1 00:00:00,240 --> 00:00:04,680 Speaker 1: From UFOs to psychic powers and government conspiracies. History is 2 00:00:04,760 --> 00:00:09,080 Speaker 1: riddled with unexplained events. You can turn back now or 3 00:00:09,200 --> 00:00:12,079 Speaker 1: learn the stuff they don't want you to know. A 4 00:00:12,200 --> 00:00:25,439 Speaker 1: production of I Heart Radio. Hello, and welcome back to 5 00:00:25,480 --> 00:00:27,720 Speaker 1: the show. My name is Matt, my name is Nol. 6 00:00:28,240 --> 00:00:31,120 Speaker 1: They called me Ben. We're joined as always with our 7 00:00:31,200 --> 00:00:36,000 Speaker 1: super producer Paul Mission Controlled deconds. Most importantly, you are you, 8 00:00:36,000 --> 00:00:39,200 Speaker 1: You are here, and that makes this the stuff they 9 00:00:39,320 --> 00:00:42,599 Speaker 1: don't want you to know. This is a follow up 10 00:00:42,800 --> 00:00:47,520 Speaker 1: from an earlier Listener Male segment where our fellow conspiracy 11 00:00:47,560 --> 00:00:52,640 Speaker 1: realist Ryan hipped us to a strange story about cheese 12 00:00:52,840 --> 00:00:56,040 Speaker 1: in the United States. We talked a little bit about 13 00:00:56,040 --> 00:00:58,880 Speaker 1: our favorite cheese commercials. I mean, some of that is 14 00:00:59,080 --> 00:01:02,680 Speaker 1: clearly a genre now. You know, it's sometimes called although 15 00:01:02,720 --> 00:01:05,520 Speaker 1: I hate the name food porn. Uh, we all know 16 00:01:05,600 --> 00:01:08,959 Speaker 1: what we're talking about. The slow motion clips of like 17 00:01:09,120 --> 00:01:11,520 Speaker 1: um no, you had a good example of a mozzarella 18 00:01:11,600 --> 00:01:15,479 Speaker 1: stick that's a classic being pulled. And then we had 19 00:01:15,600 --> 00:01:19,160 Speaker 1: you know, Taco bell Que, Sadilla's Casa Lupas, which play 20 00:01:19,200 --> 00:01:22,920 Speaker 1: a big role here and uh, this this kind of 21 00:01:22,920 --> 00:01:26,559 Speaker 1: commercial is almost a troupe nowadays. And in fact, there 22 00:01:26,600 --> 00:01:31,800 Speaker 1: are trendy restaurants of plenty in this country dedicated entirely 23 00:01:31,880 --> 00:01:34,800 Speaker 1: to one sort of cheesy food or another. Like, have 24 00:01:34,959 --> 00:01:37,800 Speaker 1: you guys ever been to a macaroni and cheese restaurant 25 00:01:37,880 --> 00:01:40,880 Speaker 1: or a place that only sells grilled cheese sandwiches. It's 26 00:01:40,959 --> 00:01:43,520 Speaker 1: very New York. Yeah, there's a place I went to. 27 00:01:43,600 --> 00:01:46,520 Speaker 1: I think it was in Oakland. It was called Homework 28 00:01:46,959 --> 00:01:49,480 Speaker 1: or something like that. I was meant to evoke feelings 29 00:01:49,520 --> 00:01:51,880 Speaker 1: of like childhood and like lunch room stuff, but it 30 00:01:51,920 --> 00:01:56,800 Speaker 1: was these bespoke kind of personal pan like little um, 31 00:01:56,840 --> 00:01:59,160 Speaker 1: you know, those dishes that are meant to be put 32 00:01:59,160 --> 00:02:01,680 Speaker 1: in the oven, kind of like bakeware or whatever, like 33 00:02:01,760 --> 00:02:04,920 Speaker 1: little Dutch oven kind of guys, with baccaroni and cheese 34 00:02:04,960 --> 00:02:07,560 Speaker 1: of all different flavors and varieties. And it was obviously 35 00:02:07,680 --> 00:02:10,200 Speaker 1: very overpriced for mac and cheese, but it was delicious. Um. 36 00:02:10,200 --> 00:02:11,920 Speaker 1: What about fawn Do you guys have been to a 37 00:02:11,960 --> 00:02:16,400 Speaker 1: fawn do restaurant? There's a chain here in Atlanta. I 38 00:02:16,440 --> 00:02:18,840 Speaker 1: think it's called the Melting Pot or something like that. 39 00:02:20,040 --> 00:02:22,919 Speaker 1: For me, it was all about the pirate ship. Oh, 40 00:02:23,040 --> 00:02:27,520 Speaker 1: Dante is down the hatch. You're Dante's guy, Yeah it 41 00:02:27,600 --> 00:02:33,840 Speaker 1: was so good. Uh yeah, we yea grilled cheese food trucks. 42 00:02:34,080 --> 00:02:36,920 Speaker 1: Do not sleep on those in Atlanta. There are a 43 00:02:36,960 --> 00:02:41,520 Speaker 1: ton of them, uh, grilled cheese food trucks. Guys. Oh 44 00:02:42,080 --> 00:02:46,000 Speaker 1: so we're we're both cheesy and fans of cheese. So 45 00:02:46,120 --> 00:02:48,680 Speaker 1: if you do not like this is the weirdest trigger 46 00:02:48,720 --> 00:02:51,280 Speaker 1: warning we've ever done. If you do not like puns, 47 00:02:51,680 --> 00:02:54,359 Speaker 1: this is not the episode for you, and you should 48 00:02:54,360 --> 00:02:58,840 Speaker 1: just go to the next episode. But but the thing is, 49 00:02:59,240 --> 00:03:00,960 Speaker 1: you know, a long time listens, you know, I have 50 00:03:01,000 --> 00:03:04,399 Speaker 1: a personal obsession with Casa Dilla's. They're basic, They're kind 51 00:03:04,400 --> 00:03:07,920 Speaker 1: of a grilled cheese, I would argue. Um. And in fact, folks, 52 00:03:07,960 --> 00:03:10,079 Speaker 1: all of us here at this stuff, they don't want 53 00:03:10,120 --> 00:03:13,440 Speaker 1: you to know. Team we all love cheese and we 54 00:03:13,480 --> 00:03:16,720 Speaker 1: all love talking about food, and we wanted to make 55 00:03:16,720 --> 00:03:19,560 Speaker 1: sure that was an accurate statement. So right before we 56 00:03:19,600 --> 00:03:22,320 Speaker 1: went to air, I went ahead and pained our super 57 00:03:22,400 --> 00:03:26,800 Speaker 1: producer code named Doc Holiday, and said, Matt and I 58 00:03:26,919 --> 00:03:29,799 Speaker 1: worked on how to write this question. Said, hey, Doc, 59 00:03:29,880 --> 00:03:32,720 Speaker 1: this is a work question. Do you like cheese? If so, 60 00:03:32,919 --> 00:03:37,360 Speaker 1: what kind? Doc says, I love cheese, but I don't 61 00:03:37,400 --> 00:03:39,640 Speaker 1: eat it as much as I would. And she kind 62 00:03:39,640 --> 00:03:42,680 Speaker 1: of alluded to, you know, health concerns, but she says, 63 00:03:43,440 --> 00:03:46,040 Speaker 1: I am literally the eating shredded cheese out of the 64 00:03:46,040 --> 00:03:49,360 Speaker 1: bag person. So we're gonna hit her with same we're 65 00:03:49,360 --> 00:03:53,560 Speaker 1: reading this on air, sorry, doc So. And you know 66 00:03:53,720 --> 00:03:57,720 Speaker 1: Paul michion Control look Horse, one of the one of 67 00:03:57,760 --> 00:04:02,200 Speaker 1: the prime uh champ beans of apple Bees loves cheese. 68 00:04:03,000 --> 00:04:05,120 Speaker 1: Matt Old, do you guys have a favorite cheese or 69 00:04:05,400 --> 00:04:07,640 Speaker 1: or you are? Yes? Old cheese kind of kind of 70 00:04:07,680 --> 00:04:10,200 Speaker 1: crewe Yeah, I mean, I guess all cheeses aren't really 71 00:04:10,240 --> 00:04:12,640 Speaker 1: created equal. I'll tell you what I think does get 72 00:04:12,680 --> 00:04:14,640 Speaker 1: slept on a little bit is just good old fashioned 73 00:04:14,720 --> 00:04:18,680 Speaker 1: American cheese product, you know, not even properly. It's like 74 00:04:18,800 --> 00:04:20,960 Speaker 1: I wouldn't like I like it on I'll tell you 75 00:04:20,960 --> 00:04:23,919 Speaker 1: what I like it on melted, because it melts beautifully 76 00:04:24,160 --> 00:04:28,760 Speaker 1: on like breakfast biscuit, like an egg and a sausage 77 00:04:28,800 --> 00:04:31,320 Speaker 1: on a biscuit, and you to slap that cheese product on. 78 00:04:31,360 --> 00:04:33,960 Speaker 1: We're gonna get into this um and it just melts 79 00:04:34,000 --> 00:04:35,880 Speaker 1: beautifully and it's its own thing and a lot of 80 00:04:35,920 --> 00:04:38,839 Speaker 1: like kind of cool breakfast divy type joints do that too. 81 00:04:38,839 --> 00:04:42,279 Speaker 1: But I am I like a I like a funky cheese, 82 00:04:42,680 --> 00:04:45,119 Speaker 1: you know, like a little bit of a foot smell 83 00:04:45,320 --> 00:04:52,080 Speaker 1: to cheese. Well, I don't like you ever had a raclette. 84 00:04:52,240 --> 00:04:54,280 Speaker 1: Raclette as a one. It's like a Swiss one that 85 00:04:54,320 --> 00:04:56,719 Speaker 1: also has its own kind of fondue situation where you 86 00:04:57,080 --> 00:04:59,840 Speaker 1: melted over meats and stuff on this special little grill 87 00:05:00,240 --> 00:05:01,840 Speaker 1: that's and you drink a lot of vodka with it, 88 00:05:01,880 --> 00:05:06,880 Speaker 1: which is a bonus. Man, that sounds good for my money, Yeah, 89 00:05:06,920 --> 00:05:10,719 Speaker 1: for my money. It's you combine let's say, a pecan 90 00:05:10,880 --> 00:05:15,159 Speaker 1: slash pecan with some kind of dried fruit and a 91 00:05:15,200 --> 00:05:19,360 Speaker 1: goat cheese. Mix those together with anything else. You're good 92 00:05:19,400 --> 00:05:24,640 Speaker 1: to go. Oh yeah, baby, yeah right. I'm I'm pretty 93 00:05:24,760 --> 00:05:29,480 Speaker 1: uh as you guys know, I am pretty non discriminatory 94 00:05:29,560 --> 00:05:33,520 Speaker 1: when it comes to food in general. Uh. Doc says 95 00:05:33,600 --> 00:05:38,279 Speaker 1: that her favorites are gorgonzola, blue cheese, and gouda. Uh 96 00:05:38,360 --> 00:05:42,200 Speaker 1: And when we were talking about this, you probably have 97 00:05:42,360 --> 00:05:46,520 Speaker 1: your own favorite cheese, conspiracy realist, just statistically, but we're 98 00:05:46,560 --> 00:05:52,120 Speaker 1: talking about this, we're talking about a national phenomenon. Cheese 99 00:05:52,160 --> 00:05:55,720 Speaker 1: is almost a staple. Lights. Cheese is a staple nowadays 100 00:05:55,800 --> 00:05:57,760 Speaker 1: in the US, and it can be weird if you 101 00:05:57,800 --> 00:06:02,000 Speaker 1: travel to other countries where cheese is not really a thing. Uh. 102 00:06:02,040 --> 00:06:05,039 Speaker 1: And you know you wouldn't see it very often in 103 00:06:05,080 --> 00:06:08,720 Speaker 1: a in your normal grocery store or market. You probably 104 00:06:08,760 --> 00:06:11,200 Speaker 1: only run into it if you went to like up 105 00:06:11,240 --> 00:06:15,280 Speaker 1: pizza restaurant or something of that nature. But in different 106 00:06:15,320 --> 00:06:18,400 Speaker 1: parts of the world that are historically not on board 107 00:06:18,440 --> 00:06:22,560 Speaker 1: with cheese, uh, it is still like a growing industry. 108 00:06:22,760 --> 00:06:28,480 Speaker 1: So here are the facts. Before we get into the conspiracy. Folks, 109 00:06:28,560 --> 00:06:32,560 Speaker 1: you have to realize cheese is, as the younger humans 110 00:06:32,560 --> 00:06:35,760 Speaker 1: would say, popular a f in the US unless you 111 00:06:35,800 --> 00:06:39,400 Speaker 1: are lactose intolerant, unless you're vegan or have some other 112 00:06:39,560 --> 00:06:44,479 Speaker 1: dietary restriction. Odds are you have consumed some cheese recently. 113 00:06:45,520 --> 00:06:49,039 Speaker 1: This wasn't always the case. We'll get to it. Um. 114 00:06:49,240 --> 00:06:54,040 Speaker 1: We are adventurous cheese eaters. But if you're the average American, 115 00:06:54,560 --> 00:06:56,520 Speaker 1: you know one of your favorite cheeses, or the one 116 00:06:56,560 --> 00:07:00,560 Speaker 1: you consume most often, is gonna be cheddar. And there 117 00:07:00,560 --> 00:07:03,040 Speaker 1: are health concerns that come into cheese consumption. But I 118 00:07:03,440 --> 00:07:06,359 Speaker 1: suggest we put those two as to the side for 119 00:07:06,400 --> 00:07:09,400 Speaker 1: a moment. Uh, to the side of our cheese plate, 120 00:07:09,640 --> 00:07:14,520 Speaker 1: and look at just the straight consumption, because this is 121 00:07:15,160 --> 00:07:19,920 Speaker 1: for cheesemakers. Uh, this sounds like a resounding success story. 122 00:07:20,440 --> 00:07:23,120 Speaker 1: So if you start back in the mid nineteen seventies, 123 00:07:23,200 --> 00:07:27,120 Speaker 1: let's say nineteen seventy five, you're an average American. Look 124 00:07:27,120 --> 00:07:30,080 Speaker 1: at you, so are we. We are all average Americans. 125 00:07:30,560 --> 00:07:35,520 Speaker 1: We consumed around fourteen pounds of cheese. That's in a year. 126 00:07:36,280 --> 00:07:38,640 Speaker 1: So in a year's time, you would eat fourteen pounds 127 00:07:38,640 --> 00:07:40,960 Speaker 1: of cheese. Now, if you went to a deli right now, 128 00:07:41,000 --> 00:07:43,360 Speaker 1: wherever you are, and you said, give me a pound 129 00:07:43,400 --> 00:07:48,600 Speaker 1: of cheese, that seems like a lot right now, those 130 00:07:49,720 --> 00:07:54,120 Speaker 1: it does. I think you typically order your cheese and 131 00:07:54,160 --> 00:07:56,680 Speaker 1: maybe a quarter pounds, right. I think a quarter pound 132 00:07:56,680 --> 00:07:59,040 Speaker 1: of cheese is respectable. And if you've got a family, 133 00:07:59,080 --> 00:08:00,760 Speaker 1: it's half a pound or pound. That's just the way 134 00:08:00,800 --> 00:08:08,160 Speaker 1: you gotta go. I am a family, family of one. 135 00:08:09,320 --> 00:08:12,920 Speaker 1: Look that's my this is my experience. So perhaps I'm wrong, 136 00:08:13,240 --> 00:08:17,520 Speaker 1: but you know, just imagine fourteen pounds. That seems like 137 00:08:17,520 --> 00:08:20,120 Speaker 1: a lot already. It in my opinion, maybe that's wrong, 138 00:08:20,400 --> 00:08:23,760 Speaker 1: but it's not. It's not as much as it has 139 00:08:23,800 --> 00:08:28,480 Speaker 1: grown to. Yeah, you're right, Matt, it's skyrocketed as of 140 00:08:29,120 --> 00:08:31,440 Speaker 1: nineteen and this is a number that continued to rise 141 00:08:31,560 --> 00:08:35,480 Speaker 1: during the pandemic. But as of the average US consumer 142 00:08:35,880 --> 00:08:41,720 Speaker 1: was eating around forty four zero point four pounds of 143 00:08:41,800 --> 00:08:47,000 Speaker 1: cheese per year. Yeah, yeah, and uh that's just the 144 00:08:47,040 --> 00:08:53,080 Speaker 1: tip of the cheeseburg or we're keeping it. For the 145 00:08:53,120 --> 00:08:57,439 Speaker 1: past ten years, US per capita consumption of cheese has 146 00:08:58,520 --> 00:09:03,360 Speaker 1: risen even more, uh over five pounds um from that point. 147 00:09:03,920 --> 00:09:06,680 Speaker 1: And as this began to happen, we had other dairy 148 00:09:06,720 --> 00:09:10,120 Speaker 1: categories that had largely remained unchanged or were flat or 149 00:09:10,160 --> 00:09:13,960 Speaker 1: had even fallen UM. Things like consumption of fluid milk 150 00:09:14,160 --> 00:09:17,319 Speaker 1: had plummeted from two forty seven pounds per person in 151 00:09:17,320 --> 00:09:22,920 Speaker 1: the N two hundred and forty six pounds, Which makes sense. 152 00:09:23,200 --> 00:09:25,840 Speaker 1: I mean, like we've talked about, I think on the 153 00:09:25,840 --> 00:09:29,120 Speaker 1: the cheese segment on the listener mail. You know, there 154 00:09:29,120 --> 00:09:31,520 Speaker 1: were all these amazing ad campaigns, you know, to get 155 00:09:31,559 --> 00:09:34,080 Speaker 1: milk back up milk drinking about how it's good for 156 00:09:34,120 --> 00:09:36,679 Speaker 1: your bones and calcium and and got milk and all 157 00:09:36,720 --> 00:09:39,360 Speaker 1: of that. Uh. Now, I think most people think of 158 00:09:39,400 --> 00:09:42,760 Speaker 1: milk as an ingredient rather than a beverage. Unto itself 159 00:09:42,760 --> 00:09:44,959 Speaker 1: like something you add to a smoothie or using making 160 00:09:44,960 --> 00:09:47,960 Speaker 1: a cake. But it doesn't seem very in vogue to 161 00:09:48,120 --> 00:09:52,480 Speaker 1: just drink milk as a refreshing beverage, unless unless you're 162 00:09:52,800 --> 00:09:55,840 Speaker 1: a kid. Uh, you know, a lot of parents still 163 00:09:55,960 --> 00:09:59,160 Speaker 1: give their children milk. Like in the waters, I give 164 00:09:59,200 --> 00:10:03,000 Speaker 1: my son milk cow's milk, um and we have, we 165 00:10:03,040 --> 00:10:06,000 Speaker 1: have for a long time. But the the ability to 166 00:10:06,440 --> 00:10:10,960 Speaker 1: this always so interesting. The ability to digest lactose in 167 00:10:11,120 --> 00:10:15,480 Speaker 1: adulthood is a relatively recent mutation in part of the 168 00:10:15,559 --> 00:10:19,479 Speaker 1: human species, and it's far from uniform. Uh. So sometimes 169 00:10:20,320 --> 00:10:25,439 Speaker 1: it's funny because if you if you watch television or film, uh, 170 00:10:25,480 --> 00:10:30,199 Speaker 1: you'll see the consumption of drinking milk is almost always 171 00:10:30,280 --> 00:10:33,240 Speaker 1: a statement. Now it's not the only time you'd see 172 00:10:33,240 --> 00:10:37,640 Speaker 1: it as like a quote unquote normal everyday thing. Is 173 00:10:37,679 --> 00:10:40,720 Speaker 1: if it's an indicator of time and place, so like 174 00:10:40,840 --> 00:10:43,319 Speaker 1: our story takes place in the nineteen fifties or something, 175 00:10:43,840 --> 00:10:48,600 Speaker 1: or if it's one of those tremendously irritating breakfast scenes 176 00:10:48,800 --> 00:10:51,240 Speaker 1: where there's this huge you know that, like there's this 177 00:10:51,360 --> 00:10:53,600 Speaker 1: huge spread and they got juice and they got water, 178 00:10:53,640 --> 00:10:55,559 Speaker 1: and they got coffee and they got milk, and then 179 00:10:55,559 --> 00:10:58,480 Speaker 1: the guy, like the patriarch or the kid there or 180 00:10:58,679 --> 00:11:02,040 Speaker 1: whomever's running through the house and they're they're like getting 181 00:11:02,080 --> 00:11:04,480 Speaker 1: calls and they look at this and they like take 182 00:11:04,520 --> 00:11:06,600 Speaker 1: half a bite of toast, and they're like, all right, 183 00:11:06,640 --> 00:11:10,480 Speaker 1: love you, honey, gotta go, And no one mentions you monster. 184 00:11:10,960 --> 00:11:14,600 Speaker 1: I did all this work. I made you this continental 185 00:11:14,640 --> 00:11:17,880 Speaker 1: breakfast type situation. You didn't need all that, but I 186 00:11:17,880 --> 00:11:19,520 Speaker 1: did it for you because I love you. You couldn't 187 00:11:19,520 --> 00:11:21,560 Speaker 1: even bother yourself to eat more than a bite of toast. 188 00:11:21,880 --> 00:11:27,360 Speaker 1: I want a divorce. That's that's where that goes. But no, 189 00:11:27,760 --> 00:11:32,080 Speaker 1: it's true. Milk is not nearly as popular as it 190 00:11:32,160 --> 00:11:34,480 Speaker 1: used to be. I like chocolate milk. I think that's 191 00:11:34,480 --> 00:11:36,800 Speaker 1: still probably flies off the shells pretty well, but it's 192 00:11:36,800 --> 00:11:39,360 Speaker 1: something you put in your coffee. Now. I think I 193 00:11:39,440 --> 00:11:41,959 Speaker 1: got a sideways story for you guys, real quick about 194 00:11:42,200 --> 00:11:46,040 Speaker 1: you know, the urban legend or the rumor about chocolate milk, right, 195 00:11:46,559 --> 00:11:49,800 Speaker 1: the bad milk. Yeah, that it's the milk with the 196 00:11:49,840 --> 00:11:55,160 Speaker 1: clots in it, with the blood clots, chocolate everything. For me, well, 197 00:11:55,200 --> 00:11:57,160 Speaker 1: back in the back in the day, that may have 198 00:11:57,200 --> 00:11:59,880 Speaker 1: been more true than it is today. You're probably safe 199 00:12:00,040 --> 00:12:05,240 Speaker 1: thanks to federal health standards, So please enjoy your chocolate milk. Also, 200 00:12:05,280 --> 00:12:08,120 Speaker 1: try you who if you haven't yet. It's a good 201 00:12:08,400 --> 00:12:11,360 Speaker 1: it's a good you who is to chocolate milk? What 202 00:12:11,520 --> 00:12:14,520 Speaker 1: those cheese slices are to cheese? You? Who is a 203 00:12:14,640 --> 00:12:19,600 Speaker 1: chocolate drink product? Right? There's any real milk or chocolate 204 00:12:19,640 --> 00:12:23,920 Speaker 1: in that beverage like a powder. But but yeah, we're 205 00:12:24,440 --> 00:12:28,000 Speaker 1: we're inundated with cheese in the US. It's officially a 206 00:12:28,080 --> 00:12:32,800 Speaker 1: cheesy country and a cheesy people. How did we get here? Well, 207 00:12:32,840 --> 00:12:38,160 Speaker 1: there are some easy, kind of fun answers. First, the 208 00:12:38,360 --> 00:12:41,720 Speaker 1: popularity of cheese owes a ton to the rise of pizza, 209 00:12:41,840 --> 00:12:44,120 Speaker 1: and I, for one, I'm grateful for the rise of pizza. 210 00:12:44,440 --> 00:12:46,760 Speaker 1: I know it's maybe not the same as the legit 211 00:12:46,840 --> 00:12:49,520 Speaker 1: pizza con I know it's absolutely not the same as 212 00:12:49,600 --> 00:12:54,640 Speaker 1: legit pizza created in Italy. But because of pizza alone, 213 00:12:54,800 --> 00:12:58,040 Speaker 1: mozzarella went from being kind of this obscure thing you'd 214 00:12:58,040 --> 00:13:01,480 Speaker 1: see it a fancy cheese place to one of the 215 00:13:01,520 --> 00:13:05,000 Speaker 1: most popular varieties of cheese in the country. Like around 216 00:13:05,400 --> 00:13:08,400 Speaker 1: like cheddar status, which is Cheddar is like the Kanye 217 00:13:08,480 --> 00:13:10,480 Speaker 1: West of cheese In the state. And was that an 218 00:13:10,480 --> 00:13:15,040 Speaker 1: accidental pun the rise of pizza? No, wait, yes, yes 219 00:13:15,080 --> 00:13:17,600 Speaker 1: it was. I didn't think about just checking that, checking 220 00:13:17,640 --> 00:13:19,160 Speaker 1: one of those off the list. If anyone wants to 221 00:13:19,160 --> 00:13:21,640 Speaker 1: make a drinking game out of this, yes, we all 222 00:13:21,679 --> 00:13:25,000 Speaker 1: are not accidental or not. We have a list of uh, 223 00:13:25,080 --> 00:13:27,360 Speaker 1: I think we all have. We've got a mental list 224 00:13:27,400 --> 00:13:29,480 Speaker 1: of puns. So again, this is gonna be pun heavy. 225 00:13:29,520 --> 00:13:33,840 Speaker 1: We've done some pretty serious, kind of dark episodes that 226 00:13:33,880 --> 00:13:37,080 Speaker 1: were we think very important, so we wanted to take 227 00:13:37,120 --> 00:13:39,440 Speaker 1: We're recorded on a Friday, folks. We wanted to do 228 00:13:39,559 --> 00:13:45,600 Speaker 1: something that was not immensely depressing. So well, you're right. 229 00:13:45,840 --> 00:13:48,400 Speaker 1: Just to get to the concept that cheese owes pizza 230 00:13:48,520 --> 00:13:51,600 Speaker 1: a lot of gratitude, at least as an industry, as 231 00:13:51,640 --> 00:13:55,800 Speaker 1: a food thing. Uh, really think about think about the 232 00:13:55,880 --> 00:14:00,400 Speaker 1: local pizza chain that maybe you grew up with. Then 233 00:14:00,480 --> 00:14:04,240 Speaker 1: also think about the big ones, the Dominoes, the pizza 234 00:14:04,320 --> 00:14:07,600 Speaker 1: huts eventually the Papa John's and the Goodfellas and the 235 00:14:07,640 --> 00:14:10,640 Speaker 1: ccs and all these other ones. When when you all 236 00:14:10,679 --> 00:14:13,520 Speaker 1: of a sudden, you had companies that could just deliver 237 00:14:13,679 --> 00:14:16,960 Speaker 1: you food, which wasn't a big thing in a lot 238 00:14:17,000 --> 00:14:20,240 Speaker 1: of places, and for a lot of companies, delivering food 239 00:14:20,320 --> 00:14:23,440 Speaker 1: was not a big thing, but it kind of changed 240 00:14:23,480 --> 00:14:27,880 Speaker 1: the game and how Americans got food and the other thing. 241 00:14:28,040 --> 00:14:31,800 Speaker 1: Just to really remember here, these aren't Italian companies bringing 242 00:14:32,040 --> 00:14:35,400 Speaker 1: you know, Italian pizza that's done correctly in the Italian way. 243 00:14:35,680 --> 00:14:40,680 Speaker 1: These are American companies that are creating food products with 244 00:14:40,800 --> 00:14:45,600 Speaker 1: relatively cheap ingredients where they can make a ton of 245 00:14:45,640 --> 00:14:48,520 Speaker 1: money on Uh So it's just a it's a very 246 00:14:48,560 --> 00:14:51,480 Speaker 1: different it's a very different thing. Um, we don't have 247 00:14:51,520 --> 00:14:53,200 Speaker 1: to get all into pizza that. We can leave that 248 00:14:53,280 --> 00:14:58,880 Speaker 1: to the saver folks, but uh yeah, definitely shout out 249 00:14:59,560 --> 00:15:01,560 Speaker 1: what it's telling we see all the time. Right, it's 250 00:15:01,600 --> 00:15:04,920 Speaker 1: like bastardization and sort of like um, I don't know, 251 00:15:05,400 --> 00:15:08,480 Speaker 1: co opting of like culture and turning it into this 252 00:15:08,560 --> 00:15:11,200 Speaker 1: sort of like product that can be mass marketed. And 253 00:15:11,240 --> 00:15:14,520 Speaker 1: I think, uh, these chain pizza restaurants are exactly that. 254 00:15:14,600 --> 00:15:17,280 Speaker 1: Because you go to like a Antiko's really awesome pizza 255 00:15:17,320 --> 00:15:19,840 Speaker 1: joint here in Atlanta, are anywhere you know that your 256 00:15:20,800 --> 00:15:22,880 Speaker 1: name your pizza joint of choice that is like a 257 00:15:22,920 --> 00:15:26,960 Speaker 1: little more traditional, you know, it's not covered in shredded cheese. 258 00:15:27,360 --> 00:15:30,640 Speaker 1: It has like a slice of like buffalo mozzarella, like 259 00:15:30,680 --> 00:15:33,200 Speaker 1: a little circle doll up on the top, and that's 260 00:15:33,200 --> 00:15:36,320 Speaker 1: what you get. And it's delicious and it's very delicate 261 00:15:36,360 --> 00:15:38,360 Speaker 1: and light h and it's not you don't get that 262 00:15:38,480 --> 00:15:41,120 Speaker 1: massive cheese pull. You get like one. But you know again, 263 00:15:41,360 --> 00:15:43,920 Speaker 1: they've got bags and bags and pounds and thousands and 264 00:15:43,920 --> 00:15:46,240 Speaker 1: thousands of tons of this stuff that they're trying to ship, 265 00:15:46,280 --> 00:15:48,360 Speaker 1: they're trying to sell, and it's like, uh, it is 266 00:15:48,400 --> 00:15:52,640 Speaker 1: a conspiracy between big pizza and big cheese. Well, let's 267 00:15:52,720 --> 00:15:55,720 Speaker 1: let's step back a second before we use the C word. So, 268 00:15:56,200 --> 00:15:59,680 Speaker 1: uh well, let's keep painting the picture, right. Did did 269 00:16:00,000 --> 00:16:02,720 Speaker 1: someone who went on the found Dominoes and Pizza Hut 270 00:16:02,800 --> 00:16:05,840 Speaker 1: go to Italy and say, look at this anything you 271 00:16:05,880 --> 00:16:10,160 Speaker 1: can do, I can do feeda or was this just 272 00:16:10,200 --> 00:16:15,560 Speaker 1: a natural outgrowth of changing trends and taste in the 273 00:16:15,560 --> 00:16:19,960 Speaker 1: American public? Did it happen as marketers would put it, organically? 274 00:16:20,440 --> 00:16:22,840 Speaker 1: We know that part of the that We know that 275 00:16:22,880 --> 00:16:29,120 Speaker 1: pizza has also given a lot of heft to shredded cheese, 276 00:16:29,120 --> 00:16:35,000 Speaker 1: in particular, about six billion dollars of the natural cheese 277 00:16:35,080 --> 00:16:42,200 Speaker 1: categories shredded cheese and uh, this the natural cheese category. Overall, 278 00:16:42,760 --> 00:16:44,960 Speaker 1: there is a little less than eighteen billion. We're talking 279 00:16:45,000 --> 00:16:50,920 Speaker 1: a lot of money here. During the coronavirus pandemic, Kraft Hinds, 280 00:16:50,960 --> 00:16:53,760 Speaker 1: which is a giant in the food industry, said it 281 00:16:53,840 --> 00:16:57,840 Speaker 1: saw an unprecedented double digit growth in this sale of 282 00:16:57,880 --> 00:17:01,320 Speaker 1: those craft singles, which is in the rest of the 283 00:17:01,320 --> 00:17:04,200 Speaker 1: world people would probably call that American cheese. It's those 284 00:17:04,240 --> 00:17:08,199 Speaker 1: processed yellow cheese slices we're talking about. But pizza and 285 00:17:08,240 --> 00:17:14,399 Speaker 1: a pandemic and organic taste cannot entirely explain America's weird 286 00:17:14,480 --> 00:17:19,320 Speaker 1: relationship with cheese. When we look into the dawn of 287 00:17:19,440 --> 00:17:24,439 Speaker 1: the American Age of cheesiness, we were surprised to find 288 00:17:24,800 --> 00:17:29,280 Speaker 1: there is literally a conspiracy of foot not a conspiracy theory, 289 00:17:29,600 --> 00:17:32,840 Speaker 1: not something that happened in the past. It is going 290 00:17:33,040 --> 00:17:37,080 Speaker 1: odd today. As as we pointed out, there really is 291 00:17:37,160 --> 00:17:41,359 Speaker 1: a thing called big cheese. And they're after you. What 292 00:17:41,440 --> 00:17:43,600 Speaker 1: are we talking about. We'll tell you after a word 293 00:17:43,680 --> 00:17:53,640 Speaker 1: from our sponsors. Here's where it gets crazy. So we've 294 00:17:53,680 --> 00:17:58,400 Speaker 1: all heard of government cheese, right, We'll get to that. 295 00:17:58,800 --> 00:18:02,640 Speaker 1: And if you, if you were in eighties or nineties, kid, 296 00:18:02,760 --> 00:18:05,600 Speaker 1: you would doubtlessly heard of this stuff, even if you 297 00:18:05,640 --> 00:18:09,840 Speaker 1: never saw it in person. But when we're talking about cheese, 298 00:18:10,480 --> 00:18:14,160 Speaker 1: really talking about milk, right, because you can't have cheat, well, 299 00:18:14,200 --> 00:18:16,959 Speaker 1: you can't have good cheese without milk, that's right. So 300 00:18:17,040 --> 00:18:19,520 Speaker 1: let's that's that's wind the clock back to the good 301 00:18:19,560 --> 00:18:22,480 Speaker 1: old nineteen eighties, the late eighties on the cusp of 302 00:18:22,520 --> 00:18:24,679 Speaker 1: the nineties, that that gray period where it sort of 303 00:18:24,680 --> 00:18:27,200 Speaker 1: still feels like the one era, but things are starting 304 00:18:27,200 --> 00:18:30,480 Speaker 1: to change a little bit. Night seven the mediaan dairy 305 00:18:30,480 --> 00:18:34,600 Speaker 1: farm had eighty cows or fewer. Uh. Today that number 306 00:18:34,800 --> 00:18:39,440 Speaker 1: is closer to nine hundred. So economies of scale drive 307 00:18:39,520 --> 00:18:43,520 Speaker 1: costs down and bolster output. Right. So on top of that, 308 00:18:43,560 --> 00:18:46,639 Speaker 1: you've got the average cow producing more milk than ever before, 309 00:18:47,040 --> 00:18:50,800 Speaker 1: and that is because of better breeding practices. They've kind 310 00:18:50,800 --> 00:18:52,399 Speaker 1: of figured it out and honed it down to a 311 00:18:52,480 --> 00:18:55,680 Speaker 1: science to get the most output for their buck. Uh. 312 00:18:55,720 --> 00:18:59,000 Speaker 1: And there are also those like heinous machines that you've 313 00:18:59,000 --> 00:19:02,280 Speaker 1: seen in like some of the factory production of milk. 314 00:19:02,600 --> 00:19:05,399 Speaker 1: I like the phrase heinous machines. That sounds like a 315 00:19:05,440 --> 00:19:08,320 Speaker 1: good album named I agree, but I think we all 316 00:19:08,359 --> 00:19:11,000 Speaker 1: know we're talking about those kind of like dystopian sci 317 00:19:11,040 --> 00:19:16,000 Speaker 1: fi milk suction cup tube machines that we're talking about, Matt. Yeah, 318 00:19:16,119 --> 00:19:18,760 Speaker 1: and some of them are completely fine. I mean, well, 319 00:19:18,800 --> 00:19:22,080 Speaker 1: who's who's to say people would argue that fact, but um, 320 00:19:22,960 --> 00:19:27,119 Speaker 1: many are less impactful on the cows than others. U 321 00:19:27,320 --> 00:19:32,080 Speaker 1: some I would use the term heinus. So yeah, anyway, 322 00:19:32,160 --> 00:19:36,840 Speaker 1: at the very least, it doesn't seem pleasant for the animal, right, Yeah, absolutely, 323 00:19:37,000 --> 00:19:41,959 Speaker 1: But as we know in our previous work on livestock conspiracies, 324 00:19:42,000 --> 00:19:46,480 Speaker 1: the feeling of the animal being used doesn't often come 325 00:19:46,480 --> 00:19:49,760 Speaker 1: into play. And there's also we should also, by the way, 326 00:19:49,880 --> 00:19:53,560 Speaker 1: folks do a an episode in the future on chicken farms, 327 00:19:53,800 --> 00:19:57,879 Speaker 1: just chicken, chicken, the chicken industry, poultry industry in particular. 328 00:19:58,280 --> 00:20:02,160 Speaker 1: It's bad, bad, I learn some weird stuff. But wait, 329 00:20:02,200 --> 00:20:05,280 Speaker 1: this is our light episode. We're keeping it light. Sorry, sorry, guys, 330 00:20:06,040 --> 00:20:08,000 Speaker 1: We're back to it. So you know what it is, 331 00:20:08,640 --> 00:20:10,800 Speaker 1: even at the cost I mean, and it's hard to 332 00:20:10,880 --> 00:20:14,919 Speaker 1: even talk about, but at the cost of the experience 333 00:20:14,960 --> 00:20:20,359 Speaker 1: of the cow. The whole point of all of that production, 334 00:20:21,000 --> 00:20:24,320 Speaker 1: it's to get more food for humans that need food. 335 00:20:24,600 --> 00:20:29,960 Speaker 1: In theory. In theory, yes, because it's responding to a market. Right, 336 00:20:30,400 --> 00:20:33,800 Speaker 1: there's a need for this milk, so we must produce 337 00:20:33,880 --> 00:20:37,000 Speaker 1: more of it. How do we do that more efficiently? Well, 338 00:20:37,160 --> 00:20:39,800 Speaker 1: this is how So I just put that out there. 339 00:20:39,960 --> 00:20:42,680 Speaker 1: Yeah there is a logic, Yeah, there is a logic. 340 00:20:42,720 --> 00:20:46,120 Speaker 1: The farmers weren't sitting around saying, how can we make 341 00:20:46,200 --> 00:20:51,400 Speaker 1: things worse? People don't usually think that way. And you know, often, 342 00:20:51,800 --> 00:20:55,600 Speaker 1: especially in smaller farms, they have a very human connection 343 00:20:55,960 --> 00:20:58,280 Speaker 1: with their livestock, you know, and they and they want 344 00:20:58,880 --> 00:21:01,280 Speaker 1: they want these cow us to live good lives and 345 00:21:01,320 --> 00:21:04,600 Speaker 1: be happy. Because if the cow is in a good 346 00:21:04,640 --> 00:21:07,199 Speaker 1: mental state, because it is a higher order mammal, if 347 00:21:07,240 --> 00:21:09,679 Speaker 1: it's in a good mental state, then that's going to 348 00:21:09,720 --> 00:21:13,280 Speaker 1: affect its diet, it's nutrition, and therefore the milk it produces. 349 00:21:13,960 --> 00:21:17,000 Speaker 1: We have to acknowledge at this point the dairy industry, 350 00:21:17,280 --> 00:21:22,240 Speaker 1: and we're talking about uh, several years or decades so ago, 351 00:21:22,840 --> 00:21:27,560 Speaker 1: they were not just producing milk for a domestic consumer, 352 00:21:27,600 --> 00:21:30,240 Speaker 1: they were producing it for other countries. And then a 353 00:21:30,400 --> 00:21:35,040 Speaker 1: crash hit China's economy slowed down. This drove the global 354 00:21:35,080 --> 00:21:38,480 Speaker 1: demand from milk down. At the same time, the EU 355 00:21:39,040 --> 00:21:42,960 Speaker 1: said they had had these domestic caps on how much 356 00:21:43,080 --> 00:21:47,400 Speaker 1: milk a European or EU farmer could produce, and they 357 00:21:47,480 --> 00:21:49,840 Speaker 1: remove those caps, so all of a sudden, the EU 358 00:21:50,000 --> 00:21:52,240 Speaker 1: is making a bunch of milk, which means they need 359 00:21:52,320 --> 00:21:56,640 Speaker 1: less from the US. Russia slapped sanctions on foeign cheese 360 00:21:56,720 --> 00:22:00,240 Speaker 1: because they were mad about some Western sanctions, and then 361 00:22:00,440 --> 00:22:04,000 Speaker 1: the US dollar was stronger, and that meant that American 362 00:22:04,080 --> 00:22:08,280 Speaker 1: dairy farmers had to sell milk at a more expensive 363 00:22:08,359 --> 00:22:12,760 Speaker 1: rate internationally. And these factors combined into this like bubbly 364 00:22:12,880 --> 00:22:16,399 Speaker 1: caso dip of problems. There was a massive ongoing cheese glut. 365 00:22:16,400 --> 00:22:19,320 Speaker 1: There was not a tortilla chip of a solution. Insight, 366 00:22:20,359 --> 00:22:23,400 Speaker 1: This did not occur in a vacuum at all historically, 367 00:22:23,720 --> 00:22:26,359 Speaker 1: because if you look back, you'll see that the US 368 00:22:26,480 --> 00:22:31,960 Speaker 1: government has a very long history of supporting dairy farmers 369 00:22:32,280 --> 00:22:39,399 Speaker 1: when prices collapse, through through subsidies, through marketing campaigns, through 370 00:22:39,600 --> 00:22:43,000 Speaker 1: government cheese. Uh, maybe we we should talk a little 371 00:22:43,000 --> 00:22:45,480 Speaker 1: bit about government cheese, I think before we continue, what 372 00:22:45,480 --> 00:22:47,600 Speaker 1: do you guys say? I think we should because I, 373 00:22:47,760 --> 00:22:49,880 Speaker 1: for the longest time sort of thought it was a joke, 374 00:22:50,560 --> 00:22:54,720 Speaker 1: um that it was sort of a euphemism for, you know, 375 00:22:54,880 --> 00:22:57,439 Speaker 1: welfare in some way. But it turns out it's a 376 00:22:57,480 --> 00:23:01,040 Speaker 1: real thing. It's an actual cheese product that the government 377 00:23:01,080 --> 00:23:06,000 Speaker 1: would distribute to low income individuals UH and families UM 378 00:23:06,000 --> 00:23:09,280 Speaker 1: in place of money or as a supplement to their 379 00:23:10,000 --> 00:23:15,000 Speaker 1: you know nutrition. Right. Yeah, it's processed cheese. It's been 380 00:23:15,040 --> 00:23:18,400 Speaker 1: around since the World Wars. It was used in military 381 00:23:18,480 --> 00:23:21,600 Speaker 1: consitions during World War Two. It's been in schools since 382 00:23:21,640 --> 00:23:24,959 Speaker 1: the nineteen fifties. So if you went to a public 383 00:23:25,000 --> 00:23:27,480 Speaker 1: school in the US, you probably at some point did 384 00:23:27,480 --> 00:23:32,000 Speaker 1: eat government cheese. It got a it became politically charged 385 00:23:32,040 --> 00:23:36,719 Speaker 1: because it was associated with welfare beneficiaries, people who received 386 00:23:36,760 --> 00:23:40,400 Speaker 1: food stamps or SNAP, people who are elderly and got 387 00:23:40,440 --> 00:23:44,560 Speaker 1: Social Security checks, and then people who visited food banks. 388 00:23:44,600 --> 00:23:49,959 Speaker 1: But there's absolutely nothing shameful about that. And and you know, 389 00:23:50,359 --> 00:23:53,680 Speaker 1: odds are outside of schools. We've got plenty of people 390 00:23:53,720 --> 00:23:57,360 Speaker 1: in our audience today who have at some point been 391 00:23:57,440 --> 00:24:00,840 Speaker 1: on government assistance. And not to a box too much, 392 00:24:01,200 --> 00:24:03,359 Speaker 1: but yeah, you should not feel bad about it. That 393 00:24:03,560 --> 00:24:06,399 Speaker 1: is what that assistance is there for. Every time you 394 00:24:06,480 --> 00:24:09,360 Speaker 1: buy something and Uncle Sam takes a little it takes 395 00:24:09,400 --> 00:24:12,040 Speaker 1: a little vig from you at the cash register. It's 396 00:24:12,080 --> 00:24:14,879 Speaker 1: supposed to go back to you in some way. Yeah, 397 00:24:15,000 --> 00:24:18,119 Speaker 1: I'm not gonna lie. I was myself when I was 398 00:24:18,160 --> 00:24:21,520 Speaker 1: a young new parent, um, you know, kind of getting 399 00:24:21,560 --> 00:24:24,560 Speaker 1: started in my career. Even before really I was there 400 00:24:24,600 --> 00:24:26,800 Speaker 1: was a period where we were living with my my 401 00:24:26,880 --> 00:24:30,560 Speaker 1: mom um when my daughter was born, and we got 402 00:24:30,560 --> 00:24:32,639 Speaker 1: a little bit of government assistance for a brief period 403 00:24:32,640 --> 00:24:35,040 Speaker 1: of time when we lived in Athens, Georgia. It was 404 00:24:35,040 --> 00:24:37,400 Speaker 1: actually kind of great because they had a system where 405 00:24:37,720 --> 00:24:40,320 Speaker 1: you could use those SNAP benefits to shop at the 406 00:24:40,320 --> 00:24:43,720 Speaker 1: local farmers market and you would get to to one 407 00:24:44,400 --> 00:24:48,720 Speaker 1: um value for your Snap dollars. So that was actually 408 00:24:48,720 --> 00:24:51,720 Speaker 1: a really progressive, great way of doing it, I thought, 409 00:24:51,880 --> 00:24:55,880 Speaker 1: And it would also support local farmers and that's that's 410 00:24:55,920 --> 00:24:59,520 Speaker 1: part of that's part of what this uh, that's part 411 00:24:59,520 --> 00:25:05,280 Speaker 1: of the good intention behind programs like this. The cheese, though, 412 00:25:05,520 --> 00:25:09,520 Speaker 1: where did all this government cheese come from? That's the question. 413 00:25:09,640 --> 00:25:12,959 Speaker 1: That's where we we see some wrinkles in the story 414 00:25:13,440 --> 00:25:17,840 Speaker 1: because it really was the motivation to get people who 415 00:25:18,040 --> 00:25:21,200 Speaker 1: needed food more food, or was there something else behind 416 00:25:21,560 --> 00:25:26,920 Speaker 1: that move? Exactly, Matt, Exactly. So the cheese often came 417 00:25:27,040 --> 00:25:31,600 Speaker 1: from food surpluses that were stockpiled by the government as 418 00:25:31,720 --> 00:25:36,680 Speaker 1: part of these this huge subsidies system. Butter was also 419 00:25:36,760 --> 00:25:39,640 Speaker 1: stockpiled and then it was given under the same program. 420 00:25:39,680 --> 00:25:41,359 Speaker 1: But you don't hear a lot of people talking about 421 00:25:41,400 --> 00:25:45,600 Speaker 1: government butter doesn't. Yeah, it doesn't sound as good exactly. 422 00:25:46,040 --> 00:25:50,760 Speaker 1: So while this program it's not nefarious, it also isn't 423 00:25:50,800 --> 00:25:54,800 Speaker 1: fully altruistic. Uncle Sam is killing two birds with one 424 00:25:54,800 --> 00:25:58,040 Speaker 1: wheel of parmesan. Here. Yes, they are feeding the hungry, 425 00:25:58,040 --> 00:26:03,960 Speaker 1: but they're also supporting a massive, sometimes faltering dairy industry 426 00:26:04,000 --> 00:26:07,280 Speaker 1: by guaranteeing farmers will be able to sell their products 427 00:26:07,680 --> 00:26:13,439 Speaker 1: even if the actual demand does not exist. This is 428 00:26:13,480 --> 00:26:16,840 Speaker 1: exactly what Matt was talking about earlier when we kept 429 00:26:16,880 --> 00:26:20,320 Speaker 1: saying in theory to each other, because in theory is 430 00:26:20,359 --> 00:26:24,040 Speaker 1: not the same thing as in practice. Gentlemen, it is 431 00:26:24,119 --> 00:26:27,600 Speaker 1: time for us to share with our fellow conspiracy realists 432 00:26:27,800 --> 00:26:32,000 Speaker 1: the story of the Illuminati of cheese, which is a 433 00:26:32,160 --> 00:26:34,480 Speaker 1: real thing. Like, can we say that one more time 434 00:26:34,480 --> 00:26:36,720 Speaker 1: and Paul maybe give us a like a whatever is 435 00:26:36,760 --> 00:26:45,560 Speaker 1: a cheesy, sinister sound effect, the Illuminati of cheese perfect. 436 00:26:46,480 --> 00:26:49,760 Speaker 1: I thought you meant literal cheese sound effect, but well, 437 00:26:49,920 --> 00:26:53,520 Speaker 1: we'll see what Paul comes up with magic Man, what 438 00:26:53,560 --> 00:26:59,720 Speaker 1: are you talking about? We already know where Yeah, we 439 00:27:00,119 --> 00:27:04,040 Speaker 1: we paused the recording and brainstormed about this for twenty minutes. Right. So, 440 00:27:04,960 --> 00:27:10,240 Speaker 1: as we said, American consumption of cheese has skyrocketed. But 441 00:27:10,400 --> 00:27:14,680 Speaker 1: even that skyrocketing consumption, which was not organic, by the way, 442 00:27:14,840 --> 00:27:18,800 Speaker 1: has not been enough to meet demand because we are 443 00:27:18,800 --> 00:27:22,879 Speaker 1: grappling with a system that has been in place since 444 00:27:22,960 --> 00:27:26,400 Speaker 1: the nineteen thirties, since the Great Depression, when the federal 445 00:27:26,440 --> 00:27:31,720 Speaker 1: government bailed out America's dairy industry with all these subsidies, 446 00:27:31,960 --> 00:27:34,800 Speaker 1: and many people who are not in in the dairy 447 00:27:34,840 --> 00:27:38,680 Speaker 1: industry themselves are not aware of this, but those subsidies 448 00:27:38,760 --> 00:27:42,960 Speaker 1: are in effect today, and part of the knock on 449 00:27:43,520 --> 00:27:47,719 Speaker 1: consequence of this is that Uncle Sam is underwritten this 450 00:27:47,840 --> 00:27:53,320 Speaker 1: gigantic surplus of milk in the dairy industry that far 451 00:27:53,760 --> 00:28:01,480 Speaker 1: far exceeds the appetites of Americans like we even even 452 00:28:01,600 --> 00:28:06,040 Speaker 1: the cheesiest cheese lover in this country, like the number 453 00:28:06,080 --> 00:28:11,439 Speaker 1: one cheese eater person. Uh, if everybody was like that person, 454 00:28:11,640 --> 00:28:14,800 Speaker 1: we still would not be able to buy all the 455 00:28:14,840 --> 00:28:17,719 Speaker 1: milk and all the cheese that was being produced. And 456 00:28:17,840 --> 00:28:22,080 Speaker 1: when when the US government realized that there was this 457 00:28:22,240 --> 00:28:31,000 Speaker 1: huge surplus. They found themselves in a difficult political situation. Uh, 458 00:28:31,200 --> 00:28:34,520 Speaker 1: they knew that it would be and it's something we 459 00:28:34,600 --> 00:28:37,399 Speaker 1: talked about and passed off air. But you know, during 460 00:28:37,400 --> 00:28:40,840 Speaker 1: this time, post World War two, things like that. Uh, 461 00:28:41,080 --> 00:28:44,640 Speaker 1: they knew that they had two choices. They could remove 462 00:28:44,720 --> 00:28:48,680 Speaker 1: the subsidies, which would lead to over time a reduction 463 00:28:48,920 --> 00:28:52,160 Speaker 1: and the surplus of production, or they could find a 464 00:28:52,200 --> 00:28:55,600 Speaker 1: way to buy this to create a market that did 465 00:28:55,600 --> 00:29:02,840 Speaker 1: not exist at this level. And rural vote, the farmer vote, 466 00:29:03,160 --> 00:29:07,240 Speaker 1: was very, very important. So so imagine you're you're, you know, 467 00:29:07,280 --> 00:29:09,800 Speaker 1: a presidential administration. Are you going to go to these 468 00:29:09,800 --> 00:29:12,960 Speaker 1: people who voted for you and then pull the financial 469 00:29:13,040 --> 00:29:15,080 Speaker 1: rug out from under them? Yes? And I want to 470 00:29:15,080 --> 00:29:17,560 Speaker 1: give you a quick quote from a professor of agricultural 471 00:29:17,600 --> 00:29:21,920 Speaker 1: economics at Cornell University. This quote comes from a video 472 00:29:22,320 --> 00:29:25,640 Speaker 1: created by CNBC. You can check it out on YouTube, 473 00:29:25,680 --> 00:29:30,000 Speaker 1: I believe right now, maybe on CNBC, But this quote 474 00:29:30,040 --> 00:29:32,360 Speaker 1: is dealing with exactly that Ben. He says that at 475 00:29:32,400 --> 00:29:36,360 Speaker 1: this time in the nineteen thirties, when these subsidies first arrive, quote, 476 00:29:36,480 --> 00:29:40,760 Speaker 1: rural America presented over half of the population of Americans 477 00:29:41,280 --> 00:29:46,400 Speaker 1: and farmers presented over half of rural America. So there 478 00:29:46,480 --> 00:29:49,560 Speaker 1: was this sense that as a politician, as the government, 479 00:29:49,600 --> 00:29:52,520 Speaker 1: if you are helping out farmers, you are helping out 480 00:29:52,600 --> 00:29:57,200 Speaker 1: a lot of citizens slash, as you said, been voters. Yeah, 481 00:29:57,240 --> 00:30:00,520 Speaker 1: that's the thing that's and this happens a lot in 482 00:30:00,920 --> 00:30:04,720 Speaker 1: the kind of political system that the US has, Right, 483 00:30:04,840 --> 00:30:10,160 Speaker 1: you have, Uh sometimes they're vilified as being special interests, right, 484 00:30:10,200 --> 00:30:14,280 Speaker 1: but you have these voters and these businesses or these 485 00:30:14,280 --> 00:30:18,040 Speaker 1: institutions who have a very specific set of things they 486 00:30:18,080 --> 00:30:23,280 Speaker 1: want to see in place. And at times these forces 487 00:30:23,400 --> 00:30:27,800 Speaker 1: can be relatively a political you know, the name brand 488 00:30:27,880 --> 00:30:32,600 Speaker 1: of the political party doesn't matter as much as their actions. 489 00:30:32,680 --> 00:30:35,480 Speaker 1: So you know, if there's a third party called them 490 00:30:36,080 --> 00:30:42,080 Speaker 1: provolone rangers, Uh, it just gets worse, It gets worse. 491 00:30:42,520 --> 00:30:45,680 Speaker 1: Did Uh If if you have that, you know, this 492 00:30:45,840 --> 00:30:50,640 Speaker 1: other party and there their policies are, you know, not 493 00:30:50,720 --> 00:30:53,040 Speaker 1: too extreme, but one of their big things is they 494 00:30:53,040 --> 00:30:56,920 Speaker 1: support a subsidy that supports your industry, then you're probably 495 00:30:56,920 --> 00:30:58,600 Speaker 1: going to vote for them, you know what I mean, 496 00:30:58,680 --> 00:31:03,560 Speaker 1: or at least donate to their cause. So that's what happened. Uh, 497 00:31:03,840 --> 00:31:07,720 Speaker 1: the US made the decision to and you know, I 498 00:31:07,760 --> 00:31:09,520 Speaker 1: think a lot of historians agree it was the right 499 00:31:09,560 --> 00:31:12,840 Speaker 1: decision to enact these subsidies for the dairy industry and 500 00:31:12,880 --> 00:31:16,160 Speaker 1: other parts of US business during the Great Depression. But 501 00:31:16,320 --> 00:31:19,920 Speaker 1: once you open that door, it can be political suicide 502 00:31:20,120 --> 00:31:24,160 Speaker 1: to close it, to step it back, And so they continued, 503 00:31:24,760 --> 00:31:29,920 Speaker 1: they continued subsidizing this industry. And now in recent years, 504 00:31:29,960 --> 00:31:34,320 Speaker 1: the US dairy surplus has reached a record high, and 505 00:31:34,400 --> 00:31:37,320 Speaker 1: it's it's been going this way for a while. Uh. 506 00:31:37,440 --> 00:31:40,320 Speaker 1: The U s d A quickly realized as a result 507 00:31:40,400 --> 00:31:44,239 Speaker 1: of these subsidies, they they could not figure out what 508 00:31:44,320 --> 00:31:46,520 Speaker 1: to do with all the milk, and so the band 509 00:31:46,560 --> 00:31:51,360 Speaker 1: aid solution was, I don't know, make it cheese last 510 00:31:51,440 --> 00:31:54,160 Speaker 1: longer than milk, and that's what they did. And so 511 00:31:54,400 --> 00:31:57,440 Speaker 1: in recent years, we have, as we mentioned our listener 512 00:31:57,480 --> 00:32:01,880 Speaker 1: mail segment, UH with Ryan, we have as a country 513 00:32:02,040 --> 00:32:06,920 Speaker 1: generated a surplus of one point for billion with a 514 00:32:07,000 --> 00:32:11,440 Speaker 1: b pounds of cheese. And you'll see all these breathless 515 00:32:11,560 --> 00:32:15,320 Speaker 1: reports like go on YouTube and just google cheese surplus. 516 00:32:15,600 --> 00:32:18,800 Speaker 1: Colbert's got a great spot on it. Your you know, 517 00:32:18,880 --> 00:32:20,959 Speaker 1: your local news might have done a story on it 518 00:32:21,000 --> 00:32:24,240 Speaker 1: a few years ago. But you'll see these these breathless 519 00:32:24,280 --> 00:32:29,160 Speaker 1: comparisons because we as Americans love weird measurements. One of 520 00:32:29,200 --> 00:32:33,120 Speaker 1: the ones who found was one point four billion pounds 521 00:32:33,160 --> 00:32:37,640 Speaker 1: of cheese. That's enough to wrap around the US capital. Uh. 522 00:32:37,880 --> 00:32:40,640 Speaker 1: Nobody's saying we should consume it all. Luckily you can 523 00:32:40,720 --> 00:32:46,320 Speaker 1: store cheese. But even if the nation tried to eat 524 00:32:46,440 --> 00:32:50,520 Speaker 1: all of this cheese, it's less and less likely that 525 00:32:50,560 --> 00:32:54,240 Speaker 1: we could do it because that surplus is still building 526 00:32:54,560 --> 00:32:58,760 Speaker 1: despite the fact that people are consuming more cheese. Uh, 527 00:32:58,840 --> 00:33:03,800 Speaker 1: there were side effects that uh would break havoc on 528 00:33:03,800 --> 00:33:07,160 Speaker 1: all of us. No, it's to say that's the thing though. 529 00:33:07,160 --> 00:33:09,720 Speaker 1: It's like not not to get soapbox or anything, but 530 00:33:09,800 --> 00:33:12,320 Speaker 1: like you know, with the healthcare crisis that we have 531 00:33:12,400 --> 00:33:15,040 Speaker 1: in this country, and then you start to see that 532 00:33:15,200 --> 00:33:18,400 Speaker 1: all of this consumer culture that leads to poor health 533 00:33:18,880 --> 00:33:21,720 Speaker 1: uh and and and that this lack of preventative medicine 534 00:33:22,000 --> 00:33:25,760 Speaker 1: everything's about curing when you're already like super sick and 535 00:33:25,800 --> 00:33:28,160 Speaker 1: on your lound death's door. A lot of it is 536 00:33:28,200 --> 00:33:32,040 Speaker 1: like literally the fault of the government in the first place. 537 00:33:32,120 --> 00:33:34,280 Speaker 1: This whole cheese glut thing. When you really start to 538 00:33:34,360 --> 00:33:36,760 Speaker 1: drill down into it, right like like I mean, I 539 00:33:36,760 --> 00:33:39,360 Speaker 1: think we can think of probably a better like for 540 00:33:39,400 --> 00:33:44,640 Speaker 1: the for the individuals subsidized food product like granola or something, 541 00:33:44,680 --> 00:33:46,680 Speaker 1: you know, I mean, what about the grain farmers only 542 00:33:46,680 --> 00:33:48,600 Speaker 1: need help to But it's crazy, you guys. I just 543 00:33:48,640 --> 00:33:50,960 Speaker 1: sent a link in the chat um. I was just 544 00:33:51,000 --> 00:33:54,239 Speaker 1: googling government cheese and an eBay listing came up for 545 00:33:54,360 --> 00:33:58,040 Speaker 1: a cardboard packaging box. It's like vintage. It's like like 546 00:33:58,080 --> 00:34:01,960 Speaker 1: sixty dollars on eBay. Uh, and it says pasteurized process 547 00:34:02,080 --> 00:34:07,160 Speaker 1: American cheese donated by US Department of Agriculture for food 548 00:34:07,240 --> 00:34:11,440 Speaker 1: help programs, not to be sold or exchanged. It just 549 00:34:11,480 --> 00:34:14,480 Speaker 1: says it all there, and it's like, there's that's it's 550 00:34:14,640 --> 00:34:18,080 Speaker 1: literally helping the government. It's not helping the people that 551 00:34:18,160 --> 00:34:21,560 Speaker 1: need you know, better food. Well, it's like helping them 552 00:34:21,640 --> 00:34:27,560 Speaker 1: is is kind of a side effects. It's cyclical together, right, Yeah, 553 00:34:27,640 --> 00:34:30,640 Speaker 1: But there's there's nothing wrong with multitasking, you know. And 554 00:34:30,680 --> 00:34:34,080 Speaker 1: if you're not hurting anybody and you're satisfying two things 555 00:34:34,120 --> 00:34:36,920 Speaker 1: at once, it's it's understandable, but aren't you They are 556 00:34:37,000 --> 00:34:40,360 Speaker 1: hurting people because she's clogs your arteries if you're eating 557 00:34:40,400 --> 00:34:43,120 Speaker 1: it like for every meal, and it and it leads 558 00:34:43,160 --> 00:34:46,000 Speaker 1: to this, like I think obesity at pepademic that we 559 00:34:46,080 --> 00:34:48,680 Speaker 1: have and like you know, heart heart disease, one of 560 00:34:48,719 --> 00:34:51,160 Speaker 1: the unhealthiest countries in the world. And I would argue 561 00:34:51,200 --> 00:34:54,600 Speaker 1: that the government is culpable in that. And on two 562 00:34:54,640 --> 00:34:58,520 Speaker 1: separate occasions in t sixteen, first in August and then 563 00:34:58,520 --> 00:35:03,040 Speaker 1: on October uh the FEDS announced they were gonna quote 564 00:35:03,040 --> 00:35:06,320 Speaker 1: bail out dairy farmers. They were gonna purchase twenty million 565 00:35:06,360 --> 00:35:11,040 Speaker 1: dollars more worth of surplus dairy products a kh cheese 566 00:35:11,239 --> 00:35:14,160 Speaker 1: to distribute in food pantries and some milk as well. 567 00:35:14,960 --> 00:35:18,160 Speaker 1: As this is occurring, by the way, as this surplus 568 00:35:18,320 --> 00:35:22,719 Speaker 1: is increasing, there's a global drop in demand for dairy. 569 00:35:22,880 --> 00:35:26,439 Speaker 1: Plus there's that technology we mentioned that's making cows give 570 00:35:26,560 --> 00:35:31,800 Speaker 1: more milk, which results in the lowest milk prices since 571 00:35:31,840 --> 00:35:35,600 Speaker 1: the Great Recession, not depression, but Great Recession ended in 572 00:35:35,640 --> 00:35:38,680 Speaker 1: two thousand and nine. This is something we alluded to 573 00:35:38,840 --> 00:35:44,920 Speaker 1: in our listener mail segment. Farmers were literally pouring gallons 574 00:35:44,960 --> 00:35:48,600 Speaker 1: and gallons and gallons of unsold milk into holes in 575 00:35:48,640 --> 00:35:51,960 Speaker 1: the ground. Fifty million gallons. That's according to the U 576 00:35:52,040 --> 00:35:56,480 Speaker 1: s DA itself in in August letter Uh, the National 577 00:35:56,560 --> 00:36:00,520 Speaker 1: Milk Producers Federation asked the U. S d A for 578 00:36:00,600 --> 00:36:04,960 Speaker 1: a bailout of a hundred and fifty million dollars. This 579 00:36:05,080 --> 00:36:09,040 Speaker 1: was an ongoing, continual issue. It just kind of like 580 00:36:09,200 --> 00:36:13,280 Speaker 1: would reach a crisis point, maybe go down one step 581 00:36:13,320 --> 00:36:15,400 Speaker 1: and then go up two steps, down one step. It 582 00:36:15,520 --> 00:36:19,320 Speaker 1: was an incremental increase and this led to the creation 583 00:36:19,400 --> 00:36:24,480 Speaker 1: of something with a somewhat innocuous Dame Dairy Management Incorporated. 584 00:36:24,480 --> 00:36:27,239 Speaker 1: Their website is super wholesome. As wholesome as a kid 585 00:36:27,239 --> 00:36:29,520 Speaker 1: in the nineteen fifties drinking a glass of milk. Can 586 00:36:29,560 --> 00:36:31,080 Speaker 1: I just say, there's got to be a better way 587 00:36:31,120 --> 00:36:33,680 Speaker 1: to dispose of fifty million gallons of milk and pouring 588 00:36:33,680 --> 00:36:35,560 Speaker 1: it into a hole in the ground that's gonna be 589 00:36:35,600 --> 00:36:41,520 Speaker 1: the smelliest hole ever. I can't imagine. I can't imagine 590 00:36:40,840 --> 00:36:49,560 Speaker 1: that's going to be this So, yes, we're keeping it. 591 00:36:50,200 --> 00:36:53,440 Speaker 1: So this is it's I mean, it's true because milk 592 00:36:53,560 --> 00:36:55,960 Speaker 1: doesn't have a very long shelf life, you know what 593 00:36:56,000 --> 00:37:00,759 Speaker 1: I mean? And you could say, why don't we distribute 594 00:37:00,800 --> 00:37:03,960 Speaker 1: this as part of an international aid or charity thing? Um, 595 00:37:04,280 --> 00:37:07,840 Speaker 1: But if you've listened to our previous episodes on farming 596 00:37:08,000 --> 00:37:11,360 Speaker 1: in the US and across the world. You know, farmers 597 00:37:11,360 --> 00:37:16,239 Speaker 1: are often put in a very risky situation with a 598 00:37:16,239 --> 00:37:19,920 Speaker 1: lot of forces that are beyond their control. So it 599 00:37:20,080 --> 00:37:22,920 Speaker 1: is understandable, as strange as it sounds, that they might 600 00:37:22,960 --> 00:37:27,600 Speaker 1: be in a situation where it is literally less expensive 601 00:37:27,840 --> 00:37:30,239 Speaker 1: for them to get rid of the milk than it 602 00:37:30,360 --> 00:37:32,880 Speaker 1: is to try to transport it somewhere and pay for 603 00:37:32,880 --> 00:37:36,280 Speaker 1: the gas and pay for the storage. And Dairy Management, Inc. 604 00:37:36,800 --> 00:37:40,239 Speaker 1: Was set to was set to try to solve this 605 00:37:41,360 --> 00:37:44,520 Speaker 1: the They were created by something called the National Dairy 606 00:37:44,560 --> 00:37:48,279 Speaker 1: Promotion Board in the mid nineties. Here's what they do. 607 00:37:48,360 --> 00:37:51,200 Speaker 1: Their job is to act as an umbrella company for 608 00:37:51,320 --> 00:37:58,160 Speaker 1: state and local programs promoting the dairy industry. In blunt terms, folks, 609 00:37:58,640 --> 00:38:04,959 Speaker 1: this organization was the quasi governmental organization was explicitly created 610 00:38:05,160 --> 00:38:10,040 Speaker 1: to push as much milk, cheese, butter, and yogurt as 611 00:38:10,120 --> 00:38:15,600 Speaker 1: possible into products and into people, both in the US 612 00:38:15,920 --> 00:38:19,319 Speaker 1: and in other countries. They're the hidden hand behind what 613 00:38:19,400 --> 00:38:23,760 Speaker 1: appears to be rising cheese consumption. They're the group behind 614 00:38:23,920 --> 00:38:27,680 Speaker 1: Got Milk. That campaign we mentioned. Everybody remembers Got Milk 615 00:38:27,800 --> 00:38:32,440 Speaker 1: there were Uh. It started with like some normal you know, models, 616 00:38:32,800 --> 00:38:36,840 Speaker 1: commercial lactors, and then it escalated to like celebrities and 617 00:38:36,880 --> 00:38:39,680 Speaker 1: everybody would have a little little milk mustache, right, and 618 00:38:39,719 --> 00:38:42,000 Speaker 1: then what they're called, yes sir, that we saw in 619 00:38:42,040 --> 00:38:46,279 Speaker 1: subway stations, you saw them in billboards as television commercials. 620 00:38:46,320 --> 00:38:48,200 Speaker 1: It was absolutely ubiquitous. I mean, it's the kind of 621 00:38:48,239 --> 00:38:51,279 Speaker 1: campaign that clearly a lot, a lot, a lot of 622 00:38:51,320 --> 00:38:55,040 Speaker 1: money and marketing resources went into. Yeah, it's it was. 623 00:38:55,600 --> 00:38:59,880 Speaker 1: It was direct to your eyes and ears advertising, that's 624 00:39:00,040 --> 00:39:02,319 Speaker 1: what they did. And it was an era defining too. 625 00:39:02,400 --> 00:39:04,680 Speaker 1: We think of it and it like almost defines like 626 00:39:04,760 --> 00:39:07,160 Speaker 1: that period in history in our minds. It's like a 627 00:39:07,200 --> 00:39:10,800 Speaker 1: nostalgia thing at this point, right, Yeah, in a weird 628 00:39:10,840 --> 00:39:17,040 Speaker 1: way their their mind lobbyists for on the individual level 629 00:39:17,880 --> 00:39:20,839 Speaker 1: of people sitting there watching television you know which which 630 00:39:20,920 --> 00:39:25,840 Speaker 1: was television shows? Man think about as especially kids watching 631 00:39:25,880 --> 00:39:28,040 Speaker 1: TV shows with all of those like us, I think 632 00:39:28,080 --> 00:39:30,640 Speaker 1: our generation, I mean it is the nineties, we got 633 00:39:30,719 --> 00:39:34,200 Speaker 1: hit hard with the got Milk campaign with a lot 634 00:39:34,239 --> 00:39:40,000 Speaker 1: of the promotion of dairy products and we it and 635 00:39:40,080 --> 00:39:43,680 Speaker 1: it formed a lot in in our minds. We're gonna 636 00:39:43,760 --> 00:39:46,160 Speaker 1: tell you a lot of these other things that you're 637 00:39:46,200 --> 00:39:48,879 Speaker 1: going to remember, probably from your childhood and growing up, 638 00:39:49,680 --> 00:39:53,160 Speaker 1: that that this company was behind I'm gonna pause it 639 00:39:53,239 --> 00:39:59,879 Speaker 1: again here. I believe wholeheartedly that the Ninja Turtles Tell 640 00:40:00,000 --> 00:40:04,120 Speaker 1: Division show that I watched as a child had some 641 00:40:04,239 --> 00:40:08,680 Speaker 1: kind of behind the scenes interaction with this group to 642 00:40:08,760 --> 00:40:12,000 Speaker 1: get me to like and want pizza all the time. 643 00:40:12,400 --> 00:40:14,920 Speaker 1: I think I think they're behind it. I can't prove it. 644 00:40:15,520 --> 00:40:18,560 Speaker 1: I think you're right. And this goes to This also 645 00:40:18,640 --> 00:40:21,840 Speaker 1: goes to Ryan's original question about the origin of the 646 00:40:21,920 --> 00:40:26,520 Speaker 1: cheese powder used in mac and Cheese. Uh want a pause. 647 00:40:26,640 --> 00:40:29,080 Speaker 1: We've got some of our fellow listeners who are who 648 00:40:29,120 --> 00:40:31,279 Speaker 1: are gonna You're gonna meet at the end of the 649 00:40:31,320 --> 00:40:34,080 Speaker 1: episode on Twitter with their cheese punts of their own. 650 00:40:34,520 --> 00:40:37,520 Speaker 1: But I wanted to point out this excellent tweet we 651 00:40:37,560 --> 00:40:41,320 Speaker 1: got from someone who lived in Wisconsin. Uh, this is Steph, 652 00:40:41,560 --> 00:40:44,080 Speaker 1: and Steph, you said, one of my favorite things while 653 00:40:44,120 --> 00:40:47,000 Speaker 1: living in Wisconsin were the billboards that were just black 654 00:40:47,040 --> 00:40:50,640 Speaker 1: text on a yellow field that said cheese, no other 655 00:40:50,680 --> 00:40:53,799 Speaker 1: info needed. They knew why you were there. And that's 656 00:40:53,840 --> 00:40:56,680 Speaker 1: like that. Whenever you see those really vague this is 657 00:40:56,680 --> 00:41:00,520 Speaker 1: an important point. Whenever you see those really vague prommercials 658 00:41:00,880 --> 00:41:03,920 Speaker 1: that are not about a specific brand name or product. 659 00:41:04,360 --> 00:41:07,760 Speaker 1: They're just about a kind of thing, right Like got 660 00:41:07,840 --> 00:41:11,239 Speaker 1: milk is never saying, you know, go like, it's not 661 00:41:11,400 --> 00:41:19,800 Speaker 1: Greenfield pastures milk. It's just the idea of freaking milk. Exactly, yes, exactly. 662 00:41:19,840 --> 00:41:22,560 Speaker 1: If that's what's for dinner, it's what's for dinner. I 663 00:41:22,600 --> 00:41:25,080 Speaker 1: love those kind of commercials, you know, I want to 664 00:41:25,080 --> 00:41:28,880 Speaker 1: see more of them where um. You know, sometimes they're wholesome, 665 00:41:28,920 --> 00:41:32,200 Speaker 1: they're thing. They're like literacy movements. They're like read books. 666 00:41:32,760 --> 00:41:37,000 Speaker 1: We don't care which one. Uh so we see we 667 00:41:37,080 --> 00:41:38,880 Speaker 1: see this, and what they're What d m I is 668 00:41:39,000 --> 00:41:42,160 Speaker 1: doing at this time in the in their early days 669 00:41:42,600 --> 00:41:45,319 Speaker 1: is um, like you said, Matt, they're doing direct to 670 00:41:45,560 --> 00:41:51,160 Speaker 1: consumer advertising. They're they're playing there. They are playing literal 671 00:41:51,239 --> 00:41:55,280 Speaker 1: burnet style mind games. They want you to be thinking 672 00:41:55,320 --> 00:41:58,040 Speaker 1: about milk. That wants you to be thinking about cheese 673 00:41:58,080 --> 00:42:02,080 Speaker 1: and yogurt when you're in the grocery store. Dream about it, 674 00:42:02,520 --> 00:42:06,040 Speaker 1: dream about it. I really want some cheese right now. 675 00:42:06,880 --> 00:42:11,560 Speaker 1: The dairy rainbow. So I don't know why dairy rainbow 676 00:42:11,640 --> 00:42:15,880 Speaker 1: makes me uncomfortable, but yeah, so something happened though in 677 00:42:15,960 --> 00:42:20,919 Speaker 1: the late nine, people in this country began eating out 678 00:42:21,000 --> 00:42:23,200 Speaker 1: more often. They were going to restaurants and stuff. They 679 00:42:23,200 --> 00:42:27,240 Speaker 1: weren't cooking as often at home. So d M I 680 00:42:27,280 --> 00:42:31,320 Speaker 1: did what Corporate America calls a pivot, and they started 681 00:42:31,480 --> 00:42:36,480 Speaker 1: working with companies that sold food to people in fast 682 00:42:36,480 --> 00:42:40,000 Speaker 1: food joints, in restaurants and what is it called approachable 683 00:42:40,040 --> 00:42:44,360 Speaker 1: fine dining? You know in in uh in the hallowed halls, 684 00:42:44,600 --> 00:42:46,920 Speaker 1: the Chilies, and the t g I f s throughout 685 00:42:46,920 --> 00:42:51,200 Speaker 1: the nation. But what about what about them quick them 686 00:42:51,239 --> 00:42:54,399 Speaker 1: quick food places? What do you call them? The fast 687 00:42:54,440 --> 00:43:00,319 Speaker 1: food joints? Yeah, yeah, yeah, Pizza Hut, Taco Bell. We 688 00:43:00,520 --> 00:43:05,120 Speaker 1: kid you not. They have been infiltrating. How weird is 689 00:43:05,160 --> 00:43:09,279 Speaker 1: that they have been infiltrated by t M I. Uh Like, 690 00:43:09,960 --> 00:43:13,120 Speaker 1: think about this. If you're nineties kid, you know, and 691 00:43:13,239 --> 00:43:17,440 Speaker 1: you you enjoyed book It, you enjoyed that personal pan pizza, 692 00:43:18,160 --> 00:43:22,399 Speaker 1: you probably you probably have this memory, yeah duds off 693 00:43:22,440 --> 00:43:29,160 Speaker 1: to crust pizza in Yeah, and specifically from maybe you 694 00:43:29,160 --> 00:43:32,120 Speaker 1: don't even remember eating it necessarily, but you certainly remember 695 00:43:32,239 --> 00:43:35,280 Speaker 1: seeing that cheese pull when and then the whole notion 696 00:43:35,320 --> 00:43:39,200 Speaker 1: of eating your pizza backwards. That was the whole impetus 697 00:43:39,239 --> 00:43:42,359 Speaker 1: behind us, and a little old guy, uh we might 698 00:43:42,400 --> 00:43:45,440 Speaker 1: have heard of named Donald Trump appeared in some of 699 00:43:45,440 --> 00:43:48,440 Speaker 1: those ads, um eating his pizza backwards and making it 700 00:43:48,440 --> 00:43:51,000 Speaker 1: sound like, you know, I'm Donald Trump, and I'm obviously 701 00:43:51,040 --> 00:43:53,719 Speaker 1: a smart guy, and I eat my pizza backwards because 702 00:43:53,760 --> 00:43:57,920 Speaker 1: that's just what you do, idiots. I think they cut 703 00:43:57,920 --> 00:44:02,200 Speaker 1: the part where he said it. That would have been funny, 704 00:44:02,239 --> 00:44:05,160 Speaker 1: That would have been edgy for nineties kid. But I 705 00:44:05,200 --> 00:44:09,680 Speaker 1: think you're right. Eating pizza backwards was already this new, exciting, weird, 706 00:44:09,840 --> 00:44:15,000 Speaker 1: cool thing. Uh. And by the end of the year, 707 00:44:16,000 --> 00:44:21,040 Speaker 1: this initiative had increased Pizza Hut sales by three million, 708 00:44:21,400 --> 00:44:23,960 Speaker 1: which sounds like a lot of money, but really it's 709 00:44:24,000 --> 00:44:28,120 Speaker 1: it's about a seven percent improvement over the previous year. 710 00:44:28,480 --> 00:44:32,279 Speaker 1: But still, think about all that extra cheese that went 711 00:44:32,360 --> 00:44:35,120 Speaker 1: in each one of those pizza pies. And if you 712 00:44:35,160 --> 00:44:38,239 Speaker 1: think about if you've got a stuffed crust pizza with 713 00:44:38,440 --> 00:44:42,120 Speaker 1: extra cheese, good god, d M. I is just killing 714 00:44:42,160 --> 00:44:45,280 Speaker 1: it here. Yeah, they're killing it, and they're killing you 715 00:44:46,120 --> 00:44:50,600 Speaker 1: slowly from the inside out. Greater good. If you eat 716 00:44:50,640 --> 00:44:54,280 Speaker 1: too much stuffed crust, eventually you become the stuffed crust yourself. 717 00:44:54,680 --> 00:44:57,200 Speaker 1: That's that's the problem. But you know, this reminds me. 718 00:44:57,320 --> 00:45:00,279 Speaker 1: I was trying to find while we're researching this um 719 00:45:00,440 --> 00:45:03,520 Speaker 1: between our listener mail and this episode, I was trying 720 00:45:03,560 --> 00:45:08,000 Speaker 1: to find this tweet that I loved from a while back. 721 00:45:08,320 --> 00:45:10,920 Speaker 1: It was someone I didn't know very well, but they 722 00:45:10,960 --> 00:45:14,799 Speaker 1: posted this this kind of explicit So Paul, please beat me. Uh. 723 00:45:14,960 --> 00:45:18,799 Speaker 1: Someone said, I was on a marketing call years ago 724 00:45:19,320 --> 00:45:23,319 Speaker 1: with Pete, with marketing people at Pizza Hut, and this 725 00:45:23,400 --> 00:45:27,960 Speaker 1: one guy said, we do it with what stuffed crust pizza. 726 00:45:28,200 --> 00:45:30,600 Speaker 1: No one asked us about that, We just did it. 727 00:45:30,880 --> 00:45:33,680 Speaker 1: And then the tweet ended. I think about that guy 728 00:45:33,719 --> 00:45:37,920 Speaker 1: a lot, and I love I love that idea that 729 00:45:38,080 --> 00:45:41,520 Speaker 1: someone is so gangster about stuffed crust. But now it 730 00:45:41,600 --> 00:45:44,200 Speaker 1: makes a little bit more sense because they had help 731 00:45:44,360 --> 00:45:48,279 Speaker 1: from the illuminati of cheese. They're not the only example. Uh, 732 00:45:48,400 --> 00:45:51,359 Speaker 1: Taco Bell is a huge example. Remember at the top, 733 00:45:51,440 --> 00:45:53,160 Speaker 1: we said the case a Loupa is going to come 734 00:45:53,160 --> 00:45:56,920 Speaker 1: into play in two thousand and twelve, a d M 735 00:45:56,960 --> 00:46:01,520 Speaker 1: I operative, we could call them that food scientists. It 736 00:46:01,640 --> 00:46:04,319 Speaker 1: was probably a fantastic person. We haven't spoken with her 737 00:46:04,360 --> 00:46:09,480 Speaker 1: directly named Lisa McClintock started working with the Taco Bell 738 00:46:09,800 --> 00:46:14,600 Speaker 1: product development team. Pause here for a second, Taco Bell fans, 739 00:46:15,880 --> 00:46:18,120 Speaker 1: does that not sound kind of like a dream job? 740 00:46:19,280 --> 00:46:22,640 Speaker 1: Like you go home from work and you're stressed out 741 00:46:22,719 --> 00:46:26,439 Speaker 1: and and your roommates or your partner, your friends are 742 00:46:26,480 --> 00:46:29,080 Speaker 1: are saying, oh, hey, what's got you down, and you're 743 00:46:29,080 --> 00:46:32,800 Speaker 1: like kicking rocks and you're like, they're just not They're 744 00:46:32,840 --> 00:46:37,160 Speaker 1: just not listening. With the burrito design, Okay, you don't 745 00:46:37,200 --> 00:46:40,640 Speaker 1: want leakage and I don't. I don't, And you're like, yeah, 746 00:46:40,680 --> 00:46:43,959 Speaker 1: you're like Samantha, they don't get my vision. Think about 747 00:46:44,000 --> 00:46:46,520 Speaker 1: the beauty. I mean, we've talked about it before, about 748 00:46:46,560 --> 00:46:48,440 Speaker 1: the beauty of Taco Bell and pizza. At some of 749 00:46:48,440 --> 00:46:51,279 Speaker 1: these other places, you've got a limited number of ingredients, 750 00:46:52,160 --> 00:46:56,440 Speaker 1: but anything you can do to create a new product 751 00:46:56,640 --> 00:47:01,440 Speaker 1: out of those ingredients can bring in millions and millions 752 00:47:01,440 --> 00:47:04,399 Speaker 1: of dollars. And that's like Mexican food, or at least 753 00:47:04,400 --> 00:47:07,799 Speaker 1: Americanized Mexican food in general, right, where like everything on 754 00:47:07,840 --> 00:47:10,000 Speaker 1: the menu is kind of a different combination of like 755 00:47:10,080 --> 00:47:13,200 Speaker 1: five or six ingredients. Taco Bell takes it to the extreme, 756 00:47:13,640 --> 00:47:16,879 Speaker 1: and you know, injects massive marketing dollars into it. Ben, 757 00:47:16,920 --> 00:47:23,120 Speaker 1: you were talking about what the case Uh what, there's 758 00:47:23,160 --> 00:47:26,919 Speaker 1: also the case Arito. Let's not forget about the case rito. 759 00:47:27,080 --> 00:47:30,160 Speaker 1: And when you look at it on the Taco Bell website, Um, 760 00:47:30,440 --> 00:47:34,600 Speaker 1: the cheese pouring forth from it is clearly that powdered 761 00:47:34,719 --> 00:47:38,520 Speaker 1: yellow stuff. Like there's not an ounce of actual cheese 762 00:47:38,680 --> 00:47:41,640 Speaker 1: in this. I'm I'm conjecturing it's just it's this the 763 00:47:41,640 --> 00:47:45,680 Speaker 1: wrong color. It's like this kind of weird beige yellow, pale, 764 00:47:45,840 --> 00:47:48,200 Speaker 1: odd color. When you see it pulling apart in this 765 00:47:48,320 --> 00:47:52,040 Speaker 1: perfectly symmetrical image. Uh, it also has just an insane 766 00:47:52,040 --> 00:47:56,440 Speaker 1: amount of saturated fat um and just more you know, 767 00:47:57,040 --> 00:47:59,680 Speaker 1: sodium than anyone should probably consume in a day. Um. 768 00:47:59,719 --> 00:48:03,439 Speaker 1: But it's absolutely true, Ben, it's genius marketing paired with 769 00:48:04,000 --> 00:48:07,040 Speaker 1: this actual conspiracy in this idea, we gotta move some 770 00:48:07,160 --> 00:48:13,160 Speaker 1: cheese units. Yeah. So Lisa McClintock started working with the 771 00:48:13,280 --> 00:48:18,040 Speaker 1: senior manager for product Development, Steve Gomez over Taco Bell, 772 00:48:18,360 --> 00:48:21,000 Speaker 1: and they said, we need to develop a new kind 773 00:48:21,080 --> 00:48:25,280 Speaker 1: of cheese, a new cheese filling, a new kind of cheese, 774 00:48:25,719 --> 00:48:28,560 Speaker 1: a new kind of cheese. You say, here at the 775 00:48:28,640 --> 00:48:33,080 Speaker 1: Taco Bell so, uh yeah, I'm at the food science lab, 776 00:48:33,160 --> 00:48:36,080 Speaker 1: I'm at the Taco Bell, I'm at the combination food 777 00:48:36,120 --> 00:48:39,839 Speaker 1: science and Taco Bell. There there's stretching and so they 778 00:48:39,880 --> 00:48:43,759 Speaker 1: want to make a stretchy cheese that that pulls like 779 00:48:43,880 --> 00:48:46,719 Speaker 1: taffy when it's heated. They want to figure out how 780 00:48:46,719 --> 00:48:49,719 Speaker 1: to mass produce it, and they want to invent some 781 00:48:49,800 --> 00:48:54,960 Speaker 1: proprietary machinery to make this. And the proprietary machinery is 782 00:48:55,400 --> 00:49:00,080 Speaker 1: really interesting, really clever. Point. So this the result of 783 00:49:00,160 --> 00:49:04,640 Speaker 1: this experimentation is something called the case Lupa, and I 784 00:49:04,680 --> 00:49:07,320 Speaker 1: think the case of Rito is clearly part of this too. 785 00:49:07,760 --> 00:49:10,440 Speaker 1: The case a Loupa, as you mentioned earlier, has five 786 00:49:10,480 --> 00:49:15,080 Speaker 1: times as much cheese as a basic crunchy taco. Taco 787 00:49:15,160 --> 00:49:18,080 Speaker 1: Bell had to buy four point seven million pounds of 788 00:49:18,160 --> 00:49:22,080 Speaker 1: cheese just too produce the shells. So that puts the 789 00:49:22,160 --> 00:49:27,000 Speaker 1: dent in that one point three billion pounds surplus. It's 790 00:49:27,000 --> 00:49:30,279 Speaker 1: weird if you read, if you read interviews with people 791 00:49:30,360 --> 00:49:33,120 Speaker 1: who are in Taco Bell management at the time, then 792 00:49:33,160 --> 00:49:35,600 Speaker 1: they will say, you know, for the longest time, we 793 00:49:35,680 --> 00:49:39,840 Speaker 1: thought cheese, sour cream stuff like that was kind of 794 00:49:39,880 --> 00:49:43,640 Speaker 1: more of a garnish or an add on to our products, 795 00:49:43,680 --> 00:49:46,360 Speaker 1: but now it's one of the main things beef and cheese, 796 00:49:46,680 --> 00:49:51,000 Speaker 1: So cheese use at Taco Bell since they partnered with 797 00:49:51,120 --> 00:49:56,960 Speaker 1: DMA it's increased by over twenty and that's that's why 798 00:49:57,200 --> 00:50:01,640 Speaker 1: d m I gets this strange, somewhat half sinister, half 799 00:50:01,719 --> 00:50:07,520 Speaker 1: hilarious nickname, the Illuminati of Cheese. We're gonna pause for 800 00:50:07,640 --> 00:50:13,880 Speaker 1: word from our sponsors. I'm wondering. I'm wondering too, and 801 00:50:14,080 --> 00:50:19,040 Speaker 1: we'll be back. We'll be back to explore some more 802 00:50:19,239 --> 00:50:25,960 Speaker 1: of unintended consequences of this again active ongoing conspiracy. Do 803 00:50:26,000 --> 00:50:29,879 Speaker 1: you guys think the Bacon Illuminati worked with Wendy's when 804 00:50:29,880 --> 00:50:32,239 Speaker 1: they came up with a bacon eater? D m I 805 00:50:32,320 --> 00:50:42,160 Speaker 1: definitely did. And we're back. Here's the thing. There's there's 806 00:50:42,239 --> 00:50:47,000 Speaker 1: no if and or but about it. D m I 807 00:50:47,120 --> 00:50:50,680 Speaker 1: will tell you and you speak with him that their 808 00:50:50,800 --> 00:50:57,200 Speaker 1: work has helped slow America's declining desire for dairy And 809 00:50:57,760 --> 00:51:02,040 Speaker 1: it's true that she demand for cheese is going like this, 810 00:51:02,360 --> 00:51:05,399 Speaker 1: and demand for milk is going like this. Uh so 811 00:51:06,920 --> 00:51:13,239 Speaker 1: do that do that for the non visual yes? So? 812 00:51:13,640 --> 00:51:16,760 Speaker 1: Uh if you look at a grid in your head, uh, 813 00:51:17,320 --> 00:51:21,640 Speaker 1: demand for cheese is starting at the lower left corner 814 00:51:21,800 --> 00:51:23,680 Speaker 1: and it's going all the way to the top right. 815 00:51:24,360 --> 00:51:27,839 Speaker 1: Demand for milk is starting sort of midway up your 816 00:51:28,040 --> 00:51:33,280 Speaker 1: y axis, and it's going down into the right beautiful networks. Okay, 817 00:51:33,400 --> 00:51:37,920 Speaker 1: So demand for milk has gone from thirty five pounds 818 00:51:37,960 --> 00:51:43,040 Speaker 1: per person in nine seventy five to fifteen pounds today. 819 00:51:43,239 --> 00:51:47,640 Speaker 1: So they've they've it's reversed. Like what was it? No, 820 00:51:47,840 --> 00:51:53,440 Speaker 1: I think, as you said, um, demand for uh, cheese 821 00:51:53,640 --> 00:51:57,040 Speaker 1: was what fourteen something? Oh yeah, that's right. In n 822 00:51:58,280 --> 00:52:02,400 Speaker 1: Americans were consuming fourteen pound of cheese. Then in like 823 00:52:02,640 --> 00:52:05,279 Speaker 1: essentially the modern day, it's around forty five pounds of 824 00:52:05,320 --> 00:52:09,960 Speaker 1: cheese per person per year. Milk has decreased a ton, right, Yeah, 825 00:52:09,960 --> 00:52:12,960 Speaker 1: I think it was a two close to fifty pounds 826 00:52:12,960 --> 00:52:17,839 Speaker 1: per person in nineteen and uh in um more like 827 00:52:17,920 --> 00:52:22,359 Speaker 1: one s So a significant decrease. And this is effective 828 00:52:22,480 --> 00:52:27,440 Speaker 1: for the a lot of the stakeholders, not counting the consumers. 829 00:52:27,920 --> 00:52:32,080 Speaker 1: Texas A and M economists did a cost benefit analysis 830 00:52:32,120 --> 00:52:35,600 Speaker 1: and in two thousand twelve they found that for every 831 00:52:35,640 --> 00:52:40,319 Speaker 1: single dollar a dairy producer invests in d M I UH, 832 00:52:40,360 --> 00:52:44,720 Speaker 1: they get two dollars and fourteen cents worth of return 833 00:52:44,840 --> 00:52:48,600 Speaker 1: for milk, four dollars twenty six cents worth of return 834 00:52:48,640 --> 00:52:52,760 Speaker 1: for cheese, and nine dollars in sixty three cents return 835 00:52:52,920 --> 00:52:56,920 Speaker 1: for butter. So yeah, so investing in d m I, 836 00:52:56,920 --> 00:52:59,200 Speaker 1: if you're if you're a dairy person, this is this 837 00:52:59,360 --> 00:53:05,440 Speaker 1: really helped. But of course, as we said, d m 838 00:53:05,520 --> 00:53:09,640 Speaker 1: I has, by nature of its mission its endeavor. They 839 00:53:09,640 --> 00:53:14,280 Speaker 1: have promoted lots of saturated fat, lots of cholesterol. Eating 840 00:53:14,400 --> 00:53:19,880 Speaker 1: cheese can add up. And for a federal agency that 841 00:53:20,080 --> 00:53:25,800 Speaker 1: is dedicated to improving overall nutrition and giving dietary guidance, 842 00:53:26,320 --> 00:53:31,000 Speaker 1: these partnerships can seem kind of like a contradiction, right, 843 00:53:31,239 --> 00:53:34,640 Speaker 1: Oh yeah, absolutely, And experts out there who talk about 844 00:53:34,680 --> 00:53:38,480 Speaker 1: this would completely agree with you. Ben. Uh. There's there's 845 00:53:38,520 --> 00:53:43,919 Speaker 1: a food oppression expert named Andrea Freeman who believes that 846 00:53:45,360 --> 00:53:48,360 Speaker 1: you know this, This group d m e s efforts 847 00:53:48,440 --> 00:53:53,479 Speaker 1: quote impose health costs on Americans generally, but disproportionately harm 848 00:53:53,600 --> 00:53:57,360 Speaker 1: low income African Americans and Latinos who live in urban 849 00:53:57,440 --> 00:54:03,120 Speaker 1: centers dominated by fast food restaurants. And we've just described 850 00:54:03,480 --> 00:54:09,200 Speaker 1: how these two giant, well now one giant chains that 851 00:54:09,239 --> 00:54:12,160 Speaker 1: we at least all went to when we were younger, 852 00:54:12,400 --> 00:54:16,239 Speaker 1: Like my family still eats at some of these restaurants. Um, 853 00:54:16,280 --> 00:54:18,840 Speaker 1: in these fast food chains, you can see where d 854 00:54:19,000 --> 00:54:23,040 Speaker 1: m I had large impacts at least on those brands 855 00:54:23,080 --> 00:54:27,200 Speaker 1: slash companies. And you can definitely see that. You can 856 00:54:27,239 --> 00:54:30,680 Speaker 1: see the evidence of what what Andrew Freeman is saying 857 00:54:30,680 --> 00:54:33,080 Speaker 1: here just in what we described with Pizza Hut and 858 00:54:33,120 --> 00:54:36,759 Speaker 1: Taco Bell. You can really see it. Yeah. And you know, 859 00:54:36,880 --> 00:54:41,239 Speaker 1: also farmers themselves aren't entirely on board with d m I, 860 00:54:42,200 --> 00:54:46,200 Speaker 1: because some will argue this primarily helps out the big guys, 861 00:54:46,280 --> 00:54:48,920 Speaker 1: the big dairy players, and not the family farms or 862 00:54:48,960 --> 00:54:52,239 Speaker 1: the smaller outfits. And that's a that's a valid criticism. 863 00:54:52,320 --> 00:54:54,960 Speaker 1: We see that happening with other parts of what is 864 00:54:55,000 --> 00:54:59,680 Speaker 1: called agribusiness on a high level. But either way, it 865 00:54:59,680 --> 00:55:04,040 Speaker 1: app yours. The cheesy Illuminati is real and is set 866 00:55:04,360 --> 00:55:08,080 Speaker 1: to stay. At this point, we want to pass the 867 00:55:08,160 --> 00:55:10,320 Speaker 1: torch to you folks who want to get a sense 868 00:55:10,400 --> 00:55:14,239 Speaker 1: of what you think, because there there there's a web 869 00:55:14,280 --> 00:55:17,840 Speaker 1: of connections. Here is this number one. A good idea 870 00:55:18,360 --> 00:55:20,799 Speaker 1: is this um. You know, some of us may argue 871 00:55:20,840 --> 00:55:23,919 Speaker 1: it's too much government intervention. Some of us may say 872 00:55:24,000 --> 00:55:27,000 Speaker 1: it's too dishonest for the public. Other people may say, 873 00:55:27,080 --> 00:55:33,120 Speaker 1: well it is overall, it's more beneficial that it is damaging. 874 00:55:33,760 --> 00:55:35,839 Speaker 1: We want to we want to hear from you. And 875 00:55:35,920 --> 00:55:39,160 Speaker 1: because we purposely wanted this to be to be a 876 00:55:39,920 --> 00:55:44,440 Speaker 1: lighter episode, and we promised some puns. Uh, we went 877 00:55:44,760 --> 00:55:50,000 Speaker 1: on Twitter uh and asked people if they would if 878 00:55:50,040 --> 00:55:52,399 Speaker 1: they would like to help us out with some puns. 879 00:55:52,440 --> 00:55:55,319 Speaker 1: So the tweet I put out, which is terrible, was 880 00:55:55,920 --> 00:55:58,520 Speaker 1: about to record this cheese episode. If you'd like to 881 00:55:58,520 --> 00:56:00,040 Speaker 1: be a part of the show, hit us up in 882 00:56:00,080 --> 00:56:02,480 Speaker 1: your favorite cheese puns in the next hour or so. 883 00:56:02,960 --> 00:56:08,440 Speaker 1: Dot dot dot This will be a guda one. Oh man, God, 884 00:56:08,480 --> 00:56:11,920 Speaker 1: I hate it. I hate Why do we do this? Anyway? 885 00:56:12,080 --> 00:56:16,840 Speaker 1: Here's here are some responses from your fellow listeners. Vaults 886 00:56:16,920 --> 00:56:19,239 Speaker 1: of x Talk, who we all know and love here 887 00:56:19,280 --> 00:56:25,600 Speaker 1: on the show, says, e damn, it has an eat um. 888 00:56:25,680 --> 00:56:27,600 Speaker 1: I'm assuming I can't think of any that would be 889 00:56:27,600 --> 00:56:32,920 Speaker 1: good enough for you eating cheese. No, that's not what 890 00:56:32,960 --> 00:56:41,200 Speaker 1: I meant to say. Eat Um. Is that a cheese ye? Yes? Um? 891 00:56:41,239 --> 00:56:44,879 Speaker 1: But then Vaults Vaults follows up with and now I'm 892 00:56:44,880 --> 00:56:48,240 Speaker 1: getting a pop up ad hot cheese singles in your area. 893 00:56:48,680 --> 00:56:52,880 Speaker 1: Thanks guys, that's great um our own O world, close 894 00:56:52,960 --> 00:56:55,600 Speaker 1: friends and colleague that you've heard on our show before. 895 00:56:56,000 --> 00:56:59,200 Speaker 1: Annie Reese Uh, one of the co creators and co 896 00:56:59,360 --> 00:57:07,680 Speaker 1: host the Amazing Saver podcast, says, unbreathlievable. You're too big 897 00:57:07,680 --> 00:57:10,520 Speaker 1: of a cheese to ask for my pun expertise. Now, 898 00:57:10,960 --> 00:57:15,319 Speaker 1: you munster spelled how I want to think? Yeah, cheese puns, 899 00:57:15,360 --> 00:57:19,240 Speaker 1: you monster, you relieve you're mildly cheddar than us. I 900 00:57:19,520 --> 00:57:24,960 Speaker 1: camembart being so fed up. That's funny because there were 901 00:57:25,000 --> 00:57:28,280 Speaker 1: some crossovers there between those two. Yeah, and then what 902 00:57:28,400 --> 00:57:31,000 Speaker 1: our buddy Nick Takowsky did a good one. I'd step up, 903 00:57:31,000 --> 00:57:34,160 Speaker 1: but I got the blues pretty bad this week. B 904 00:57:34,480 --> 00:57:39,080 Speaker 1: l e U s y and good friend of the show, 905 00:57:39,200 --> 00:57:42,080 Speaker 1: the one and only Mike Johns, who recently had a birthday. 906 00:57:42,160 --> 00:57:45,400 Speaker 1: Mike is a colleague of ours, a fantastic writer, musician, 907 00:57:45,520 --> 00:57:50,320 Speaker 1: producer here in Atlanta. Uh. He says something encouraging. He says, 908 00:57:50,360 --> 00:57:53,240 Speaker 1: you've got some pretty sharp followers. You chedd to get 909 00:57:53,320 --> 00:57:57,160 Speaker 1: some good suggestions here. I've got half a rind to 910 00:57:57,360 --> 00:58:00,000 Speaker 1: suggest a few, but I don't want to wedge myself 911 00:58:00,080 --> 00:58:05,240 Speaker 1: into the conversation. Wow, okay, here we go. Um, let's see, 912 00:58:05,240 --> 00:58:10,040 Speaker 1: he kept going by the way. Yeah, could we This 913 00:58:10,080 --> 00:58:14,200 Speaker 1: is one from at grape Whanger. That's a great twitter 914 00:58:14,280 --> 00:58:21,600 Speaker 1: named vigil um. Could we not craft less cheesy puns? Craft? No, 915 00:58:21,840 --> 00:58:28,840 Speaker 1: we can't wangerh Wow? Can we kind of jump to 916 00:58:28,920 --> 00:58:33,280 Speaker 1: Andy really quickly? Andy? He said, Ben, I believe you 917 00:58:33,320 --> 00:58:35,720 Speaker 1: have enough puns for us all but fed us safe 918 00:58:35,760 --> 00:58:39,360 Speaker 1: and sorry, here's a joke. What hotel do cheese lovers 919 00:58:39,400 --> 00:58:46,680 Speaker 1: stay in? The Stilton? Wonderful? My Stilton Honors Club can't 920 00:58:46,680 --> 00:58:51,640 Speaker 1: wait for that? And then uh, Steph, who we mentioned earlier, 921 00:58:51,680 --> 00:58:54,160 Speaker 1: says I'd make a joke about American cheese, but it 922 00:58:54,200 --> 00:58:59,479 Speaker 1: wouldn't age well. Shots fired, Shots fired. You can find 923 00:58:59,560 --> 00:59:01,600 Speaker 1: you can find more of these puns. We'd love to 924 00:59:01,680 --> 00:59:06,480 Speaker 1: hear your your favorite cheese puns or your least favorite 925 00:59:06,800 --> 00:59:09,680 Speaker 1: if you hate puns and ignored our warning and sat 926 00:59:09,720 --> 00:59:14,280 Speaker 1: through this episode. And equally importantly, we'd like to hear, um, 927 00:59:14,360 --> 00:59:17,000 Speaker 1: what what you think about these kind of things. They're 928 00:59:17,040 --> 00:59:20,240 Speaker 1: part of what's known as check off programs. We mentioned 929 00:59:20,240 --> 00:59:24,120 Speaker 1: this earlier. The idea is that it is so important 930 00:59:24,200 --> 00:59:27,920 Speaker 1: to have your own domestic sources of food that you 931 00:59:28,080 --> 00:59:30,240 Speaker 1: will you know, you'll go out of your way to 932 00:59:30,320 --> 00:59:33,840 Speaker 1: support them more than you may some other businesses. Oh 933 00:59:33,880 --> 00:59:41,440 Speaker 1: and uh, Jenna V. Says sweet dreams are made of cheese. Cheese. 934 00:59:42,400 --> 00:59:49,960 Speaker 1: Oh am, I to breathe. We gotta get out of 935 00:59:50,000 --> 00:59:53,960 Speaker 1: this the Paul. I can feel Paul growing so uh 936 00:59:54,080 --> 00:59:56,440 Speaker 1: so let us know your thoughts. We can't wait to 937 00:59:56,440 --> 00:59:58,520 Speaker 1: hear from you. We try to be easy to find online. 938 00:59:58,560 --> 01:00:00,120 Speaker 1: That's right. You can find us on the end your 939 01:00:00,120 --> 01:00:03,720 Speaker 1: net where you can also find that vintage government cheese 940 01:00:03,720 --> 01:00:06,440 Speaker 1: box that I mentioned. The listing is still live on 941 01:00:06,560 --> 01:00:08,760 Speaker 1: eBay if you're if you're down for that, um, just 942 01:00:08,800 --> 01:00:11,360 Speaker 1: search for vintage Government cheese box on eBay. I think 943 01:00:11,400 --> 01:00:13,680 Speaker 1: the bid is up to around sixty bucks. But if 944 01:00:13,680 --> 01:00:16,480 Speaker 1: you're looking for us, you can find us on Instagram 945 01:00:16,960 --> 01:00:19,800 Speaker 1: or we are conspiracy stuff show Twitter and Facebook or 946 01:00:19,840 --> 01:00:22,480 Speaker 1: were conspiracy stuff. You can also give us a telephone call. 947 01:00:22,680 --> 01:00:24,640 Speaker 1: We are one eight three three s T d W 948 01:00:24,880 --> 01:00:28,240 Speaker 1: y t K. Hit him with it, Matt, Yeah, hit 949 01:00:28,320 --> 01:00:33,000 Speaker 1: him with the rules. I was gonna hey, is there 950 01:00:33,000 --> 01:00:39,000 Speaker 1: a hey? Is there a cheese that rhymes with hey? Yes? 951 01:00:39,080 --> 01:00:41,240 Speaker 1: When you call, please tell us the name you would 952 01:00:41,240 --> 01:00:43,120 Speaker 1: like for us to refer to you as, doesn't have 953 01:00:43,160 --> 01:00:44,720 Speaker 1: to be your real name, could be a code name 954 01:00:44,800 --> 01:00:48,320 Speaker 1: like Doc Holiday or Mission Control. Super cool. Then let 955 01:00:48,360 --> 01:00:50,520 Speaker 1: us know if we can use your message on air. 956 01:00:50,960 --> 01:00:54,480 Speaker 1: That's very helpful for us. Thank you, lawyers say cheers. 957 01:00:55,400 --> 01:00:58,600 Speaker 1: Then leave your message. Please only call once per subject, 958 01:00:58,720 --> 01:01:01,400 Speaker 1: that would be very helpful as well. And if you've 959 01:01:01,440 --> 01:01:03,840 Speaker 1: got a message you want to say personally to us, 960 01:01:03,840 --> 01:01:05,840 Speaker 1: please put it right in the end. There you've got 961 01:01:05,880 --> 01:01:10,920 Speaker 1: a quick update. This justin. Uh, code named dot Holiday 962 01:01:11,120 --> 01:01:15,400 Speaker 1: adds that have our tie and grew yer are are 963 01:01:15,640 --> 01:01:18,440 Speaker 1: very high on our list. And I just I promised 964 01:01:18,440 --> 01:01:22,200 Speaker 1: her we would mention that, Oh that's important. It's important stuff. 965 01:01:22,520 --> 01:01:24,840 Speaker 1: And you call us tell us your favorite cheese is 966 01:01:24,880 --> 01:01:27,560 Speaker 1: what you know about the cheese spiracy going on here? 967 01:01:27,840 --> 01:01:35,120 Speaker 1: Whenever you're front, that's perfect, that's right, oh man. And uh, 968 01:01:35,480 --> 01:01:37,560 Speaker 1: if you don't want to do that stuff, you don't 969 01:01:37,600 --> 01:01:38,760 Speaker 1: want to leave us a message, you don't want to 970 01:01:38,760 --> 01:01:40,680 Speaker 1: find us on the internet, definitely check out the YouTube 971 01:01:40,760 --> 01:01:44,760 Speaker 1: channel conspiracy stuff over there. And if you have something 972 01:01:44,800 --> 01:01:46,760 Speaker 1: to tell us and you want to send links or 973 01:01:46,800 --> 01:01:48,880 Speaker 1: you wanna, you know, really write a lot of stuff out. 974 01:01:49,160 --> 01:01:52,440 Speaker 1: Please use our good old fashioned email address. We are 975 01:01:52,880 --> 01:01:57,080 Speaker 1: conspirat cheese at i heeart radio dot com, conspiracy at 976 01:01:57,120 --> 01:02:18,400 Speaker 1: iHeart radio dot com. Yeah, stuff they don't want you 977 01:02:18,440 --> 01:02:21,080 Speaker 1: to know. Is a production of I heart Radio. For 978 01:02:21,120 --> 01:02:23,520 Speaker 1: more podcasts from my heart Radio, visit the i heart 979 01:02:23,600 --> 01:02:26,360 Speaker 1: Radio app, Apple Podcasts, or wherever you listen to your 980 01:02:26,360 --> 01:02:27,040 Speaker 1: favorite shows.