SPEAKER_00: there is a lot of innovation happening now. And it's faster than ever. And it's coming in the way of new types of models, open source models, agentic reasoning, and voice. And so in the second half of this year, J-Cow, we are going to have some surprises that no one was anticipating.

SPEAKER_02: Okay, there you go. So that's inside information. That's the inside line or a prediction.

SPEAKER_00: Generally, I'm telling you, we're going to be more surprised in the back half of this year than we were when OpenAI first came out. Oh, it's a big claim. It's a big claim. I don't know how

SPEAKER_07: we phrase it as a bet. But what you're saying is everything that happened up to now is the epilogue and that the story is coming. The real story is coming. The story is coming now.

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SPEAKER_15: to get 10% off. All right, everybody. Welcome back to This Week in Startups. Madra Mondays are back. Here is my guy, Sandeep Madra. I don't know if we're releasing this on

SPEAKER_16: a Monday. We're taping it on a Monday. Taping it on a Monday. Welcome back to This Week in Startups.

SPEAKER_19: Good to be back. People are like, yeah, where you been? Where you been? Everybody's asking,

SPEAKER_00: where's Sunny? Where's Sandeep? Business called fundraising. And you know how it is when you're doing that. The calendar got prioritized for meeting investors. And you've had to do it too. I don't know, Jacob. Actually, I'd like some advice from you. You're able to keep the shows going and you have multiple shows and fundraise. But for me, fundraising, it's kind of like took over my calendar. It's like, hey, we're meeting with this and you can't really move them around. So

SPEAKER_21: how did you manage that? I broke myself is how I managed it. I mean, if I'm being honest,

SPEAKER_15: I didn't manage it. It literally broke my brain. We are living in the toughest fundraising environment since probably the dot-com era, possibly right ahead of the Great Recession, I would say. And so with an AI company, that's a notable exception, obviously. But even still, because there haven't been a lot of returns, if you look at, you know, IPOs, they've been few and far between and they've been muted, right? Instacart coming out at 8 billion, private valuations being 30 or 40 billion for that company, which means LPs and VCs are less apt to put money into products. So we've had a little bit of indigestion. The gears are a little gunked up. We talked about it on this pod a couple of times. M&A is really anemic. We used to have a very vibrant

SPEAKER_07: mid-market for M&A since the Biden administration hired Lena Kahn. That has destroyed the startup space. And I think putting aside any politics, how you feel about different presidential candidates, this is an area that really matters to America's viability. If there's no mid-market for selling

SPEAKER_15: Figma to Adobe or Instacart to DoorDash, whatever it is. Or the little teams, the little acquisitions. And the tuck-ins, like you're referring to, 10 million to 100 million tuck-ins. That mutes investment. So now you've got LPs and VCs either not investing in as many companies, not investing as many dollars in companies, companies not being able to raise funding, which then means less job creation, which means a less competitive America. You have to have two different concepts for M&A. M&A for Facebook, Meta, Google, Amazon, Microsoft should behave one way. In other words, companies are over a trillion dollars. They should have one rulebook for M&A. For the mid-market, there should be a different rulebook. So if Uber and Airbnb, Coinbase, call it the 30 billion to 250 billion crowd. If they want to do interesting things, I say, let them. You can review them. And then I would say any acquisition under, no, let's pick a number, $1 billion. Just, you can do whatever you want. Under a billion dollars, you can do whatever you want. And if you've watched, we've now had two AI acquisitions in the space where they were acqui-hires with leaving the shell company. And somehow money's flowing to the investors. They're getting some of their money back somehow through like backdoor contracts. It's all a giant scam, a hack to route around Lina Kong. So if you tighten your grip, more deals will slip through your finger. So Lina Kong's a complete

SPEAKER_36: disaster and binds a complete disaster when it comes to this M&A situation.

SPEAKER_00: It also ties into like incentives drive behaviors, right? Like these big companies can also put offers in front of people, which look like acquisition, right? Which,

SPEAKER_39: you know- Say more, explain.

SPEAKER_00: Well, look, like, you know, if you're Microsoft and you want to staff up a new AI, you know,

SPEAKER_41: division, like as they did, right? Or you're Amazon and you want to staff up an AI division, like they did with the, you know, adept thing that just happened last week. You offer the founders

SPEAKER_00: 10 plus million dollars a year or over two years. You know, if you stay there, let's just say you get 10 million a year and you stay there for five years, right? That's, that'd be the, probably the

SPEAKER_42: equivalent of you selling a startup for $500 million and you owning like 10% of it after a couple of years.

SPEAKER_15: Yeah. You and your co-founder are 10%. Exactly. So now what is all this, you know, crazy M&A philosophy of future competition done? They just routed around you, Lunacon. They routed around you and you accomplish nothing. And then what happened was the big companies get stronger because now they don't have the competitors in the smaller companies. So you're actually having the opposite

SPEAKER_27: effect. And the investors kind of get weary where the investors are like, Hey, I'm not sure

SPEAKER_00: I want to do this because what if the team gets scooped away or decides to go away or walk away?

SPEAKER_49: Yeah. So there's unintended consequence. Show me an incentive. I'll show you an outcome.

SPEAKER_36: And here's what happened. So you got to be very careful. If you want to put your thumb on the scale in a con, you, you know, could just tilt the scale and everything goes flying off the scale and you don't catch any of it. You don't do any of the things you intended. So it's just a terrible approach. It's been executed very poorly, but let's get to demos. Everybody wants to see demos. I've been playing with chat GPT 4.0 and the new clause on it. And I pay for both of those, obviously for my companies. And, um, it's getting pretty damn impressive.

SPEAKER_42: Well, yeah. Claude Sonnet is where I wanted to start today. You know, as we get into it,

SPEAKER_41: I just want to give credit here to this really incredible thread that, um, Min Choi put together, and then I'll, I'll show a demo from it, but I will give this, uh, credit real quick. He really went in and, uh, went across sort of a bunch of different ideas and came and, you know, aggregated them. So, you know, here's build an iPhone app prototype from scratch. So really the thing that we're going to talk about when we do Claude is they have this mode now where there's like a, on the right-hand side, you have that enabled where it can do sort of an output for you that's different than the text output. And so here, you know, someone made an app. I'm going to demo this next, which is make it interactive dashboard from an earnings report. You can make games, you can go through this, right. You can get insights on business, one click SEO tool and not. So I'll, we'll put a link to this, but what I did here was I basically dropped in, you know, Tesla's Q1 2024 earnings report. A PDF. Yeah. A PDF. And I said, come up with a detailed analysis and summary with stock recommendation by carefully reading this report, put together a nice and interactive, but highly detailed report. And so here's sort of the summary, which we were all used to getting.

SPEAKER_42: What I got on the right-hand side is an interactive report. Wow. So this is the presentation layer. So

SPEAKER_57: instead of just seeing texts that you would copy and paste and start building a deck, it knows that

SPEAKER_15: you want an interactive report. And that means it's making something beautiful. Now, I don't know what

SPEAKER_46: format that is. It looks like HTML, but well done. Yeah. You have to enable this thing called artifacts.

SPEAKER_00: If you turn that on right in the, in the features preview, you know, I'll just copy this prompt over

SPEAKER_41: and I'll add in a, you know, here's Nvidia's last 10 queue. Right. And, uh, you know, you're sort of limited to the upload side, which I think is like 30 megabytes or something like that more than enough

SPEAKER_63: for any kind of text. Yeah. And I like how you tell it to be like thoughtful or something, or to be

SPEAKER_65: thorough, like the AI needs to know that. Just, just, you know, just in case it doesn't know, right. Just in case it doesn't, it doesn't want to do its best work. You've given an encouragement

SPEAKER_41: to do its best work. We have to be mindful to our future overlords. Absolutely. And so, and so what you see here, um, it's obviously doing the standard analysis and what it's going to start doing, once it's done this, it's going to basically open a tab to the right hand side. Wow. You see here is where it's starting to create the interactive dashboard. So it's basically, you know, writing all the, um, the code for it. Right. And so this is being done in HTML. This looks like JavaScript or

SPEAKER_00: something. Right. And so that it can be interactive. And so at the end of that, you'll get this. And so

SPEAKER_63: now. So it says vehicles delivered, operating margin, the production, and then even a chart.

SPEAKER_00: Yes. There are deliverables, revenue, net income. Exactly. And then a quick summary. And, you know, you can download this, you can copy it out. So think about how powerful this is. Like, if you have a bunch of data from say an event you just ran, like the liquidity summit, if you have all that data about, you know, how people interacted and how many people went to different sessions or whatever it was, you throw that in there, ask it to do the work. And basically it comes out with a really, really nice report for you. Whereas before all we were getting were these kinds of text updates, and you had to take these and go and, you know, copy and paste them into PowerPoint or do something. Now it's creating the dashboard for us. This is a huge jump forward.

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SPEAKER_76: So I don't know if you've tried this with your teams yet, J-Cal, but give me your thoughts.

SPEAKER_07: I haven't. It's absolutely extraordinary. And we have a lot of information that I would like to put

SPEAKER_15: through this. So one of the things we've been doing since we're an at-scale venture firm doing 100 investments a year, think like 25% of what Y Combinator does a year, maybe 20%. They do 500 companies, we do 100. We get a lot of information from those companies back. We put them in a database. We're currently using Notion as our database. And to be able to pull that data from them and then say, hey, make us a report which of our companies are in group one, two, or three. And we have a very basic, I like creating simple systems. We put our startups into three buckets. One is high growth, consistent growth. In other words, they're probably series A, series B, they figured something out, they have product market fit. Bucket two, figuring out product market fit. And then number three is they've run out of money. I don't know, the co-founders broke up. In other words, they're still active in some way, but something's in all likelihood going to wind down and they're trying to find a buyer. They're in the middle of M&A. And we have humans figuring that out, analysts and researchers. But eventually, AI should be able to figure that out. And we should be able to then do some analysis over time of companies and say, hey, which companies look like they might be the next to move from group three to group two, from group two to group one.

SPEAKER_00: Yeah. And look, this is the one that we just did. So it's slightly different. So you can really tune this and it comes up with incredible. Incredible. I've been using this quite a bit because I think it's, you know, if you ever have any task to do, and even if you just want to brainstorm for yourself just to get this visualization. So I find, you know, like I said, like this type of prompt, which definitely,

SPEAKER_41: you know, came from one of the threads that Min shared. So I want to give him kudos for it, but it's a really useful prompt and it's powerful and it's a great way to analyze a bunch of things.

SPEAKER_15: Yeah. These things are definitely getting smarter. I give this, this is an A plus for me.

SPEAKER_36: I'll be honest. I kind of finally feel like these LLMs are not doing random moments of interesting potential. I feel like they're nailing it. And I'll give you one example. Maybe you could just

SPEAKER_15: fire up chat GPT 4.0 and since you got screen there, we're opening up an accelerator and a studio in Austin, Texas. So you heard it here first. And so, and so I'm spending a little more time here and imagine you were looking for, I don't know, a personal assistant, right? I don't know how much a personal assistant, I know what that costs in the Bay Area. I don't know what that costs. So here, just write this sentence. Please tell me how much a personal assistant costs on an hourly basis in Austin, Texas. Please cite five sources and please put the high, low and medium salary in a table. And I did this in 4.0 and the result to me was extraordinary. Okay, great. Oh, here it goes. Okay. So when I did this, it was unbelievable to me. So here you go. A personal assistant in Austin, low salary, medium salary, high salary. And I just said, Hey, can you link to it? And it gave me a similar result here. And so it's really, and you remember when it used to

SPEAKER_36: go out to Bing last year, you would do the search and it never worked and it crashed. And like we'd ask

SPEAKER_15: it to put stuff in a table and sometimes it'd put it in the table, right? Sometimes it wouldn't, but here it is. It's now quoted salary.com. And now just give it a follow up and say, give me five different sources of data and average those sources. And so when I did this and I said, give me five different sources, it gave me Glassdoor, indeed salary.com. And I was just blown away because when I got, here you go, zip recruiter, is that the, I don't know that one, Glassdoor, indeed salary. And now you start to see a range of them and you see the sources. This is what a college educated researcher would have spent in our companies in Silicon Valley. How many hours? Two, three, four,

SPEAKER_00: half a day. This is like, you know, the way I think about it, you'd come in in the morning, you tell someone and maybe in the afternoon, you'd get an email back with this. Right. And so this is

SPEAKER_100: wasted work for a human to do now in the age of AI. So when you see that companies aren't adding

SPEAKER_15: a lot of positions and you're wondering, why is that happening? Well, how is Uber and Google and Facebook growing, whatever they're growing, 15%, 30% year over year, adding billions of dollars, but they didn't add any people. This is a perfect example of this. I would normally ask somebody on my

SPEAKER_100: team to do this in operation. The amount of time for me to explain it to them and get the result is greater than the amount of time it takes me to test. It would take you longer to write the email

SPEAKER_00: because you'd have to be a little bit more formal and detailed. Correct. And I'd have to tell them

SPEAKER_15: why I'm doing it and all that stuff. So this now is really becoming, as I said, extraordinary. So I just want to say to the team over at OpenAI and ChatGPT4, incredible job on giving citations here, which we're sitting here a year or two ago, we would have been complaining about citations. But this idea that the work is actually good enough to put into actual production is something we saw with developers checking the work. Now we're seeing it in operations. We're seeing it in data

SPEAKER_109: analysis. This is heating up, right? We're trying the same thing just in Claude Sonnet, right?

SPEAKER_104: So yeah. And Claude Sonnet did a very good job at this. Yeah. So here you go. I mean, boom,

SPEAKER_36: and you could actually create a visualization and now what you have to worry about here, I think is this goes back to our discussion about, is this stealing or not? And so why would I ever click through to ZipRecruiterSalary or indeed.com? There is no reason for

SPEAKER_15: me to ever go to those websites again. 100% of my attention and money is going to Claude

SPEAKER_36: and to OpenAI. I'm paying 20 bucks each for these services, maybe 30, I can't remember. Yeah. And so not only is the human being taken out of this, that it would have worked for me previously. When I say previously, last year, last year, I had humans on my team working on this.

SPEAKER_95: I don't know. I no longer have those humans working on our team.

SPEAKER_00: This is definitely a real life problem for you where you were like, Hey, I need to get an assistant. Please help me. So I know. Yeah.

David Friedberg: Now the next phase of this would be write a job description. Okay. It's going to do that very easy.

SPEAKER_15: So now just say, write me a job description for this job for somebody with five years of experience and give me 10 bullet points to choose from for the skillset. That would be something again, I would ask a human being working in Silicon Valley or working remote, let's just call it 50, to a hundred thousand dollars a year salary, depending on if they were right out of school

SPEAKER_07: or if they had five or 10 years. And so here we go, boom, about the role, key responsibilities.

SPEAKER_15: To write up a job description would have been a ton of work as well. So now the job description is done. So if you're an HR professional who's been in the field for 10 years and you work with management to tell them in an interactive discussion, how much it costs to hire people in a city and to write a job

SPEAKER_36: description is done. Now, the next phase of this would be post this to five services, use my corporate card. And then the next phase of this is sort through the candidates, ask them five questions on email, and then rank them for me and put them in a table and schedule

SPEAKER_00: an appointment with them. You want an agent that's designed specifically to work on this for you, both in, in real time and offline, right? Where it starts and then it, once it's going, it's like, Hey, look, post it, come back. And, you know, even set up interviews, maybe even do a screen of these folks. Well, yeah, that's kind of the next part of it.

SPEAKER_15: So if you're an HR startup, these kinds of things are easy. The next hard part is of course, the agent doing stuff. And now when we talked last year about the Maestro concept I had where I'm in Maestro running a company, if you're a startup, you're not hiring an HR department, obviously. And you might've hired an HR consultant, or you might've hired the proverbial jack of all trades, right? The utility player on your startup. It was a place for the utility player on the startup. I don't think there is anymore because you can just do this. If you're the sales executive, you can just do that. So I think there's a large group of administrators, operators

SPEAKER_07: who are on the cusp of just being 10 to one. So if you had 10 of these people who are operators in

SPEAKER_15: a hundred person company, which is what I would say, like 10% of people are in operations. I think

SPEAKER_90: they could just go down to one, maybe two, if you wanted redundancy and people to answer requests

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SPEAKER_00: One really good AI native, you know, we used to say like, uh, internet native and then cloud native, cloud, exactly. Mobile natives. AI native. Yep. AI native. That just thinks the first thing

SPEAKER_41: they do is go here and try to, you know, get it done. I just want to give clothes on it and a plus

SPEAKER_136: and I want to give 4.0 an A plus officially, because we've been, we've been off for a month. We've been busy fundraising. Congrats on getting that done. Hopefully. And so I just want to get both these

SPEAKER_15: A pluses. These are ready for prime time. And if you are listening to this and you're wondering why you're not getting a job, you were previously considered a very valuable contributor. And now you're wondering why you got laid off and why you're not finding a job. It's because the stakes have gone higher. The stakes have just gotten higher. You have to provide more value. And what is the more value? Well, what can't this do? I describe what it can't do. It can't post it to

SPEAKER_36: indeed and it can't sort through the resumes, et cetera. And so every company right now is just figuring out, you know, I have 10 people in ops. I need four. I have four people in ops. I need two.

SPEAKER_137: I have two. I need one, whatever it is. Right. Or twice as much productivity. And I may have to

SPEAKER_15: switch some people up. Yeah. And so this is just what's happening and, you know, at startups and big companies and big companies, I think are not AI natives, but they will be soon. And so if you work at a big company and you see me do something like this and your HR department's the same size, you probably need, and listen, I don't want anybody to lose their jobs, but those people need to, you know, half the people need to be cut loose and reassigned to something more productive in your org. Maybe it's sales, maybe it's marketing. There might be some other place, but I suppose this level of efficiency is coming to everything. And in sales, unlike operations,

SPEAKER_40: being more effective just means you increase your sales per person.

SPEAKER_101: And can I add one thing, J. Cal? I think we both give them A pluses. I'm an A plus on both as well. I think really well done. More and more powerful by the day. You know, the other thing that's happening is the model rollouts are just happening on a continuous basis now, right? And so you just

SPEAKER_00: have to start using the tools and maybe use more than one of them and you'll see the advantages that come from it. The other thing that, you know, is related to, you know, so you have to change your

SPEAKER_101: behavior. You have to become an AI native. If you just did sort of what J. Cal was asking for and dropped it in Google, look, and I have generative search enabled, look at how weak this result set

SPEAKER_00: is now. Like what is going on? Like with, you know, in fact, I feel like Google is going backwards here.

SPEAKER_15: I mean, it's giving you the links that Sonnet and 4.0 ingested very quickly. I mean, the speed is also the really, you know, a very important Google asset, you know, if you make things faster, consumption goes up and usage goes up. Here we go. Like the these things are going really fast. I do think ZipRecruiter and Glassdoor in these places have to block the crawl, have to block chat. Unless there's a relationship on it. Unless they pay per citation, per query, per whatever, because these things are going out to the web and there is no reason to click through to ZipRecruiter or Indeed or Glassdoor and go to their website and get bombarded with ads, pop-ups, whatever it happens to be, uh, calls to action, et cetera.

SPEAKER_42: Just since we're here, Jacob, this wasn't even part of the plan, but I'm going to drop it in

SPEAKER_145: Gemini advanced as well. Okay. Gemini advanced. Yeah. So Google's Gemini advanced. Okay. It didn't do

SPEAKER_69: the table. Oh no, it did. It did. It did down here. Yeah. Oh, and let you export the sheets,

SPEAKER_07: which is, I remember that feature, such a great feature. Okay. I mean, it looks comparable. I mean, they're all comparable. I started also doing this with product searches. So I was looking for a new

SPEAKER_15: portable speaker. I was just curious, like what the highest rated ones. And I asked that specifically, tell me what's on Wirecutter. Yeah. Oh, okay. So go ahead and do this. What are the best portable

SPEAKER_36: speakers? Please cite Wirecutter, put it in a table with a link to Amazon. Give me up to 10. Now,

SPEAKER_15: what's interesting about this is the way Wirecutter and other people who do the review sites make money is they get an affiliate link. And so here it didn't put the links in, but if you do the, if you cut and paste this and you put the same one into chat GPT 4.0 or Omni, it gave me an Amazon

SPEAKER_07: link, obviously without the wire cutters thing. Now, what you're going to see is like the wire cutters behind a paywall. So how does it know? Not always, not always. Sometimes it's not. Yeah.

SPEAKER_158: Um, cause I do this all the time. Oh, here you go. All right. And there's the Amazon link.

SPEAKER_104: Okay. Um, and I think you could put in here, give me, uh, when you do the followup, just say,

SPEAKER_36: uh, give me the same table with a quote from Wirecutter about each one. Yeah. Modify the table

SPEAKER_136: with a quote from Wirecutter for each product. I hope somebody from the New York Times is watching

SPEAKER_162: this because here's the wire cutter quote. And by the way, this is a much better presentation.

SPEAKER_101: I don't know, but, but it's really good. I mean, I've not done this, but I'm going to start doing it. Cause I'm, I always do whatever I might go to is this search, but like on, you know, uh, on Google,

SPEAKER_22: and then I basically end up on Wirecutter, right? Like that's it. So I use Google as navigation in

SPEAKER_15: those cases. So, you know, if you have other rating sites, like I also like PC magazine,

SPEAKER_36: they have a lab or something like that. So then you can start to say, put the, a quote from PC magazine, put a quote from here, put a quote from here. That's what a personal assistant does. So I did this and I'm like, well, why am I getting a personal assistant again? And

SPEAKER_104: then it's for real world stuff, right? So if it does this, then we know it's hallucinating,

SPEAKER_138: though. You gotta be careful. But let's see. And they had a quote from PC mag for each product. That's it. Yeah. There you go. Oh, it says, I can't do it. There you go. It says restrictions.

SPEAKER_01: Oh, so wow. Whoa. That's the first I'm seeing it. It seems that I am unable to retrieve the PC

SPEAKER_36: magazine directly due to restrictions on their website. However, I can provide general speakers based on

SPEAKER_170: available. That's interesting. Oh, wait, general PC mag insights. It did it anyway.

SPEAKER_62: No, I know. But I think that's like, it's hallucinating a little bit. Like, yeah.

SPEAKER_15: Interesting. Well, we'd have to cross-reference this, but again, you know, you start thinking about where you're going to start your journey. What do we use Google for? I use Google to find restaurants. I use it to buy products. I use it to hire people, you know, and this is a better experience. The results here are better. And they give me a shout out. But I do think if you're PC magazine or Wirecutter, you need to say, if you use any of our data, you need to pay us. And if you are in fact, you know, in the lawsuit from the New York Times and OpenAI, Wirecutter is why I pay for New York Times. It's like Wirecutter then New York Times in order. Yeah. I am no longer visiting the Wirecutter website. So if you want a piece of evidence in

SPEAKER_178: your lawsuit, clip this and submit it in your, you know, briefs. That is super wild.

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SPEAKER_101: about the service here on this week in startups, you get 10% off. I mean, I was not expecting that. We use these things all the time. So I really like this. I got to say that is a way to...

SPEAKER_170: And this is what being AI native is about. I think AI native is, you know, the name of this episode.

SPEAKER_177: Yeah, you've become AI native. When I'm in the car with my daughters now, they asked me questions. And I told you, I put my quick key on my iPhone to be the chat GPT for

SPEAKER_15: dialogue where you talk to it, the talk interface, and that's actually getting better and better. Yeah, getting better and better. So you can do these similar type things. Tell me the best three sushi

SPEAKER_36: restaurants near me and what their top dishes are, and that will work. You know, tell me the top three

SPEAKER_07: sushi restaurants in San Francisco and what their top two signature dishes are.

SPEAKER_194: You know, if you're driving in a car and you can't look at your phone, this is pretty amazing. And then

SPEAKER_36: when this has a hookup to make the reservation amazing, it really is game over. Sometimes with

SPEAKER_15: these changes in technology we see, there's like a tipping point, as we've talked about in, you know,

SPEAKER_07: the world. And when a tipping point gets hit, I think people are going to collectively realize, wait a second, I'm not an AI native. If I am an AI native, I'm just going to get a lot more done

SPEAKER_15: a lot faster, and then you'll be super competitive. And I just look at what my startups are doing when they are planning an event, a retreat, a offsite, you know, a new product, a press release, it's just going faster and faster and faster. So velocity is going up.

SPEAKER_00: Yeah, definitely at liquidity, I could tell this year, Jason, and obviously your teams are always getting better because you're pushing them, but like, it felt like there was more AI behind the scenes for organizing. Oh, yeah.

SPEAKER_07: A thousand percent AI is starting to work a lot better for planning events, 100%.

SPEAKER_177: Yeah. Any key insights from that?

SPEAKER_15: I think if you were to just ask, like when I was doing the all in summit and we moved it to LA last year, you can pull up, uh, chat to be four. Oh, I started asking questions like, tell me arenas and theater spaces that could have over, you could have between 2000 and 4,000 people and seats. And it was kludgy slow. And it would find me some, it had, you know, half the information was wrong, but it did find some places that I may not have thought of. So I would just ask it, what theaters have between 2000 and 5,000 seats in the Los Angeles area for events and put it in a

SPEAKER_36: table and put the address and you, you know, you do some sort of interesting search like that. It didn't work, you know, 18 months ago when we were planning that one, it didn't work comprehensively. So I worked with a producer and then I told them, Hey, you didn't, you didn't have these three places. And they're like, Oh yeah, I didn't think of that. And like, one of them was the YouTube theater. It turns out there's like a YouTube theater and there's what used to be called the Nokia theater, then the Microsoft theater. And here, you know, you're starting to see links to the theaters for more information, their address. And now if you ask, give me 20 theaters and put it in a table, it's going to give you 20. Uh, and it's going to find the YouTube theater

SPEAKER_15: and the other ones. And that was the comprehensiveness is moving up and the speed is moving up. Uh, so here you see, it's searching the web. It's re and it found 10 different websites, dude, 10 different websites at once and started doing this, you know, it's got the same ones so far, but there's the Adobe theater. I mentioned there's the Hollywood Palladium. There's a shrine and, uh, there's the Greek theater Walt Disney concert hall, uh, El Capitan, the region theater, man.

SPEAKER_22: I mean, this is incredible. This is more than a morning. This would you, you'd give someone this

SPEAKER_15: task and they'd come to you a couple days later. Yeah. Yeah. This is a weak project. Now, if you said add a column with the price to rent the theater, now that's where, you know, sometimes they have the information on the website. Sometimes it's third party websites that have it. This is where you start getting into the job of, you know, an event producer. And so, you know, we're going to add a price here to rent this. That would be the next thing your client would ask if you were, and, you know, it couldn't come up with anything. And here we'll see if these are, you know,

SPEAKER_41: if these are actually, you have a good sense, right? I think the Dolby theater might be more

SPEAKER_62: expensive than 5,000 an hour, but anyways. Yeah. I mean, these prices are probably wrong.

SPEAKER_15: So, you know, and that's because a lot of times these prices aren't available. So it's probably estimating here. So what you'd want to say is cite the source. If you can cite a source of the cost per day to rent this, let me know, or you could say, get, get me the email of the contact person or find me a person on LinkedIn, right? Remember we're trying to do LinkedIn searches. Like, these are all the next steps. And so I don't know if there are somebody at open AI looking at the search stream and saying, Hey, solve this next, or, um, the AI is just watching people do it and trying to figure it out, or it's ingesting more information. Um, but brave new world here. Right.

SPEAKER_00: Well, I think to answer your question, I think what, you know, the focus really is, and we're going to see more of this in the back half of this year is we're going to see, you know, a greater focus on agentic type use cases where I think the first aha, when this came out, it was like, Oh my God, I can just type something comes up. But now it's like, Hey, go do a bunch of stuff for me and come back. I think that's where we're going to see the biggest improvements. And when we think of the next generation of these coming out and it's like, we did here, all of this didn't, you know, we're, we're doing what's called multi-shot, right? We're working through our problem, but we're going to see the agents just kind of work through it on their own. And, you know, you saw that in the very first iteration, people were trying that remember with auto GPTs would be like, yeah, come up with like a work plan and then try to go fill it out. I think what we're going to see now is it's going to be able to do that just for a question you ask. And it will be able to sort of figure out, Hey, I should probably put this in a table and figure out prices and do that because you can, you can literally farm the task off to a bunch of different agents and come back and, you know, aggregate the results.

SPEAKER_15: Yeah. So the ability to do the same search against five different things, five different language models and have it then build one table from five different models. Some master one, do it or say, you know, what other questions should I know about renting a space? And it's like, well, you need an AV company and you know, you're going to need sound and you're going to need food.

SPEAKER_07: And, you know, um, okay, let's go through it. And you have more, um, I do. And I know there were a lot of image stuff that came out. So I'm very interested in the image stuff. I don't know if you saw those trending and if you have it on your docket, but somebody was making, they were taking

SPEAKER_36: famous memes and then bringing them to life on Twitter. Oh yeah. Yeah. I, I, I mean, I have that

SPEAKER_00: one in Twitter. I didn't, I didn't demo it, but like I can show you who did it. Let's pull it up.

SPEAKER_42: That was a fun one. I have a demo slated with these guys. I didn't do the meme real thing.

SPEAKER_41: Yeah. I have them pulled up for my next demo, which is awesome. It's a good call up. You always

SPEAKER_00: do that. You have this super power. This is like Phil Hellmuth. Like he knows how to read your cards and he'd be like, oh, you had this, but you want a range of hands. Yeah. So here it is.

SPEAKER_228: This video is created using Luma AI. Yeah. So we have them queued up for the next demo, but we'll just start here. So it's just, this is the start of the video.

SPEAKER_233: Yep. Okay. There's the guy with the beard, the bear guy. There's Keanu eating a bagel or something.

SPEAKER_234: Oh, this is good. The girlfriend and the whistle at the, the, uh,

SPEAKER_236: there's the other girl with the fire behind her. There's the crying guy from some TV show.

SPEAKER_73: Here's Charlie in the chocolate factory. I know that one.

SPEAKER_236: Yep.

SPEAKER_73: Oh, here's the guy thinking guy. Yeah. I love that. We have this temple. Okay. Here's space balls.

SPEAKER_245: Wow. Luke Skywalker. I mean, I didn't know that was a meme. Rick, are you just looking at things in the office and saying that you love them?

SPEAKER_252: There you go. Uh, Pulp Fiction. Incredible. Oh, you were the chosen one?

SPEAKER_256: The chosen one.

SPEAKER_236: Star Wars. I don't even know you. Or her. Yeah. Oh yeah. That girl with the, the little girl with the crazy look and actually putting on glasses.

SPEAKER_258: I mean, it's nuts. And this is like, all right, we get it.

SPEAKER_261: Oh, there's Putin and Kim Jong-un driving together. Oh, wow. I didn't watch it all the way through.

SPEAKER_05: I didn't watch it all the way through. I didn't know they did modern ones. Oh, and they're having them do the drop off. I wonder if they told it, make that story about memes. Rick rolled us at the end. So there's the guy at the Celtics game. Who's just

SPEAKER_20: up there's Doge Dog. Shima, Inu, whatever it's called. And then finishes with it. Back to the kids. Wow. Wow. I mean, pretty impressive.

SPEAKER_190: Yeah.

SPEAKER_280: I'm going to have to go through our bet list. I think I'm going to owe you a lot of money. You always took the short end of these.

SPEAKER_190: Right. Well, because, you know, I'm, I'm, I'm a technologist, right? So I always think, you know, slow than fast, but, um, so, so that was Luma Labs that did those. I did this one

SPEAKER_00: earlier because it was taking some time, uh, to do them. And I said, a panda, you know, riding a bicycle through New York city. And, uh, these things are just incredible now. Right.

SPEAKER_283: In terms of their capabilities and what they're able to do. Um, it's, it's just mind blowing.

SPEAKER_57: I mean, it hasn't crossed the uncanny valley for me. It still looks like it's AI generated. I, I would not have been fooled by either of these. I would have said, oh, that's cool. AI is getting

SPEAKER_15: better. Yeah. I think they would have to put these into post-production to full minute.

SPEAKER_76: And I think so too. And I think we should, you know, the framework we should use is just

SPEAKER_42: enhancement, right? What I would say, J. Kel is, you know, let's say you're trying to produce something and you need some screens or some storyboards. Storyboards is what they're called.

SPEAKER_77: Yeah. We're in the storyboard era. And I think it's not that far away. I think when we see the really,

SPEAKER_00: really highly produced stuff, well, basically Luma was one and we'll just rate two back to back. I had another one queued up here as well. This is a Chinese company and they really

SPEAKER_121: blew some people's minds away over the last couple of weeks. And it's called Kling. Did you see this

SPEAKER_290: one? No. Show me. Kling. K-L-I-N-G. Yeah, the video that these guys had. You know, you could just see

SPEAKER_36: some of these. Wow. Yeah. Look at those flowers. Here's a panda playing a guitar. Yeah. I mean, so close to the Uncanny Valley. Uh, this one of the parrot, it did cross the Uncanny Valley for me. I wouldn't have known that was fake. Yeah. Obviously a rabbit drinking coffee. Even this

SPEAKER_63: one. I think if you were making a product. Yeah, that's a flat white in a Cortado glass, you know, like the glass glass. That's a Cortado. And that's one of my favorite beverages of coffee

SPEAKER_90: beverages. And that looks like it's from a stock image. Now, what company is this? Kling is the name of

SPEAKER_77: Kling. Now you can't use it because you have to download an app and you have to be in the Chinese

SPEAKER_00: app store to do it. So I can only demo the screen here. But I think the point on these, you know, maybe the next episode we'll review the bets is I think some of these have really crossed. Now, I do think these require a lot of tuning. So it's like being in mid journey and creating things. And so you, in order to get there, you really have to do, you know, you have to kind of do many, many iterations of it. Yeah. But I do think it's, it's definitely crossed sort of the point of convincing us that, you know, these are real. I think we've crossed the uncanny valley, but.

SPEAKER_65: Fantastic. What do you give those two? I give those B pluses. Okay. Yeah. And what, what, what, what do they need to be better?

SPEAKER_177: I'm grading them on crossing the uncanny valley. So I don't know it's AI created. So

SPEAKER_15: if you were saying, judge it on it being a storyboard tool, like a creative tool to give you ideas, I give it an A. I give it a solid A. Now, if you said, you know, to actually make production videos, not storyboards, I give it a B, B plus. I think they're six months away from

SPEAKER_36: crossing the uncanny valley. Like even the coffee one, if you said, look at and tell me AI or not AI, I probably would've got AI from just some of the artifacts, the parrot, maybe not. So I felt like out of the maybe five or six we saw. Yeah. It's not going to get me the majority of the time.

SPEAKER_00: Okay. Yeah. I'm going to give them an A minus. Cause they're going to help me win bets against

SPEAKER_306: you so they can get to. Okay. Great. Awesome. So you're just, yeah, the fix is in. Got it.

SPEAKER_87: If you're trying to motivate them. Yeah, exactly. Guys, get an A plus. Let's, let's, let's,

SPEAKER_104: let's win some money here. Whatever bet you're saying, I'm taking the under whatever you're proposing going forward. I'm going to get shellacked here. No, no, no. What I was,

SPEAKER_00: what I was going to suggest is we do like a little bake off where we do it real image and AI image. And you, we'll do that. How about next episode? We do that. Yes. We'll just do it for a hundred

SPEAKER_310: dollars a pop. It'll be just like flips. It just flips. Just flips. Just flips. AI flips. Deal.

SPEAKER_49: Next, next episode. We'll do in two weeks. AI flips. Get two full weeks to do it.

SPEAKER_313: Oh, that is so fun. I think it'd be good. Producers get on it.

SPEAKER_15: Make us one Cortado from a stock image library. Get one of these ones, you know, take the watermarks off and let's see, let's see if we can do flips.

SPEAKER_00: Now, the last one I want to do here is just actually there's two things I want to do really quickly. Just one, I want to lightning around, talk about open source. So we'll do a quick lightning round and a few things that have also happened, uh, since Sonny's been out. Um, so a couple of models

SPEAKER_101: have come and the, this Quentu, it's this, uh, team out of Alibaba people that are not tracking. This is probably right now the best open source model. And so it's come out. It's really powerful.

SPEAKER_00: They, you know, uh, it's fully open source. They have a lot of different sizes ranging from 500 million all the way to 72 billion trained on a bunch of different languages and a really big context, like 128 K tokens. You can put a lot in. So this is available. You can try it out on at hugging face. If you want to, they have a version here. That's running. Okay. So this is open source,

SPEAKER_77: open source from the Alibaba team. And this is really interesting because what we're seeing is

SPEAKER_00: these teams in China are pushing for these open source models and they're coming to the top of the

SPEAKER_320: leaderboard. Interesting. Yeah. There's a great tradition of not respecting IP and just

SPEAKER_15: wholesale copying stuff. I'm curious in these code bases or new projects from China, are these built from scratch or do you think these are, you know, inspired by common LLMs? Do you think these are, you know, backdoor hacks into proprietary stuff? I'm just curious.

SPEAKER_00: Well, I'm going to actually share something slightly different. I don't want to comment, you know, a lot about where they're getting the data from because it's just not known, but I know one thing that's happening because I, you know, definitely heard some of the real, you know, professionals in the space talk about it is they're using the current large language models that are available to generate synthetic data so that they can make their model better. Got it. So think about like what makes a model really good, not so much as just like open wild data on the internet, but it's matched paired information. So it's like, if we just took the stuff that we were doing, which is like a question and an answer and then mapped it all. And you gave that as training

SPEAKER_77: data. It's really, really high quality data. Interesting. So that search we did on, you know,

Chamath Palihapitiya: salaries or speakers, then you say, get me some more data similar to this, and then you feed it in.

SPEAKER_15: So it's kind of meta. Yeah. And there's also the chance in China that, you know, they would just rip the entire New York times archive, the entire magazine archive from somewhere where other people in the United States now would respect that IP. And, you know, we just saw the lawsuits against two of the music companies that you and I played with last year. Like those companies are gone, by the way.

SPEAKER_36: I'll just tell you right now. I don't think. Oh, you're calling it? I'm calling it right now. Both of those companies are going to, you're calling the Napster. I'm calling it. I'm just saying it's straight up Napster. There's two of them. I think that got sued. I think they're both going to be Napster roadkill, or they will have a $50 million fine against them and they will have to work it off. And then I don't know who's paying for that $50 million fine, because they're going to need a perpetual license to the music. So they'll be fined 50 million, 25 million for what they've done already. Then they're going to get charged on top of that, right? For future use. And they don't have enough revenue to make that work straight up. So I think they're anybody who invested in those companies. And I don't know if you saw, they said in their training data, I forgot the name of the company. There's two. There's two. There's two.

SPEAKER_15: There's two. And I think Suno is saying they can't tell them how they train their music.

SPEAKER_90: This is proprietary in discovery, which means they're screwed. But in China, they'll just take

SPEAKER_15: every song ever written and they'll make a better open source LLM. And then what is the music industry going to do? They're going to have to do an injunction. This is what I predict will happen. This is as crazy as it's going to be. They're going to do an injunction, the music industry, because they are the most powerful. Well, they're organized.

SPEAKER_00: And co-organized, correct. More than anyone, because they have the RIAA, right? Reporting Industry Association of America. And then you have the songwriters,

SPEAKER_15: and then you have the live stuff. You have the labels. I mean, you're going to get it from all angles. And you're just trying to understand how many different ways you're going to get sued requires a major legal team, because they're going to come at you from all ends. So what will happen is, a Chinese or whatever firm that is operating in an area that doesn't have restrictions,

SPEAKER_36: or even Israel or India or Pakistan with LinkedIn data, right? With spray data, you're going to have an LLM come out of LinkedIn and other Facebook data, Instagram data. And you're going to be able to use that LLM to search and say, Hey, I'm looking for CTOs at this thing. And it's going to just spit it out, like things you can't do now. And that's going to then wind up on Hugging Face as an open source project. Yeah. And the weights and everything and the data. And then they'll do it for the music industry. They'll do it with every movie ever made. And then those language models are going to get sued and GitHub Hugging Face are going to get sued if they publish them. So just think about that. If you host

SPEAKER_15: them, they're going to say you're contributing, you know, that this is built off stolen data, they're going to get sued. Now, I don't know if that lawsuit works or not, or Hugging Face hosting an open source

SPEAKER_42: project, but I could see that happening. That's how much is at stake. You paint a very doomed picture for a wonderful place. We got to find a better compromise, OJ Cal. We can't just shut innovation

SPEAKER_345: down either. Yeah. It's a very simple one. The US companies pay the man his money, pay the man his

SPEAKER_346: money. Yes, exactly. Yes. It's like we did to OG Ananobi at the Knicks. We paid the man his money.

SPEAKER_101: Yeah. All right. Last one. And I want, I'm really excited by this. So, you know, there's been a lot of energy and notion around like large language models, but there's

SPEAKER_00: been, there's a lot of innovation happening in the space of voice and Cartesia AI, what they're doing is, so they do something called state space models and specifically space models. Okay. Yes.

SPEAKER_101: And so what does it mean in English? It's basically a model that uses a different

SPEAKER_00: area for the search space, which is not necessarily language. Right. Okay. And I'll, I'll do a more

SPEAKER_228: detailed explanation of it because I have to get up to speed. I don't think I'm like, okay, fair enough. And so it shows the product. Yeah. Okay. Of course. And so what they've done here is

SPEAKER_22: they've created these voices using this model and these voices are incredible. So let me just make sure the audio comes. So this is a 1920s radio man. And I know you got to do this voice.

SPEAKER_354: Ladies and gentlemen, ladies and gentlemen, gather around the wireless as we delve into a world of

SPEAKER_357: speakeasy, the tragedy of the Hindenburg roaring excitement. Yeah. I mean, it's literally the classic. And I know you'd love this right there. Have a seat and relax during for the best haircut

SPEAKER_359: and some great conversation. Oh, ASMR barbershop guy. Yeah. Yeah. I feel like he's about to slip my

SPEAKER_361: throat. Yeah. Like, you know, here's the Indian man. Every journey starts with a single step.

SPEAKER_365: I'm not doing my Indian accent. I'm not getting canceled. You nice try, Sonny. I will not do my

SPEAKER_134: Indian accent. Yeah. But they've done an incredible job. You're Indian. You're allowed to. No, no, no. I'm not going to do it. I'm not going to do it. I don't do a good one. But

SPEAKER_369: the wizard, how about this is the last one? With a flick of my wand and a whisper of incantations,

SPEAKER_371: mysteries unravel and magic fills the air. All we have to do is decide to do with the time that's

SPEAKER_373: been given to us, Sadiq. Yes. It's Ian McAllen. Yes, it is. Exactly. Exactly. But

SPEAKER_162: do they have one for her and for Scarlett Johansson in here?

SPEAKER_376: No, they don't. I don't see one here. But no, what's really happening here is like,

SPEAKER_101: what, you know, there's so much innovation happening now, J. Cal, and maybe in one of the next episodes, we'll just do a, that's what, you know, we talked about it a little bit in liquidity,

SPEAKER_00: but, or at liquidity, there is a lot of innovation happening now and it's faster than ever. And it's coming in the, in the, you know, way of new, new types of models, open source models, agentic reasoning in voice. And so one thing that I want to go into is in the second half of this year, J. Cal, we are going to have some surprises that no one was anticipating. Okay. There you go. That's just a,

SPEAKER_02: that's something that, you know, running developer.brock.com or that's something. So that's inside information. That's the inside line. No, I don't know. I'm not, it's not inside.

SPEAKER_00: I'm just saying like being in the thick of it, you know, seeing how everyone is innovating. I have a very, very, and I don't know exactly what it'll be. I mean, I have insights in a few places that I can't share, but. Okay. But beyond like, just sort of generally, I'm telling you, we're going to be more surprised in the back half of this year than we were when opening high first came out.

SPEAKER_07: Oh, it's a big claim. It's a big claim. I don't know how we phrase it as a bet, but what you're saying is everything that happened up to now is the epilogue and that we're going to really see the stories coming, the real stories coming now. Okay. There you have it folks.

SPEAKER_213: Stick with us. X.com slash sun deep X.com slash Jason. This is this week in startups rate, subscribe, write a review, and then make sure you tell Sonny how much to love him and that you missed him for a month so that he is no longer MIA on the pod. We need him here doing these demos. We'll see y'all next time. Bye bye. Bye.