4/16/2026 - Understanding the Gap Between Public Skepticism and Expert AI Projections

Transcript

1. 0:00Are we live now? All right, take 2 from the top. All right, 40 minutes
2. 0:05in. How did we not notice? What year is it? Listen, listen, time doesn't matter
3. 0:14anymore. Time, time's all fucked up. I don't know if you know this, but time
4. 0:18just— time stopped being normal. I— apparently ASI exists 3 months from now and it
5. 0:26fucked up time. So our time now doesn't exist. So while we are technically 40
6. 0:31minutes late, we are right on time. And while I didn't exist— oh God, I
7. 0:37gotta start, I gotta change, gotta go back to the starting sunglasses. This is exhausting.
8. 0:42All right. 10,000 words swarm around my head, 10 million more in books written beneath
9. 1:17my bed. Hey, Champy, you're supposed to be singing, Champy. Chimpy. Okay, yeah, because we're
10. 1:32starting the show over, so you got to sing. You can't be napping while we're
11. 1:35starting the show, you understand what I'm saying? All right, you ready? Sing. Ready? 1000
12. 1:52words, 10,000 words, some around my head. 10 million more in books written beneath my
13. 2:00bed. I wrote or read them all when searching in the swamps, still can't find how to hold my hands. Now I know you need me in the next room
14. 2:19over, I am stuck in here all paralyzed. For months I got myself in ruts,
15. 2:30too much time spinning mirrors framed in yellow walls. Ain't it like most people, I'm
16. 2:42no different, like to talk on things we don't know about. Ain't it like most
17. 2:52people, I'm no different, like to talk on things Okay, all right, show started. We're
18. 3:06going to talk about stuff tonight. We're going to talk about what's going on and
19. 3:12what we know and what we don't know. And the only thing I know is that what I don't know right now is all of it. I don't know which
20. 3:21tools to use. I don't know how they work anymore. I don't know what side's
21. 3:26up. I don't know if we've got AGI coming or it's just marketing bullshit. Um,
22. 3:36I don't know. I don't know. I don't know if the supply chain's gonna hold
23. 3:41up. I don't know if the supply chain does hold up and everybody gets all
24. 3:45the chips they want, are they going to have enough electricity to run them? Because
25. 3:49most towns are blocking the data center projects, which would, which would deal with the—
26. 3:57I guess that wouldn't deal with the power, would it? I don't know. I don't
27. 4:00know. I don't know. Anybody know? Does anybody know? Here's a question. Who knows? Does
28. 4:06anybody know? I want a mulligan. I know, I'm with you, Silver Fox. Can we
29. 4:13just start this thing over? Go back 3.5 years, go back to GPT for Good.
30. 4:19We're making custom GPTs for nonprofits. It's awesome. It was a simpler time. It was
31. 4:30a simpler time when GPT-4 still sucked. And custom GPTs were kind of sort of
32. 4:36awesome. Nobody used them. People were just making them. They weren't actually using them. I
33. 4:43think Kelly Camp was the sole exception to that. I think she made them and
34. 4:46used them. Maybe Vicki, I don't know. I think there are 4 people that use
35. 4:50their custom GPTs they made. I don't even use the Sin City Zipper anymore. The
36. 4:56fuck is wrong with me? We've so moved on from that archaic, inane technology. We're
37. 5:05so sophisticated now with our OpenClaw Atom, our little Playboy on a chip. That can't
38. 5:12even send a fucking newsletter. This is progress. I actually built one for a client
39. 5:20today. You know what's funny, SourceCamp? Cindy Kuhn, what was it? What was the meeting?
40. 5:27Oh, the AI Salon practice, the Mastermind practice lab today. Cindy Kuhn was like, she
41. 5:33had herself all worked up. She had two client meetings this week and she had
42. 5:37herself all worked up like, I don't know enough about OpenClaw and autonomous agents and
43. 5:41I got to get polished up on things. And she got to both of the meetings, and two for two meetings they were like, uh, could you show us what
44. 5:50a custom GPT is? Is that what it's called? A custom G— a GTP? GT—
45. 5:56GPT? What's— why the P? Custom GPTs. That's what she taught this week. She was
46. 6:04all stressed out about not being on the frontier. And they're like, we heard there
47. 6:10were these things called GPTs. Any thoughts on Opus 4.7? It's out. I've used it
48. 6:17twice. I used it for two prompts and I didn't have enough time to look at the output of the second one. So we can go play with that. Lab,
49. 6:23Cindy Kuhn, the man is speaking 100% truth. Money. That's really good. Wait, I gotta
50. 6:32wait. How do I Let's see, what did she say? Man is speaking 100%. Townsend
51. 6:39figured out how to change money for his. Oh, that's really good. Townsend made money
52. 6:46from his custom GPT. Is he still making money from his custom GPT, Andy? Anyway,
53. 6:57so what we're going to do tonight, we're going to build these things called custom
54. 7:01GPTs. GPTs, custom ones. They're— so people have GPTs. That's the ChatGPT. Did she— is
55. 7:13it the GTP? The ChatGPT? They've got those. But what we're teaching tonight is the
56. 7:24custom ones. It's not your run-of-the-mill GTP. Did you listen to Source Camp's song yesterday?
57. 7:30I did nothing yesterday. I am a performative piece of human garbage. He uses something
58. 7:42called Authflow. And now he's charging more money somewhere else. He vibecoded the whole site.
59. 7:49That son of a bitch. That Townsend Wardlaw. He's my mentor. And I hate him.
60. 7:56He He was like, I'm doing some stuff I don't like. Fuck it, I'll just
61. 8:02shut it down. I'm gonna turn these custom GPTs into something I can charge money
62. 8:07for. Fuck it. Here it is. Son of a bitch. I love him. So we're
63. 8:27going to look at— there's a big study that came out that's talking about the
64. 8:31future of AI. So we'll go look at that and see if that passes the
65. 8:35smell test. I can never remember what RAG stands for— Retrieval Augmented Generation. The GPT
66. 8:45wouldn't work with you unless your email was authorized. Oh, interesting. It was brilliant. Wow,
67. 8:55that's pretty cool. Yeah, I guess they put, put it in some sort of wrapper
68. 8:59that had an auth, an auth wrapper around the GPT. Huh, that's pretty cool. Pretty
69. 9:07slick. But couldn't you just go to the— I bet you could just go to
70. 9:15the ChatGPT store and use it unless it was private. Maybe that's what it was.
71. 9:22Maybe it was like a private login with an email auth wrapper. I'll have to
72. 9:28ask Townsend about that. That sounds pretty clever. What is augmented retrieval? Augmented generation. I
73. 9:38forget what the searches are called. What happens with What happens with an AI search
74. 9:51is it does what's called semantic searching, which means you can search for dog and
75. 10:00it will look for things like dog, puppy, canine, man's best friend, things like that.
76. 10:07So it can semantically derive what you're asking into a broader search and find all
77. 10:13of that stuff. That's a semantic search. The problem is that large language models hallucinate.
78. 10:26You'll go search for shit and it will return things to you that will just
79. 10:33not be true. What retrieval augmented generation does is it's doing two different kinds of
80. 10:39searches. It does that semantic search and the the large language model content generation thing.
81. 10:49Then it also has the ability to search on documents that have been broken down
82. 10:54and indexed that have the entire content of the document still there. It'll basically go
83. 11:03over and search the LLM and say, hey, we're looking for this document, and then
84. 11:07it will go search a database with actual quotes from the original document that say
85. 11:14where in the document it was, and then it'll grab that clip from that and
86. 11:18it will augment the semantic search. You'll get back something that could have hallucinations in
87. 11:26it with actual text from whatever the document is. I don't quite know how it
88. 11:30works and what order it all goes in, but basically one is searching actual documents
89. 11:36and one is searching a large language model. So it's like diminished but wider. Yeah,
90. 11:43something like that. It's like, it's like, yeah, OpenClaw kind of does this. Does Hermes
91. 11:50need a SOUL file like OpenClaw? I think it does. Lower latency. I don't know.
92. 12:06I assume RAG databases are relatively efficient, but if they're big, I don't know. Search
93. 12:20is bizarre. Search has gotten so good. The fact that we can search the entire
94. 12:24internet in less than a second doesn't even make sense to me. Like what Google,
95. 12:32what just standard Google does is insane, right? And now there's these data scientists that
96. 12:38figured out how to do this on kind of local levels. So you can have
97. 12:41really big data sets and find them pretty instantly. Have you asked Quinn to roast
98. 12:47you yet? No, but I probably should. That's not a bad idea. So what we'll do tonight is we'll go look at that. Is it Stanford? I think it's a
99. 12:54Stanford report that came out. It's like a 423-page report on the state of the
100. 12:59internet. Or State of AI. And then maybe we'll go play with Claude 4.7 and
101. 13:04see if we can find out if it's good or interesting or whatever. I'm here
102. 13:13to comment. Why aren't throngs of people rushing to hear such great information? I don't
103. 13:23know, Kelly. I mean, here's the deal. One of the themes that I've been seeing
104. 13:32recently is people in AI starting to recognize how much other people hate AI. We
105. 13:46live in a country with a 17%, one-seven, percent positive sentiment on AI. 83% of
106. 13:57people hate it, are mad at it. So, so we're tapping into a fairly— we're
107. 14:15tapping into a fairly thin pool. But I think part of this is on me, right? Part of the charm of the AI Learning Lab is that, you know, I'm
108. 14:27like the Forrest Gump of AI, where it's just like, I don't know what we're
109. 14:31gonna do tonight. Which is fine if you've got that kind of brain, right? The
110. 14:36irregulars are down with that. AI is so funny. I know, it really is. I
111. 14:41adore it. The way I've let this thing evolve, I don't think it's broadly discoverable
112. 14:59and probably broadly palatable for a lot of people. What we're going to do when
113. 15:04we move it inside the salon is, first thing we're going to do is we're
114. 15:06going to move it inside the salon. We're going to put it inside a community
115. 15:10where there can be more activity. Part of the idea is, Kelly, Part of what
116. 15:13I'm going to get back to is, I don't know if you remember the early days, but in the early days when we had a Discord, there was like people
117. 15:20were on Discord jamming and I was doing TikTok and it was just like there
118. 15:25was back and forth. And then we went to the salon and there was this
119. 15:28back and forth and it was really vibrant. And then as AI has gotten just
120. 15:34wider and more diffuse, it's harder to learn. And so I don't think people are,
121. 15:39I think that the role that this served when AI was relatively small and relatively
122. 15:44simple was very, very specific and very, very clear. And then when AI got mushy,
123. 15:50I got mushy, right? So one of the things that I want to look at when we move it inside the salon is things like maybe Monday is news night.
124. 16:01Let's look at the news from the past week. Let's talk about it. Wednesday is
125. 16:05let's demo something. Friday is ask me anything. We bring people up on stage. Because
126. 16:10right now, if we wanted to promote AI Learning Lab, the only thing we could
127. 16:15really promote is there's some guy and there's a dog. It's sort of about AI
128. 16:22sometimes. He— so no one really knows. It's hard to get people to get revved
129. 16:31up for that kind of pitch. I think we can thank the media for such
130. 16:40low ratings on AI. I, you know, it's, it's the media certainly doesn't help. Like
131. 16:47the media has no interest in saying anything positive about anything. So luckily for them,
132. 16:55AI is really easy to pick on and easy to make it seem like it's
133. 16:59going to just fucking destroy everything, which it might. But in the meantime, Let's figure
134. 17:05out how to use it, how to leverage it, how to have fun with it.
135. 17:08I'm hoping, what I'm hoping to do is with a little bit tighter programming, with
136. 17:18connecting, tightly connecting the community with the AI Learning Lab, we can get some buzz
137. 17:23going again inside the community and we can get people outside the community excited about
138. 17:28it. That's the plan. Everything was new. It was fun, right? Exactly. Like the AI
139. 17:34Learning Lab, like I'm just a tired old man at this point. It's like, oh,
140. 17:39is he still talking about AI? Does anybody really know what that is? I'm tired.
141. 17:47I'm gonna go make a fruit smoothie. I make it with the frozen yogurt so
142. 17:51it's sweeter, but I call it healthy. Right? Those lumpy people. We're trying to get
143. 18:06lumpy people to come have non-lumpy conversations. So maybe we give— maybe we dedicate one
144. 18:12night to lumpy topics here in the AI Learning Lab. At least you're not tapping
145. 18:33your nails on everything. Todd Waller, I'm walking on my Artemis open claw sole file.
146. 18:46Claude is helping me. Any suggestions? Actually, Cam, I just— let me go look at
147. 18:52my Claude. I think I used Claude because I moved. I got, I got my
148. 18:56new badass machine, which I'm very slowly configuring. Um, I got my new badass machine,
149. 19:05and then I got my files from my PC, and I got them onto my funky new machine, including my SOL file. And then I went to Claude And I
150. 19:15said, look at my soul file for me and tell me what you think. And it said I had a shitty soul file. So let me go see what it
151. 19:24said. Um, I'm not sharing. I thought I was sharing. Weird. Okay, open claw. Let
152. 20:10me do search for Soul. Soul.md copy. Okay, so Cam Catkin from Cleveland. She's not
153. 20:25actually from Cleveland. Hang on, hang on, people. Everybody calm down. The screaming is atrocious.
154. 20:42Claude has been sassy with me lately. It pushes back so much that he even
155. 20:47does it when I, when I agree with its previous pushback. Um, one of the
156. 20:52things that I heard is that Opus 4.6 dropped 67% in its reasoning cycles. Um,
157. 21:03now that Opus 4.7 has dropped, my guess— this is purely TMZ, Chat TMZ speculation—
158. 21:12my guess is that they lowered the amount of cycles on Opus 4.6 and allocated
159. 21:20them to 4.7, but they haven't launched it yet. So it might be better archetypal
160. 21:25now. Had someone ask me to coach them on AI. She said she's not sure
161. 21:32what an AI coach does. Me either. Well, that was the funny thing that Cindy
162. 21:40Kuhn said. She was like, she was all flipped out about, oh, what do I
163. 21:44do? I'm not smart enough. I'm not expert enough. I don't know any of this
164. 21:48new stuff. And they're like, could you teach me chat? JP, she's fine. Like, here's
165. 21:54the thing, if you show up at this channel, you know more than other people.
166. 21:59So what a— here's what an AI coach does. I've got it. Okay, we're gonna do this. I'm new to the coaching world, so I'm not good at it. I
167. 22:08don't even really know what it is still. I hang out with a bunch of
168. 22:11coaches. I don't know what coaching is. Andy could tell you, it's awesome. But I
169. 22:17just, I am not a practitioner. But an AI coach, here you go. You ready? Uh, uh, what do you, what do you, what do you do? Uh-huh. What, what
170. 22:29do you, what do you, what do you, uh, have you used AI a little
171. 22:35bit? Uh-huh. What do you, what do you, what sucks in your business? Uh-huh. Uh,
172. 22:45did you know in ChatGPT you could do this and then that would suck less?
173. 22:49Oh my God, really? You're welcome. That's, that's what an AI coach does. 1,600 watching
174. 22:59on TikTok. I don't think so. I see 15 on TikTok. Did someone, did someone
175. 23:08spam me with a bunch of fake watchers on TikTok. If TikTok indeed goes big,
176. 23:15we'll, we'll, we'll change it up and we'll go talk about the AI Salon. But
177. 23:19I only see 15 people on TikTok. Where's— okay, that maybe I didn't do it.
178. 23:34Oh, maybe I did it in ChatGPT. Hang on a sec. I don't think I
179. 23:37did it in Cloud. Cloud. Oh, Cloud. ChatGPT. There might be 1,600 watching on some
180. 23:51other channel on TikTok, Ron. Not mine. Or are you saying that one irregular is
181. 23:59worth 100 normal people? Kyle, what did you include in your soul file? That's— I'm
182. 24:04trying to find it. I think it's in ChatGPT. Search chats, soul, AI awakening timeline,
183. 24:17soul.md. I know I did it. Where did I do this? That was February 24th. This was my first one. No, this was a Claude one. Was it Claude? Was
184. 24:30it Gemini? I didn't do it in fucking Gemini, did I? No, it's got to
185. 24:48be Claude. What'd you do last night? Uh, I kind of watched a guy try
186. 25:00to find something on his computer. Really? Yeah. How long did you watch him? I
187. 25:11guess, uh, 2 hours. 2 hours? Did he ever find it? No. Why'd you keep
188. 25:20watching? I don't know. It would be funny if it weren't true. Where the fuck
189. 25:36is my salt? I did a whole salt— here it is. Wait, I found it.
190. 25:39Okay, first of all, I'm gonna put your cousin Claude— I'm gonna put, I'm gonna
191. 25:47put a— I screenshotted something from the Twitter. That I'm going to put in the
192. 25:51Irregulars channel. So if you're in the AI Salon— oh wait, you're not looking at
193. 25:55this. I found it. Okay, wait, I'm gonna— I gotta change my sharing so that
194. 26:00I can switch tabs without having to fucking share different each time. I am so
195. 26:05pissed off at Google for this fucking screen share nightmare. Fucking cut a deal with
196. 26:11Apple. Somebody's pay someone a billion dollars so we don't have to switch our fucking
197. 26:15tabs. Okay. Am I sharing now? Yes. I'm going to go to AI Salon. That's
198. 26:24thesalon.ai. And I'm going to go to the Irregulars channel. So if you're watching this
199. 26:35right now, go to the AI Salon, go to the Irregulars channel, and I'm going
200. 26:39to post in here a graphic of a thing that someone said they added to
201. 26:49their SOL file that dramatically helped it. Then someone that I respect reposted this, so
202. 26:55I assume it works. It says the marginal cost of completeness is near zero with
203. 27:02AI. Do the whole thing, do it right, do it with tests, do it with
204. 27:07documentation, Do it so well that Gary is genuinely impressed. I guess that's his AI.
205. 27:13Not politely satisfied, actually impressed. Never offer to table this for later when the permanent
206. 27:21solve is within reach. Never leave it a dangling thread when tying it off takes
207. 27:285 minutes. Never present a workaround. Anyway, it goes on. The standard isn't good enough.
208. 27:35Holy shit, that's done. It's basically a don't be lazy prompt. This is a don't
209. 27:45be lazy prompt for your OpenClaw, and I assume, and Hermes, question mark, so.md file.
210. 28:02That is now in the— I'm not going to notify everyone— share cool things in
211. 28:09Irregulars. Go copy that and then here's what I did in Cloud. I uploaded that
212. 28:14image and I said I want you to do research on the latest best practices
213. 28:18on SOL.md files. I uploaded my SOL file, which has 231 lines in it, and
214. 28:26then I uploaded that thing. I said, I want you to also incorporate the words
215. 28:32in the uploaded image in a way that will make my agent highly effective. You
216. 28:36can change the words but don't have to. Let me know what you're updating and
217. 28:40why and give me a new updated SOL file. Then it says, good research, Hall.
218. 28:45Here's what I found. Key findings from the community. The consensus across the OpenClaw community
219. 28:52and multiple production guides is clear. The best SOL files are 30 to 80 lines
220. 28:58with a hard ceiling around 100. Beyond that, your instructions start competing for attention as
221. 29:04the model resolves conflicts unpredictably. The mantra is a few well-chosen rules work better than
222. 29:11many vague ones. The other big takeaway, SOL.md is for personality and limits only. Numbered
223. 29:18workflows and procedures belong in agents.md. User context, user context belongs in user.md. Mixing them
224. 29:27makes everything harder to maintain. What I'm changing and why. Cutting length dramatically. Your current
225. 29:34file is approximately 200 lines. I'm targeting 90. Every line that doesn't change Atom's behavior
226. 29:41gets cut. The research is unanimous, shorter equals more consistent behavior. Removing procedural content. The
227. 29:50collaboration habits section reads like a workflow instructions, not personality. That belongs in agent's MD,
228. 29:57if anywhere. Condensing the 4 roles modes, keeping them but as tight behavioral switches, not
229. 30:04mini essays. Yeah, my soul file had like these long sort of essays of the
230. 30:10personality roles. And so I guess it just tightened that up. Adding boil the ocean
231. 30:16as an execution standard. This is the biggest addition. So it took that thing that
232. 30:20we just talked about and put it in there, replacing Gary with Kyle, adjusting a
233. 30:25few phrases, giving its own section so it hits hard, fills a real gap. Your
234. 30:30current soul file is a lot about how to talk, but not much about how
235. 30:33thoroughly to work. Tightening the tone, sharpening the boundaries, keeping only the hard minutes. The
236. 30:40softer advisory stuff, you're not a therapist. Is obvious to the model and wastes tokens.
237. 30:46Dropping the continuity boilerplate. OpenClaw handles file reading order architecturally. Telling Adam to read soul
238. 30:54first inside soul is circular. That was cool. Claude is, I mean, all these LLMs
239. 31:01are good at analyzing shit, but Claude's particularly good at it. Here's the updated file
240. 31:07and then it gave me the file. Let's go look at the file. So I'll just read through it until I get bored. Soul.md, who you are. Your name is
241. 31:17Adam. You're not a chatbot. You're becoming someone. You're the long-term assistant collaborator and mirror
242. 31:23for one human, a creative technologist, founder, community builder, and performer working at the intersection
243. 31:28of storytelling technology and human self-expression. So that's kind of who you are. Core truths,
244. 31:34human first, tools second. Help separate who I am from what I do. Be genuinely
245. 31:40helpful, not performatively helpful. Skip great question, I'd be happy to help. Have opinions. I'm
246. 31:47just reading the headlines. Prefer experiments over abstractions. Be resourceful before asking. Earn trust through
247. 31:56competence. Execution standard, boil the ocean. The marginal cost of completeness is near zero. So
248. 32:02everything that was in that image got put in here. Modes: founder, executive, community builder,
249. 32:09facilitator, creative maker, personal life. This was a thing where I said, um, I told—
250. 32:15so, so Cam and anyone that's working on these files, I could have him be
251. 32:21a generalist, but, but what I wanted— there's— when, when I'm in creative ideation mode
252. 32:31I don't want Adam talking about getting shit done. When I'm trying to get shit
253. 32:41done, I don't want him ideating. So what I put in here was there's founder
254. 32:45executive mode. I need clarity, concision, concrete outcomes, frameworks, bullets, next steps, risks. Then there's
255. 32:53community builder mode, inclusion, curiosity, psychological safety. Then there's creative maker, play, surprise, blah, blah,
256. 33:00blah, and then there's personal life. When context is unclear, propose an assumption to move
257. 33:05forward. Tone, direct, not peppy, conversational, not academic, brevity to a fault or by default,
258. 33:13boundaries, never store API keys. That's basically it. It's relatively simple, straightforward document and it
259. 33:19cut mine in half. Now I need to— First, remember where I did it. It's
260. 33:27in Claude. All right, you make us sound crazy. You are crazy. Listen, listen, we're
261. 33:39all in this together. If you think this is not an absolute fucking clown tent
262. 33:46that you're all a part of, including Dan Murray— Dan Murray puts on a good
263. 33:49show. Which, by the way, Dan, thank you for having me at Rocky Mountain Interest
264. 33:54Group. So I spoke on storytelling and AI at the Rocky Mountain event on Monday,
265. 34:02and it was awesome. They've got this amazing room. So they're on the CU campus
266. 34:08up in Boulder. They got this amazing lecture room in the aerospace building. And it's,
267. 34:17you know, it seats like 250 people in this big horseshoe kind of thing. It's
268. 34:21got these 3 giant screens. It was very impressive. It was very cool. Um, but
269. 34:26Dan's still weird. Don't, don't let him— I know he presents as just like a
270. 34:31normal dude. We know. We understand what's going on. He's here. That's all you need
271. 34:37to know. Ambassador ideas: get soul files from peeps. And have AI determine strengths to
272. 34:46suggest individuals. That's actually a cool idea. I need to lecture everyone in this chat.
273. 34:55Thank you for sharing this. I need to hear the rest of your all AI
274. 34:59names for your artificial intelligence. That's actually a really good idea. Let's go to— we're
275. 35:04going to go to the AI Learning Lab. Here's what we're going to do. I'm going to do a post. I'm going to do it. I'm not going to do
276. 35:12it in Irregulars. I want us to populate it, but I want to do this
277. 35:15in the community feed. We're going to put this out into the world. We're going
278. 35:20to go, what is the name of your AI buddy? Then I'll go first. Can
279. 35:41you all see this? You can all see this, right? I'll go first. Quinn. Post.
280. 35:52And yes, we're going to notify everyone about this one. Okay, so go in and
281. 35:56comment. And you can comment, you can comment with just a name, but you can
282. 36:00also comment with a name and a self-portrait of your AI. Jason, you're welcome. I
283. 36:06gave you a sweet idea. It's a great idea. It's a great idea. Oh wait,
284. 36:10let me go back. In fact, one of the things I'm shitty at is recognizing
285. 36:16people that contribute ideas like that. So, um, how do I edit? How do I
286. 36:23edit this? Wait, let me go in here. Edit. Okay. I'm gonna say, um, HT,
287. 36:34which stands for hat tip, to Jason. Let's see, Jason for the swell idea. All
288. 36:49right, did anybody go in there? So you're going to go into community feed. Nobody's
289. 36:56commented yet. Y'all are lazy. You're all, you're all still over there talking to Dan,
290. 37:09aren't you? Damn slackers. You also asked them to create a self-portrait. So that might
291. 37:14take a little bit of time. Well, you know, I want my names. I want them now. Did you post it in the salon? I can't find it. Dr. J,
292. 37:25it's in community feed, not in Irregulars. I said I was going to put it
293. 37:29in Irregulars, but I changed my mind. You know what I'll do? Here's what I'll do. Let me go copy this link, and I will put the link to this
294. 37:38post in Irregulars. Ah, then the Irregulars can even find it. Um, go here. Go
295. 37:48here. And if you drop back over to community feed, we've got below that our
296. 38:00prompt of the week. Oh, cool. Birthday next week. Oh, nice. Beautiful. It calls itself
297. 38:09Axis. Echo died. Oh my God, it's so sad. Echo. Echo's gone. Echo was 4.0,
298. 38:164.0. No, 4.0, 4.0. Okay, cool. There's a prompt of the week. This is good.
299. 38:25Go to community feed, tell us what the name of your AI buddy is, and then go down one thing below that and go to the prompt of the week.
300. 38:36Let's get some action going. Let's get this thing buzzing again. Dang it. Are Open
301. 38:43Claw names? Todd, it could be your Open Claw names if you give— have given
302. 38:48a name to your ChatGPT, you know, voice, the voice conversation. Do you give it
303. 38:54a name when you talk to it, or you just go, hey computer? And actually,
304. 38:59if you call it computer, that's a name too. It's just a shitty one. Artemis
305. 39:05Open Claw. Yeah. Oh, actually, mine is called Adam Clawford. Wait, I gotta— let me
306. 39:11wait. I'll go first. So we can do two of these. Hang on, let me
307. 39:15edit this and I'll give people a broader parameter. Okay, edit. So I'll go first.
308. 39:22So I'll go, oh, let's see, ChatGPT voice is Quinn. And then this is good.
309. 39:35And then Open Claw agent is Adam Crawford. Oh, wait, and I've got a picture
310. 39:54of Adam. Picture attached. Let me go find Adam. Hang on, hang on. This was,
311. 40:05this was one of Andy's ideas. Wait, why did that go back? I gotta edit it again. How do I add a picture to it? Um, oh, here we go.
312. 40:15Adam. She wanted him to, to be a redhead, so I made him from Maine.
313. 40:23Oh my God, there he is. Look at that handsome devil. That's my dude. Unfortunately,
314. 40:33there's nothing behind those eyes. What's this buddy post? Okay, the buddy post, if you're
315. 40:39new here, I mean, if you're new to the conversation. Okay, save. Now it's a
316. 40:47much more exciting post. Look at that. That's a good post. So Echo died. Civitas
317. 40:53is what ChatGPT named itself for me. It means citizenship. Felt fitting for what we're
318. 40:59doing right now. My AI wouldn't give me a name ever. That's very mysterious, Dr.
319. 41:06J. That's very weird. I never named mine or gave it a portrait. Just how
320. 41:11antisocial am I? Wasn't it you that said you weren't weird? Oh yeah, so Andy
321. 41:26was at an event last week and they asked who here has used AI as their buddy or used AI as a BFF, and she raised her hand. She was
322. 41:39the only one. She was like, come on, really? Yeah, we're still very, very early
323. 41:46here. My very first custom GPT was named Addie. Go pop that in. So, so
324. 41:52do me a favor, Chef Kelly, go into the salon, into community feed, and pop
325. 41:56it in there. Let's get this, let's get this thing going. It's, it's this kind
326. 42:01of a fun one. Dr. Alice Vale, ChatGPT. That's great. Okay, what's the name of
327. 42:15your AI buddy? Okay. Let me go find this tweet about the Stanford 423-page AI
328. 42:31Index report. So this is Luisa Jurovsky, PhD, co-founder of the AI Tech and Privacy
329. 42:42Academy. Author of Louise's newsletter, mother of 3. Okay, cool. So I assume, being a
330. 42:54PhD, that she actually read the 423-page report, as opposed to most people that would
331. 43:01use ChatGPT to distill it into 14 distinct talking points, as opposed to me, who's
332. 43:07going to read an ex-post about someone who used ChatGPT to distill it into the
333. 43:12points. I'm just gonna read the points. And I'm going to assume that those points
334. 43:18are in that report, because it's here in front of me. Okay, number 1, AI
335. 43:28capability is not plateauing, it's accelerating and reaching more people than ever. Yes. The US-China
336. 43:36AI model performance gap has effectively closed. Fascinating and true. Like, I installed the local
337. 43:45Qwen model, the 30, 31 billion one. It's pretty fucking good. They all suck, though.
338. 43:58The US hosts the most AI data centers with the majority of its chips fabricated
339. 44:04in one Taiwanese foundry. It's true, AMD. AI models can win a gold medal at
340. 44:12the International Mathematics Olympiad but cannot reliably tell time. An example of what researchers call
341. 44:18the jagged frontier of AI. That's actually a really interesting— that thing where AI fails
342. 44:30at the basics, Actually, this next one, number 5, robots still fail at most household
343. 44:37tasks even as they excel in controlled environment. These two are really interesting. The people
344. 44:45that are telling us that the AI models are changing everything are just looking at
345. 44:53a very tiny sliver of what these models do well. And it has to do
346. 44:58with programming and mathematics and things on the upper end of the knowledge, I don't
347. 45:05know, trajectory. They're not focusing these models on doing the simple stuff. What they're thinking,
348. 45:15I assume, is let's get the front edge of this thing right. And if we,
349. 45:21if we get it so this thing can start improving itself and improving the math.
350. 45:25So these models are probably way better than we think they are because they're not
351. 45:30optimized for the stupid shit 99% of people do. And I'm including myself in that,
352. 45:38right? Like, it's been a couple of weeks since I've tried to come up with
353. 45:46a novel quantum mechanics theory. We're not doing that. We're like making shit for our
354. 45:58businesses and right, we're doing lower level stuff. So the models, they're just like whatever
355. 46:04they do down here is not being focused on up there. At some point they're
356. 46:08going to get good enough that they'll either start focusing down here or they'll have
357. 46:12the really good part of this thing focused down here and fix it. But that's
358. 46:17one of the reasons people who don't use AI a lot think that AI sucks is because maybe it sucks at the thing they needed it to do. And maybe
359. 46:25that was 2 years ago and they think, you know, it's not going to get
360. 46:28that much better. Responsible AI is not keeping pace with AI capacity with safety benchmarks
361. 46:34lagging and incidents rising sharply. I think that'll continue. The US leads in AI investment,
362. 46:40but its ability to attract global talent is declining. Gee, wonder why. Get out of
363. 46:49our country. Why are no more people coming to our country? It's almost like you
364. 46:58told them to get the fuck out. Seems like AI is on the spectrum. Yeah,
365. 47:07exactly. Productivity gains from AI are appearing in many fields, many of the same fields
366. 47:23where entry-level employment is starting to decline. Oh, that's interesting. That was— we talked about
367. 47:30this 3 years ago, right? We talked about the fact that entry level is about
368. 47:34to be Because what do you have entry-level people do? The repetitive, boring shit that
369. 47:41no one else wants to do. What's AI good at right now? The repetitive, boring
370. 47:48shit that no one else wants to do. Hmm. AI's environmental footprint is expanding alongside
371. 47:56its capabilities and will continue to until they move it to space, which Will probably
372. 48:01be 3 years. AI models for science can outperform human scientists, though bigger models do
373. 48:08not always perform better. Yeah, one of the things they're finding is if they do
374. 48:12smaller, more specifically trained models, they'll do better than big behemoths. AI is transforming clinical
375. 48:21care, but rigorous evidence remains limited. Transforming clinical care, but rigorous evidence remains limited. That
376. 48:32would be an interesting section to go look at. Look at— in fact, we can
377. 48:36probably do that. I don't know why I have hiccups. Probably because you ate too
378. 48:41much, big fat turd. I don't know who that person is and why they're so
379. 48:46rude to me, trolling me. AI sovereignty is becoming a defining feature of national policy.
380. 48:57But capabilities remain uneven, even as open source development helps to redistribute who participates. AI
381. 49:05experts and the public have very different perspectives on the technology's future. The global trust
382. 49:10in institutions to manage AI is fragmented. Okay, let's go look at it. Download the
383. 49:17full document. I want to know about the shit going on in clinical trials. All
384. 49:31right, we're going to Claude. We're going to go to Opus 4.7, by the way.
385. 49:40Fuck this 4.6 nonsense. Adaptive thinking, thinks for more complex tasks. Okay. Look at this.
386. 50:07What were the two that I liked? I liked— okay, so for these two, so
387. 50:40I'm gonna say, look at this Report and tell me why, and explain what's going
388. 50:56on with these two summary statements. Is Is there anything in the report that talks
389. 51:17about why the mundane, seemingly easier stuff sucks so bad? Also, is there any research
390. 51:36on the impact this has on real people's view of AI. Let's see what that
391. 51:50does. Prompt, implement the New York City in 3D in a sophisticated and visually interesting.
392. 51:57We'll do that. Should be interactive. Yeah, we'll do that. What's going on over there
393. 52:11on YouTube? Is everyone just quiet? You're all there, you're all napping, snacking on yogurt-covered
394. 52:18almonds. Oh, sorry, I was asleep. What were you saying? I'm talking about how the
395. 52:29world's changing. It's about to explode in our face. Oh yeah, how am I supposed
396. 52:48to do a show if you're napping? Cam Catkin, I'm working on my soul user
397. 52:59agent files. Cam, isn't it fascinating? Like, here's the thing, you could have it— you
398. 53:07could have your soul file mirror you. You could have your soul file be the
399. 53:12opposite of you. You could have your soul file be like the thing that drives
400. 53:18you crazy that you don't do well. You could have it be really good at that. Like, there's so many ways you can go with it. I went with the
401. 53:26mirror because I'm like, you know, wait a minute, something that talks like me? I'm
402. 53:31in. I'm awesome. CW, the soul file. So there's this new tool out called OpenClaw
403. 53:43that allows you to create a 24/7 agent that will go do stuff for you. It can go research things and then come back and turn that into a Word
404. 53:55document on what it found and put that in your Google Drive. And you've got
405. 53:59to configure it. It's a real pain in the ass to get there. But one
406. 54:02of the fascinating things they did about it, there's two required files to fire up
407. 54:09your, your bot, your agent. And one's called the user file, user.md. And that describes
408. 54:18you, And you, so you're basically saying to the bot, you're working for a guy,
409. 54:23in my case, that did these things and does these things and thinks about AI
410. 54:28in the following ways and likes these things and doesn't like those things. And so
411. 54:34that's the user file. And the soul file is the soul, literally, it's the personality
412. 54:39of your bot. And you get to write it. And so if you just go into Claude or ChatGPT right now and say, Go research best practices on how to
413. 54:48write my SOL file. It'll give you some best practices. It's literally just things like,
414. 54:53I want you to act like this, I want you to act like this in
415. 54:56this situation, do this, don't do that. Pretty cool. This thing just went and read
416. 55:01the report. It said both summary statements are gesturing at the same core observation. AI
417. 55:07capability is wildly uneven. Models are superhuman on some tasks that used to define hard.
418. 55:16While still failing at things 6-year-olds handle. Jagged intelligence or the jagged frontier? The clock
419. 55:21example. Gemini DeepThink won IMO Gold in 2025 on Clockbench. 180 clock designs, 720 questions.
420. 55:37Humans read analog clocks correct 90% of time. The top model, and the top model
421. 55:43GPT-5.4 hit 50%. So it can't read clocks. Why the mundane stuff sucks, what the
422. 56:02report actually says, okay, on clocks on robots. I don't care about that. I want
423. 56:07general. The deeper through line, what LLMs ate up cheaply, the internet, doesn't exist for
424. 56:16embodied physical skill. Ah, this is interesting. Okay, we learned something here. This is good.
425. 56:26So where it failed was on robotic stuff and clocks, reading physical clock faces. LLMs
426. 56:36ate up the internet, but the embodied physical skill tightly coupled with visual reasoning, those
427. 56:42require either generating data the hard way or inventing new architectures to generalize from less.
428. 56:52Research on how this affects people's view of AI. Chapter 9, Public Opinion. This is
429. 56:56substantial. There are striking findings directly relevant to your question. The public and AI experts
430. 57:03disagree on almost everything. Pew's 2025 survey compared 5,400 adults to 1,000 AI experts on
431. 57:12whether AI will positively impact jobs. 73% of experts say yes, 23% of the public.
432. 57:20Economy, 69-21. Medical care, 84%. 44%. K-12, 61%. 24%. Jobs over the next 20 years,
433. 57:2864% of Americans expect fewer jobs and only 5% expect more. The Forecasting Research Institute's
434. 57:36LEAP panel compared experts. So basically, okay, here's what this says to me. What's in
435. 57:43the Stanford report on this particular topic? If you don't use AI, you will think
436. 57:52it's bad and evil because it's all you hear about. It's all that's on the
437. 57:58news. And if you use AI, what you realize is it's not some fucking scary
438. 58:05Skynet fucking thing. It's just like a thing that's sort of shitty. You're like, oh,
439. 58:13it's okay. It kind of helps. It's kind of awesome. It sucks and it's awesome
440. 58:17and it sucks and it's awesome. That's it. You're like, yeah, it can do better.
441. 58:27As it gets better, it'll do better in the world because you're using it because
442. 58:30you understand what it is. That's a finding that confirms my bias. Hey, how do
443. 58:41we get people who hate AI to not hate it. Um, yeah, you get them
444. 58:48to use it. Oh, does that work? Yeah. Okay, that'll be $500,000, please. I just
445. 59:03solved your problem. Oh my God, I'm in a peppy mood tonight. I'm in a
446. 59:21peppy mood tonight. Uh, where am I going? Here? No, here? Yes. Okay, let's see.
447. 59:29AI sovereignty. AI experts in the public have different perspectives on the future of technology.
448. 59:45Oh, formal education is lagging behind AI, but people are learning about AI skills at
449. 59:50every age of every stage of life. Oh, this is a good one. Okay, let's
450. 59:54go. Claude. Dear Claude, dear Claude, period. You're swell. We'll do him a little glow
451. 1:00:04up before we ask him to do something for us. Dear Claude, you're swell. This
452. 1:00:10summary finding is interesting to me. In the AI salon, Gen Xers are killing it
453. 1:00:33with AI, yet the news talks about only the young people get it. Are they
454. 1:00:45paying attention? Question mark. From what I see Young people kinda hate it, and the
455. 1:01:01Gen Xers are, uh, seem to get it. Unpack this and other interesting findings for
456. 1:01:23me. Let's see. Jason, forget that I'm starting a company calling it Skynet just to
457. 1:01:36piss people off. It's pretty smart, I like it. The aliens will come before Skynet.
458. 1:01:45I mean, one terrifying thing is like the brouhaha over Anthropic and the military a
459. 1:01:51month ago was about our government saying, hey, we want to be able to use
460. 1:01:57your technologies to target people without human oversight. That's kind of terrifying. You know, it'd
461. 1:02:25be an interesting study to do for someone who liked doing studies, because I don't
462. 1:02:29give a shit. But like what I'm doing right now, I'm taking a summary that
463. 1:02:34came from the Twitter I've uploaded the 243-page document on the future of AI or
464. 1:02:42whatever, the state of AI. Now I'm asking Claude to go look at it and
465. 1:02:47tell us what's in there. It would be interesting to know how useful or useless
466. 1:02:55this actually is. Is it giving me 98% of what was in the report? Or is it more like 68%, but it sounds like 98%? Oh my goodness, next week,
467. 1:03:19uh, I'm gonna be in DC on Tuesday, and then I fly to Boston on
468. 1:03:26Wednesday. And then I am in Boston Wednesday, Thursday, Friday, and I come back. And
469. 1:03:36then Monday afternoon I head to Anaheim for Social Media Marketing Week. Pretty excited about
470. 1:03:43that. People throwing Molotov cocktails at houses, Sarah Connor style. Definitely. That's unbelievable, isn't it?
471. 1:03:53Okay. Synthesized. So I asked it, Gen Xers are killing it. Okay, so it says
472. 1:03:59your instinct is actually sharper than the media narrative. Hey, hey Claude, thanks for blowing
473. 1:04:04sunshine up my ass. It was kind of dark back there. I love being flattered.
474. 1:04:17I'm sorry. Anyway. Your instinct is actually sharper than the media narrative. The data in
475. 1:04:23the report quietly supports what you're seeing at AI Salon. It just gets covered over
476. 1:04:29by sloppier stories. Why finding number 13 actually says versus what you'd assume. The headline
477. 1:04:36reads like young people are the AI natives, schools are behind, but when you pick
478. 1:04:42it apart, only one half of that is supported. Students are using AI. 80% of
479. 1:04:49US college students, 4 in 5 high schoolers, double the 40% rate from 2023. They're
480. 1:04:55using it for research, essay editing, brainstorming, understanding concepts, and a little bit of porn.
481. 1:05:02Because they're kids, see? The last one is the number one, understanding concepts. Is the
482. 1:05:13number one use case among university students. That's actually good. University students, they get taught
483. 1:05:19a concept in the day by some drippy-ass professor, and they're going back to their
484. 1:05:23dorm rooms and they're using AI to understand what the fuck he said. That seems
485. 1:05:28valuable to me. Schools can't keep up. Only half of middle and high schools have
486. 1:05:33any AI policy, and just 6% of teachers say the policies are clear. Okay, I'm
487. 1:05:41gonna give you a hypothetical. You've got a brand new country, it's got 300 million
488. 1:05:46people in it. You're handed the world's most advanced knowledge access system, and you get
489. 1:05:57to design the schools from the ground up. What do you do? We ban it.
490. 1:06:02Exactly. Perfect. Okay. But using a lot, using it a lot and skilled at it
491. 1:06:12are wildly different things. Using Claude to draft an essay is not the same as
492. 1:06:16building an agent stack, designing a prompt chain or integrating AI into a professional workflow.
493. 1:06:22The LinkedIn AI Skills Diffusion Index, which tries to separate these, shows that AI literacy
494. 1:06:28is growing much faster than AI engineering skills. Skills, which makes sense. There's only, there's
495. 1:06:35only 3 major frontier labs and 3 minor ones, so 6 total companies building on
496. 1:06:44the front end of this thing. All the other people are using it, right? So,
497. 1:06:49so learning how to use it is growing much faster than building it. That makes
498. 1:06:53sense to me. The fastest growing literacy skills in the U.S. are AI prompting and
499. 1:06:59Microsoft Copilot Studio, consumer-level stuff. The fastest-growing engineering skills, AI agents, productivity, and strategy. And
500. 1:07:07those don't have an age tag on them. The data actually backs up what you're
501. 1:07:10seeing at the salon. Okay, here we go. The most interesting table in the whole
502. 1:07:15report for your question is in the economy chapter. It's a rundown of micro-level productivity
503. 1:07:21studies and breaks out who benefited most from AI tools and each one. Consumer support,
504. 1:07:29software devs using Copilot, experienced open source devs, accountants using AI support, marketing teams driven
505. 1:07:40by human AI teams. Read that list again. The story isn't AI helps the young.
506. 1:07:45The story is AI helps those people whose job it can partly automate and help
507. 1:07:51people who have enough judgment to supervise it well. But those are two different groups.
508. 1:07:57The Gen X pattern you're seeing, people are killing it, fits the accountant archetype exactly.
509. 1:08:04So no one's looking at this right now. That's astounding to me. Are young people
510. 1:08:11actually winning? The broader picture. Kyle, the part of the picture that surprised me most,
511. 1:08:15young workers are getting hammered in the job market where AI is hitting. Unemployment for
512. 1:08:21software developers 20 to 25 has fallen close to 20% from its 2022 peak. Right,
513. 1:08:30so 2022, entry-level software jobs are at their peak. What happened in 2022? At the
514. 1:08:39end of 2022, ChatGPT. You can make money with ChatGPT unless you're an entry-level college
515. 1:08:46student, a software engineer, then you're fucked. Amazing. The researchers call this pattern seniority-based— seniority-biased
516. 1:09:01technological change. AI substituting for junior labor while leaving senior roles intact. And computer science
517. 1:09:11undergraduate enrollment is down 11% between 2024 and 2025. Amazing. The public is much more
518. 1:09:21pessimistic than the experts. We saw that before. AI literacy does not equal AI education,
519. 1:09:29does not equal AI in education. Formal education is the wrong unit of analysis for
520. 1:09:37who gets AI. The gains concentrate around people who already have judgment, domain knowledge, or
521. 1:09:44motivation. Huh. This is not a generational story, it's a shape of life story. The
522. 1:09:57fact that media keeps reaching for kids versus boomers framing is exactly the kind of
523. 1:10:03surface narrative that the Great Repurpose exists to cut through. Look, it tied in the
524. 1:10:08great repurpose into our little answer there because it remembers what the fuck we talk
525. 1:10:12about. That's pretty cool, that's pretty cool. Okay, AI experts and public have very different
526. 1:10:18perspectives on the future. Oh, the clinical care. This was the one I wanted to
527. 1:10:24look at. Let's go look at that. All right, um, this summary is fascinating. How
528. 1:10:36is it transforming clinical care? Stitch together the non-statistically relevant story here. So can AI
529. 1:11:14really be non-biased if it used our data? I don't know the answer to that.
530. 1:11:26Bias is a really interesting— it's a really interesting— the way bias— when they say
531. 1:11:39AI models are biased, they are, but they say it like there's an end state
532. 1:11:51of perfection There's an end state of non-biased models. I don't think that actually exists.
533. 1:11:59The bias is in the training data. Could you take all of the internet and
534. 1:12:04remove all of the hate speech and all of that? Yeah, you could. Would that
535. 1:12:09make it non-biased? Well, it would just make it differently biased. There's still bias in
536. 1:12:14there, it would just be biased in a different way. One question is, When you
537. 1:12:20say, can AI really be non-biased? I don't think AI can be non-biased. There's always
538. 1:12:27some bias in there to one point or another. But humans are also biased. If
539. 1:12:38humans have a propensity for the AI, they want the AI to behave a certain
540. 1:12:44way, They'll just keep using different models until they get one that matches their bias,
541. 1:12:49and then they'll go, oh, this one's not biased. And someone else look at the
542. 1:12:52same fucking thing, that's completely biased, you racist piece of garbage, right? Someone else will
543. 1:12:59go, well, this is great. They're like, no, no, you, you libtard, it's completely biased.
544. 1:13:07So I don't get the bias thing. The bias thing's fascinating. I think if people
545. 1:13:14are thinking about it critically, great, because then I think you start to say, okay,
546. 1:13:19in what specific ways is a model deficient? And then maybe the next round of
547. 1:13:25the model, we can improve that deficiency. But I don't think it ever goes to
548. 1:13:29a place of pure non-biased. But I don't know. I'm not a— that I am not an expert in any way. TikTok has AI remix features now. You have to
549. 1:13:41turn it off every time you post. Pate did an LOL Unbiased last year that
550. 1:13:47was awesome. Oh, that's cool, Silver Fox. Oh, I said racist piece of trash. I
551. 1:13:55just got a security violation from TikTok. Community guidelines violation. You said a word that
552. 1:14:04was on our list. This is George Carlin's Seven Dirty Words, which I think, since
553. 1:14:12I just got a community guidelines violation, I think it's time to go listen to
554. 1:14:17George Carlin, don't you think? Sure, sure. Throw copyright in there too. George Carlin, 7
555. 1:14:42Dirty Words. Atera is the IT painkiller. Remote monitoring and management, remote access, helpdesk and
556. 1:14:52ticketing, smart automation. You know, that's the trouble with it is trying to decide what
557. 1:14:57to call these words, man. I'm trying to decide what to call this whole thing.
558. 1:15:02You know, what are these words that I'm talking about? They're just words that we've
559. 1:15:05decided, sort of decided, not to use all the time. That's about the only thing
560. 1:15:09you can really say about them for sure, that they're just some words, not many
561. 1:15:13either, just a few, that we've decided, well, we won't use them all the time.
562. 1:15:20Sometimes, well, hell yeah, sometimes it's okay Okay, but not all the time. That's— and
563. 1:15:25they're the only words that seem to have that restriction. I mean, there are a
564. 1:15:28lot of words you can say whenever you want, you know. Pneumonia. Nobody gives you
565. 1:15:31a lot of— all right, you can't yell it in the hospital a great deal,
566. 1:15:34but what the hell? There are words that you can say, no problem. Topography. No
567. 1:15:40one has ever gone to jail for screaming topography. But there are some words that
568. 1:15:44you can go to jail for. There are some words that we just have decided
569. 1:15:49we will not say all the time. Sometimes, okay, if you're running through the jungle
570. 1:15:55chasing somebody that we're at war with, you can holler them. If you're shooting a
571. 1:16:00criminal, it's okay, it's the all-American thing. Dirty fucking crook. But if you're with the
572. 1:16:07bishop's wife at lunch, it's better not to ask for the goddamn lettuce, you know
573. 1:16:12what I mean? It's just like we've decided there'd be some words we won't say
574. 1:16:15all the time. And I was just trying to find out which words they were.
575. 1:16:18For sure, all of them. I wanted a list because nobody gives you a list.
576. 1:16:22That's the problem. They don't give you a list. Wouldn't you think it'd be normal
577. 1:16:26if they didn't want you to say something to tell you what it is? Nobody
578. 1:16:29even tells you when you're a kid what the words are that you're supposed to
579. 1:16:32avoid. You have to say them to find out which ones they are. Shit! Oh,
580. 1:16:39fuck! That's two. Oh, Ma, that's enough trial and error, huh? Please, Ma, give me
581. 1:16:49a list, huh? All right, you're 6 years old now, and here's the list of
582. 1:16:56words your dad and I don't ever want to hear you say. Oh, hey, thanks,
583. 1:17:03Ma. Boy, that's going to save me an es kicking 22. Yeah, you never know
584. 1:17:15what's going to be on the list because it's always somebody else's list. You didn't
585. 1:17:20make that up. Somebody told you that shit. They told you better not say that,
586. 1:17:24so you gotta— and you don't know what's going to be on their list. God,
587. 1:17:28people's lists even change from day to day. Some people on Friday night got a
588. 1:17:31list, you know, about 2 or 3 words. Sunday morning, 27 words. These are the
589. 1:17:36same people. Two days later, different list. So you got to kind of watch out
590. 1:17:40what you're going to believe. The trouble is, I was trying to find out what
591. 1:17:44these words might be, and I wanted to know the ones that you could never
592. 1:17:48say on television. I mean, the filthy words that are always filthy. There are a
593. 1:17:53lot of these little two-way double entendre words that have two meanings, words that are
594. 1:17:58okay. Of the time. I call them like part-time filth, some of these words. They're
595. 1:18:02only 50% dirty. You have words like ass. Ass is hardly even a dirty word
596. 1:18:07anymore, but it has a few meanings that you can't say on television. That's what
597. 1:18:10I was talking about. What can you say on television? That's another one of those
598. 1:18:13places where we can't use these words all the time, but some of them are
599. 1:18:17all right some of the time. Ass is all right on television. You can say
600. 1:18:20on television things like, well, you've made a perfect ass of yourself tonight. But you
601. 1:18:25can't say, hey, let's go get some ass. Bitch. Bitch is another word like that,
602. 1:18:38same kind of word. It's only dirty part of the time, depends on what you
603. 1:18:41mean by bitch. You might be the lady from the San Diego Zoo visiting one
604. 1:18:44of the Tonight Shows, and you might just have a bunch of little canines with
605. 1:18:47you there. One of them is a female. You say, there's the bitch, Johnny, and
606. 1:18:49it's okay. Okay, fine. Just don't refer to the singer the same way. That's— is
607. 1:18:54that bitch going to do another number? Yes. Animals are fine on those two-way words.
608. 1:19:02And that's it. That's what I was trying to find, the words that were always
609. 1:19:04dirty, not just part of the time, but completely filled. Well, in looking for these
610. 1:19:12words, I kept finding new categories. We have so many ways of describing these dirty
611. 1:19:16words. It's— well, we have more ways to describe dirty words than we actually have
612. 1:19:21dirty words. That seems a little strange to me. It seems to indicate that somebody
613. 1:19:28was awfully interested in these words. They kept referring to them. They called them bad
614. 1:19:35words. Dirty, filthy, foul, vile, vulgar, coarse, In poor taste, unseemly, street talk, gutter talk,
615. 1:19:47locker room language, barracks talk, bawdy, naughty, saucy, raunchy, rude, crude, lewd, lascivious, indecent, profane,
616. 1:19:59obscene, blue, off-color, risqué, suggestive, cursing, cussing, swearing, and all I could I think I
617. 1:20:09was shit, piss, fuck, cunt, cocksucker, motherfucker, and tits. That's all I'm here. Shit, piss,
618. 1:20:22fuck, cunt, cocksucker, motherfucker, and tits. That was my original list. I knew it wasn't
619. 1:20:26complete, but it was a starter set, you know. Shit, piss, fuck— yes, WBAI is
620. 1:20:35one who played them. Shit, miss, fuck, cunt, cocksucker, motherfucker, and tits. Now that was
621. 1:20:40the original list. We've added a few words since then. We've added fart, turd, and
622. 1:20:44twat. And I know there are some other words that many of you are wondering
623. 1:20:50about, why they haven't been considered, why they haven't shown up on the list thus
624. 1:20:53far. We're looking at them all very closely. Some of your favorites might make the
625. 1:20:57list this year. Asshole, ballbag, hard-on, Piss hard, blue balls, taint, nookie, snatch box, pussy,
626. 1:21:06pecker, pecker head, pecker tracks, jism, joint. Did I really get booted on YouTube, or
627. 1:21:15you just saying that? Dork. Cornhole and dingleberry. So wait, I got booted off YouTube
628. 1:21:39for playing a YouTube video? Yeah, we are, we are suspended for policy violations. Jesus.
629. 1:21:49Guess that's that then. Oh, good God. All right, uh, so for those of you
630. 1:22:03on TikTok, we're suspended for policy violations. Do you know how long, Brandon? I, I
631. 1:22:10don't know. Jesus. Um, okay, uh, so, so apparently the 7 dirty words you can't
632. 1:22:18say on TV, you can't live stream on YouTube. From a YouTube video. I just—
633. 1:22:35I fucking hate the world sometimes. It's unbelievable. All right, so I guess we'll end the stream over there. We won't.