SPEAKER_00: I think it's interesting. As a podcast host, I can tell you, a lot of these podcast hosts suck. I guarantee you their AI will be better than 50% of podcast hosts in under three years. And that's

SPEAKER_01: not really a reflection of their ability at WonderCraft AI or AI. That's just a function

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SPEAKER_05: All right, everybody. Welcome to this week in startups. And really it's this week in AI. We've never seen anything like this. So I made an audible. I called an audible. I decided to make

SPEAKER_00: a call in the middle of the year to have one of my great friends on Sonny Madra and Vinny. Vinny's out today. And we just decided, gosh, instead of talking about all the nonsense around

SPEAKER_05: where we think AI is going, we just thought we would show you what AI is doing today. And this will serve as an incredible roadmap for you. This is your AI MBA. This is your master's degree. This is your PhD. Tune into this week in startups. And we'll just show you what's being built.

SPEAKER_00: And Sonny Madra is here. He, of course, is the co-founder of Definitive Intelligence, which I have a little slice of. I put a little investment in there. And that lets people do

SPEAKER_05: analysis of information on the blockchain and other data mining analysis services. I think I described

SPEAKER_08: pretty good. Your own personal data analyst. Your own personal data analyst. Ooh, this is a new mission statement. Updated. Updated. Updated. Evolved. Okay. I like that. Your own personal

SPEAKER_00: data analyst. So we talked about this in ChatGPT4. I don't know if it was maybe a month ago, three weeks ago. It felt like three years ago, but I think it was literally three. You could upload a CSV and ask the data analyst at ChatGPT4, hey, tell me about this file. And it would make charts and give you some analysis. So you're going to build the industrial strength version of that, or you're building the industrial strength version of that, yeah?

SPEAKER_15: Yeah, we already have it. You know, ChatGPT is good if you have 100 megabytes of data. If you have terabytes or petabytes, then come talk to us. All right. So if you're screwing around,

SPEAKER_18: you can use the code interpreter on ChatGP4. I wouldn't even say screwing around if you're

SPEAKER_15: just doing something with small data. But if you're a big enterprise and you've got transactions for the last 20 years of your customers, that's going to be several terabytes or even approaching petabytes for you. And you need to use a different set of tools.

SPEAKER_00: Big news. ChatGPT launched their iOS app.

SPEAKER_15: Yes.

SPEAKER_00: Which really does change your behavior. I have been trying to get, you know, I had this weird thing. I realized mobile Chrome does not let you set the default browser. I didn't know why that's the case. I guess Chrome is getting greedy. Google's getting greedy. Safari's getting greedy. They want to own that real estate. I like to set my homepage up at what I want it to be. But this is now, I'm going to just stop using browsers. I'm just going to go straight to the ChatGPT4 app. And Kurt Interpreter is now built into it. So did you download the app? And I had the app, by the way, like three months ago. They put me in the test flight.

SPEAKER_30: Yeah, you were on the test flight. Yeah.

SPEAKER_00: Yeah. And it was single use. Now it seems like they're trying to get parity between the web experience and the app experience. It's not perfect yet, but it's getting there.

SPEAKER_21: Yeah. And I mean, it doesn't have all the advanced features, like some of the stuff that we've been demoing. But what I found interesting is they did do like a, like a, you know, like a home screen widget. So I think that's what you were looking for at one point, right? J. Cal. And so

SPEAKER_15: you can, with the latest one, you can make it a home screen widgets. You can just push something on your home screen and it pops up and you can ask your question.

SPEAKER_00: But what we really need is I need to be able to say, Hey, ChatGPT, as opposed to saying, Hey, Siri.

SPEAKER_05: And so that's the change that we really need is can I use Siri as an interface for it? And why do I

SPEAKER_39: need to use Siri for it? I guess maybe on an Android phone, I could do that. Because Android lets you integrate into the operating system a little bit deeper than iOS does. All right. So we've been playing

SPEAKER_00: with a bunch of stuff this week, big conversations all over the internet about regulation. We'll talk

SPEAKER_18: about that in a bit, but what have you been playing with this week?

SPEAKER_21: Well, you know, let's start with someplace fun and then we'll get, we'll get into sort

SPEAKER_15: of the meatier topics. And I know you're a big fan of this J. Cal. So I think this will be a fun one for us. I make sure I share this properly, but okay. So Google this week, uh, or, you know, launch this or made available to some folks, uh, what they call music LM. Mm-hmm. And what this is, is basically a, um, you know, a model that is able to generate

SPEAKER_21: music based on prompts. And so, um, you know, what, what we can do is we can take some of the recommended ones here. And so you can do, let's start with something simple. You click that

SPEAKER_15: here and it'll give it just a few seconds and it will generate this. And so, okay. So we're

SPEAKER_50: playing some funky music here. What was the, um, so that was the prompt here. I'm just

SPEAKER_21: going to read out to everyone. It was drums that sound like rain and thunder. So that one is not all that exciting. Um, you know, well, I'll do another interesting one here, which is a chill out elevator music. And then we'll get into some, some more kind of fun ones in a second. Uh, and for the folks watching here, you can see some recommended prompts it comes up with. So, you know, it's okay. So this is, you know, it's, it's kind of interesting,

SPEAKER_15: feels like a bit, um, it, you know, early music generation. We've gone through this before,

SPEAKER_21: but where it gets kind of much more interesting is when you prompt it yourself. So I'll do one J Cal and then you'll, you'll probably make it better for me. So let's do Tulum, uh, beach

SPEAKER_15: party. The old Tulum beach party. Yeah. With, um, uh, a bit of, uh, what do you want to put

SPEAKER_31: in there? Let's say cyberpunk. Yeah. Cyber punk. Okay. We're blade or something. Let's see. See if we get anywhere close. So there we go. Maybe, maybe I heard some cyberpunk

SPEAKER_15: in there. Yeah. Yeah. Yeah. Um, you know, you can do Atlanta, uh, trap music with a new

SPEAKER_67: York 80s flare. Oh, okay. Yeah. You can see it's got a sounds like Jay Z's voice and no

SPEAKER_81: voice. It's just music. I thought I heard it's just music, but you can hear like Jay Z's voice going to something like that. You could, you totally could. Yeah. So it's kind of getting

SPEAKER_87: something. So this has been trained on copyrighted music, I assume, or on maybe a library of,

SPEAKER_00: okay. What is this? New York 80s rap. Yeah. This is New York 80s rap. Yeah. Back in the

SPEAKER_90: day. We used to on the block, the, the, the, the, the, the, the, the, the fly shoes. Can't

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SPEAKER_21: off an annual premium plan. Yeah. So, um, you know, I, I think this is pretty fascinating. Like where, where is this applicable? So, um, I don't know if you're familiar with Rick Rubin, right? Legend in the industry. Big fan of the all in pot. He just sent me a copy of his book. Oh, awesome.

SPEAKER_99: Yeah. Oh, really? Okay. That's incredible. So where, where does this go? Let's talk about that a

SPEAKER_21: little bit. So, you know, he gave this pretty cool and interesting 60 minutes interview. Yes. Not that long ago. Right. And one of the things that, yeah, there we go. Awesome. We got a cute up here. And one of the things that he talks about here is that he has no musical abilities. He does not know how to play instruments or use the mixer board. Maybe play, let's play the clip real quick. It just became a

SPEAKER_104: meme with Anderson Cooper, but exactly what he does and how is difficult to describe.

SPEAKER_106: Do you play instruments? Barely. Do you know how to work a soundboard? No. I have no technical

SPEAKER_108: ability and I know nothing about music. You must know something. Well, I know what I like and what I don't like. And I'm, I'm decisive about what I like and what I don't like. So what are you being paid for? The confidence that I have in my taste and my ability to express what I feel has proven helpful

SPEAKER_25: for artists. So curating, really having a great ear and curating stuff and being confident in your

SPEAKER_21: selection process. And if you take that alongside, you know, a tool like this, I think there's a lot of people that, you know, fall in a similar category that don't have the skills to, you know, maybe play an instrument and or run a soundboard, but know the type of thing that they want to listen to. And so I think tools like this will evolve to support folks that are not just songwriters, but that have

SPEAKER_15: a great ear for something that had no way of creating it and didn't know how to pull up like any of the music tools. I don't know if you've ever tried to use GarageBand or something like that.

SPEAKER_118: I mean, this stuff is, it will take you 10,000 hours, you know, the proverbial 10,000 hours. And

SPEAKER_25: in fact, I just saw a clip of the lead singer of the Smashing Pumpkins talking about how, you know,

SPEAKER_118: people will be able to get 50 different riffs. And instead of having to spend a decade or two in

SPEAKER_00: their parents' basement, you know, like he did finding interesting ones, they'll be able to just do that with the software and it will still be a selection process. So you're defending creativity in this process. I'm wondering, this is going to, this also was a copyright, uh, and there was a copyright, uh, hearing last week that kind of went unnoticed where people are just talking about who gets the

SPEAKER_05: rights to this. So did Google say how they train this? Did they find stock music, which is available royalty free? And so what that means is somebody creates a beat based upon what beats in the eighties were like, but they don't, um, copy anybody specifically. They just copied the genre and

SPEAKER_00: then they put it available in a thing like splice, or there's a couple of other places you can buy beats that are royalty free. You can put into any song. You don't have to pay a royalty. You don't even have to give credit. So I'm wondering how Google trained this because Google is smart

SPEAKER_39: enough to know that this, they should not train this on Spotify or something. So did they,

SPEAKER_15: yeah. So I think, uh, what, and, um, you know, Nick is sharing the same thing here. I think it's

SPEAKER_21: trained on samples from YouTube, which is a very fascinating thing because they know what's copyrighted in YouTube. Obviously they do a ton of work around that. Jason, you've, you've talked about that a lot. And then they have a bunch of stuff that is just done organically and people are creating. That's what, you know, YouTube has so much content on it. Now, if you're a creator and

SPEAKER_126: you've created a beat, it's been uploaded there. So my understanding is that they've taken clips from YouTube to got it here. I guess we didn't. Hey guys, you didn't put where this, these quotes

SPEAKER_39: are. You put two great quotes in here. Where are those quotes from? Oh, sorry. Um, that is from

SPEAKER_131: Google's data set on Kaggle, which is where they host everything. I can pull it up on the screen.

SPEAKER_05: Okay. So, but is that how this was trained or you're guessing it's from their GitHub, uh, for, for the music. All right. And so, uh, producers just pulled up that, uh, they built this, they trained it audio set consists of audio set consists of an expanding ontology of 632 audio

SPEAKER_134: event classes and a collection of 2 million human label, 10 second sound clips drawn from YouTube videos, huh? 10 second sound clips drawn from YouTube videos doesn't say if those are copyrighted or not. The music caps database contains 5,521 music examples, each of which is labeled with an English aspect list and a free text caption written by musicians. Hmm. So if they, if those 2 million human labeled clips, uh, were, um, of copyrighted content, I think there's a copyright claim and maybe

SPEAKER_05: they're saying they're only using such a small amount of it, or maybe they're using people, um,

SPEAKER_00: in the public as a way to defend themselves against copyright violation by saying those people made these clips. So then therefore you have to blame them.

SPEAKER_21: So in my scan of this, Jake, we were kind of looking at this when we were preparing. Yeah. All the clips seem to be of like, uh, user generated content. Not okay. Not yeah. So not like,

SPEAKER_119: oh, here's 10 seconds of rolling stones or something like that.

SPEAKER_25: The reason I bring this up is because we have talked about sunny many times, uh, Google

SPEAKER_00: waited to release this because Google is so aware of copyright claims. Cause they bought YouTube at a time when YouTube had a multi-billion dollar, you know, lawsuit with Viacom and YouTube was going to be shut down for all the copyright violations going on there. And they were able to navigate that. So Google is incredibly savvy on navigating fair use. They use robot.txt in their Google search. So you can opt out of being crawled. They get permission from people. Sometimes

SPEAKER_05: generally they let people find music, um, that other people are using in their videos and then let them shut down those YouTube videos or claim the monetization of that.

SPEAKER_21: Wasn't that the big unlock Jay Kelly? I've heard you talk about this a few times is when you can put music in, but they'll basically, they'll demonetize you for that video and they'll pick up the monetization. So if you have a video that has been a million views, we made our own

SPEAKER_149: track here and then we copyrighted it. So again, I, I guess we could take these tracks

SPEAKER_00: and then we could claim copyright on them. Even though the AI built it, I wonder how that works. I wonder if that's in the terms of service. So if you and I sang over one of these beats, then we put it into on YouTube and we use distro kit to publish it to Spotify, whatever we claim copyright on it. Now, if somebody else uses it in their video, we can then get an alert based on it

SPEAKER_151: being, um, tagged in a, in a database content ID is the system con ID ID flags. It tells us, and we can

SPEAKER_00: just say automatically shut it down. So the Beatles might want to just shut down anybody using their video,

SPEAKER_152: music and videos, but somebody else, the rolling stones might say, yeah, let anybody use it by

SPEAKER_00: default, just take their money. And so you may have seen all in two weeks ago, we put the Balenciaga AI video that somebody made at the end of the show. And then it turned on ads on the show. So then we had a choice, either turn the show off, re-upload it, or just deal with the fact that

SPEAKER_39: somebody else is going to make, you know, $5,000 off that episode, which I think we just decided the

SPEAKER_15: ladder. Wow. Yeah. So, and that, that's an interesting one. Just, okay. So then it,

SPEAKER_123: was it the music or was it the, the music that they use or somebody owned the right

SPEAKER_25: set of music. So they didn't make AI music. They use somebody else's track that I guess was used in a Balenciaga stuff before I'm not going to play it here. Oh, okay. Crazy.

SPEAKER_61: And then here's the quote from, this is a really interesting discussion. Here's,

SPEAKER_162: um, the lead singer of the Smashing Pumpkins. My general, uh, thought is that, um, AI will change music forever because once a young artist figures out that they can use AI to game the system and write them a better song, they're not going to spend 10,000 hours in a basement

SPEAKER_166: like I did. There's not. But are you, do you still have the ability to get real art that way?

SPEAKER_162: Okay. Well, ultimately art is about discernment, right? So, uh, like somebody was telling me the other day about, uh, how an, uh, a famous rap artist would work. They would bring in all these different people and they would sort of pick the beat that they were most attracted to. Okay. Now let's change it to AI. Hey, AI, give me 50 beats. Listen, I'm not really feeling. AI, give me 50 beats from the 50 most famous rap songs of all time. Okay. Well, I like number 37.

SPEAKER_25: That, that inspires me. Yeah. So he basically nails what you just said. You can use this as a tool

SPEAKER_21: to be inspired, use your curation, your thoughts on. Yeah. I was actually gonna just put in what he said in, in, in the tool, right? Like, should we, should we give it a try? Oh yeah. Good. Uh, right.

SPEAKER_67: He said, uh, give me a beat, uh, that is, uh, inspired by the 37th greatest song in hip hop.

SPEAKER_171: And rap. Yeah. 37th. Okay. Yeah. He said, just see if it goes and finds a list on Rolex. Rap song. Okay. Ever. All right. See what it does. I don't think it has the thirties.

SPEAKER_177: No, no, maybe. No, see, it's not. Well, that could be like an M and M kind of beat.

SPEAKER_179: Um, yeah. Okay. Maybe it's got some work to do, but I think, look, here's the thing.

SPEAKER_00: This is gonna come out with weak results because it's not trained on copyrighted music. Yeah. So I think this is weak and it's a 1.0, right? Yeah. But you know, uh, what I would say

SPEAKER_21: is like, YouTube is full of very talented people that, you know, can produce like almost the exact

SPEAKER_15: same quality, uh, of, you know, copyrighted music. So you could hire people. Yeah. But here's what would

SPEAKER_00: be interesting. You're gonna be able to take that language model, run it on your laptop and take every single dire straight song or every single M and M song, Jay-Z song, biggie, put it in there. And then you're gonna be able to do it on the copyrighted stuff. All right. Yeah. Yeah. Yeah. And so that's what people are going to be doing. And that's something Google is not going to release, but this is in fact an opportunity. And I guess this goes back to what Grimes is saying, you know, like, Hey, just use, you know, what's her tool like health something. Um, so she's got a tool that they, she partnered with somebody who just put out there and she said, people are making songs that couldn't possibly be me because I have a speech impediment of some type, like a lisp or something. And they elf dot tech, the enunciation in this is better than I can enunciate. Yeah. Um, I think

SPEAKER_194: one got published, right? Just last week. Um, I think it did. Yeah. And so shout out to dot tech,

SPEAKER_20: our new sponsor here, Ralph dot tech with Grimes. I wonder if Grimes secured a bag for using dot tech. Um,

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SPEAKER_05: How would people tell, I guess, is the question where this came from, because it's not giving you

SPEAKER_00: sources. And this is the thing I think all these AIs need to do. And I think this is where the legal issue is going to come into play. I don't want to obsess over legal stuff, but either you regulate yourself or you get regulated. That's the nature of business of life. If these people don't show their work and it would be possible to show here's what the AI, here are some of the source material. The AI used to make this beat. It is possible to do that. It is possible, right? It is like taking

SPEAKER_21: bits of it, but it may be, it may be, you know, several bits, maybe, you know, and it's all possible. Yes. And I think that's where we're probably going to have to head towards because the black box nature just, that's also very much on the closed side of things. And I, and I, you know, what I anticipate is as we move to the more open models, J. Cal, what we'll see is the, those ones will have the kind of references baked into them. So people will want to see where it came from and that'll force the folks that are doing it in the close. I also think the challenge is for, um, and this is interesting because, um, uh, it came up earlier today in terms of, I think a lot of folks are, let me try to find

SPEAKER_15: this link. We're talking about like what these models have been trained on. And I'm going to try to find this link as we're just talking about it here and, and send it to the, uh, to the guys. But like, um,

SPEAKER_21: there is a really interesting, um, share here that people talked about what the training set was. And one of the things included the Enron emails, uh, for many of the models. Like, so there's a big open source set from, you know, back in the late nineties, 2000 era. Yeah. So that got dumped. There's no copyright. Yeah. It's just in the open because of the lawsuit,

SPEAKER_214: right? Yeah. The lawsuit, but each of those individuals, I wonder who owns the copyright to

SPEAKER_05: those emails. It's not public domain. It would be Enron's IP. Yeah, here it is. There's no Enron left to sue. So I guess, you know, in order to have, this is a really edge

SPEAKER_00: legal case is sort of interesting to me, but not anybody else. The interesting thing is in order,

SPEAKER_39: to have, you have to have somebody bring the case and there's nobody to bring the case for the Enron emails. You know, when a company goes out of business, it's like, you know, who, who's going to defend, you know, you have to find who owns that copyright. There is nobody who owns that copyright. No, there might be a shell company somewhere that does, um, which is super interesting.

SPEAKER_225: But, uh, the Grimes, uh, what are you, oh, here's a chart. What is this chart?

SPEAKER_15: Yeah. So this shows, uh, what someone was, let me read this, uh, for the folks listening,

SPEAKER_21: what's in the pile dataset from AI Euler that is being used to train a lot of the open LLMs, right? The following chart, analyze the tokens and the test set. There are 22 sets in pile from sources like Wikipedia, GitHub, uh, you know, books three, et cetera. And, you know,

SPEAKER_227: if we kind of zoom in here, uh, you see at the bottom, it's Enron emails. So there's like 600,000

SPEAKER_05: Enron emails in the tree. Yes. Yeah. So, uh, hacker news. Yeah. Is in there. Uh, I guess that

SPEAKER_00: must be open sourced. Um, and there's YouTube subtitles. Now that's interesting. I don't see Twitter in here. Yeah. I do see stack exchange, which didn't give their permission. I'm sure. So that's an issue. PubMed extracts. Somebody probably owns that. So this is really interesting. We're going to have to have data sets. The reason AI is going so well is the data sets, right? And the combined

SPEAKER_39: with the hardware combined with the software, you take out one of those, it's kind of could narrow the,

SPEAKER_15: um, the success of this. Correct. Correct. Yeah. I mean, they, you know, at the end of the day, these are systems which use, you know, math to predict, um, the most likely kind of next character in a sequence that the human would like to see based on what has been trained on. So if it's not trained on as good material, you're not going to get as good predictions. And so,

SPEAKER_21: um, yeah, I, I can see that really changing. And then, and you know, you've talked about this before

SPEAKER_15: with what's happening with Poe and others, right, where they've started to close their data sets off and say, come to us directly. If you want to sort of use an, um, use our LLM, if you want to experience

SPEAKER_05: our data on this. And by the way, in music, like there are all kinds of crazy drive by lawsuits

SPEAKER_00: where people try to, you know, claim that their song from 30 years ago inspired somebody and, uh,

SPEAKER_87: Ed Sheeran just got sued. Yep. He decided to take it to the mat. Usually these things settle out of court. Sometimes people do actually steal people's beats and they settle with them. Other times people are using chord progressions, you know, GCD, CFG, whatever, uh, G, you know, whatever they just chord progressions that are obvious. Um, and that exists in many songs. Here's Ed Sheeran explaining

SPEAKER_233: this. What did you play for that jury? I was the jury. Yeah. What did you say to them? So it was,

SPEAKER_236: um, so my one is, um, when your legs don't work like they used to before. And then there's, have I told you lately that I loved you? And then, um, um, people get ready. There's a train

SPEAKER_238: coming. Um, and then, uh, what was the looks like we made it. Look how far we've come my baby. And

SPEAKER_240: oh, she breaks just like a woman. I mean, there was, there was 101 songs that what I was saying is

SPEAKER_241: like, yes, it's a chord sequence that you hear on successful songs, but if you say that a song in 1973 owns this, then what about all the songs that came before we found songs like from like the 1700s that had similar, uh, melodic stuff. And so what's the URL for this? Uh, just so people

SPEAKER_39: who know, um, if people want to play with that test kitchen, just type in Google's music LM,

SPEAKER_242: you should be able to find it. Yeah. Google's AI test kitchen. That's the best way to get to it.

SPEAKER_243: AI test kitchen is the name of where they're putting these experiments. Yeah. It's, uh,

SPEAKER_15: ask AI test kitchen dot with google.com. So great. Just type in AI test kitchen. You don't mind.

SPEAKER_00: Yeah. Yeah. You'll, you'll find it. So they're just sharing all their work now because they were getting their butts kicked by at least in public perception. And so Google's in the game,

SPEAKER_25: just generally speaking, you feel like Google's catching up. The gap is closing. What do you think?

SPEAKER_21: Yeah. Yeah. The gap is closing, you know, it's all hands on deck. It's a different mentality. It's shipped quickly. You know, all these things that have been in around beta for a long time or private or becoming available. Um, you know, they are realizing, you know, there's a war for developer mindshare as much as there is for, you know, sort of the, you know, the, the financial side of the

SPEAKER_15: world wanting to hear about it, the developer mindshare is really important. So they've really started to make a big push in terms of engaging with developers and making, you know, the tools available so people can play with them and see them. So that's been a distinct shift in the last, like, you know, I have to say even 30 to 90 days. Yeah. This hugging face, uh, dot co just keeps

SPEAKER_00: coming up over and over and over again, having, I guess, one, some amount of just getting in early and being a place where developers can find the latest stuff, but what do we got next?

SPEAKER_15: Okay. So we're going to look at assembly AI. I thought, I thought this one was really, really interesting. Okay. Um, they, they do a, you know, very interesting job. You can basically, so if we just, uh, let me, I'm going to go to a different window for a quick second, because this is one's in progress. Um, so you show up and basically, um, you can, you can drop in a

SPEAKER_21: URL, uh, for, for any, anything with a transcript. So I, what I did was, cause it's, and it's a good discussion point. I took last week's all in pod. Okay. I dropped in the URL. It, it, it pulls, it does a transcription itself. And so here's a transcript on the left. And then what it does is it lets you create like a Q and a around it on your own. And so what I did, I have a couple of examples queued up here. Uh, I said, you know, what was Friedberg's position on the AI hearing? And, you know, I think this is a good one. Friedberg's position is that regulating open source

SPEAKER_15: AI models will be impractical due to the rise of edge computing and many models already available, making it hard for regulators to track and audit all AI models. That was his position.

SPEAKER_05: Yeah. Pretty good. Yeah. So you have signed up for this. This isn't available to everybody yet.

SPEAKER_15: This is in a beta. Yeah. And, uh, I signed up and I thought it was really kind of neat.

SPEAKER_119: What are they charged to do a transcription? Um, I think if it's under, there's like $24 a month or something like that. I don't quote me on the, the prices. Yeah. Oh, well, I'm looking at it right

SPEAKER_214: now. Yeah. Audio intelligence is 0.005 per second. So I don't know what that is for an hour.

SPEAKER_260: I'm doing the math right now. Yeah.

SPEAKER_25: 0.05 times, uh, 60 times 60. I don't know what that equals, but it looks like two bucks an hour.

SPEAKER_263: If I had to guess, I don't think I did the math right there. Fascinating. And so you could ask,

SPEAKER_05: uh, what does Saks think of Biden? Okay. Uh, did Saks mention Biden or Ukraine? Okay. Then, uh,

SPEAKER_266: I'll just do Saks mention. I mean, this is the kind of internal joke. Like everybody who's on the left,

SPEAKER_00: even some people on the right, or they play a drinking game and, uh, be careful with this drinking game. They do a shot. Anytime he says Ukraine or Biden, and you take two shots when

SPEAKER_15: he says, yeah. Yeah. If he says the media. Okay. No, he did not mention Biden or the Ukraine.

SPEAKER_87: So it's interesting. Interesting. Well, this is like, I'll tell you what's interesting is very,

SPEAKER_05: uh, sometime around last year, maybe 18 months ago, people started contacting us saying, Hey, we can do all of this week in startups, all of all in, we'll just transcribe everything.

SPEAKER_00: And I was talking to Nick about like, Hey, I would like to do this for every, all 1700 episodes of this week in startups. And we had budget originally like $500 per episode

SPEAKER_05: to have human transcription. Then machine transcription was like 150 bucks an episode.

SPEAKER_00: Plus we wanted to clean it up, put the people's names in it. And thank God we didn't do it. Cause we started this process of doing like 10 episodes and looking at the results.

SPEAKER_05: And now I think this is all going to get so good that even doing it is a waste of time because it's going to be built into every app. In other words, when you're on YouTube, the transcript is going to be so rich and great on YouTube. I don't know if we should even publish one. Does that make sense? Like, I feel like this is going to go from 80% to 99%

SPEAKER_15: in the next six months in terms of fidelity. Yeah. So I'm just, for anyone that's watching, I'm basically went to assembly.com slash playground V two. And all you have to do,

SPEAKER_21: go here, drop the link. I won't do this again. And basically you're off processing it. Um, I did another kind of quick summary from here. I said,

SPEAKER_152: does it know the people? How does it know the people and separate different people?

SPEAKER_00: Cause that is what, like, we had a service called Temi T E M I that we're using for a while.

SPEAKER_05: We'd use descript. And I think we have to identify the people's names. Is that correct? Producer Nick?

SPEAKER_276: Yes. Descript is the best one at, they actually can identify different voices and then you just name

SPEAKER_131: the voices. So they identify them and you, you tag them basically, but it all, it does it automatically.

SPEAKER_194: It's pretty great. I think there should be a thing at the beginning of podcasts. When you're doing this, where you say, hi, this is Jason Calacanis. My Twitter handle is twitter.com slash

SPEAKER_00: Jason. My website is calacanis.com. And then it, uh, so if everybody did something like that,

SPEAKER_87: Nick, at the beginning of a podcast, Sonny, then when you run it through these, it would just go find you. And then it would add to the signature. Hey, if you ever hear this voice again, attribute it to these two URLs. It's like the new robots.txt. Yeah. It would be like just a way of defining

SPEAKER_281: over and over again, who somebody is. I have to say, just in my time working here,

SPEAKER_276: I started here in 2019 at the beginning of 2019, the transcript, the auto transcription world has completely changed. It's gone from like a dollar a minute to like this one was what? $2 for an hour

SPEAKER_284: and a half of all. Yeah. It went literally from a hundred dollars an hour to $2 an hour.

SPEAKER_276: You're exactly right. Cause it used to be rev member rev.com was the original and they would do human transcription. That was their big thing. And it was a dollar a minute. Then Tammy came out, but they were, you know, not a, not perfect. And now it's Descript and they do every single thing for you. And it's a monthly, they don't even charge you on a per transcription basis. It's just a

SPEAKER_25: monthly charge. Crazy. And then the use of these transcripts is something I've got to get the

SPEAKER_00: legality around what I want to do. There's somebody who can do this for me. I don't know if we can hire a developer to do this for this week in startups. I would like to just take the entire

SPEAKER_290: this week in startups corpus and normalize it and publish it to coda notion, you know, publish it to

SPEAKER_15: the web. Um, and then, uh, I've got a great firm that you should work with the small like AI focused law firm. We should, we should, uh, yeah. Well, I don't want to sue anybody. I just want to create my own

SPEAKER_07: website. No, but I thought you said you wanted the legality around it. So they'll help you.

SPEAKER_05: No, no, no, no. I mean, the legality of what I want to be able to do is come up with a license

SPEAKER_87: kind of like Grimes did, where I'd let people come up with a license. That is, if you can do

SPEAKER_00: whatever you want with the archive, as long as you link back to the show page and, um, yeah,

SPEAKER_61: it's non-commercial. Yeah. Maybe even let people do commercial, do interesting things with it and let them monetize a little bit. Rev share. Just a rev share. Yeah. Maybe rev share. Yeah. 50, 50, 50 rev share. Uh, do whatever you want. 50, 50 rev share. Interesting. Uh, all right,

SPEAKER_21: let's continue on this one. So like the next question is a summary of the Congress hearing section. And I think it does a really good job, like going back to your point on like, it's identifying what each, you know, of the host is talking about. So sort of what Sam's claim is.

SPEAKER_15: And then, you know, what Chamath jumps on about. What does he say about Chamath?

SPEAKER_21: Chamath says like, I'm just going to highlight it. Chamath says, Sam went further than he initially suggested by proposing licenses for compiling and training models as a form of know your, you know, KYC, know your customer verification. Right. Um, Saks argues that Sam is seeking regulatory capture

SPEAKER_15: to maintain open AI's lead by creating red tape. Um, you know, Friedberg, we already got that. Pretty accurate. Yeah. Yeah. And so, um, I, I thought, I thought, I thought this was one of the better summaries pulling out each person's position and, you know,

SPEAKER_21: like to your point, no one had tagged themselves. We've not done any work in terms of identifying,

SPEAKER_15: uh, you know, who is who. And it, it, it really, I think it really did a good job here.

SPEAKER_113: Yeah. It's fantastic. Yeah. Okay. Great. So this is Lemur L E M U R.

SPEAKER_142: Assembly.ai assembly.ai or assemblyai.com. Yeah. So that's the company and that's their product. Uh, very cool. What else do you got in the

SPEAKER_01: delightful demos that we do here? Demos on Monday. Monday demos.

SPEAKER_227: Demo or die. Demo or die. We usually do them on Fridays, but like, uh, at, uh, at work, but now we've got Mondays as well.

SPEAKER_15: Well, I'm just going to queue up the next one. So this one was really fascinating.

SPEAKER_00: Um, okay. By the way, there's gotta be 20 people doing transcription. Yeah. Like stuff like this.

SPEAKER_39: So I, it, it, I wonder if they all just wind up in the same place, which is perfection.

SPEAKER_291: Okay. Everybody who's a transcriber is now out of a job. The concept of transcribing

SPEAKER_44: is over. I don't think it's over yet, but you should really level yourself up with these tools

SPEAKER_134: is what I think. So if you're a transcriber, you use these tools and then you have to build a product or service on top of it. And the product and service on top of it would be

SPEAKER_00: as best I can see. I would want somebody to make sure that the names were attributed correctly and any, uh, you know, pertinent links would be added to it. So if somebody was talking about assembly AI lemur deep link to it, to the Google AI sandbox, deep link to it. Right. So, but of course, AI is, I bet you get to ask AI to go through and find the relevant links. And this, somebody is going to make something that puts hyperlinks into documents that are correct. I think so.

SPEAKER_312: That job too.

David Friedberg: All right, everybody, you know, I've invested in 300 early stage startups and, you know,

SPEAKER_198: having a great name is critical, but it's hard to find a great domain name sometimes. So people will then compromise their search for a name based on what's available. And you know what? There's a new domain name. I need you to check out. It's called dot tech. Yes, there are now dot tech domain names, and this is the one go to namespace to build anything in tech. A bunch of people are using it. And I even got Jason dot tech, which I'm using for a micro blogging site that I'm coming up with. It's not exactly Twitter. It's kind of like not exactly like Tumblr. It's just going to be Jason dot tech, where I give updates all day long about little tiny things that I don't want to share on social, but that maybe I want to share with y'all. So go ahead and get your own dot tech domain name, and you can lock them down. Now there's tons available one year domain 10 bucks five year domain 50 bucks. It's a great deal. Now's the time to go get a great dot tech domain name. Again, the word go dot tech slash twist is where you're going to go get these great domain names. You type geo dot T E C H slash twist to lock in a great deal. Right now you want to do it today. You want to get in there and lock down a great domain name. And listen, if your company's already up and running, maybe you want that for your tech team or to have it as a possible brand extension later on.

SPEAKER_202: So if you've got a great brand, you want to get the dot tech version of it as well.

SPEAKER_15: Okay. So let's go to this next one. So I'm going to open this one up. This is really cool. Uh, wonder craft dot AI. Okay. Wonder craft dot AI. And, um, it basically can create a podcast from a bulleted set of points. And so in this example, I said, let's do the twist weekly AI. What is the podcast about? It's, uh, the AI, a weekly AI podcast discussing

SPEAKER_21: and showing the latest in AI products. I took three sections from the notion that we normally use

SPEAKER_15: here. So we talked about, uh, open AI, uh, perplexity, which we haven't got to yet here. And then Apple bands, chat GPT. And, uh, you just put some links in there to, uh, to the source

SPEAKER_21: links in there. Yep. Correct. And it, you know, you kind of hit it off and then it's going to generate here. We don't, we don't have to generate it cause it's already generated. So here we go. So I'm going to play this now, make sure the audio comes out. So it's generated. Here's the transcript it generated. So all we gave it was a title topics and you know, some links, and I'm just going to

SPEAKER_15: play the little intro here and we'll get started already. Corny music. Welcome to twist weekly AI,

SPEAKER_325: the podcast that keeps you up to date on the latest developments in AI. Join us every week as we discuss and showcase the newest AI products. Don't miss out on any of the exciting breakthroughs in

SPEAKER_328: this rapidly evolving field. Okay. Insufferable. That voice is insufferable. And now she's going to

SPEAKER_14: ask for likes and subscribes. Oh, but, but hear me out here. It gives you an option to use your own

SPEAKER_21: voice when you're doing this. Yeah. And yeah. And as, as I was going through the process here, you'll see, you know, it, one of the sections was to, to choose your own voice and you can

SPEAKER_15: do a lot of editing here. You can do a speaker or you can, it's got a lot, a lot of choices here.

SPEAKER_330: You can add your own voice. Kevin, Julia. So it has all these different voices.

SPEAKER_15: And it's also add your own voice and add your own music, which I didn't do. I just kind of ran through this relatively quickly. So you can go through this process and you can basically, uh, add your own voice. Now, who's going to use this? Um, well, one, if you're a one man show or one woman show and you're trying to one person show, that's the apparently more accurate. Yeah. Everybody's one person show. And you're trying to put something together and you don't have the resources that the great resources that twist does. Uh, and you know, you basically, you do your

SPEAKER_21: research, you find things that are interesting, you prop it up here and you can basically get a podcast going, you know, quickly, and you can use that as maybe not your primary. So what I've seen people start to do is people that are publishing newsletters who are already doing the work or using this to create a podcast just as another media for them. And then you can imagine if you're an enterprise, you got something interesting you want to talk about, so you can do that in there. So I'll pause there because this one probably strikes closer to home for you guys. So I'll let

SPEAKER_87: you react. Yeah. I mean, like many things, uh, you know, you want the humans actual reaction.

SPEAKER_05: We talked about, um, Rick Rubin and his reaction to stuff and his taste. So what this doesn't have is taste. It doesn't have comedic impact. It doesn't have the surprise, the banter that you and I would have now. Yep. How much of people tuning into this is for the banter and the personality? I would argue significant. How much of is it for the news value and the information? Well, podcasts are entertainment, therefore, uh, and information, right? So this weekend startups all in are giving you information and they're entertaining you. It's going to be very hard for this to be entertaining, uh, authentic and informative, but I could see this working really well. If it was just reading you stock quotes, headlines, et cetera. So for something, but you know, if I listened to it and I didn't know, and then I found out I would feel dirty, I would feel like I'd been tricked. So maybe that's just a short-term thing. Maybe an AI personality is the future and there'll be some version where this Joe

SPEAKER_39: Rogan, uh, and we talked about this before, you know, if I could have, I don't know, I'm trying to pick somebody who's no longer with us, Johnny Carson, and I can have Johnny Carson interview Jesus Christ or Johnny Carson interview. I don't know, somebody who's died recently, who's an

SPEAKER_343: incredible artist, but you know, um, Howard Stern live on forever. Right. Or Biggie Smalls,

SPEAKER_00: you know, like, so now you've got Johnny Carson interviewing, you know, Biggie Smalls. It's like, well, that's fascinating. So there, there are kind of permutations here that you can't get.

SPEAKER_05: Yep. Um, but I do wonder, uh, how soon it would be, how soon it will be before these are witty and entertaining. Can an AI entertain you? And do you authentically want to be entertained by an AI is I think where we're going to get to. So if the AI made you laugh,

SPEAKER_87: you know, there, there's like a, there's a level of, can it provide information that's accurate? And we all agree it's on the road to being accurate. Yeah. Right. But it's still delusional sometimes and hallucinations and, you know, whatever. So, but, and then the next step is, can it actually be entertaining and can you bond with, uh, the

SPEAKER_00: personality? And I think that's what the movie her was about is, can you cross that uncanny valley? Yep. In which you could fall in love with, and listen, people fall in love with podcast hosts. They love, listen to the show. They use that word love. Could you fall in love with this artificial podcast host? Of course you could. And so will that happen? Of course. And will we be competing against, you know, I don't know of the top 110 years, will five or 10 of them be virtual? Sure. Why not? Completely possible. I actually don't feel, I, I feel like there's an artist out there

SPEAKER_08: who can make, there'll be an auto GPT or what they call it, baby GPT.

SPEAKER_07: Yeah. Auto baby. There's a few of them now, right?

SPEAKER_08: Yeah. So now let's make it a baby auto GPT where we, we unleash this thing on the world. And we just

SPEAKER_00: say, go find interesting people and then create a fake interview with them. And could that be interesting? Yeah, it could. And could you make a custom interview? So I wanted to talk to this person about the topics you're interested in. So, I mean, the, the mind kind of can go to weird places. You could have, you know, if I really love Johnny Carson as an interviewer and he, you know, or Howard Stern, he retired, could I have Howard Stern having an absurdist interview with Dolly, you know, or,

SPEAKER_20: I don't know, pick a, you know, an actor who's no longer with us. Um, and that could be fascinating

SPEAKER_346: and sure. And why not? This is a good segue to the last thing I just wanted to show quickly.

SPEAKER_288: And then let, let's come back. Wait, so the startup that's building this wondercraft, uh, a wondercraft.ai. I think it's interesting as a podcast host. I can tell you a lot of these

SPEAKER_00: podcast hosts suck. I guarantee you their AI will be better than 50% of podcast hosts in under three

SPEAKER_01: years. And that's not a really reflection of their ability at wondercraft AI or AI. That's just a

SPEAKER_15: function of how bad some podcast hosts are. Yeah. And the sheer number that are showing up. So

SPEAKER_192: exactly. Yeah. Well, this is an interesting, maybe you take, I didn't get your reaction.

SPEAKER_351: there. What do you, I gave my whole analysis there. Do you think I'm being objective?

SPEAKER_21: Or do you think I'm being precious because I'm a podcaster? I think you are, but I want to tie it to this last demo. Then we'll close out on the discussion. You think I'm being precious?

SPEAKER_15: A little bit. Okay. Great. Fair. Fair enough. You know, cause, uh, and so, so let, let's kind of tie it to this. So this is the last really cool demo. So this is a combination of

SPEAKER_21: stable diffusion control net EB synth and fusion where they took, uh, a couple of scenes from Cowboys versus alien, Cowboys and aliens, and they replaced current Harrison Ford with young

SPEAKER_15: Harrison Ford. So I was going to play this video here real quickly. Tell him he's a fool.

SPEAKER_14: She's in a better place. All right. So your reaction first to that. I've never seen this movie before. Cowboys versus aliens. I've never seen it.

SPEAKER_39: Cowboys and aliens, I think. Cowboys and aliens. When did that? I don't know when that film came out. I don't know anything about it. Um, I, I, I think Harrison Ford is kind of my style icon along with Daniel Craig. So this kind of hit both notes for me. Uh, everybody can obviously see the likeness and the personality and the ruggedness that I do have. Uh, and I do exude that male rugged confidence, uh, and style icons, uh, of my two style icons. I like them old. I like them young,

SPEAKER_134: but it was a bad example for me because I feel like old Indiana Jones, old Harrison Ford

SPEAKER_00: and aging Daniel Craig is kind of strikes a nerve with me that they get better with age, you know, like Sean Connery got better with age. I like these guys who don't get a bunch of like

SPEAKER_39: Botox and they just are like, yeah, listen, it's gonna, I'm gonna, maybe they do. I don't know,

SPEAKER_142: but yeah, they kind of like, yeah, I'm gonna be old and weathered. So I like the weather,

SPEAKER_00: but it, it, it crosses the uncanny valley, the Luke Skywalker deep fake from, uh, Mandalorian three years ago. It didn't some kid fixed it. They hired that kid. And now I feel like this, the aging stuff is a whole awesome space. And, uh, I would watch, you know, uh, Alec Guinness is a perfect example who played Obi-Wan. I would watch an old Obi-Wan series. And if they could

SPEAKER_05: get Alec Guinness as a state to secure the bag for 10 million bucks, Disney plus, please do the old adventures of Obi-Wan. That's incredible. Can you imagine how like on adventures as a Jedi Knight?

SPEAKER_15: Yeah. Incredible. Uh, and I already did, and I, you know, I just saw a tweet about, uh, empire strikes back and the deal that he made to be in empire strikes back. It was like some rev share deal that netted him like tens or hundreds of millions of dollars. Which at the

SPEAKER_192: time. Yeah. You talking about Alec Guinness? I think so. Yeah. He played Obi-Wan in the,

SPEAKER_21: yeah. He got paid a lot. He secured the bag. Yeah. Yeah. Um, but in, in empire strikes back, he did some weird rev share thing. Yeah. Yeah. Well, he, George Lucas funded with his own money

SPEAKER_00: and some loans and the money from the toys. He had the sequel rights. He funded empire strikes back

SPEAKER_39: himself. And so that's the other story that's crazy is Georgia Lucas just was like the entrepreneur's entrepreneur and he almost went bankrupt doing empire strikes back because they were filming in like Finland or something or Norway, the hot scenes. And then a blizzard happened. And then that, I don't know if it was Pinewood studios or wherever, like a bunch of sets burned out. I mean,

SPEAKER_01: it was like problem after problem after problem. There's an amazing tweet thread that has all those

SPEAKER_192: details in it. Yeah. So if you're a star Wars fan, you know all this because he's talked about it

SPEAKER_123: over and over again. Yeah. Um, yeah. And I didn't realize, but like Luke Skywalker got injured. That's why some of the, in the motorcycle motorcycle accident. Yeah. Yeah. And that's why the Tauntaun

SPEAKER_20: uh, scene, he gets his face ripped by the Tauntaun. Yeah. And then they put him in the back, the tank and they did that because he actually got, yeah. All right. Yeah. So this was a deal. So I

SPEAKER_15: think it was, uh, like Guinness, right? Half a day, half a day and was paid, uh, you know, quarter

SPEAKER_212: point of the film's gross. Yeah. I mean, I'm skis. Yeah. I mean, I'm skis. All right. Okay.

SPEAKER_21: Coming back to it. Uh, just kind of closing out on, on your question. So I think there's a couple

SPEAKER_15: of different ways to think about it. I think one, um, we always want to be able to produce more content and we're not capable going back to the sort of the music example. So I think giving people the ability to create more podcasts is excellent. I think where the technology has to come together is like, what I really, really like to see is a combination of what we just saw with the, uh, Harrison Ford

SPEAKER_21: piece combined with a generative AI where someone can take the style. And we've seen that in these, the music ones that we've seen are like the AI Drake, where the underlying music and the tempo and the beat is done by someone else. And then the voice is replaced. Now, if those two things can sort of come together where someone can understand the style of J Cal and then, you know, you're willing to do the revenue share and say, Hey, I would really like J Cal to be my world's greatest moderator of my podcast. And, you know, he doesn't have the time to do it, but he's willing to do it through sort of what you talked about, like through his voice, he's made available through a platform that he has, and it understands all the inflections and it has like a way of coming up with unique and interesting questions. I think that would be really great. I think that would be sort of, and I feel like we'll get there sooner than three years where someone will take

SPEAKER_15: all the nuances that you've put together in the, you know, hundreds of episodes, it's gotta be more than that. It's gonna be thousands of episodes that you have, right? 1,700 episodes. 1,700 episodes and it can come up with sort of all the uniqueness of J Cal as a moderator. Love it. And yeah, that, that's what, you know, we'd love to see that if you're doing something like that, we'd love to demo that.

SPEAKER_05: Tell me what you think about this Harrison Ford clip and then me being precious, uh, about it.

SPEAKER_00: Like, do you actually think that these things will replace podcasters? I said, like I could see in three years, you know, it would be these folks at wondercraft

SPEAKER_118: AI or another, or, you know, even like a generic model, uh, or you can roll your own, would be able to make a host that's better than 50% of hosts, but I don't think they're gonna ever replace the top 25% or top 10%. I think if you're in the top 10%, you're a virtuoso, you're just so good at it that you're not going to be replaced, but maybe there'll be an AI that could crack into the top

SPEAKER_61: 10, but I don't think you replace the top 10%. What do you think?

SPEAKER_21: Yeah. Yeah. I sort of think of content as this like pyramid and at the top, it's like you said, it's the

SPEAKER_15: folks that have really, you know, tuned into the craft and put the work in and then sort of, you know, at the bottom of the pyramid, you have everything else. I think there'll be a lot more at the bottom, but I do think we'll see something crack into maybe not the top, top of the

SPEAKER_21: pyramid, but just below that where we're comfortable. The cost base is more affordable for someone that wants to listen to it. And so I don't think you're being precious, but I, I think I really like what you said earlier, you should embrace this and you should kind of lead the charge and say, Hey, let's create, you know, the world's greatest moderator as an AI for podcasting that can be there to moderate your pods. If, if you kind of led that as a charge, I think then, you know,

SPEAKER_118: then you're not being precious about it. I get permission right now. Somebody can train an AI and make me as the world's greatest moderator. And you can make the J Cal AI totally cool with it. And, uh, as an experiment, uh, non-commercial experiment, whatever, we'll see

SPEAKER_00: where it goes and people can then create me interviewing Jesus Christ. I mean, I think that's what you gotta, that's the benchmark. You gotta have me interviewing Jesus H. Christ or who would

SPEAKER_389: be another historical figure? Well, I mean, I don't think we, the Jesus Christ is maybe a bit different,

SPEAKER_50: more difficult, but, um, you don't have video of Jesus Christ. How about you doing Steve Jobs? There's a great video of you and Steve Jobs. Yeah, there's a great video of me and Steve Jobs

SPEAKER_152: interacting. Right. Yeah, do me interviewing Steve on the program. I like you and Jesus.

SPEAKER_87: No, no. Well, that's kind of like Steve Jobs is tech Jesus. So have me interviewing this. Okay. Whoever makes the best version of this, I will give a ticket, a VIP ticket. I'll give

SPEAKER_08: sightings VIP ticket to all in summit. If somebody can create, how many minutes, 10 minutes or more?

SPEAKER_87: Yeah, 10, 10 minutes or more. Whoever creates the best J Cal interviewing Steve Jobs, but it has to be now and it has to be, it has to hit on current events. So he has to talk about how bad Siri is. Yeah. Him freaking out about Siri, me telling him, like me criticizing Siri, it's gotta be one of the

SPEAKER_20: themes, um, talking about, um, maybe he could talk about, uh, AI, um, and what he thinks the future of it is. Maybe he could talk about, uh, what would be a current affairs topic? Trump, him talking about Trump. We never got to hear his take on Trump. Uh, hmm, China, maybe talk about China, the relationship with China. I don't know. People don't care about his politics. Yeah. It was not

SPEAKER_87: his thing, but I think they don't attack. I think it's talking about VR and AR. Yes. Me interviewing him at the launch of the augmented reality. That's okay. That's the scenario. Okay.

SPEAKER_413: So there's a $7,500 VIP ticket at stake here. There's a bunch of AI people are racing to make

SPEAKER_00: J Cal interviewing Steve jobs on the launch of the AR glasses. And you just throw the AR glasses in there because there's people who have them. Um, does it have to have video too of us? I think it does,

SPEAKER_08: right? Has to be some video bonus points, bonus points, bonus points for some video. Yeah. Uh,

SPEAKER_118: or at least images. Cause you could just have the audio have images, but, uh, yeah, we'll do creative and, uh, we'll announce the winner, whatever at some point. That's about a $7,500 prize. That's a big

SPEAKER_344: one. Well, I mean, it's also going to be sold out. So those tickets, like I got 25 tickets, 10 for

SPEAKER_39: family. Yeah. One for this contest, one for my Twitter contest. And then I've got for my LPs. And then I've got my close friends who are now telling me that I need to give them tickets because they're co-hosting this, uh, AI segment. So, uh, I have to get you and Vinny a ticket.

SPEAKER_142: Apparently I think Vinny's ha w what's the vibe on tickets? Have you, have you, uh, what's the vibe

SPEAKER_39: in our circle on tickets? Are people losing their minds or what's going on? What's the back channel

SPEAKER_15: on the oil and summit? Uh, well, you know, I think, uh, everyone, it's a, it's an interesting spot because I think everyone assumes they have a ticket. Cause I think as much has been said.

SPEAKER_08: Oh, I see. Everybody assumes that they have it to everybody in our poker circle. Yes. They have a ticket. Yes. Hmm. Because as much has been said, there's 20. See, here's the thing. There's 25 tickets per bestie. I told my wife five, six for family,

SPEAKER_00: immediate family members. Yeah. I have to give like 10 LPs, my major LPs. I got to invite them

SPEAKER_39: to the event. Of course. I can't do any of my startup founders. Uh, sorry. Uh, but I can let them buy maybe the generation. And then I guess I got some close friends I got to think about. Hmm. It's a tough one. It's a tough one. How do you feel about working the, uh, the badge station being a

SPEAKER_433: volunteer, Sonny? No, the volunteer, everybody wants to be a volunteer from this thing too. Uh, all right, everybody. We'll see you next time. Bye. Bye.