1 00:00:04,240 --> 00:00:06,520 Speaker 1: Hey, and welcome to the Short Stuff. Chuck's on a roll. 2 00:00:06,600 --> 00:00:09,480 Speaker 1: Let's get it up. Jerry's here too, Let's go it's 3 00:00:09,480 --> 00:00:10,280 Speaker 1: short stuff. 4 00:00:10,880 --> 00:00:14,120 Speaker 2: And speaking of Jerry, does this not start with a 5 00:00:14,160 --> 00:00:15,600 Speaker 2: Seinfeld ref? 6 00:00:15,800 --> 00:00:19,919 Speaker 1: Yeah, the Boyfriend episode part one. 7 00:00:19,360 --> 00:00:21,520 Speaker 2: I don't remember that one, walk me through it. 8 00:00:21,720 --> 00:00:24,000 Speaker 1: I don't remember the episode either, but I definitely remember 9 00:00:24,000 --> 00:00:26,680 Speaker 1: this part. So they're talking about meeting new friends in 10 00:00:26,720 --> 00:00:30,800 Speaker 1: your thirties and how it's just basically impossible to do that. 11 00:00:30,920 --> 00:00:33,680 Speaker 1: I think Jerry says, whatever group you've got is the 12 00:00:33,680 --> 00:00:36,440 Speaker 1: one you're going with. You're not interviewing, you're not looking 13 00:00:36,479 --> 00:00:39,880 Speaker 1: at any new people, not interested in seeing any applications. 14 00:00:41,200 --> 00:00:44,680 Speaker 1: And it's such a truism, like, as you get older, 15 00:00:45,080 --> 00:00:48,080 Speaker 1: the chances of you making new friends or especially adding 16 00:00:48,120 --> 00:00:54,000 Speaker 1: to your group, especially pre social media, was really low. 17 00:00:54,240 --> 00:00:57,520 Speaker 1: The chances were relatively low, especially compared to how you 18 00:00:57,560 --> 00:01:01,080 Speaker 1: were as a kid. And it's not just necessarily because 19 00:01:01,080 --> 00:01:05,160 Speaker 1: you lose interest in it. You potentially max out your 20 00:01:05,200 --> 00:01:08,760 Speaker 1: friends by a certain age, say your thirties, And the 21 00:01:08,840 --> 00:01:11,120 Speaker 1: idea that we can even max out the number of 22 00:01:11,200 --> 00:01:15,080 Speaker 1: friends we have suggests that there's like some sort of 23 00:01:15,720 --> 00:01:20,880 Speaker 1: cognitive load that having friends puts on us and that 24 00:01:21,040 --> 00:01:23,880 Speaker 1: we can only do so much, so we are limited 25 00:01:23,880 --> 00:01:25,759 Speaker 1: to a certain amount of friends. 26 00:01:26,160 --> 00:01:29,039 Speaker 2: Well, my friend, I would agree that in most cases 27 00:01:29,080 --> 00:01:32,640 Speaker 2: that's true. But it's funny that I fly in the 28 00:01:32,640 --> 00:01:36,240 Speaker 2: face of this because I have met a lot of 29 00:01:36,280 --> 00:01:40,480 Speaker 2: friends since my thirties and in my forties even Wow, 30 00:01:41,040 --> 00:01:42,759 Speaker 2: But a lot of it is because of this job. 31 00:01:45,840 --> 00:01:48,320 Speaker 2: And you know me, I'm kind of a friend collector anyway. Sure, 32 00:01:50,320 --> 00:01:52,200 Speaker 2: And I just I don't know, I was just thinking 33 00:01:52,240 --> 00:01:54,920 Speaker 2: about it because of this stuff you sent me that, Like, 34 00:01:55,200 --> 00:01:57,480 Speaker 2: I've met a lot of really really good friends. You know, 35 00:01:57,520 --> 00:02:00,560 Speaker 2: I've got my my my crew from way way back, 36 00:02:00,600 --> 00:02:03,440 Speaker 2: from like high school and even college. But I've made 37 00:02:03,480 --> 00:02:07,160 Speaker 2: a bunch of new friends and some are really close, 38 00:02:07,320 --> 00:02:09,480 Speaker 2: but some are just sort of professional colleagues that I 39 00:02:09,520 --> 00:02:14,760 Speaker 2: consider friends. But it's interesting and I've really enjoyed meeting 40 00:02:14,840 --> 00:02:16,680 Speaker 2: new people in my thirties and forties. 41 00:02:16,760 --> 00:02:19,960 Speaker 1: That's awesome. Well, if this whole episode didn't make me 42 00:02:20,000 --> 00:02:22,639 Speaker 1: feel like a loser before, it definitely does now. 43 00:02:23,200 --> 00:02:25,079 Speaker 2: Well, I mean, you and I are different. You're you're 44 00:02:25,680 --> 00:02:31,320 Speaker 2: more likely to keep your your tribe small. True, and 45 00:02:31,360 --> 00:02:32,840 Speaker 2: there's nothing wrong with that either. 46 00:02:33,040 --> 00:02:34,760 Speaker 1: Well, it is how I put it. 47 00:02:35,200 --> 00:02:37,360 Speaker 3: Yeah, but there's no right way to be. 48 00:02:37,600 --> 00:02:39,720 Speaker 1: I know, I'm just teasing, of course, but. 49 00:02:39,680 --> 00:02:42,000 Speaker 2: I mean that's sort of the yin and yang of 50 00:02:42,080 --> 00:02:44,280 Speaker 2: us and why we work I think as partners. 51 00:02:44,560 --> 00:02:47,000 Speaker 1: So let me ask you this, then, Chuck, would you 52 00:02:47,080 --> 00:02:49,679 Speaker 1: say that your number of friends people you would call 53 00:02:49,760 --> 00:02:53,040 Speaker 1: friends to one degree or another, exceeds that one hundred 54 00:02:53,040 --> 00:02:53,600 Speaker 1: and fifty. 55 00:02:54,120 --> 00:02:55,800 Speaker 3: Well, no, that's a lot of friends. 56 00:02:56,040 --> 00:02:58,360 Speaker 1: It is a lot of friends. And the reason that 57 00:02:58,360 --> 00:03:00,680 Speaker 1: that numbers even out there is because of a British 58 00:03:00,680 --> 00:03:06,400 Speaker 1: anthropologist I believe from Oxford named Robin Dunbar, who I'm 59 00:03:06,400 --> 00:03:08,840 Speaker 1: just going to say it became obsessed with the idea 60 00:03:09,520 --> 00:03:14,760 Speaker 1: that there was a magic number, a limited number of friends, 61 00:03:15,440 --> 00:03:18,520 Speaker 1: which is, okay, that's something in and of itself, but 62 00:03:18,639 --> 00:03:21,920 Speaker 1: that you could actually predict the number of friends a 63 00:03:22,000 --> 00:03:26,360 Speaker 1: species would have based on the size of their neocortex. 64 00:03:26,400 --> 00:03:32,480 Speaker 2: Right, that there was a ratio between your neocortex and 65 00:03:32,880 --> 00:03:35,000 Speaker 2: the amount of people that you could It's almost like 66 00:03:35,040 --> 00:03:38,120 Speaker 2: the amount of people you can manage before it starts 67 00:03:38,160 --> 00:03:42,440 Speaker 2: falling apart. And he studied, he studied this. He didn't 68 00:03:42,440 --> 00:03:45,640 Speaker 2: just come up with a number. He studied primates at first, 69 00:03:46,480 --> 00:03:49,880 Speaker 2: and you know, kind of at least in that world 70 00:03:49,880 --> 00:03:52,480 Speaker 2: found it to be true, and basically said, the size 71 00:03:52,480 --> 00:03:56,760 Speaker 2: of your neo cortex relative to your body size, and 72 00:03:56,800 --> 00:03:58,880 Speaker 2: that's the part of the brain that handles language right 73 00:03:59,280 --> 00:04:00,440 Speaker 2: in cognition mm hmm. 74 00:04:01,280 --> 00:04:05,280 Speaker 1: And also like just managing people and interacting with people, 75 00:04:05,320 --> 00:04:06,720 Speaker 1: it starts there, which is. 76 00:04:06,640 --> 00:04:07,720 Speaker 3: How it figures in here. 77 00:04:08,600 --> 00:04:14,200 Speaker 2: But that ratio basically will limit how complex of a 78 00:04:14,280 --> 00:04:16,600 Speaker 2: cohort group that you can be a part of. 79 00:04:17,000 --> 00:04:20,560 Speaker 1: Right, And so he took these primate studies that he conducted, 80 00:04:21,240 --> 00:04:26,159 Speaker 1: doing fMRIs of neocortices of primates and then looking at 81 00:04:26,240 --> 00:04:28,679 Speaker 1: the size of their social groups, and then said, okay, 82 00:04:28,839 --> 00:04:31,880 Speaker 1: let's apply this to humans. Humans are social animals, we're primates. 83 00:04:33,160 --> 00:04:36,200 Speaker 1: Let's measure the size of the human average size of 84 00:04:36,200 --> 00:04:41,960 Speaker 1: the neocortext and a human and just guess, like or 85 00:04:41,960 --> 00:04:45,320 Speaker 1: I guess, extrapolate based on our findings, how big the 86 00:04:45,400 --> 00:04:48,560 Speaker 1: average human social network would be. And he did that, 87 00:04:48,640 --> 00:04:50,720 Speaker 1: and he came up with one hundred and fifty. And 88 00:04:50,760 --> 00:04:54,440 Speaker 1: then he set about finding things that proved it right. 89 00:04:54,640 --> 00:04:57,640 Speaker 2: But first he took a commercial break, and we'll be 90 00:04:57,720 --> 00:05:30,119 Speaker 2: right back to talk about what he discovered, all right, 91 00:05:30,160 --> 00:05:33,679 Speaker 2: so he landed on one fifty, and he it wasn't 92 00:05:33,760 --> 00:05:37,520 Speaker 2: just like friends. He looked at you know, working groups 93 00:05:37,560 --> 00:05:44,400 Speaker 2: and factories and military squadrons and you know, ancient villages 94 00:05:44,440 --> 00:05:47,200 Speaker 2: in England and Christmas card lists, all kinds of things, 95 00:05:47,960 --> 00:05:51,520 Speaker 2: and he found that one fifty sort of stuck in. 96 00:05:51,720 --> 00:05:54,960 Speaker 2: Anything above that it would either you know, people would 97 00:05:54,960 --> 00:05:56,839 Speaker 2: turn on each other and you know the case of 98 00:05:56,920 --> 00:06:00,320 Speaker 2: like many many years ago, or it would just come 99 00:06:00,360 --> 00:06:03,760 Speaker 2: to unwieldy to manage and splinter off into smaller groups. 100 00:06:04,080 --> 00:06:07,000 Speaker 1: Yeah, your Christmas card list goes over one fifty, all 101 00:06:07,040 --> 00:06:09,080 Speaker 1: the people on are going to turn against each other. 102 00:06:09,360 --> 00:06:09,720 Speaker 3: Uh huh. 103 00:06:10,760 --> 00:06:16,000 Speaker 1: So he also said that even more fascinatingly, beyond the 104 00:06:16,080 --> 00:06:19,720 Speaker 1: number one fifty, that's just one of several numbers that 105 00:06:19,760 --> 00:06:23,560 Speaker 1: pop up and mind bogglingly, they're all factors of five. 106 00:06:24,200 --> 00:06:24,640 Speaker 3: Yeah. 107 00:06:24,680 --> 00:06:28,120 Speaker 1: In fact, in fact, oh it seems super hinky. In fact, 108 00:06:28,120 --> 00:06:31,760 Speaker 1: he said, you have five the closest people, the people 109 00:06:31,800 --> 00:06:36,000 Speaker 1: you consider your loved ones, usually number five. After that, 110 00:06:36,080 --> 00:06:41,720 Speaker 1: you've got fifteen good friends, fifty friends, one hundred and 111 00:06:41,760 --> 00:06:48,560 Speaker 1: fifty meaningful contactskay, five hundred acquaintances, fifteen hundred people you 112 00:06:48,600 --> 00:06:52,680 Speaker 1: can recognize. No and then he said also like this 113 00:06:52,800 --> 00:06:56,400 Speaker 1: is not these aren't like static lists, Like depending on 114 00:06:56,520 --> 00:07:00,520 Speaker 1: how frequently you interact with these people, somebody from your 115 00:07:00,960 --> 00:07:04,640 Speaker 1: recognized group can end up becoming one of your good 116 00:07:04,640 --> 00:07:06,719 Speaker 1: friends if you see them enough and you hit it 117 00:07:06,760 --> 00:07:09,520 Speaker 1: off and you connect, so you know, they're not locked 118 00:07:09,560 --> 00:07:12,560 Speaker 1: in any list, and you know your loved ones can 119 00:07:12,600 --> 00:07:14,760 Speaker 1: turn on you and end up with its just people 120 00:07:14,800 --> 00:07:17,560 Speaker 1: you can recognize. Who knows it depends on how your 121 00:07:17,600 --> 00:07:18,120 Speaker 1: life goes. 122 00:07:18,760 --> 00:07:23,000 Speaker 2: Yeah, and obviously it's a you know, it's in an arrange. 123 00:07:23,040 --> 00:07:27,920 Speaker 2: If you're an extrovert, you might have more you know, acquaintances. 124 00:07:27,960 --> 00:07:32,040 Speaker 2: At least I think they found that women have a 125 00:07:32,160 --> 00:07:35,600 Speaker 2: smaller number of I guess what would be considered good 126 00:07:35,600 --> 00:07:40,440 Speaker 2: friends than men do. Yeah. And it's interesting though that 127 00:07:40,680 --> 00:07:43,600 Speaker 2: some organizations and you got, you know, some of this 128 00:07:43,600 --> 00:07:46,440 Speaker 2: stuff from this was an article on the BBC. 129 00:07:46,080 --> 00:07:49,560 Speaker 1: BBC Courts Biology Letters, a few others good stuff. 130 00:07:49,600 --> 00:07:52,400 Speaker 2: But some organizations have sort of adhere to this, like 131 00:07:52,400 --> 00:07:55,560 Speaker 2: they buy into it, like the Swedish Tax Authority. Apparently 132 00:07:56,440 --> 00:07:58,640 Speaker 2: with their offices they don't have more than one hundred 133 00:07:58,640 --> 00:08:01,520 Speaker 2: and fifty people in any like particular location. 134 00:08:01,360 --> 00:08:03,600 Speaker 1: Which is hilarious I saw it pointed out in one 135 00:08:03,600 --> 00:08:06,080 Speaker 1: of our sources. I don't remember which one they said. 136 00:08:06,240 --> 00:08:08,920 Speaker 1: I guess the Swedish tax authority is just presuming that 137 00:08:09,000 --> 00:08:12,400 Speaker 1: its employees don't have friends or loved ones outside of work, 138 00:08:12,960 --> 00:08:15,720 Speaker 1: because that totally undermines their entire pursuit. There. 139 00:08:16,440 --> 00:08:19,440 Speaker 2: Oh, interesting, you know, I see what you mean. Yeah, yeah, 140 00:08:19,480 --> 00:08:22,520 Speaker 2: And some people say this is all bunk anyway. Some 141 00:08:22,560 --> 00:08:24,880 Speaker 2: people say it's completely bunk, and then some people say 142 00:08:25,080 --> 00:08:26,920 Speaker 2: there's something to that. But I just don't know about 143 00:08:26,960 --> 00:08:28,440 Speaker 2: that one fifty number, right. 144 00:08:28,560 --> 00:08:32,040 Speaker 1: So people have studied this and performed their own their 145 00:08:32,080 --> 00:08:35,360 Speaker 1: own studies and have tried to reproduce Dunbar studies and 146 00:08:35,400 --> 00:08:38,120 Speaker 1: have come up with different numbers. But they can come 147 00:08:38,200 --> 00:08:40,280 Speaker 1: up with a number. It's just some of them are 148 00:08:40,280 --> 00:08:42,959 Speaker 1: like two hundred and ninety. I saw one of them 149 00:08:42,960 --> 00:08:45,720 Speaker 1: came up with six hundred and eleven. But the thing is, 150 00:08:46,200 --> 00:08:51,840 Speaker 1: Dunbar was convinced that it was a regression line where 151 00:08:52,200 --> 00:08:55,920 Speaker 1: there was a there was a The data forms basically 152 00:08:55,920 --> 00:08:59,360 Speaker 1: a bell curve where the average is the highest point 153 00:08:59,440 --> 00:09:02,400 Speaker 1: and then the outliers are the smallest. And what a 154 00:09:02,440 --> 00:09:05,880 Speaker 1: bunch of other studies have found is that it's probably 155 00:09:05,920 --> 00:09:08,800 Speaker 1: actually what's called it power law, which is a huge 156 00:09:08,840 --> 00:09:12,360 Speaker 1: steep curve that starts really high and then comes down 157 00:09:12,400 --> 00:09:15,480 Speaker 1: and then evens out toward the bottom. And power laws 158 00:09:15,520 --> 00:09:20,080 Speaker 1: happen when some people really skew the numbers upward. But 159 00:09:20,120 --> 00:09:23,520 Speaker 1: then most people have far far fewer, say, contacts than 160 00:09:23,559 --> 00:09:26,320 Speaker 1: those people who are the actual outliers, So rather than 161 00:09:26,360 --> 00:09:29,600 Speaker 1: the outliers being the fewest, the outliers have the most. 162 00:09:30,440 --> 00:09:31,360 Speaker 3: And yeah, that. 163 00:09:31,360 --> 00:09:34,120 Speaker 1: Changes a lot of stuff, so much so that I 164 00:09:34,160 --> 00:09:37,680 Speaker 1: saw there's a one study that found that the ninety 165 00:09:37,679 --> 00:09:42,400 Speaker 1: five percent confidence interval had a range of between four 166 00:09:42,559 --> 00:09:45,840 Speaker 1: and five hundred and twenty contacts that the average person had, 167 00:09:47,080 --> 00:09:49,640 Speaker 1: so they kind of thiderrate out the window. Yeah, exactly. 168 00:09:50,320 --> 00:09:52,920 Speaker 2: Wow, that's interesting. And of course, with the advent of 169 00:09:53,000 --> 00:09:57,559 Speaker 2: social media in more recent years, depending on what how 170 00:09:57,600 --> 00:10:01,040 Speaker 2: old you are, you might you know, some people our 171 00:10:01,120 --> 00:10:04,520 Speaker 2: age might not consider those people friends, even though they 172 00:10:04,559 --> 00:10:06,880 Speaker 2: may be like Facebook friends, even though neither one of 173 00:10:06,960 --> 00:10:07,800 Speaker 2: us are on Facebook, you know. 174 00:10:07,800 --> 00:10:08,280 Speaker 3: What I'm saying. 175 00:10:09,840 --> 00:10:13,000 Speaker 2: Whereas I think a younger generation might say like, oh no, 176 00:10:13,120 --> 00:10:17,280 Speaker 2: those are my friends and my gaming network that I 177 00:10:17,320 --> 00:10:19,400 Speaker 2: play like, these people are my friends. We never met 178 00:10:19,480 --> 00:10:24,240 Speaker 2: or anything. So the idea of what friendship is means 179 00:10:24,280 --> 00:10:26,440 Speaker 2: different things to different people, and a lot of times 180 00:10:26,800 --> 00:10:29,000 Speaker 2: depending on how old you are, what generation you're in. 181 00:10:29,120 --> 00:10:33,000 Speaker 1: Right, And then Dunbar was saying that even still he 182 00:10:33,160 --> 00:10:36,800 Speaker 1: sees the same things hold on the online world as well. 183 00:10:37,800 --> 00:10:40,480 Speaker 1: In that BBC article, he puts it that it's like 184 00:10:40,600 --> 00:10:43,000 Speaker 1: the same design features of the human mind that are 185 00:10:43,040 --> 00:10:46,320 Speaker 1: imposing constraints on the number of individuals like in the 186 00:10:46,360 --> 00:10:51,079 Speaker 1: real world also do so in the gaming world as well. Yeah, 187 00:10:51,120 --> 00:10:54,080 Speaker 1: you know, which is pretty interesting. And then you also 188 00:10:54,240 --> 00:10:56,000 Speaker 1: chuck us to wrap it up. You might be sitting 189 00:10:56,040 --> 00:10:58,520 Speaker 1: there like this is all fascinating, but it just feels 190 00:10:58,559 --> 00:11:01,880 Speaker 1: like navel gazing to me. Why does any of this matter? Right, Well, 191 00:11:01,920 --> 00:11:05,240 Speaker 1: if you're like a demographer or an economist, coming up 192 00:11:05,280 --> 00:11:11,240 Speaker 1: with a way to reliably predict group size starting at 193 00:11:11,240 --> 00:11:15,400 Speaker 1: the individual level, would let you count huge groups like 194 00:11:15,480 --> 00:11:18,240 Speaker 1: really accurately, would let you count groups that are hard 195 00:11:18,320 --> 00:11:21,079 Speaker 1: to count, like victims of crimes who don't come forward, 196 00:11:21,640 --> 00:11:26,800 Speaker 1: the homeless. There's a lot of people whose entire field 197 00:11:26,840 --> 00:11:29,800 Speaker 1: would be revolutionized by being able to look at the 198 00:11:29,840 --> 00:11:34,440 Speaker 1: size of a cortex and predict the number of people 199 00:11:34,559 --> 00:11:37,760 Speaker 1: that person is friends with, but it just doesn't seem 200 00:11:37,840 --> 00:11:40,760 Speaker 1: to be fully holding up. It holds up enough that 201 00:11:40,800 --> 00:11:44,360 Speaker 1: the Dunbar Dunbar's number has stuck around this long, but 202 00:11:44,400 --> 00:11:48,199 Speaker 1: it's it's not just proving reliable in case after case. 203 00:11:48,800 --> 00:11:50,839 Speaker 2: Yeah, you know, I kind of went down this rabbit 204 00:11:50,880 --> 00:11:54,520 Speaker 2: hole interestingly recently because when I went to LA for 205 00:11:54,640 --> 00:11:55,199 Speaker 2: spring break? 206 00:11:55,280 --> 00:11:56,480 Speaker 1: Did you make more friends there? 207 00:11:57,000 --> 00:11:57,160 Speaker 3: No? 208 00:11:57,200 --> 00:12:02,520 Speaker 2: I didn't make any friends that week, maybe, but I 209 00:12:02,600 --> 00:12:04,800 Speaker 2: was I wanted to throw I rented this house up 210 00:12:04,840 --> 00:12:07,200 Speaker 2: in the Hollywood Hills with the pool, and I was like, hey, 211 00:12:07,400 --> 00:12:09,200 Speaker 2: you know, I get to go back to LA and 212 00:12:09,400 --> 00:12:12,840 Speaker 2: live like a hot shot for a week, and I 213 00:12:12,920 --> 00:12:15,320 Speaker 2: wanted to throw a party, like a pool party, because 214 00:12:15,320 --> 00:12:16,720 Speaker 2: I have a lot of friends out there with kids, 215 00:12:16,720 --> 00:12:19,240 Speaker 2: and so I've made a list and that went. I 216 00:12:19,280 --> 00:12:21,400 Speaker 2: went down that rabbit hole of making a list of 217 00:12:21,400 --> 00:12:24,120 Speaker 2: my LA friends and I'm looking at it now and 218 00:12:24,160 --> 00:12:27,120 Speaker 2: it was like, you know, a list of friends. And 219 00:12:27,160 --> 00:12:32,360 Speaker 2: then I even made a second list of fringe. 220 00:12:31,360 --> 00:12:32,880 Speaker 3: So like people I consider my friends. 221 00:12:32,800 --> 00:12:36,280 Speaker 2: Like Joe Randazzo and Janet Varney and her partner Brand 222 00:12:36,280 --> 00:12:39,360 Speaker 2: and like people that I'm like tight with, Like I 223 00:12:39,400 --> 00:12:42,680 Speaker 2: consider those people people like if they needed something, I 224 00:12:42,679 --> 00:12:44,920 Speaker 2: would be there for them, no matter what kind of 225 00:12:44,920 --> 00:12:49,199 Speaker 2: friends a friend. And then the fringe was everything from 226 00:12:49,400 --> 00:12:52,000 Speaker 2: professional colleagues that I've met here and there over the years, 227 00:12:53,360 --> 00:12:56,000 Speaker 2: all the way down to like I had this personal 228 00:12:56,040 --> 00:12:59,000 Speaker 2: movie crush once and we really hit it off that day, right, 229 00:12:59,520 --> 00:13:01,840 Speaker 2: but he didn't keep in touch at all. But I 230 00:13:01,960 --> 00:13:04,240 Speaker 2: just invited everyone. I just threw a really cast a 231 00:13:04,280 --> 00:13:05,839 Speaker 2: wide net and like a. 232 00:13:05,800 --> 00:13:06,600 Speaker 3: Lot of people came. 233 00:13:06,640 --> 00:13:08,800 Speaker 2: That really surprised me, And it was kind of fun 234 00:13:08,840 --> 00:13:11,360 Speaker 2: and cool to have all these different people from thirty 235 00:13:11,440 --> 00:13:13,680 Speaker 2: years of my life, because some people went way back 236 00:13:13,720 --> 00:13:16,680 Speaker 2: with me from Atlanta that live out there. Yeah, and 237 00:13:16,720 --> 00:13:18,959 Speaker 2: it was just kind of interesting to look at this 238 00:13:19,080 --> 00:13:22,439 Speaker 2: list of people and now how it relates to this episode. 239 00:13:22,720 --> 00:13:25,000 Speaker 1: So you had new friends and old friends mingling together. 240 00:13:25,040 --> 00:13:25,600 Speaker 1: How did it go? 241 00:13:26,200 --> 00:13:28,840 Speaker 2: It was great people that like a lot of the 242 00:13:28,960 --> 00:13:31,079 Speaker 2: very little crossovers, so a lot of people didn't most 243 00:13:31,120 --> 00:13:34,040 Speaker 2: people didn't know one another and just getting to know 244 00:13:34,080 --> 00:13:36,280 Speaker 2: each other, and it was like, it was fun, It 245 00:13:36,360 --> 00:13:36,920 Speaker 2: was really neat. 246 00:13:37,160 --> 00:13:39,480 Speaker 1: I could see putting your old friends and new friends 247 00:13:39,480 --> 00:13:43,360 Speaker 1: together as a relatively low risk exercise because friends of 248 00:13:43,440 --> 00:13:45,560 Speaker 1: Chucks aren't going to not get along with other friends 249 00:13:45,600 --> 00:13:47,520 Speaker 1: of Chucks. You know, it was all good folks. 250 00:13:47,559 --> 00:13:50,600 Speaker 2: And you know what, big congratulations our buddy Josh Beerman. 251 00:13:50,679 --> 00:13:54,160 Speaker 2: You remember Josh. Yeah, he came and I didn't expect 252 00:13:54,240 --> 00:13:56,080 Speaker 2: him to come. And he walks in with a brand 253 00:13:56,120 --> 00:13:56,640 Speaker 2: new baby. 254 00:13:57,080 --> 00:13:58,280 Speaker 1: Oh wow was it his? 255 00:13:58,840 --> 00:13:59,600 Speaker 3: Yeah? 256 00:13:59,440 --> 00:14:01,800 Speaker 2: Okay, talk about a fun way to enter a party. 257 00:14:01,840 --> 00:14:03,240 Speaker 2: I was like, oh my god, you get You've got 258 00:14:03,240 --> 00:14:05,840 Speaker 2: this great, great new baby. And it was kind of 259 00:14:05,840 --> 00:14:07,480 Speaker 2: a fun reveal, very nice. 260 00:14:08,440 --> 00:14:11,040 Speaker 1: He had a cape that that made me reveal even better. 261 00:14:11,080 --> 00:14:14,720 Speaker 1: He's like, I've got a baby. Holy cow. Uh well, 262 00:14:14,760 --> 00:14:16,319 Speaker 1: I guess that's it for short stuff, right. 263 00:14:16,880 --> 00:14:18,720 Speaker 3: I think that means we're out right yes. 264 00:14:21,160 --> 00:14:21,200 Speaker 1: You. 265 00:14:22,040 --> 00:14:24,920 Speaker 3: Stuff you should Know is a production of iHeartRadio. For 266 00:14:25,000 --> 00:14:29,160 Speaker 3: more podcasts my heart Radio, visit the iHeartRadio app, Apple Podcasts, 267 00:14:29,280 --> 00:14:31,120 Speaker 3: or wherever you listen to your favorite shows.