440 Speaker 3: rank in the uniform who come in there. They didn't 473 00:25:47,480 --> 00:25:50,520 Speaker 3: have to go to basic training and you know, again 474 00:25:50,560 --> 00:25:52,920 Speaker 3: they're not called up to a battlefield, and might might 475 00:25:52,960 --> 00:25:56,480 Speaker 3: I might say, geez, you know, these guys took a 476 00:25:56,560 --> 00:25:59,920 Speaker 3: shortcut to get where they are, so we don't know 477 00:26:00,119 --> 00:26:02,800 Speaker 3: how that's going to work. After talking to them, I'm 478 00:26:02,840 --> 00:26:07,000 Speaker 3: only not convinced that this couldn't have been done with 479 00:26:07,240 --> 00:26:09,440 Speaker 3: a regular consultancy sort of arrangement. 480 00:26:09,880 --> 00:26:14,000 Speaker 1: But that that is not the only problem here, right Actually. 481 00:26:13,640 --> 00:26:17,240 Speaker 3: The biggest problem, the biggest problem, I say, is one 482 00:26:17,960 --> 00:26:24,320 Speaker 3: that comes from the future of technology specifically, so right now, 483 00:26:25,440 --> 00:26:31,959 Speaker 3: open Ai, Meta, other companies, you know, Google, Microsoft, Anthropic, 484 00:26:32,000 --> 00:26:39,119 Speaker 3: they're creating this technology that they call super intelligence. HI 485 00:26:39,880 --> 00:26:44,120 Speaker 3: could do anything. And there's a question that they talk 486 00:26:44,200 --> 00:26:49,879 Speaker 3: about that this could present as essential threat to humanity 487 00:26:49,960 --> 00:26:52,639 Speaker 3: and they are you know, they say that they're valed 488 00:26:52,680 --> 00:26:55,040 Speaker 3: to make it safe. And one of the things that 489 00:26:55,080 --> 00:26:59,400 Speaker 3: these companies and AI open AI specifically says Anthropics says 490 00:26:59,440 --> 00:27:04,199 Speaker 3: the same thing that we don't want our AI to 491 00:27:04,280 --> 00:27:08,000 Speaker 3: do harm to people, and we don't want our AI 492 00:27:08,640 --> 00:27:13,840 Speaker 3: to be used to create weaponry. Okay, so here's something 493 00:27:14,080 --> 00:27:18,119 Speaker 3: seems good, right, that seems great, and it seems, like 494 00:27:18,119 --> 00:27:19,960 Speaker 3: I tell you necessary. 495 00:27:19,680 --> 00:27:22,080 Speaker 1: Because because we all don't want to die. 496 00:27:23,640 --> 00:27:26,560 Speaker 3: Or our hope. If you accept their premise that this 497 00:27:26,600 --> 00:27:29,359 Speaker 3: is a shattering technology that is going to change everything, 498 00:27:29,400 --> 00:27:32,479 Speaker 3: not everyone does. If you accept that premise, then you say, well, 499 00:27:32,560 --> 00:27:36,399 Speaker 3: let's build it in a way where there's these guardrails 500 00:27:36,680 --> 00:27:40,560 Speaker 3: that they can't jump that even if the AI wants 501 00:27:40,600 --> 00:27:44,240 Speaker 3: to figure out, you know, to get that kill god, 502 00:27:44,760 --> 00:27:47,239 Speaker 3: you know, some humans have to go. They can't do that, 503 00:27:47,400 --> 00:27:51,360 Speaker 3: right right. Okay, here's these people who are now working 504 00:27:51,720 --> 00:27:55,879 Speaker 3: for the military whose goal is the opposite. They've said 505 00:27:56,119 --> 00:28:01,639 Speaker 3: that you're there to increase lethality. So what the military 506 00:28:01,680 --> 00:28:06,440 Speaker 3: wants to do is weaponize AI to do exactly what 507 00:28:06,520 --> 00:28:10,640 Speaker 3: the companies are telling us should never be done. So 508 00:28:10,840 --> 00:28:14,520 Speaker 3: here's these folks who are on one hand, sitting in 509 00:28:14,680 --> 00:28:17,600 Speaker 3: conference room saying, gee, how do we make sure our 510 00:28:17,640 --> 00:28:20,720 Speaker 3: AI will never be used as a weapon, and then 511 00:28:20,960 --> 00:28:27,040 Speaker 3: go to meet with their military compadres and say, gee, 512 00:28:27,080 --> 00:28:28,639 Speaker 3: how do we make AI more deadly? 513 00:28:28,920 --> 00:28:32,560 Speaker 1: I mean, it seems like a recipe for disaster with 514 00:28:32,720 --> 00:28:33,280 Speaker 1: all of AI. 515 00:28:33,440 --> 00:28:36,800 Speaker 3: We're an unknown territory here. We don't know how this 516 00:28:36,880 --> 00:28:39,080 Speaker 3: is going to check out. But you know, it would 517 00:28:39,120 --> 00:28:42,960 Speaker 3: seem that this was something that you'd have to get 518 00:28:43,000 --> 00:28:46,880 Speaker 3: thought to before you had people who were working for 519 00:28:46,960 --> 00:28:53,800 Speaker 3: the companies building advaincet AI then also working to use 520 00:28:53,840 --> 00:28:57,160 Speaker 3: it in a way that is going to harm human beings, 521 00:28:57,360 --> 00:29:00,720 Speaker 3: which is against the you know, stated ethics of the 522 00:29:00,760 --> 00:29:01,680 Speaker 3: companies they work for. 523 00:29:01,880 --> 00:29:03,600 Speaker 1: I would love you to explain to us what the 524 00:29:03,720 --> 00:29:05,640 Speaker 1: saw would be here. 525 00:29:06,000 --> 00:29:10,720 Speaker 3: Again, as you pointed out, we need this kind of 526 00:29:10,760 --> 00:29:12,960 Speaker 3: smarts and this kind of technology. We have to have 527 00:29:13,040 --> 00:29:16,000 Speaker 3: our military to be more nimble. We can't be spending 528 00:29:16,040 --> 00:29:19,160 Speaker 3: billions of dollars for things that don't work, you know, 529 00:29:19,160 --> 00:29:21,720 Speaker 3: white elephants, And there's a smarter way to do this 530 00:29:21,760 --> 00:29:24,960 Speaker 3: there's a company called anderl formed book by this game 531 00:29:25,000 --> 00:29:28,360 Speaker 3: of the guy named Palmer Lucky, who incidentally was fired 532 00:29:28,440 --> 00:29:32,640 Speaker 3: from Meta because he supported Donald Trump when that wasn't 533 00:29:32,640 --> 00:29:34,720 Speaker 3: cool at Meta, and he loved the military. 534 00:29:35,120 --> 00:29:38,120 Speaker 1: Right now, that's very cool at Meta. 535 00:29:38,240 --> 00:29:40,600 Speaker 3: Yes, as a matter of fact, after they fired him, 536 00:29:40,640 --> 00:29:43,400 Speaker 3: Mark Zuckerberg fired him. Just in the last few weeks, 537 00:29:43,440 --> 00:29:46,120 Speaker 3: Mark Zuckerberg says, we're going to partner with him, the 538 00:29:46,160 --> 00:29:49,720 Speaker 3: same guy, and there's a picture of them smiling together, 539 00:29:50,160 --> 00:29:54,160 Speaker 3: you know, when Zuckerberg fired him for the things that 540 00:29:54,240 --> 00:29:58,240 Speaker 3: now Zuckerberg is doing himself, which is supporting Trump and 541 00:29:58,480 --> 00:30:03,160 Speaker 3: you know, becoming you know, alitary fanboy. So yeah, so 542 00:30:03,720 --> 00:30:07,920 Speaker 3: we've got that. But back to andrel you know, they're 543 00:30:07,920 --> 00:30:11,360 Speaker 3: making the army smarter. You know, they're making our soldiers 544 00:30:11,760 --> 00:30:15,160 Speaker 3: more safe by giving a better view of the battlefield, 545 00:30:15,240 --> 00:30:19,480 Speaker 3: and you know and really moving us into the modern age. 546 00:30:19,760 --> 00:30:22,800 Speaker 3: You know, the question is with AI, are we building 547 00:30:23,240 --> 00:30:27,120 Speaker 3: and technology with a new level of destruction? Is it 548 00:30:27,200 --> 00:30:31,880 Speaker 3: like the next nuclear power that you know, we've been 549 00:30:32,080 --> 00:30:35,600 Speaker 3: careful fortunately not to use since we dropped the bomb. 550 00:30:36,120 --> 00:30:39,440 Speaker 3: Yeah forty five right. The scariest thing that I heard 551 00:30:39,640 --> 00:30:44,600 Speaker 3: researching this story is the spokesperson for the detachment or 552 00:30:44,600 --> 00:30:46,720 Speaker 3: actually there was a talent officer said when it was 553 00:30:46,760 --> 00:30:48,880 Speaker 3: conceiving the plan, he said, yeah, we wanted to create 554 00:30:48,960 --> 00:30:53,120 Speaker 3: like an Oppenheimer like situation. And I'm thinking j Oppenheimer 555 00:30:53,200 --> 00:30:56,040 Speaker 3: create like the Adam bomb. Maybe we don't want an 556 00:30:56,040 --> 00:30:57,360 Speaker 3: Oppenheimer like situation. 557 00:30:57,800 --> 00:31:00,520 Speaker 1: Yeah, what did they not know who Appenheim was? 558 00:31:01,080 --> 00:31:03,320 Speaker 3: I think he was referring to the idea that the 559 00:31:03,440 --> 00:31:07,880 Speaker 3: Manhattan Project used America's you know best. 560 00:31:07,520 --> 00:31:10,880 Speaker 1: Scientists, yeah, and scientists. 561 00:31:10,200 --> 00:31:13,560 Speaker 3: To help fight you know a quote good war, right, 562 00:31:13,640 --> 00:31:17,720 Speaker 3: But you know it has implications considering what AI might 563 00:31:17,760 --> 00:31:20,280 Speaker 3: be that perhaps he didn't intend. 564 00:31:20,480 --> 00:31:24,520 Speaker 1: Are you shocked at how little tech regulation Congress has done. 565 00:31:24,680 --> 00:31:26,800 Speaker 3: It's one of those cases and you hear this aw 566 00:31:26,800 --> 00:31:29,680 Speaker 3: a lot. Is this expression not surprised but shocked, you know, 567 00:31:29,800 --> 00:31:33,080 Speaker 3: right shocked. You know. A couple of years ago, Sam 568 00:31:33,120 --> 00:31:37,400 Speaker 3: Altman went to Congress, the CEO of Open AI, and said, 569 00:31:37,440 --> 00:31:40,000 Speaker 3: regulate us, and everyone in the industry was saying, we 570 00:31:40,040 --> 00:31:43,520 Speaker 3: need this regulation, we give this You're so powerful. And 571 00:31:43,560 --> 00:31:45,440 Speaker 3: now they're not saying that so much. 572 00:31:45,240 --> 00:31:47,600 Speaker 1: And because they lost interest, or because. 573 00:31:47,680 --> 00:31:50,160 Speaker 3: Because now they see they don't have to regulate, because 574 00:31:50,400 --> 00:31:53,680 Speaker 3: the Trump administration has said, you know, JD. Vance went 575 00:31:53,760 --> 00:31:55,840 Speaker 3: to a big AI conference and said, you know, I'm 576 00:31:55,840 --> 00:31:59,520 Speaker 3: not concerned with AI safety. I'm concerned with competitiveness. I'm 577 00:31:59,560 --> 00:32:04,000 Speaker 3: concerned the United States has to get AGI before China 578 00:32:04,040 --> 00:32:09,040 Speaker 3: gets it, and we'll speed ahead, right and that. And 579 00:32:09,080 --> 00:32:11,400 Speaker 3: the companies didn't say hoble and hold bold here, we 580 00:32:11,440 --> 00:32:13,400 Speaker 3: need regulation like they did a couple of years ago. 581 00:32:13,680 --> 00:32:17,280 Speaker 3: They're saying, Okay, yeah, let's go faster. And you know, 582 00:32:17,280 --> 00:32:19,400 Speaker 3: as a matter of fact, now that we're doing this, 583 00:32:20,040 --> 00:32:24,080 Speaker 3: why don't we classify all this copyrighted material we're using 584 00:32:24,400 --> 00:32:26,640 Speaker 3: to train our models that are being sued for Why 585 00:32:26,640 --> 00:32:28,440 Speaker 3: don't we just classify. We can do that and get 586 00:32:28,480 --> 00:32:30,800 Speaker 3: rid of these lawsuits because we have to be competitive. 587 00:32:31,000 --> 00:32:34,760 Speaker 1: I was at a party and a member of Congress 588 00:32:34,880 --> 00:32:37,840 Speaker 1: was complaining about how there's no fact checking on the internet, 589 00:32:38,280 --> 00:32:42,800 Speaker 1: and I was like, you know, you could have. Is 590 00:32:42,840 --> 00:32:49,040 Speaker 1: there any world in which we see members of the 591 00:32:49,080 --> 00:32:52,880 Speaker 1: government deciding to regulate, I mean with something. So here's 592 00:32:52,920 --> 00:32:56,000 Speaker 1: my anxiety. We get so far down this road that 593 00:32:56,040 --> 00:32:59,200 Speaker 1: we can't there are no fail safes, there are no 594 00:32:59,760 --> 00:33:01,640 Speaker 1: you know, by the time they realize they have to 595 00:33:01,680 --> 00:33:03,760 Speaker 1: regulate it's too late, you know. 596 00:33:04,280 --> 00:33:07,440 Speaker 3: I did a story about Anthropic, one of the big 597 00:33:07,440 --> 00:33:10,200 Speaker 3: AI companies, and that's a company that's going out of 598 00:33:10,240 --> 00:33:14,360 Speaker 3: its way to brand itself as we're the ones most 599 00:33:14,400 --> 00:33:20,160 Speaker 3: interested in safety and worried about these long term implications, 600 00:33:20,200 --> 00:33:22,840 Speaker 3: and we're going to do it right, and we're going 601 00:33:22,920 --> 00:33:24,760 Speaker 3: to have what they call a race to the top 602 00:33:24,920 --> 00:33:28,840 Speaker 3: by showing it could be done, you know, safely. Other 603 00:33:28,880 --> 00:33:35,680 Speaker 3: companies will emulate their methods. But even Anthropic says, you know, gee, 604 00:33:36,120 --> 00:33:40,720 Speaker 3: the timeline for AGI kind of comes sooner in the 605 00:33:40,760 --> 00:33:45,240 Speaker 3: timeline for our being able to control AI, right, And 606 00:33:45,320 --> 00:33:48,360 Speaker 3: I said, you know, so they are the one company 607 00:33:48,680 --> 00:33:52,880 Speaker 3: that's saying the right kind of regulation is it might 608 00:33:52,960 --> 00:33:57,160 Speaker 3: might be necessary. And I asked the CEO, Dario Amedek, 609 00:33:57,360 --> 00:33:58,800 Speaker 3: do you think we're going to need some sort of 610 00:33:58,800 --> 00:34:02,280 Speaker 3: AI pearl harbor before we really act on this? And 611 00:34:02,320 --> 00:34:04,360 Speaker 3: he said yes, I don't know what that looks like, 612 00:34:04,640 --> 00:34:05,280 Speaker 3: but is bad? 613 00:34:05,480 --> 00:34:08,320 Speaker 1: Oh good? All right, well I plan to never sleep again. 614 00:34:08,680 --> 00:34:11,280 Speaker 1: Thank you, thank you, thank you, Steven. 615 00:34:11,600 --> 00:34:13,080 Speaker 3: Thank you, Molly. I enjoyed it. 616 00:34:13,600 --> 00:34:17,959 Speaker 1: That's it for this episode of Fast Politics. Tune in 617 00:34:18,280 --> 00:34:23,720 Speaker 1: every Monday, Wednesday, Thursday and Saturday to hear the best 618 00:34:23,800 --> 00:34:28,080 Speaker 1: minds and politics make sense of all this chaos. If 619 00:34:28,120 --> 00:34:31,200 Speaker 1: you enjoy this podcast, please send it to a friend 620 00:34:31,640 --> 00:34:34,840 Speaker 1: and keep the conversation going. Thanks for listening.