SPEAKER_03: We now have 123456 brands. This is a message to Sundar. We've
talked about this you and I, okay, branding at Google is challenging. It's challenging. You call it Google bard, a much better idea would have been to take the YouTube approach and just call it bard and get the domain name bard.com and let people go to bard.com and have a completely unique verticalized experience without the Google branding. Yeah. And then nobody
SPEAKER_03: needs to know about DeepMind. Nobody needs to know about Gemini. It's just bard. Yeah. And bard is a new product from
SPEAKER_02: Google. Yeah, the end this week and startups is brought to you
SPEAKER_01:
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SPEAKER_03: Alright, everybody, welcome back to this week in startups. Let's get to work. It's Monday. That means it's a Madra Monday. My god, Sonny Sandeep Madra people don't know this. He's one of my besties when I go skiing. Every season, Sonny and I do a bit skiing. We love talking lifts. We talk on the lips talks about life parenting business, everything last down the
SPEAKER_04: mountain. You're you're I think your high speed is far faster. I
think my highest ever was 40.2 miles per hour and you blew past that I am slowing down. I don't want to attempt to be here but
SPEAKER_03: 54 mile per hour record is not a record I want to repeat. We got Oh, man, moans we got under 50 under 50 not to not do it again.
SPEAKER_03: So yeah, but this year I'm going for this year. I've set a target of 50 days on the mountain. 5050 but I you know, I like to go for two hours a day. I don't you and I like to go out we do a little bit we have a little maybe we have a cocktail. You know, it's a pretty good scan today we get that little, you know, spiked beverage not not bad for the old bones. I like it and I'm not drinker but there's something about that. You know, little a little something at the end of the day, little Kahlua in the coffee. But let's get to work here. Everybody loves Mondays. Mondays is our AI Mondays and I want to just announce that for 2024 we're locking this in for the entire frickin year 52 episodes next year of the AI Mondays with Sandeep Madra and we're actually selling the ads specifically for Monday. So if you're an AI company, you can talk to our partnership team or email me jason at calacanis.com. There's only three ads every Monday. So if you get one of those in your hugging phase or your AWS or something you wanted to reach people who are obsessed about AI, we're going to make this the greatest AI habit that anybody who's an AI developer, startup or investor has to come every Monday to listen to this and you've been getting great feedback.
SPEAKER_04: Thank you. And it's been really fun. And it honestly, it helps me in my day job. And it basically is, you know, really insightful. I even went and did a little session with our other best each month. Oh, yes. Yeah.
SPEAKER_05:
Pushing code with trim off. We were standing next to you guys
SPEAKER_03: did pair programming. I love it. We did pair programming. Yeah, he
SPEAKER_04: was on the keyboard. I got to give him credit. So it was really nice thing to do. You know, I don't have the head for
SPEAKER_03: programming. It's like a little too much focus. And yeah, you know, I get distracted or whatever my ADHD but I do like the idea that you helped me sort of pop it up and learn about it. We'll do one. We'll do one. We'll do a session. We'll do a 2024. For sure. Let's do it. We should do it on the air. That'd be fun for people. All right. But you know what, everybody loves to come here to watch us and listen to us do these AI demos. So if you're listening, no problem. We're going to explain what's happening on the screen why it's important. If you're watching great, go to youtube.com slash this weekend. And then you can just hit the videos and you'll find a playlist there. My teammate a playlist for the AI Mondays, we just look at the playlists, you find that playlist, you can watch all the demos. And you can see you know, even if you missed a couple, going backwards is a great idea. So we're entering year two, consider this year two of the AI revolution. In terms of chat, GBT 3.30. It was the one year birthday. Yeah,
SPEAKER_04: perfect. Happy birthday, everybody. Congratulations. And
SPEAKER_03: you know, the big news, obviously, was Google has a bunch of new updates. So we'll talk about that today. In the notes. What do you got first? What's what's on first? What's our first demo? Well, I want to start with it kind of got lost
SPEAKER_04: in the shuffle. And I think it's a really, really cool product. And it's called notebook lm. I know about this. This is a
SPEAKER_03: product by Steven Johnson, who is a writer who I knew from New York, he's written a ton of books, Steven Berlin, Johnson is a writer. And there is a tool, I think it's called Scrivener. That is a writing tool that like, Tim Ferriss might have gotten into and he was into. So when you're writing a book, you probably have 20 different sources. Let's say you're writing a book like Malcolm Gladwell does like the tipping point. Yeah, you might have 20 different sources of information, right? And you're, you they write these kind of meta books where they read 20 studies and then try to make an overarching thing. So what's very interesting about this is I was on this product this weekend, because I saw a wired story about it trending in the news. And I played with this weekend. So this is great. The Google product, the Google product notebook. Well, and is a book for it. Basically, there's a like I said, there's I think it's Scrivener is the product. Yeah, and Steven Berlin, Johnson, I remember him from New York, really cool cat. He's at a Netflix show. He's written a dozen books. He's like a real like, why old school wired guy, you know, like a Gen X wired guy? Yeah, yeah. And so this is a tool based on my understanding of it to help you collect lots of thoughts and then make sense of them, perhaps in a book format or whatever it is.
SPEAKER_04: Yeah. So look, I think you nailed it. And what Google's done, and I've kind of, you know, started this demo here is, and I wasn't thinking about it in the context you talked about, which is interesting, because you and I just bring different perspectives. But I think about this as a collaborative space. If you've got a document or something and what, you know, Google's done an excellent job of we want to keep giving them credit. Here, I can add documents by uploading them, but I can also grab stuff from my Google Drive. Got it. Okay, so we're looking at an interface. It's almost like a whiteboard in
SPEAKER_03: front of you or like, would you say a con board where you're putting like little objects boxes? Yeah, I think you upload your source material, the source material, typically copied text, PDF, or something on your Google Drive. So that could be a Google Sheet or a document. Amazing. Yes. And you add those in. And
SPEAKER_04: then once those are in, you can start a chat and you can say, tell me about this nda and I just uploaded the definitive mutual nda. And basically, it will, you know, start to do its analysis, you know, using the LLM. And it'll explain, you know, I just asked it here, like, tell me about this nda and explains, you know, what it's for. And I can ask, like, is this a mutual? And yeah, yeah, yeah. Can I be sure? Right. And
then, you know, I like that question. You can also do the sharing. So you can add people to this notebook. Ah, so you could add me right now. Yeah. So yeah, so I can add you to the
SPEAKER_04: notebook. And then we can kind of collectively jam on this. And then I can add notes to this. And I think sort of the use case you're talking was a little bit different, but I like it as well. And, you know, if you go back into the kind of the, this is a good job with they've done, they have some example notebooks here, where you can kind of see, you know, how how to use it. And so this is a little bit more like what you're talking about, like a bunch of notes and other things that are leveraging. Left here, let me describe it. In this one, they have an
SPEAKER_03: announcement, a letter from the CEO pros and cons of I guess, a product, some product specs. So if you're thinking about a project, and a project had a collection of documents, everybody says, Oh, I want to create a language model. But that's, you don't want to get a developer and hugging phase repli, whatever. I may just want to take 20 documents that my team's worked on. So here are the 20 documents about project Condor, whatever we're doing at the company could be an acquisition, could be an investment. And I just very simply upload those 20 documents. And it could be the pitch deck from the founder, it could be the deal memo, internal deal memo, external last deal memo for this, let's say that we were doing an investment deal, or an acquisition project Condor was, you know, Google acquiring YouTube, you might put all this different information in here. And then people can ask questions. But when they get asked the question, and they get the answer, all of those become little notebooks in this shared space. So I can see what you're asking the LM, I can ask the LM. And then we both get the benefit of those queries. Am I correct? 100%. What I really love is you can basically also then use kind
SPEAKER_04: of LLMs to ask about the documents like I was doing in my example, right. And I can kind of build on that. So I think, I think that's really powerful. I think this is a really nice interface done super clean, collaborative, I like integration into the Google ecosystem. So I'm going to start out and I'm going to give this one like an A minus, I think improvement is basically give it more sources here beyond sort of these. And it's really up there. There's not a lot more they can do. I want to start using this kind of in our work process or things and I like it. I think it's a great tool.
SPEAKER_03: I like it. I give it a B. I think it's pretty straightforward right now. It's a little confusing until you look at the examples of what you would use this for. But I think we gave you two if you're writing a book, if you're doing an M&A acquisition, if you were doing a term paper, I could see you using this, you know, if you're working on a project, you know, in graduate school or your term paper with a you know, a study group, you might put all your notes of each week's notes in here, you might put every chapter of the books you're reading. So let's say you were doing, I don't know, a cinema studies class, I might put in the script and we were doing Japanese film studies, I might put all the Kurosawa scripts in here. Perfect example, I'm a my book Kurosawa's biography in here. And that was the first thing I was thinking was, I want to put some books in here. But there's a limit, I think, to the size of it. And then I was like, Wait a second, how do I get a book in here? Because they're all locked down with digital rights management, DRM. Yeah. So I was like, Oh, so how do I get something like a biography, what I wanted to do with this was take all my favorite business biographies, put it in there, and be able to query my top 10 business biographies or 50. But I can't do it. I have to get a pirated copy, like a PDF of it. Now, unfortunately, yeah, you know how you would do that.
SPEAKER_03: Alright, listen, we work with startups, and they are all over the map. Most of them very early stage precede seed, you know, going on to their series A, but some of our investments have gone on to raise those late stage funding rounds, they've gotten acquired, hey, and a number of them have gone public. And there is one thing that unites them all they need to have their business insurance tight if they want to succeed. This is obvious. A lot of founders ignore it, and they ignore it at their peril. If it's not tight, it's not right. And we need tight and right and we send them to in broker and broker is business insurance built specifically for startups. Their single application helps startups get four quotes for four lines of coverage in just 15 minutes. broker, they'll connect you with one of their expert brokers for unmatched service, and it goes beyond your policy. They'll make the process painless and transparent, especially when you compare them to the incumbents, which are slow. So try in broker today with the code twisting at 10% off. There are already amazing prices their startup package in broker comm slash twist, EMB are okay er.com slash twist and use the code twist for 10% off we love and broker thank you for all the amazing support over the years both on this program and the love and care you give to our startups.
We asked you a morality question here and this is let's say we had we had a friend not me, not you, but we had a friend that I have an ethical question for you. I've bought these 50 business biographies. I also see you know, if I do a search of
SPEAKER_03: Google Drive on the web, that, you know, something like an autobiography or born standing up Steve Martin's great one or, you know, shoe dog is somebody has the shoe dog PDF in a Google Drive, right? You can just search site colon drive.google.com
for any PDF, you'll find every copyrighted PDF if you want them and I don't suggest you do that and don't steal my book, go buy it support authors buy every book, but let's say I bought all 50 bucks. And then I put all I put my top 20 in Google notebook
SPEAKER_03:
from the web where I found them without DRM on is that morally
and ethically acceptable to you? Not saying you do it, but what would you tell your friend?
gray zone? Okay. So here's how I think about it. It's like the
SPEAKER_04: time of music sharing before we had iTunes. So and look, I participated in like downloading music, because it was the only way to get digital music, right. But once iTunes came around, I been using it probably since 2002. It was around very early. And I've been using it because, you know, I think artists should be compensated for their work. And I'm happy to pay them because that's how the ecosystem works. And so I would say until that
type of functionality is available in Apple, iBooks, or Kindle, or other places. Yeah, because, you know, I want to, yeah, I bought it and I want that functionality and the companies that making it available. So I feel like it's okay. I think the minute those platforms offer that functionality, then I should correctly do it correctly. That's kind of how I feel about it. You know, yeah, you should
SPEAKER_03: be able to so this is for Steven Berlin Johnson, I believe is his name. It's been 20 years since I seen him. Cool cat. Hey, Stephen, come on the show would be really cool if you could press a button there. And instead of upload from PDF, it said, sync with Google, Apple, Apple Books, sync with Amazon Kindle sync with Audible, like, why can't I just sync and put those things in there? That'd be an incredible feature. And so it should be allowed. And so until it's allowed, I would tell my friend, I'm okay with it. I'll allow it. The spirit of the law
is you bought it, you should be able to do what you want with it. This is why I may start going back to buying Blu rays just to not go on a total tangent here. But I feel like, you know, like, when sometimes the internet's down, or I'd like to have physical copies of like, the top 100 films that you know, are we watchable or whatever to have? Yeah. And you know, that trade them with friends and stuff like that. You can't trade your digital library. So you if you buy a movie, I can't like, just give it to you, whatever. And then they have all these cool things like the director's cuts and the audio tracks, and they can't find those online. Like if I want to listen to the director's commentary, I can't find the director's commentary. Like Apple, it's not there. So anyway, this is a really cool thing. So it's called Google notebook, LM, Google notebook,
SPEAKER_04: LM, correct. Oh, search for it. It's pretty cool. I have to say.
SPEAKER_03: And they should actually know what they should do is Google has Google Books for all the books that are in magazines and stuff like that. They have all that stuff there. So it'd be very cool if they connected it to their magazine and newspaper archive. And you could put the New York Times in there and pull New York Times archival stories or whatever. So if I have a New York Times subscription, why can't I add that to here, you know, and just add a couple of New York Times stories. I'm constantly that's another thing I'm constantly doing is trying to take a New York Times story or financial time story and share it with a friend or discuss it with a friend and it's just like, yeah, so many roadblocks. It's so hard. I think that gives you like a little gift thing now where you can give a
SPEAKER_04: couple of articles but I think it's like maybe five a month or something. That is helpful. So I think that's a nice pressure cooker to
SPEAKER_03: take it off. I do like also putting stuff in speechify which now has speechify studio. I saw that first time this week where you can create your own voice. And you can read to yourself new stories. So you can train your own language model with speech if I studio will put will show it next week. Okay. But yeah, you can make like a J Cal voice from this podcast and then have me read your news to you, I guess. Yeah, and train your own audio.
SPEAKER_04: I took a note for it. Okay, so bard has been updated with Gemini pro functionality, I believe, whatever that means. So now they have Google DeepMind. Yes, bard
SPEAKER_03: and Google Gemini. Yes. Well, what what's going on with the
SPEAKER_03: branding? Google? Tell me what I'm talking about. Tell me explains the audience the difference between those three things just so we can level set. Okay, well, DeepMind and brain
SPEAKER_04: were two independent teams inside a Google that have been merged into a single team. That's now called DeepMind.
SPEAKER_04: DeepMind. DeepMind is the remaining brand. That's their AI
SPEAKER_03: team. That's their AI team now. And that's like saying, that's
SPEAKER_03: equivalent of saying open AI. It's equivalent of saying open
SPEAKER_04: AI. Yes, correct. And historically, those two teams had different approaches, and they came from different worlds. DeepMind was an acquisition that Google did years ago that actually led to some of the fallout between Elon Musk and some of the Google founders. Yeah. And, you know, some of the key folks that were involved in the drama with open AI this week, Iliya in particular, was a DeepMind person that was recruited out to come to open AI. So it's very kind of some important details. Those two teams now have created a new model, a multimodal model, we've started talking about these, we've used them. And that model is called Gemini. And Gemini comes in three different versions. And those three different versions basically are different size parameters, which have different capabilities. And pro which is the middle size model is now available for use inside of Bart. And on an API level, it's going to become available on Wednesday. So if you're working with Google Cloud, you can use it as an API like super the same way you can build things on top of open AI and chat, GPT and GPT. And then ultra, which is the kind of the most advanced model they said will be available in early 2024. Okay, help. Yes, we have the DeepMind team. That's the group
SPEAKER_03: inside of Google. The consumer product is called barred, barred. Now, barred is a way for them to say, Hey, this isn't Google search, this is a different experimental product, it's going to hallucinate, click here to understand you shouldn't like, based your medical decisions or financial decisions based on barred bars and experiment. Then barred uses an underlying technology called Gemini, Gemini, Gemini comes in three of the first no pro ultra ultra. Yes. So we now have 123456
SPEAKER_03:
brands. This is a message to Sundar. We've talked about this you and I, okay, branding at Google is challenging. It's challenging. You call it Google barred. A much better idea would have been to take the YouTube approach and just call it barred and get the domain name barred calm and let people go to bar calm and have a completely unique verticalized experience without the Google branding. Yeah. And then nobody needs to
SPEAKER_03: know about DeepMind. Nobody needs to know about Gemini. It's just barred. Yeah. And barred is a new product from Google. Yeah,
SPEAKER_02: the end.
SPEAKER_04: Well, Microsoft nailed this to with the naming copilot, which, you know, we had a little private chat where we were sharing some names and ideas. And we said, Yeah, copilot is the way to go. It's the simplest and nobody can own that. So
SPEAKER_03: Google copilot would have been a better name. Yeah. For this, they just call it copilot. But you know what, Google strength is in the underlying technology. Yeah, weakness is in branding, and often in UX. And that they have with barred right now. Is there's no app, right? There's no Google bar app. And the interface is just, I don't want to say it's terrible, but it's like, they say the UI is like a five of 10. And I feel like chat GPT is like an 8.5. Yeah.
SPEAKER_04: Well, can I say one thing to not to defense, but like very similar in branding and naming with open AI. We have chat GPT. And we have GPT 3.5 GPT, GPT for GPT for sure. So they've kind of taken the same consumers, but they didn't have
SPEAKER_03: a legacy brand of Google, right? There is no legacy. So there they can, they can start from a little bit of a cleaner position. People know chat GPT. They don't really know open AI. They really don't care about the version numbers. You just search for chat GPT. This is why Google you should just search for barred. And then all they should say is barred let you do this. You can use barred for this, you can use barred for that. And then yeah, when you go to the dev day, they can talk about all the underpinnings. But the consumer should just experience barred and barred should have a logo and it should be barred.com This is why Google plus failed. I don't know why Google keeps doing the same mistake, which is putting things as like a sub topic under the word Google. YouTube is your biggest success.
SPEAKER_03: It is an independent, strong brand with its own domain name. Google plus got lost because it was plus.google.com it should just been called plus.com and you went to plus.com and you had that experience. Yeah,
SPEAKER_05: fascinating. Here and the branding lesson. You have to get
SPEAKER_03: this right. Google, please. We better name than barred too. Because like no one knows what a
SPEAKER_04: bard is. I mean, yes, I agree. I mean, they should have just bought
SPEAKER_03: chat.com I would have just bought chat.com and you know who bought it? Dharmesh? I think bought it for a million dollars over at HubSpot. Yes. And the huge mistake.
SPEAKER_04: Wow, what a Oh my god. And me. I mean, for Google. That's like one millisecond of search. They just had chat.com. And it'd be like, Hey, yeah, if you
SPEAKER_03: want to chat, go there or AI chat.com or AI.com. Yeah, you know, it's just there's so many different ways to do this, folks. But yeah, the way Google does it is always a mistake. There's no branding person there and nest had a great branding team. And then you screwed it by having the Google team shoehorn it into and break it into Google Home. There should be a five alarm fire. Actually, you know what, this is a Sergei thing. Sergei, find somebody who you trust. Who's your Johnny? I've heard Sergei is in there writing the code, right? Yeah, Sergei. He needs a johnny one that pushed for the release.
SPEAKER_02:
SPEAKER_04: Apparently. Oh, really? Let's go. Yeah. Yeah. I like Sergei
SPEAKER_03: after dark. He's a sir. I like I like billions of billionaires
SPEAKER_04: writing code. We have chamath and we have Sergei. I love it. Samurai Sergei. 100 billion writing code. I so much
SPEAKER_03: respect. Samurai. Sergei. Oh, I like samurai. He's in the game.
SPEAKER_05: Oh, yeah. But this is somebody send this message to samurai
SPEAKER_03: Sergei. I know. Jake out. Samurai Sergei, please hire your johnny. I find a branding person and stop taking Google's innovations and wrapping them in bad UX. This is a place to
invest like literally just find 10 great designers, put them in a room. Don't let anybody near him. I think johnny hive as
SPEAKER_04: johnny. I've as a studio now he could just acquire it. Yes, go
SPEAKER_03: get I mean, go give johnny. I have a billion dollars and have them come in for 10 hours a week. He'll do a better job. But Google team no offense to the Google team. Yeah, they're
SPEAKER_04:
doing a bunch of work for like Silicon Valley companies. Like a little design. Yeah, design studio. Okay, easy breezy. Okay,
SPEAKER_03: let's keep going here. Because I'm in bar right now. And I asked barge is bar using deep minds Gemini and said yes, it is using it. So what I want to do here, Jake, how's I want to give
SPEAKER_04: the ours Technica guys a little bit of a shout out because they put together a really world class. Okay, that and I'm going to show some of these, but I just wanted to touch on this, but they let's go they did a great comparison between original bard and the updated bard and chat GPT. And so they have a lot of examples in here. And you know, I've kind of picked on a couple of them for our demo here. So I wanted to give these guys a shout out. They did a great job. And so one of the ones that I found that was incredibly interesting from that example was, and I think you'll you'll like this, which is, I gave it some text to summarize. And it was a bunch of texts that I just copied from an article, not what it was able what it was able to do was it was able to, and this, this was sort of the article here was like, an AI generated video of Will Smith eating spaghetti. And basically, what bard was able to do was tell me you know what this like what this was about, and was also able to link me to the original article. And I think that's really powerful. Because, you know, one of these things about attribution is going to be, I think, really important when it with respect to understanding the ground truth of things and also perhaps monetization in the future. So this is one of the enhanced powers of bard in that when you give it text from the internet, it will, you know, summarize it and then use Google's vast knowledge base to kind of pull in the original source of that content. So two things you're
SPEAKER_03:
pointing out here one attribution, this is something we've talked about who should get credit for this, you took a cut and paste of the text from an article did not give bard the URL, it then found the URL and gave proper credit to it. So all of this like, oh, the LLM, we could never undo it, we can never give attribution. That is absolute BS. AI has a very easy
time doing attribution. And so this entire concept that we don't know how it came up with the answer I call BS, you can obviously figure that out. And then so that's great for two reasons. One, it's fair, and it'll probably get you around a lot of issues of a lot of lawsuits. But number two, monetization. If this sends traffic to the original article, the original article gets to get some money, which is the very delicate balance Google made with content creators, hey, if you index it, we're sending you traffic, if we send you traffic, you can figure out how to monetize it on your site, you could even use our ad network if you like or somebody else's. So it's worth giving us the snippet, just a little bit of the content to put in to give people a preview. And so much so that they even did those one box where they kind of gave you a more specific section that might give you the answer. And content providers are like, you know what, I'll let you do a
Google because net net, I get a third of my traffic on Google, or half of my traffic from Google, whatever the website's doing. So I think those are two very good reasons and a place their strength. They now know, because they have that corpus of the web crawl. So very, very good.
SPEAKER_04: Yeah. And so I thought like really good example. And, you know, touches on a bunch of things that will could really put Google in a in a in a good position with content creators all over the internet. And, you know, attribution, which is driven much of the internet business for the last few years. Love it. Okay, so that was like sort of the first example I want to touch on with Bard, which I thought was really good.
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SPEAKER_04: Another example, which I thought was really powerful, was its ability to go deeper in its responses. And so, you know, this is something, you know, talking about, like an era that you know, you were probably closer to this stuff back in the 2000. But like, you know, PowerPC versus Intel processors, this was a big debate when Mac was switching over to, you know, the Intel based processors. And so it's able to just kind of dive into much more detail than before. So if you asked this question in a previous BARD, it just wasn't as powerful. And so the you can basically what you can kind of glean from this is, they've done a good job of just increasing the amount of material that's used in its training. So it's just as a deeper knowledge set. It's not as surface level. A lot of times before BARD was a little bit surface level. And that's called out there. Yeah.
SPEAKER_03: Fantastic. Yeah. Um, yeah, so like, look, there's a kind of a few of these
SPEAKER_04: improvements that they've done. These are kind of the more interesting ones. Other ones are a little bit subtle, like it's better at jokes and things like that. That was most important was for me, multi model looked very,
SPEAKER_03: very interesting. And there was a couple of video demos, maybe we could do a reaction to a video here.
SPEAKER_04: So the video is going to play here. And they're going to show some capabilities. What they were showing here in this video for the folks listening is someone on a on a sticky note pad, drawing pictures, and then increasingly adding details to the pictures and barred responding to it. And so this is really interesting, because what it made it seem like was barred was able to watch video and then in real time, explain what it was seeing. Yeah, what was subsequently shared by
SPEAKER_03: words, casting, it was sports casting what was happening. So when you drew it, it was like, oh, it's a scribbling line, then you drew a little more. And it was like, well, that looks like a bird. Oh, because it's got a dot because it's got a bill, it's a duck. It's, it's in the water because you just drew some water. And oh, it's a blue dock, which really doesn't exist. So it was like really giving a commentary in real time of what's happening.
SPEAKER_04: Yes. And what and this is very powerful, what there's some criticism around and controversy, I'd say more criticism was that nothing is faked in this video from a sense of that, like, you know, the answers are coming from barred. But the interface to barred was not a real time video. So what they were doing was they were taking pictures, they were giving them to barred, and then barred was replying with the answers on the right hand side, but the interaction wasn't real time video. Got it. So they faked a bit of the demo for dramatic effect,
SPEAKER_03: what it really was with static images. So they were just showing like, essentially one post it note at a time. And they would come back and say, this looks like this. And
SPEAKER_04:
what is this now? And what is this now? But the truth is, they
SPEAKER_03: probably could build this in a weekend. So Sergey is probably coding it right now just to dunk on everybody, and they'll release it in the next version.
SPEAKER_04: I you know what, I was gonna say that is like, the best way to kind of, you know, demystify everything is actually just have barred do it with video. And, and at the end of the day, you know, take screenshots of the video along the way and submit them to barred and have it answer the question. You know, I had something similar to this happen when I
SPEAKER_03: was watching Han Solo on a long distance flight, I hope like the wrong version. And the version I listened to was for people who are blind. Okay. And it described what was happening on the screen. And it's like, Han Solo jumps into a speeder, this person jumps in with him, they speed off into an alleyway, they turn now they're being chased by three people. And it was explaining. There was a narrator describing what was happening on the screen. And there was still the sound effects, the dialogue, and I was like, I didn't know that existed in the world there is, and there must be a technical name for it. But there is a technical name for like a commentary track that's
SPEAKER_03: describing what's happening on scene. Now you think about that we could take any video and if somebody was blind, you could play a music video for them. Obviously, they can listen to the song. But you could have a narrator describe what's happening on the scene. And the person could say, give me more detail or less detail, or pause the video, tell me what's happening and then restart it every 30 seconds or whatever at every milestone. So they could say, Okay, wow, I'm gonna pause here. Now Britney Spears comes onto the scene. She is a ninja, then she turns into this. And yeah, there's enough time to explain, you know, something like everything everywhere all at once, like some really dense movie with a lot of activity. And just think about what that would do for somebody who was blind, you can do this in real time, on any screen for any video, including live sports. So you know, you have a sportscaster, and we're sportscasting here. sportscasting could become could be done better by a computer by an AI. So if Bard does this for a Warriors game, you can say, you know, who I really like, when George Michaels did this, or somebody who's passed away, who was a sports dancer, and you could have like some sportscaster describe what's happening in the game and clone their voice. And man, that might be really interesting, huh?
SPEAKER_04: Yeah, it's probably I think it starts off in like, maybe different languages versus that. Because like, you know, if you watch NFL, and you get to watch a game that Tony Romo is sportscasting, you get his knowledge of football and being a recent football player, which I don't think the AI can can replicate. That if you could have Jimmy the Greek, right, who is very
SPEAKER_03: famous. You know, Monday Night commentator that people loved, and nobody remembers Jimmy the Greek, but I do because I'm Greek and it was like a big deal to have somebody called Jimmy the Greek. But don't start with Jason the Greek. I mean, you can. Anyway, Jake, I'll agree. You could bring him back and have him doing Monday Night Football. You could actually probably get all of his insights from previously and get his real time reaction. I mean, if you built Yeah, if you took a model and fine tuned
SPEAKER_04: it with all his previous commentary, sure combined with video with a multimodal Yeah, that's actually an interesting idea. Muhammad Ali Muhammad Ali doing, you know, MMA fighting or
SPEAKER_03: boxing. And, you know, what about having Steve Jobs? I mean, listen, we talked about the recently dead, I want to just be clear here to family members, etc, who might be watching or, you know, friends of his, I'm not making light here. But sincerely, like, are we eventually going to have people who've moved on and passed on, you know, like, do a keynote or send a message? And they did that with Tupac, right? He did a tour. So I think this is a fat, hard decision. They did like a
SPEAKER_04: like, what do you call a gram thing holographic Tupac can come
SPEAKER_03: back? You know, Steve Jobs come back, it might be too soon. It might be too painful for people. But eventually, because Steve Jobs come back and release the next Apple product. You know, I get so many ethical, moral, emotional things to think about there. But we seem to be on the cusp of that. You know, the other thing, I was so impressed with what they showed with Gemini. The one I found really interesting is multimodal where they took a math test. So maybe we could pull that video up. And my team can do it too. But they were explaining the reasoning of in math and physics. Alright, so this is super interesting. Here you see, this is they are taking a picture and understand of right. And what he answers and explain the concepts that scored
SPEAKER_00: the test Sunday, scored the test, identify some mistakes with problems one and three here. Let's take a look at three. That's incredible. Teachers don't have to do
SPEAKER_03:
teachers no longer have to score a test. That's a big part of the job that the formula was correct. But there was a mistake
SPEAKER_00: in calculating height. We can ask Gemini takes plane and that's powerful height is 50 meters instead of six. Yeah, not even
SPEAKER_04: okay. So let's even teach you so much not even teacher so much parents like I don't know if you ever have to kind of work like sometimes you like really got to dig deep back and especially as you're getting into algebra and calculus. Like let's say here,
SPEAKER_03:
you know, they're there, they've identified which question got wrong. Then they're looking at your solution as a student and finding and they're finding your mistake, and then explaining to you how to fix it. Yeah, this is an example of the teacher doing their work when they're not in the classroom, which is scoring test that's done. And sometimes the teacher will circle the place where you made the mistake that's done the detail. And then you have to get tutoring and you have to do corrective work to not get it right the wrong time. Those are three components of work humans do that has now been replaced by an AI. So let's just think about teachers in general. Now teachers don't need to score tests, the test would be scored in real time. So you take your test up to the front of the class, and they literally just put the test in front of a webcam. Yeah, you have a webcam facing down, they just put it there, move, put it there, move, put it there, move, and it's sent to you what your score in real time, what you got wrong, they could send you to another room to go over the answers, then have you come back and take three new versions of the three questions you got wrong and restore your test. So think what that does for education. You know, there were those really cool teachers who would let you retake a test. Yes, because they wanted you to do the best possible. You could be doing that in real time, you
SPEAKER_05:
SPEAKER_04: would learn more by fixing it and coming back, then you're doing the real learning. But it's not scalable. If they have to do that for 50 2030 students, they can't. This is a great aid for teachers. I think it's even a greater aid for students, because they can sit there while they're doing their practice, their homework. And you know, when they get something wrong, they can highlight you know why they got it wrong and explain to them how they should have done it differently.
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Alright, so we should give a score for Gemini here. Yeah, I have to say I was blown away. I think that they have
SPEAKER_03:
leapfrogged chat GPT 4.0 in some modest ways. And obviously on the tests, like there's a battery of tests, I'll have you explain that in a second and pull up the list. So people understand how that works. They seem to have smashed chat GPT for smash. Now who knows if chat GPT four is going to take this lying down or not, I suspect they won't. And maybe they'll
drop like a 4.1 or 4.5 to smash bar back and say, yeah, I'm back. But this to me means Google's in the game. They're taking it seriously. I mean, people are not gonna believe this, but I'm giving this an A plus. Because I'm going to pull
SPEAKER_04: up the benchmark. Okay. Plus, wow, I'm giving it a plus
SPEAKER_03: because I do think to surpass open AI on this many items, even though I think the interface is still janky, and needs work, and they don't even have an app and they don't even have a brand and it's just confusing. And you know, if I were, I'm rating this
release as an A plus release. Let me say that. Wow. product wise, I think chat GPT is still a better product because their interface is an 8.5 of 10. And I give the Google interface like a four or five. I mean, the Google interface looks like it was not made by anybody. It looks like they literally went with some default template. You know, like that that was built into like WordPress. A deck. What are the differences you see between the
SPEAKER_04: bard interface and the chat GPT interface? Okay, so first and
SPEAKER_03: foremost, majority of people and majority of work consumption and creation is now doing being done on smartphones. So I always judge it on the app and work backwards. Okay, there is no
SPEAKER_04:
huge there's no app that's there's no. So that means you
SPEAKER_03: lose of 10 points, you immediately lose five. So now you're at five. Yeah, now we're going to just grade you on your web and your web. So you pull up bard in a web app when you pull it up on your desktop. Yeah, looks cluttered, confusing. They just put it under the Google taskbar. You know, with all the accoutrement, it doesn't have its own look and feel. So I'm minusing a point for that. Okay. And then I don't think the
SPEAKER_05:
SPEAKER_03: pagination and the formatting are as clean and crisp looking and the fonts and the topography. I'll get I'll take a minus one on that too. So I give them a three of 10. Actually, now that I'm doing my interface three of 10. But you know what,
SPEAKER_04: I was kind of looking at it different, but the chat GBT if you use that app, it is so polished, that it's
SPEAKER_03: delightful to use. I've been using Dolly now to write my blog to make my blog post headers. And my illustrations on my last two blog posts look like a million bucks. If I had hired an illustrator to do that it would have been $5,000 per illustration, I think, at a minimum $1,000. You couldn't do what I just did in Dolly, you can pull them up my team in a minute. But if you go to callick anis dot substack.com, I'm doing a weekly startup post and I changed my list to callick Jason on startups or something. And just the two header images are gorgeous. So let's talk about scores here. Let's talk about scores. And by the way, yeah, just hire somebody to do the mobile interface. Samurai Sergei, need you Samurai Sergei to take out the sword. Whoever's on the design team, you just to cut the team in half, fire them. And then you find an external team that's great at making apps, and you ask them to make three versions, you give them 5 million bucks, and then you come back to your team and say, Why don't we Why aren't we as good as this independent firm? Is this then you make you pick the people from that firm and you give them an offer they can refuse? You have Samurai Sergei, straightforward Samurai Sergei, all right, coach him Samurai Sergei. Let's do it. So
SPEAKER_02:
SPEAKER_04:
you know, a couple of things are jaco we don't have access to ultra yet, right. So what we're playing with it Bart is pro. So pro, you can see here across the board, scored lower than GPT for. And so even though in your experience, you're feeling like it's explained to people what they're seeing on the left
SPEAKER_03: their math, so they reach hard. What is this? I know. Yeah,
SPEAKER_04: yeah, I'm in it. So there's a I think there's eight, eight critical benchmarks that people use to score different models. And what you can see here in this chart that I have pulled up is Gemini ultra Gemini pro versus GPT 435, palm to cloud two inflection, grok one and llama. And so these are kind of all the major LLM that are out there right now. And it's you know, changing rapidly. And what we see here is Gemini ultra on almost all benchmarks scored higher than GPT for Can you give us an example of but one of these benchmarks and
SPEAKER_03: what it is actually testing?
SPEAKER_04: I mean, the simplest one, which is the one we were just looking at is GSM 8k, which is grade school math, right? And so that's the second one in this chart that I'm looking at, you can see that GPT for gets 92% of the questions, right and Gemini ultra gets 94.4. Now, so this is to be clear, they took grade
SPEAKER_03: school math. And they have a battery of questions like a
standardized test. So this is kind of like the sats, or I forgot what we call them in New York. I think they were called your regions. These are your testing. These are your tests. Yes, he takes the same test. And it's basically not you can't cheat on it essentially. Well, so that's a very good question
SPEAKER_04: you asked, you are not allowed to include the test in your training material. Because if you include the test in training material, you'll probably get 100%. So the the researchers and people that are doing this have to hold to to that standard and say you cannot include any of the questions from any of these different benchmarks inside the training material.
SPEAKER_03: And I like this m m l u multiple choice questions in 57 subjects professional and academic. Correct. And this was done by some university or some researchers, correct. And then they put this out in the world. And we look at that one Gemini ultra 90% on this multiple choice test Gemini Pro 79% Okay, great chat GPT 87%. So Gemini was, you know, whatever it is a couple of Gemini was a student and GPT for was a B plus
SPEAKER_04: student. Got it. But it was interesting about this as we go
SPEAKER_03: down, Brock one is at 73 already, and that's under six month old. Claude's been at it for a while there at 78%. So
they're not applying themselves well enough. And then llama two, which is Zuckerberg's one. That's an open source. Am I correct? Yes, correct. That's at 68%. And so we're seeing open source far behind the class. be interesting to catch up. Well,
SPEAKER_04: but the thing can I just comment on that the idea of llama two is it's not meant to be a model you use out of the box, it's meant to be a model you take and you find here. And we don't have it in this example. But I'd be willing to guess there's a llama to fine tune that can score higher than GPT for in Gemini ultra on mmlu, which is project on here. I guess I just it's not doing as well or they
SPEAKER_03: didn't think of putting it on here. I wonder.
SPEAKER_04: I don't know for sure. But my guess is it's not scoring as high as any of these other projects right now. Yeah. Okay.
SPEAKER_03: So yeah, great. So this is amazing. We what you find here is chat GPT for has not run away, has not. I think what we've learned now is as an organization, open AI has an
SPEAKER_03: Achilles heel. Right? The franken corporate structure, the
frank structure, the franken structure. Also, there are two other criteria. There's the core power of the language model, which we're seeing here in these tests, and chat GPT and open AI can be beat. That's what we've determined with this correct.
SPEAKER_03: They're being that I think that was the biggest thing last week,
SPEAKER_04: which is folks got comfortable that you know, what chat GPT had wasn't, you know, magic, or it wasn't so far of a technological lead that it couldn't be caught up. And the fact that someone else got there means other people can get there as well, which means we're heading towards commodity. Would you have always said that's always been your
SPEAKER_03: position. And what's very interesting about this is if you look at this chart, GPT for was trouncing the other competitors. So when you compare flawed to chat GPT for and I'm sorry, GPT for yet Claude or to llama or to palm, it really did feel insurmountable. You know, there are scores here 92 to 80. You know, 92 to 88 52 to 34. I mean, these were big leads. Yes. And now so there's that piece. Then there's the piece of user interface. And then there's a piece of proprietary data. And so if we were to look at those chat GPT for an open AI team, they have some I don't know who the web designers are in there. Somebody tip us off of who's designing the app for open AI. But that's the person Samurai Sergey's got a poach, they got to get in there and steal the design team from open AI, and just double their salaries and double their options and get them on the team or find somebody who can. So they've got a massive lead there. But that's an easy one to close. And then they've also the question is, who's got the lead on data? So I need you to answer that question for me. Because the language models are a
SPEAKER_03: parody. The interfaces can be a parody, but the data that doesn't seem like it can be parody. So explain. Well, so I think there's this answer comes in two parts. One is the
SPEAKER_04: this answer comes in two parts for me. And it's actually one of the parts come from your recent interview with David Luan from adapt. And you did a great job. I want to give you credit as one of my favorite twist episodes. And David shared just a little bit of kind of nuggets along the way as you were interviewing him. And one of the nuggets that he shared was in terms of the training material. It wasn't all public information. They went in licensed information. Yes. And one of the things that might have happened, and this is like speculation, but you know, I think we can see sounds reasonable, which is prior to the explosion of, you know, last November 30 of chat, GPT, and then early next year, which had GPT for GPT for, if you were some organization, and this open nonprofit shows up and said, Hey, we want to license some material for you, you'd probably cash or you want to pay us some money, you're doing some kind of nonprofit research, great. They may, and you know, they have a contract for it, it's all good. I think, you know, that's an interesting thing in terms of what they were probably able to do in a past life. And what we're seeing now is in over the last, even the weekend, and last week, some people were complaining about GPT, GPT, fours capabilities are coming down. And that may be some of that material may be having to come out of the training material, they may be updating their models. And we'll talk about that in one of these demos that are coming up. And so I think between open AI being at this for a long time and their approach, and then Google with just their vast access to information, you know, being kind of the company that organized worlds information. They both had this interesting approach. What I will say, and I tweeted this yesterday, I'm saying right now GPT three, five has been commoditized. So yeah, we're gonna do a demo for coming up next. And it's using a specific model we'll talk about, but I'm willing, I just want to say here, GPT three, five level models been commoditized. There's nothing special about them. Wow. Okay, this is big news, folks. And this is great
SPEAKER_03: news for the overall ecosystem, correct. Now startups are not beholden to open AI. When the API has become more rich, and the costs go down, this is all going to be commoditized in the same way storage has. Because there's a big question if you paid a lot of money to invest in open AI by the secondary shares, and listen, I'm not I'm not trying to screw up anybody's deals here. But if you invested in a large language model at a very large dollar amount, I'm concerned about your investment. Now you have downside protection, because even if you invested in something at 10 billion, you have the preference stack. So don't cry for any of these folks. They knew that going in. But the fact is, a large language model on the internet is worth a billion dollars. If it's one of the top five, it's not worth 100 billion. Am I correct? That's
SPEAKER_03: the best statement you think? I think you're great. I think
SPEAKER_04: you're right. Right. But I think literally last year, you would
SPEAKER_03: value a large language model at 50 billion, 25 billion 100 billion, because there were only two or three really good ones. Yes. Now there's going to be 50 good ones. Probably, what do you
think? And there'll be 50 worth a billion and none of them? Well, you won't even be able to tell the difference between them for nine out of 10 searches. Am I correct? I don't think we're
SPEAKER_04: going to be able to tell the difference. And that's maybe it's a good jump off point JK to the to the next thing. Okay, here we go. Demos coming. Do we need to get a score to anything?
SPEAKER_03: You didn't get your score for this release by Google. So in totality, I gave it an A plus because samurai Sergei just proved he just commoditized the market. Yeah, deep my eyes the
SPEAKER_04: market. I'm, I'm only going to give it a B plus until we get API access to, you know, pro which is coming on Wednesday. And when we and I want to also reserve until we get to actually play with truck because we we haven't had a chance and so, but I do think they have an it's this is like the example you gave if you get to come back and get your score updated. So either they release like an updated video of Gemini ultra kind of doing the it all like as they showed in that video, or they just release it to us developers and a limited sense. And you can move your way to an A plus. All right. And that
SPEAKER_03: episode is 1855. If you're looking for it, folks. It'll be right show notes. Yeah. Okay. Okay. So next, we're gonna
SPEAKER_04: actually I'm going to just jump around in our notes to this because it's just it's a natural flow. And then I'll come back to the next thing I want to show you. So next this weekend, the team at Mistral released another new model, and it's called an eight by seven B. And you can you can play with this model at
SPEAKER_04: post one of the things that you know, if you're a subscriber to po, you're able to pick which model you want to chat with. And they have like all kinds of models there. You could they have API access to GPT for they have Claude and so you don't need to have an API key. They abstract that they use their API
SPEAKER_03: keys, you pay them. That's it. They figure it out. Yeah. And then you go get a subscription to po because I really impose that Cora Cora, which is Adam DeAngelo. Yes. Who would be great to have. But we should have a guest. We should do like a Tuesday with a guest. Adam. So yeah, let's do let's do Adam on a Tuesday. We'll have a back to back but be great to just go through Adam and his thinking but he's also the one on the board of open AI and at the center of all that nexus of yeah, chaos last couple weeks. Not that he caused the chaos but he was he was he was there in the room where it happened was happening. And he was there. He was there. Yes, he was at chaos.
SPEAKER_03: So why this model is really interesting. And this is the
SPEAKER_04: when when I saw this model and I got to play with it. It basically, you know, led me to the conclusion, which I said 3.5 has been totally commoditized now. So this is what's called a mixture of experts model. So this is eight seven billion models working together. And why this is really interesting is in a few ways. One, when the models are smaller, the resources that you need to run them become much easier to obtain, and the resources required to train them become much easier to obtain. And so, generally, a rule of thumb that you can use is, if you look at a model size, and if it says 7 billion, you need 70 billion tokens to roughly train it 10 x the amount of parameters to train it. And so when you get these really large models that are 70 billion, 270 270 billion, you need trillions and trillions of tokens, which then leads to you need very, very specific compute clusters that only a few people in the world have. When you have the smaller models, you can use much more kind of readily available compute to train these things. And what Mistral released with this model is a mixture of experts. And this is generally considered how OpenAI is structured. It's, I think, you know, they may have changed it recently. But before people were saying it's like 620 billion models working together. So they did 8-7 billion models. And what they were able to show is 8-7 billion models can outperform llama 270b, they can outperform 170 billion parameter model. Okay. And, and that, you know, as we saw on the previous one, the llama 270b was lower in score, but it's designed to be fine tuned. And if you fine tune it, you could probably get it up to, you know, matching all the other better models. And now that you can take a smaller model, which is much more realistic for smaller companies to run and fine tune, you know, the 3.5 era is commoditized. And so you can play with it here. And I gave it the gonna say, tell me about the founding of Atari, and it was able to tell me, you know, who founded it and what went on. And, you know, you can run it through the benchmark. But the key thing here, talking about, talking about, you know, po and this Mistral model is we've, you know, the open source community, combined with Mistral has found a way to take on these large models. And just one year ago, GPT three, five chat GPT was announced. Now we have an open source model that anyone can take and fine tune and run without having ridiculous infrastructure. And you have the equivalent of what chat GPT was a year ago. So I'll pause there and let you react to that and ask me questions. Yeah, I mean,
SPEAKER_03: here we are. I just subscribed to po I paid $199 for the year. Yes. While we were talking as I was like, well, that's, that's a good start. Well, I mean, is the cheapest I'm going to spend and I want to be up to date on all the models. So the fact that they have abstracted it, so let me just open up the conversation to po generally speaking. Yeah, I've been looking for this because like this show kind of helps you level up. I feel like letting people see everything is great. And then they also have bots and they let you create bots and stuff like that. Yeah. So that was that was a little bit of the tension that
SPEAKER_04:
was there with GPT got launched, right? Because they had just launched these bots. And then GPT got launched, like literally a few weeks later. So that was part of the speculated tension. Maybe we can ask Adam about it directly. Well, I mean, also, no
SPEAKER_03: conflict, no interest, right? We always say that here in Silicon Valley. So listen, you're on the board of this thing, who knows who's an investor? Maybe, you know, Sam has shares in Cora, maybe Cora has got a licensing deal with open AI, and they're sending a billion dollars back and forth, who knows, you know, I think Cora has got this incredible data set. I think what Cora should do is come up with a revenue model for people like me who are super answer is in the system. Like I did a thing with Cora twice where they had me do a like live q&a, like an AMA. And I just took 25 questions, and it really builds their corpus up with an expert. Yes. So you know, I did. And it grew my following on core. And now I never figured out how Cora would work for me, how it would help me. I'm sure people see my answer there. Maybe I get a little bit of play. But I know that people who are lawyers, accountants, real estate people, they love answering questions there and pulling bullet points on it. Because it makes them famous, right? So if you're not famous, you can become a famous real estate broker in New York City by sharing your knowledge, right and being number one there. And then people call you, they know your name, they see it, you can send your profile to people. But what if, you know, every time my answers were used in an LM, I got a licensing fee. So as the number one angel investor, early stage investor, and you as the number one, you know, AI developer and Susie as the number one, you know, tech lawyer and john as the number one real estate agent, every time they got their licensing fee at po, they distributed to us as being you know, x number of answers in the corpus x percent of real estate or whatever $10,000 a year, the spotify for everyone else, the Spotify, Spotify for everybody else, Spotify for knowledge, right? Where is that model? And I think that Adam could do that where he just said, folks, hey, we know we've, here's the verified list. So if you move to verified on Cora, and we know your credentials, we have your driver's license or passport, we know it's really a human, you're not stealing the answers. And you've done over 100 answers, and you've been on the system more than two years, you get into the licensing pool. And if you want to be in the licensing pool, you got to be an expert and get this much, like, just take that. And then for all time, and then they could be telling me, hey, if you do 100 questions, we'll pay you in advance. Yeah, these are 100 questions we want to have in the corpus. If you answer these hundred, we'll give you $100 each, we'll give you 10 grand. And then that'll be against your earnings in the future. There's a really interesting model there that could work.
SPEAKER_04: Yeah, I mean, I love it. I think, you know, what you talked about is sort of what Sam hinted at with GPTs. Yeah. And you know, we demo them as soon as they came out. And I think, you know, the, the enhanced ability of, of Quora slash poll, is that they're not limited to offer it with just the open AI model, because you can mix and match and you can have models that are lower cost to run, that maybe the monetization is better on because they don't have when you know, it's mistral, they don't have to pay as much money to open AI for using chat GPT, especially GPT for which is quite expensive. So okay, so to recap what we just saw, yeah, a high order point here is there
SPEAKER_03: is a new model that has commoditized the 3.5 level. And it's essentially now free to use these. Yes, you can run it
SPEAKER_04: on pretty standard compute, and find a standard computer, a
SPEAKER_03: MacBook Air M2. I'm saying like for a service, right, like so
SPEAKER_04: for a developer, like, you know, if you want to run like a service, you're gonna run it in the cloud, you don't need to go and procure a farm of h 100 to run it. God. So you can run this
SPEAKER_03: with just standard CPUs, you know, you should use you could
SPEAKER_04: you can, but you should use GPUs, but you don't have to buy the latest and greatest from Nvidia that has so when the models get bigger J Cal, you definitely need to use the bigger chips because they have a huge memory footprint that's required to run them. And we're going to show that the next demo when you're trying to run these things. So when they're really, really big, you have to have a memory interconnect amongst these chips, and they have to run in like racks and even you know, more even beyond chunks of information, correct time to
SPEAKER_03: process and it just can't be done without Yeah, it's not gonna read that from a hard drive that'd be far too slow. It needs to have really high speed memory. What do they call the memory now? There's a specific name for the memory chips in inside of these GPUs. Just like VRAM, like, you know, for that they use for inside
SPEAKER_04: GPUs. But the idea is that when you get these large frontier models, like, you know, 100 billion 200 billion 300 billion parameters in size, they are hundreds of gigabytes, even approaching, you know, like a terabyte, well, a terabyte of memory, as you remember, even from your days, working in the computer lab, a single computer can only support so much memory. So what you have to do is have multiple machines stitched them together, have them working amongst each other and very close. So you can support it when the models become smaller, they can all fit on a single machine that really lowers the type of, you know, hardware that you have to go after for to running these things. Got it. Awesome. Okay, yeah, continue.
SPEAKER_04: Okay. And so, yeah, last thing just on PO. These are the different models that you can access if you're using PO today, you can use GPT-4, Playground, Cloud Instant, Dolly, you know, Mistral when I was just showing you there, Cloud 2 with 100k. Yeah, so all the different models. So it's really, really great to leverage if you want to try the different models and get on the subscription tier 200 bucks a year. Great. Awesome.
SPEAKER_03: So I guess we got to grade that. Yes. B plus, I don't know, what do you feel? Well, I'm going to grade two different things. I'm
SPEAKER_04: going to grade Mistral 7B, I'm going to grade Mistral 7B as an A plus. Okay. Because it will, like I said, commoditize the 3.5. Okay, yeah, I'm going to give it an A for that. That's
SPEAKER_03: how we're basing it. Okay. And then PO, I'm going to give PO,
SPEAKER_04: you know, it kind of suffers a little bit of UI wonkiness in the same way. It's like very engineer kind of like tech heavy. So I think it's a great tool. It's quite powerful, has lots of different ways for you to try all the different models all in the same place. And so I think from a UI perspective, I kind of knock it's grade down to probably a B. But I think the fact that they just make it all available, all the different models, they have all the partnerships done. Some they run locally, some they run with other, you know, startups in the ecosystem. So I think it's like a B right now. But if they continue to get it, yeah, yeah, I give I give I give PO a B. But I will give them credit
SPEAKER_03: that they do have like a desktop app now. And they have mobile mobile apps. Yeah. So I think that they I think, Adam understands consumers pretty well. Now, you know, when you do the monetization model, they have a good monetization model,
SPEAKER_04: right that, you know, people can participate in. Yeah.
SPEAKER_03: Explain the model of bots and what they're doing.
SPEAKER_04: Yeah, so the different bots can exist like either as core language models, or you can take one and you can find to an eight so you can create the Jake, the thing that you were talking about. So you can create the J Cal bot that is trained on your books, maybe all the episodes, and it's it's been fine tuned, and you can put it up there and then people can use it. And you know, basically, you can monetize it that way.
SPEAKER_03: You'll like this one. Let me share this. I'm gonna share my screen. I may have to go B minus now I may I may have to go to a C because of this. Oh, okay.
SPEAKER_03: Hey, that's a joke. But I just asked their photo underscore create, which is one of their bots operated by Angel a 10. It's got 12,000 followers. Maybe yeah. 100 97. Just I said, Hey, it's she asked, Hey, can I create images with your prompt? I said, make me a handsome version of Jason Calacanis. Yeah. And it came out with this chubby version of me as like, I don't know, like some dude in San Diego who's, you know, going to get sushi.
SPEAKER_04: Okay, this is this is good because we have it we have a kind of an updated version of this. And let me just share the monetization model here. Oh, I can't, you're gonna have to kill the show. So I'm sure I asked him to make him thinner. Let's
SPEAKER_03: see. Okay, let's see what it does generating for I mean, it is I will say it's pretty fast. That is not good. That's not
SPEAKER_03: good. I said make him dinner and it made a pig horse. Okay, suit. Make Oh, because I maybe because I said make heat thinner. I don't know. Yeah. All right. So this one is clearly hallucinating. But I'm still going with po as a bee and I'm going to do it. Okay, let's do a deep dive into po when we have here. So let's get awesome on the call. All right. And just on
SPEAKER_04: monetization, I want to close it up. It's up to $20 per user that subscribes. And so if you put your model in here, you can make $20 user you have 1000 subscribers, you're going to get $20,000 a month. Okay, here we go. Yeah. All right. Okay,
SPEAKER_04: this one is really interesting. It's been going around the internet. I'm going to see your reaction. We're going to get like a J Cal reacts moment here. Here we go live. All right. And let me just let me just live and direct. Okay. So one of the things that I've been seeing, kind of in the last couple of weeks, like really kind of uptick in is folks creating these influencers. And so, okay, so this, you know, it's a good lead up here, because what I did was I created a collab workspace. This is a Google product. It's it's sort of like a different version of replit. And it's all exactly it's an ID that has access to compute. And what I did, I was able to basically get a model in this case, the model that I am using is called juggernaut Excel. So this is what would be called like a fine tune of stable diffusion, right? SD XL, stable diffusion Excel. So it's a fine tune and the fine tunes been really kind of focused on making people and yes, and so I let me go back here. And, and what I did was I use this framework called focus. And basically focus lets you run these models locally. You can see here in this little interface, when you when you kick this off a model gets pulled in that model is about six gigs in size, you see that right here, 6.62 gigabytes. Yep. And you can see it's utilization, how much GPU RAM it's using system RAM, it's using and whatnot on the right hand side here. And so now what I have is I have a my own model running that I downloaded from Hubbingface on my own, well, yeah, hardware that's in the cloud, right? In this case, it's in Google's cloud, nothing too spectacular. Like it's I don't have to pick like H100 or anything like that. And so let's say blonde woman standing in front of the Eiffel Tower. Right, this can get dangerous. I can see it's getting dangerous.
SPEAKER_03:
SPEAKER_04: Yeah, we might have to edit this out if it goes wrong on us, but it should be fun. Let's be careful, folks. This could go I mean, no, this is
SPEAKER_03: true, though, when you do Dolly, it has a lot of rules. You can't do a regular person, you can't do nudity, you can't get to risque. But I do see people releasing AI, you know, pin ups, let's just say, or even more graphic. And people are doing this. I see a lot of women doing it online, like as a joke, like professional women are like trying to make LinkedIn photos, and then it comes back with something that looks like they're a runway model. And they're like, Yeah, not exactly appropriate. So here is a stunningly beautiful, almost impossibly perfect. Well, it's still it's still working. It's still working. But even at halfway mark, it's like Scarlett Johansson in a very revealing dress nightgown. I'm trying to figure out where it's going with this.
SPEAKER_04: The left hand side is a little bit more PG. But like, I think they're both okay. That's just a really nice. Okay. I mean, these are not perfect. You wouldn't use these.
SPEAKER_03: These would be fine if you were going out for a night on the town. These Yeah, you know, going to the club, but you wouldn't this is not an outfit you'd wear in a professional setting. Yeah. But if you're an influencer, you might wear
SPEAKER_04: something like this. But an influencer would certainly wear
SPEAKER_03: something like this. This would be the average Instagram influencers photo. And so here you go. So like, you know, we
SPEAKER_04: just did this super impressive. And you I guess my question is
SPEAKER_03: to you uncanny valley. If you were scrolling. Yes, not stopping and pinching and zooming and like doing like a detailed thing. But if you were swiping through Instagram, would you swipe passes and think it was a real person with a filter? Yeah. And I think you would I think you would see this and
SPEAKER_04: think it's a real person. Now we have one more demo after this I want to do but let me show what you can do here. So I downloaded that picture. And let's let's just change it. Let's say in front of Eiffel Tower. Let's pick something else. Let's say how about say, I don't know, or you're doing a pose on the beach, right? Okay, great. Well, this is good. Now here we go.
SPEAKER_03: This could get a little risky. I hope it keeps it PG. And so
SPEAKER_04:
basically what I've done now is I've said I want to say, you know, same thing. I'm going to give it an image input. So it's the model is going to use this this person. And I've obviously changed to doing a yoga pose. You know, on the beach wearing yoga clothing. So like, hopefully all that comes true for us. Amazing. So so the first one you did have an
SPEAKER_03:
influencer it picked a Caucasian blonde, whatever. That was its own ideas. We didn't tell it that. Correct. And now you're using that as the foundational because that's the influencer
SPEAKER_04: that I want to have. Right. And so here it goes. Here's somebody
SPEAKER_03: in a yoga pose, which is on a beach. And yeah, it looks reasonable. And yeah, and it should be a version of her. The
SPEAKER_04: first one that we created. And, and I want you to think about this, Jason, in terms of like, you know, the world of Instagram and, you know, everything that we've seen in our world and like, like the simplicity by which like these things can be created now. And what is your reaction to this? And what does this mean to content to a person's? There are some questions there. I'm trying to for very long.
SPEAKER_03: Here's what I would say. If you are a yoga brand, let's say, in your first year, and so you're, you know, Lululemon, and it's a
SPEAKER_03: startup to, you know, 20 years ago, to do these images for your catalog would be a $20,000 photo shoot. Plus the price plus, well, yeah, no, I'm saying 20,000 all in you, you would you would book a model, you would need hairstyle, hair and makeup, you need a photographer, a photographer's assistant, I'm just giving you like a baseline. And let's say you probably need three models, because you were doing a catalog or whatever. And you need a wardrobe, hair and makeup, pull permits, security, photographer, photographer's assistant, and you know, photographer's assistant would handle lighting, and then you have to develop the film, etc. So it'd be a $20,000 process. And oh, you'd also need a location scout to find the place. And you'd have to pull a permit to take pictures there if you're going to have lighting or typically if you're putting a tripod or lighting down, that's when you need to get a permit. If you're just filming. And you know, it's for your personally, you're probably not going to get have an issue. So this is why people use like a Canon 5d or like, you know, like those kind of handheld ones, because if anybody comes, and there's those things can do low light everything, you're just like, yeah, no, no, it's just personal use. So we're just yeah, influencer. It's not a whole Yeah, now when influencers came out, this dropped down to let's say 500 to $1,000 for a shoot, people would have their own get their own wardrobe. So let's just call it $1,000 for the day, the instead of paying $1,000 a day for the model, the model would do it for free for credit to put on their Instagram or maybe give them $100 per diem. So it went down 95%. Now you've gone down 100%. You now are at free. And so the question is, if you're a Lululemon in year one, or the competitor to that, you can make an unlimited brand on
SPEAKER_04:
Shopify up and coming brand on shop, you literally don't need
SPEAKER_03: any of these models. And you could be doing your pictures, based on an editorial calendar. And if f1 was happening in Vegas, you could make these people doing yoga, you know, in front of a you know, one of those f1 cars, or you could have the same models, you know, at Fashion Week in Milan, or New York, you could have them at Art Basel. So now you're traveling around the world with a cohort of models, you own 100% of their identity and their rights, and it takes one person an hour a day. It is insane. This is saving millions of dollars in marketing collateral. So this is called marketing collateral in the business. And this is the end of needing to hire models.
And so if you did hire a model, you'd be hiring them because they were unique to the brand. So Suki waterhouse. I met her one time at a party in LA, quite charming, and she's like an influencer, musician, etc. You're getting a brand information from that person because she's hip and she's got this incredible fan base, etc. Yep. Kim Kardashian fan base traffic, but for the next year, you can't afford to pay them 500 k or 50k, whatever they get, you know, I think it's probably 50 k to 500 k per engagement. You're not gonna pay that. And so or
SPEAKER_04: what if Kim made a model that was of her and made it available to download for use? Like, yeah, yeah. I think that if it was for
SPEAKER_03: commercial use, it'd be fine. And then the second you go into commercial, you got to pay a little bit more. So I think cameo came up with this. I saw somebody was using Russ, the guy who plays Russ hammerman or whatever that guy's name is that the trace comma guy with the Yeah, that guy was doing a bunch of stuff for acquired calm. And he was doing it for Andrew guys, that he would make it like really funny, Andrew guys, that he and he would do it in the Russ hammerman voice. And it was hilarious. And he would, I think like he could use that to his company. And instead of paying $50 a cameo, you pay 500 or something. So I was talking to Steve from cameo and like, they just basically came up with a tier for sort of commercial work. And I think you can use it for one year. So just you know, a set of rights that Kim Kardashian could let you do it on your site. And it's non commercial. And it's $100 an
SPEAKER_03: image or if you want to use a commercial, it's $1,000 an image and somebody just approve it. So yeah, there could be something like that. Kim wouldn't do it. But other people would. Other words. Yeah. Okay. Grimes is doing Grimes is doing that for music. Music. Yeah. And that actually what you would do is a royalty share. Now royalties don't exist for e commerce. But you could say if you want to use this on Shopify, Shopify will make an API, anything that someone to do a Shopify app for
SPEAKER_04: this. That's it. Do it as a Shopify apps. Shout out Toby,
SPEAKER_03: fan of the pod. We have Toby set that up. And then let's say you put yourself let's say they put Kim Kardashian or Suki warehouse. And they say I approve me being in these 50 outfits. And that's 50 of 250 skews. Anybody who sees her in the outfit? Yes. Track and she gets 10% of the sound. Yeah, like, yeah, like a super affiliate. Okay. So let her bring one more, one more done. Given this, man. I gotta give it an A minus. I think it needs a little more refinement. But it's almost technical because I had to load a model in collab and
SPEAKER_04: all that but this is needs to be an app. This was an app format.
SPEAKER_03: I have an A plus because but I just actually, if it was an app format, I give it an A. In this format, I give it either a B plus or an A minus, I'll give it a B plus, I don't want to go too crazy. I'll give it a B plus because I don't doesn't cause it doesn't cross the uncanny valley enough for me. And it doesn't do video yet. These need to do short videos. Okay, so if I'm going to reserve a grade point for a grade, well, some grade, I'm going to give it a name a minus because the next product
SPEAKER_04:
will wrap up quickly here because a bit over that over this is so this is a new tool called I like that picture of me I look okay. I'm that's a medium fat J cal magna. And what it can do is you drop an image here and it's an upscaler and it will and see what it's doing here. It's kind of gave me five o'clock
SPEAKER_03:
SPEAKER_02: shadow. It gave you five o'clock shadow and like an improve look
SPEAKER_04: at it improved your eyebrows and hair. Yeah, wait, which version
SPEAKER_03: is me? This is the one without that's me. Okay, that's you. And
SPEAKER_04:
this is like, older, older, older, because that's what I was I was trying to go for. Because these are younger pictures, right? Yeah, gave me a little turkey neck. So this is
SPEAKER_04: this photo of yourself. Got it. And you made me older. Made you older gave you a bit of a thing and like it cleaned up the photo. Like it made the hair look a little bit more realistic. It fixed your suit. Yeah, that's me. Yeah, maybe Yeah, fix your shoes. Yeah. Okay. So then I took another one of you and I said, this is a quite young photo of you said, Yeah, this is me. I'm 30 years old there. Yeah, make an older
SPEAKER_03: J cow. Wow. I look so distinguished when I'm thin. Right? Look at that. I look like Pierce Brosnan, a little Pierce Brosnan going on. That's from two actions from 2009. It's a 15 year old photo. Yeah. And did you tell it how many years you want to add? I just said older. I think the prompt was older.
SPEAKER_04:
Yeah. And you know, and so I mean, it gives me my it actually doesn't. It looks like the waypoint between now and 65. So
SPEAKER_03: 53. Now that was, was 14 years ago. So that was I was 40 or something. Yeah, maybe 39 in that photo. So 39 to now. Yeah, it's probably right in between.
SPEAKER_04: Okay, then for the influencers we were making and I'll just do this quickly here. You know, we were saying there's a little bit of uncanny value. But if you put it over them, you see here, gives them a little bit more realism wrinkles and so just touch it is the opposite of a touch up. This is a
SPEAKER_03: touchdown, touchdown to make them upscaled and more
SPEAKER_04: realistic. Wow. Oh, so if you put these two things together,
SPEAKER_03: it's a plus. Yes. This looks like a real yoga mom. Yes. Looks like a 40 year old yoga mom from, you know, watch the bicep. Watch the bicep. Yeah, it added instead of
SPEAKER_04:
SPEAKER_03: it all being perfectly smooth. Yes. You know, I like you know what I like about this? I have three daughters. I like not putting a standard out there that's absurd. And that's what this AI is doing is putting out an absurd standard none of us can sort of ever achieve. And I like this. You know, instead, I like the touchdown, I think touchdown for the win. Stop touching everybody up. Let's go touch down. Yes. Let's let's own it. I like being I like moving into my Harrison Ford years. Hoping my Clint Eastwood years I hope I look grizzled and, you know, chiseled, grizzled and chiseled, grizzled and chiseled is what I'm going for. And I've been doing my two I'm starting my two mile run program trying to see if I can lower my two mile time. Okay. And then just you know, I got my I got my 20 pound waist over there on the floor and I just said, Yeah, I just hit him once in a while. Alright, this has been amazing. So for the touchdown product, I'm going to give that a B plus. Yes. The other thing a B plus you put them together, I give them an A. Yes, but I'm gonna be plus on both. I think they're both fascinating. I think it's good. I think they've done a nice job
SPEAKER_05:
SPEAKER_04: like making the photos look more realistic, which is not the touch up and what we see in the Instagram filters. It's like, it's an upscale, but making them more realistic. And I think it really changes the game for ecommerce entrepreneurs or folks that want to, you know, sell products and want to have marketing material around it. What was the term you use? You called it marketing, marketing collateral, marketing, collateral.
SPEAKER_03: So yeah, marketing collateral is let's say you had that picture from the beach yoga session, and you had the three models, you know, okay, we can use this when we go to a trade show. And we put that as the backdrop. Oh, on our website. It's the marquee image. Just really quickly. If we could pull up my sub stacks, I'll just show you what I did here. So go to the top there. So this is Jason Calacanas on startups. And I said this is the greatest moment in time to start a company, the greatest moment in time to start a company question mark right now. And I explain why. And I gave it a prompt that I wanted something that would be the roaring 20s. So combine 1920s with a futuristic world. And we'll call it the roaring AI 20s. And I said, add robots and flying cars, yada yada, and then put the roaring 20s at top and it did. s the roaring 20s like it still has text and then I said put somewhere on this picture started company. So you see at that store, it says start a company. Now what's interesting is, this took me five minutes because at the bottom of my story, I just uploaded a gallery of all the other versions. Because I thought it would be funny. So here's the boring 20s. This is a cyberpunk version, start a company now. It's spelling things wrong. This one is weird. Here's another one that is a bit more like art deco next one. And I did all this with Dolly. And this one was kind of ugly. This one was Blade Runner ish with a bunch of drones and neon. This one was also cyberpunk neon. So anyway, these illustrations and then if you go back to my previous substack, I did the productivity one. And so now in this one, I did a prompt, you know, I wanted a bulldog in it a cup of coffee and you know, a table. And so now I have like a concept here I make these marquee images for my blog posts. And I think it makes them very appealing for people. And I don't need to rely on somebody to make I used to go find a gift that I like and throw a gift in there and that or make a gift from a movie. So I go find a movie I like I download the video clip.
SPEAKER_03: The greatest time ever to be an entrepreneur, all the tools that
SPEAKER_04: just right at your fingertips, sit there and just do it, which
SPEAKER_03: is a good time as any for me to just give a final plug here definitive.io if you need work done by a genius, that genius would be my friend, Cindy mantra and our whole team. Okay, whatever. If you go to if only I started.com, you will see a video of me talking about, you know, starting a company and I my team just got the domain name if only I started calm and so I just made a 15 minute video and this is about founder university. Go watch the video go to founder.university we're starting this we give people 25k checks, two or three person teams we accept 200 teams, we give the $25 25k check to 30 of them 40 of them per cohort. So I put about a half million dollars to work a million dollars to work every quarter. So about right, my budget for this is about 2 million a year 2 million a year. Yeah, okay, 2 million a year is what I'd like to do at scale. That is obviously 80 companies 25k each. So I'd like to or 100 companies 25k each would be 2.5. I would like to actually get this to 200 companies 2.5 million. No. Yeah, 200 companies 5 million a year. So that's what I'm working towards. I want to put $5 million a year to work. In the fund, you're an LP, I'm an LP. Yes. In 25k checks, we take 2.5% of the company, it's the first check into the company helps you get organized to get your lawyer. And so just let us pick up the first 25k of your expenses, and then you got what your 5% is like nothing, you know, nothing. And if the
SPEAKER_04: shuts down, it doesn't work out. No harm, no foul, we lose 25k,
SPEAKER_03: you lose six months of your time. If it does work out, all we ask is that we get to invest in the next two rounds, another 100 250k. And so we can get that 2.5% of 10, or seven or six, whatever, whatever works out of the number is whatever the number is the number is I think it's great as an entrepreneur. Love it. I call it a pre accelerator. And so I'd like you to do a talk at it. So hey, producers can I want Sonny to do an AI talk and an AI segment with the founding university companies. I've now done I just want to let people know 60 this year I did 60 25k checks, I think. So I'm deadly serious about this. I am investing in 100 new companies a year and doing follow on 50 companies. And I think we're going to catch up to Y Combinator I don't you know, listen, watch that for launch.co slash memo launch.co slash memo is a little bit of time for QPS to get in but I think the accredited investor slots are filled. Right everybody. See you next time. Follow at Sunday, follow at Jason follow at TWI startups. We'd love you guys give us feedback. Sunny This has been a great joy. Now we play poker every week. We do a podcast episode every week and then we're gonna start chasing that pow pow. Let's let's do it. We need to go everybody. See you next time. All right.
SPEAKER_04:
