SPEAKER_00: But this Gen AI movement is the actual industrialization movement of making more images, making more software, making more mock-ups, making it easier for us to access data quickly. And so I think with that, when you apply that back to software engineer agent, we can now, the same way we went from making one card eight to 10, we can 100x ourselves.

SPEAKER_01: And so me as a software engineer, I can work on 10 different problems at the same time or 30 different problems with the help of these agents. I think we're about to have the 1,000x jump in software development.

SPEAKER_02: This Week in Startups is brought to you by Gusto, is easy online payroll, benefits, and HR built for modern small businesses. Get three months free when you run your first payroll at gusto.com slash twist. Mantle, the AI-powered equity management platform designed for modern founders and operators. Get your first 12 months free at withmantle.com slash twist. And HubSpot. Join thousands of companies that are growing better with HubSpot for startups. Learn more and get extra benefits for being a Twist listener now at hubspot.com slash startups.

SPEAKER_07: All right, everybody, welcome back to this Week in Startups. It's your boy, Jay Cal, here with my guy, Sonny Madra, GM of Brock Cloud.

SPEAKER_10: Go to console.groq.com and you can start playing with the fastest and cheapest inference in the world. Welcome back, Sonny.

SPEAKER_13: Good to be back, Jay Cal. We missed you on the all-in pod. What happened?

SPEAKER_17: Well, yeah, I mean, a lot of people are asking me because I never miss, right? It's very, very rare that I miss an episode.

SPEAKER_20: Yeah. You had the Ironman going. I think you had the Ironman going.

SPEAKER_07: I mean, I'm pretty good, generally speaking.

SPEAKER_21: What happened was I was, you know, here in Austin and I went to the Salt Lake and they have this great bison rib and I'm chewing on it. I mean, I wasn't chewing on the bone itself, but it's a chewy rib and I heard like a crack and I thought maybe I just, you know, when your two teeth, you know, crack together or something. I didn't think much of it.

SPEAKER_24: Yeah.

SPEAKER_21: It wasn't hurting. And then two days later, I'm flossing and the tooth kind of hangs halfway out. I'm like, holy cow. And so, you know, then a couple of days later, I find an emergency dentist. I get there. They're like, oh, you know, that root canal I had 20 years ago. You know, those kind of deteriorate over a couple of decades. And, you know, it's basically it's cracked. And so then I was able to get emergency surgery.

SPEAKER_10: They extracted the root of the root canal and then put in, I guess what they call an implant or something. And then I guess I got to wait three months. It heals and they put another crown on it or something.

SPEAKER_29: But, you know, for three days, I don't like taking pain meds.

SPEAKER_21: And, you know, they gave me some pain meds and that screws up my sleep. And then the pain was screwing up my sleep. I just like really was in a fog.

SPEAKER_31: And I tried to go and I'm like looking at the doc and I'm like, I can't even read the docket and my mouth, the swollen jaw. I'm now four days or five days past the surgery. But at the taping of all, and it was like, whatever, 48 hours. And I was just loopy. And I was like, you know what?

SPEAKER_35: I don't want to like disappoint the audience and be loopy. So, yeah, I just took a, yeah, I took a day off.

SPEAKER_37: Which of course is always great. Because the MAGA people are like, take a permanent vacation, Jake Allen. And other people are like, I'm not even watching this show. So, you know, to my MAGA friends who absolutely are thrilled when I'm not on.

SPEAKER_21: They're like, oh, more time to talk about Ukraine and Biden, Biden, Biden. But it was a good episode, actually. So, great job.

SPEAKER_42: I only made about three quarters through. But, yeah, it was fun. But it was missing the moderator.

SPEAKER_21: How are you doing?

SPEAKER_43: I know you've been traveling and Grok continues to grow.

SPEAKER_45: Yeah, you know what? Maybe let's like, it was a big week this week. Let's do a little bit of news before we do demos.

David Sacks: There's kind of like three important news stories that we want to highlight coming out of this week. And, you know, I had kind of a tweet about it. So, maybe we'll use that as like the grounding here.

SPEAKER_49: And this one's kind of related to us. But we won't talk about Grok here. But, you know, three major companies launched chips this week. And this is really important. So, the three companies were Meta announced a new chip called MTIA, Meta Training and Inference Accelerator. Google made some of their chips available to the public out of their conference, their TPUV5. And then Intel launched the Gaudi 3 AI chips for mass production in Q3. And that's really interesting because these are three major companies that are basically out there, you know, putting new hardware into the ecosystem.

SPEAKER_45: And, you know, let's, what are your thoughts when you read that? What do you make of that?

SPEAKER_54: Yeah, so, it does seem there's a number of things that occur when we have a boom like this. You know, there are people who've been investing in this technology all along. And they've seen, hey, there is a path here. And NVIDIA comes to mind, right? Like, they saw there was a path here. But they were working on video games, crypto, and then AI. And then, of course, crypto kind of plummets. Video games still growing on a pretty good clip. But, of course, AI then becomes a spoon. And when people see that, there are two drivers in basic human behavior. One is fear and one is greed, right? Yep. And so, if you pause and you think about, you know, okay, your Intel, your Google, your Amazon, whoever it happens to be, Meta probably has a fear that, or I should say, they see an opportunity having their own chips. What's the opportunity there? I think they're probably opportunistic. Hey, we have this data. If we have these chips, we can save money. So, sure, we're backing up the Brinks truck. I think they're one of the biggest customers of NVIDIA. But, you know, hey, this gives them a little bit of a hedge. Hey, you know, if we have to negotiate prices with NVIDIA, you know, we do have some of our own chips being deployed here.

SPEAKER_21: And then for other folks, it could just be complete fear that they're going to lose their business, you know, like Intel, right? And that other people are going to run away with it.

SPEAKER_63: So, there's usually those dual, you know, motivators.

SPEAKER_31: And what's good for everybody is, you know, most of these projects will probably fail to be significant. But in aggregate, they're going to drive prices lower, and the competition will make everybody sharper. So, you can always see in one of those tweets, I guess it was Intel was claiming, you know, they're all going to make claims. Hey, we're better on this task, and friends, we're this percentage better. And that's what you really want to see in a thriving economy is competition. And here we go, competition's here.

SPEAKER_00: It's great. Yeah. I'll just add, like, one more thing on that particular story. I think that holds true. You know, we don't really see any of these chips in production or external benchmarks just yet.

SPEAKER_49: So, that's, you know, kind of TBD, which means that it is sort of like a, you know, maybe like you said, like an internal thing slash pricing pressure thing. The other thing that I thought that Google did at their conference was not only did they launch like an AI chip, which is all the rage these days, but they also made available a new kind of CPU, which is an ARM-based CPU, which is interesting because that's them kind of going more directly after the traditional compute stack. with a chip that they've created.

David Sacks: And so, it's really fascinating how the, you know, future world of compute is playing out.

SPEAKER_54: Yeah.

SPEAKER_14: And, you know, if you look at something like Facebook, they have really pushed open source hardware, commodity hardware, because they realize that the business they're in.

SPEAKER_73: They were the first, actually. They were the first back in the day.

SPEAKER_54: Yeah. So they, they, they've pushed very hard for decades now on just making servers, commodities, data centers, commodities, the infrastructure in data centers, a commodity, because that serves them. Right. And I think Google participated in that as well. So you have the people who have a consumer application like Google search or Instagram, Facebook, a social network and the advertising businesses.

SPEAKER_14: They don't care about the hardware being commoditized. That's better for them. Now, Nvidia may care very much. Rock may very care very much. And so this just makes everybody, uh, sharper and better at their jobs. And I think that competition is going to become very real, but like you're saying, are these in anybody's hands yet?

SPEAKER_21: Um, it takes years to, to get these things into people's hands, right?

David Sacks: Yeah, this was a big project that, uh, you know, Facebook started, you know, back in 2009, right? So it has been over a decade, which is incredible.

SPEAKER_82: Yeah. Almost 15 years now.

David Sacks: Wow.

SPEAKER_10: And that really has driven a lot of server-based and cloud-based computing is this commodification of the hardware sack, right? And so, yeah, great. Awesome. It's going to be great for the industry.

SPEAKER_21: And, um, you know, there, there's other problems that need to be solved. And then I guess we'll get into some of those now.

SPEAKER_00: Okay. Switch gears, uh, two more quick news stories. So Adam shifts. Yeah. So this one is kind of near and dear to you, right?

SPEAKER_49: Because, you know, he has pushed, uh, you know, put something on the table to basically, you know, require people to disclose, right? It's called the copyright disclosure act and disclose where they got their content from. You know, this is a, this is an interesting one because it's very near and dear to you. And we've talked about this a lot. Sure. Actually, we have a demo coming up, which I think is an incredible demo, but definitely does cross into some of these lines. But what do you think about this law?

SPEAKER_90: I think this is sort of what you've been saying for a long time when it comes to.

SPEAKER_89: Yeah. I mean, I hate to say, I told you so, but yeah, yeah, I told you so listen as a founder, there are things I love doing like building products or meeting with partners, hanging out with my team and dreaming up new ideas.

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SPEAKER_14: You can't just take people's work and build a derivative product out of it without their permission and without their consent,

SPEAKER_54: without, you know, paying them. And so here we are. The industry has stolen content and they've explicitly stolen it and they knew they were stealing it. And so OpenAI, it's come out, has stolen tons of content. They built scrapers to do this. And their concept that it's fair use is laughable. You, as the person who created this IP, you have the right to create the derivative products from that IP. And the fair use doctrine is very narrow and its purpose is very clear. Educational to make society better, right? A small portion of the original work and not interfering with that person's ability to monetize it. You can look up the four-part test. There's little exceptions. So, you know, if you're doing an educational project, you get a lot more leeway.

SPEAKER_31: An example of that might be if I wanted to do, you and I decided we're going to do a critical analysis of Blade Runner.

SPEAKER_101: We could sit here and play clips from Blade Runner, but we can't create Blade Runner 2049. And so people will use like a really silly argument like, oh, well, this is how humans learn and the computers learn differently.

SPEAKER_54: None of that really matters.

SPEAKER_31: The way intellectual property works is you have some amount of ownership of it. There are fair use exceptions for education and using small amounts of content, but that doesn't give you the right to create the derivative product and exploit future products unless you have a license.

SPEAKER_07: So what I like about this bill is it just says, hey, tell us what you used. And that seems more than fair. If you used my content to build something, I should know. Yeah.

SPEAKER_105: Yeah.

SPEAKER_31: And so, I mean, look, this is going to, I do hope, what I hope happens here, and I'll just end on this, is I hope that New York Times gets an injunction against open AI and they have to pull their product from the market. And I think the New York Times, Dow Jones, Disney. All of open AI. Yeah.

SPEAKER_54: I think they should all get together and gang up on open AI at this point right now to protect the future. And this idea that, you know, oh, it can't be done and technology always wins. Tell that to Napster. Tell that to scour. Like, you can stop this. You must be coordinated in order to stop it. The music industry is a highly coordinated group. What has to happen is journalists, publishers, and content creators on the internet have to join that group. And as a united front, they should get together and they should not stop with just a settlement with open AI. They should force them to remove the product from market. And they should force them to start the product over again. Because if you stole all that content and you made the model, and then you're saying, oh, well, you know, now we'll leave you out of it. You've already created the value. And $100 billion in value has been created. And I think $2 billion or $3 billion in shares have been sold. So, essentially, what's happened is they've taken the value of the New York Times and other publications. They've built a company. That company has perceived value in the market to the point at which the most sophisticated investors in the world are buying it at a $90 billion or $100 billion valuation. That money, a portion of that belongs to the New York Times and whoever else they scraped.

SPEAKER_117: And so, you know, hopefully this time the content industry will act in unison and not fold. That's my belief on it.

SPEAKER_49: I think you and I are a little bit different sides here. I wouldn't think that open AI should go down. But I do think, you know, a lot of those organizations struggle and they should get at least some share of the revenue or something like that that's being generated while everything else is being settled. But that bill is going to be interesting and, you know, let's put another bet, J-Cal, because, you know, since we're, you know, we're on our way to 100 bets, we're probably past it, but who knows? This is just take a side. Does this bill pass or does this bill, you know, what version of it passes?

SPEAKER_121: I think that a bill will get passed.

SPEAKER_125: I don't know if it will be this one, but I do believe a bill to do this will get passed.

SPEAKER_31: Because it's going to take 10 years to, you know, to litigate this stuff or 5, 10 years for this to go through the court system.

SPEAKER_126: But Napster didn't take that long.

SPEAKER_31: Well, they were a private company sued into oblivion.

SPEAKER_128: So, you know, yeah.

SPEAKER_07: So, I mean, here, I think OpenAI, they have a lot of resources.

SPEAKER_31: And so part of this is they can fight for a long time, whereas Napster didn't really have the ability to fight for even a long time.

SPEAKER_98: Even YouTube. The reason YouTube was sold to Google was because. So they could fight the fight.

SPEAKER_31: The lawsuit and they, and they, that took years and years and years. And so, you know, I do believe that there will have to be some new laws around training specifically, because we're sitting here and we keep debating. Is it legal to train on other people's data? I guarantee you it's not legal. Okay. And I guarantee it will be found to not be legal to use other people's content to train a new intelligence that you can then go exploit that work. And so.

SPEAKER_00: Well, maybe we'll come back to this bet. We have to think about it a bit more. Okay.

SPEAKER_31: We have to think about how to frame it. But, um, and it's not that I want to see open AI go away. And we've got, you know, I do believe the technology is valid and, but if they could build this technology without the content, they would have.

SPEAKER_14: And all the papers that you and I have read and discussed say, the more data you put into it, the better the output.

SPEAKER_54: And that's obvious to anybody who uses these that it's stealing other people's content in order to give the answers. So if it's giving answers based on content, it found that the New York times and YouTube videos or whatever web content or books, and they know they stole it.

SPEAKER_14: They did it covertly and they're trying to cover it up. And I think, uh, they need to basically redo all the models. They'll have to start over. And I think that's what the New York times and wall street journal, Dow Jones, Disney, they should all Viacom, all the major content creators. They should get together and they should, as a group, sue open AI into oblivion and make them pull the product from the market and then make open AI get permission and then have them start over. And that's what I think should happen.

SPEAKER_145: Got it.

SPEAKER_31: And that's my advice to content injuries. You're going to lose your entire business. They're going to lose their entire business.

SPEAKER_147: Just like Google took over the entire ad business.

SPEAKER_48: It's a perfect segue. You know, Jacob, you always managed to do it.

SPEAKER_49: I didn't even set it up this way, but there is a new service that just got launched this week. That's backed by tech and music heavyweights called UDO, U D I O. And I'm going to pull it up here. You can see here, it's a new service that's backed by tech and music heavyweights and it has powerful capabilities. And I'm going to go right into it. J Cal. Yes. And I did this song in the style of dire straits about New York city and big money.

SPEAKER_150: And Jake, I'm going to play this for you. Okay, here we go. And I want the raw reaction.

SPEAKER_151: Okay.

SPEAKER_152: I'm going to get it.

SPEAKER_155: I mean, it's so impressive.

SPEAKER_67: It's not exactly dire straits. That was more like, I forgot that person.

SPEAKER_54: Against the wind. Who sings against the wind? Tom Petty? Against the wind. No, against the wind, Bob Seger and the silver bullet band. Oh, yeah, yeah. A little bit more Bob Seger stuff.

SPEAKER_07: But the fact is, if you played me, if that song was playing on the radio, and you and I were driving down the road, I would not know it was AI.

SPEAKER_54: I might think it was cheesy when I listened to the lyrics, but we are now past the uncanny valley.

SPEAKER_31: This is absolutely of the quality in which it would pass a Turing test.

SPEAKER_63: In other words, you would think a musician, a human musician, had written it.

SPEAKER_54: But that does sound so much like Bob Seger that I'm surprised it didn't get the dire straits piece.

SPEAKER_31: And I'm excited about this product to exist because I do think we might get some Rolling Stone songs or some dire straits songs or Bob Marley songs or Jimi Hendrix riffs that, you know, I know it sounds sacrilegious to people that you might enjoy as much as the originals.

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SPEAKER_01: You missed the beginning there because I think I was playing it before. I'm just going to do the beginning of it as well, the first 15 seconds. Because I really thought it was great.

SPEAKER_184: Around him say he's too bold, but he takes it and makes it.

SPEAKER_186: Oh, man.

SPEAKER_14: I mean, it's, yeah. I mean, it sounds a little like Counting Crows maybe. Okay. A little Counting Crows in there. So, yeah.

SPEAKER_49: I like it a lot. And so, look, they've done an incredible job here of all the ones we've tried. And like they don't, you know, have like a sort of like you can put in style of, you know, a band. Fantastic. And, um, they've really made it work. They've really put it together and it's super, super impressive. Um, and, you know, it has a bunch of ex steep mind folks that started it, um, you know, backed by one of the Instagram co-founders.

SPEAKER_54: So when you type in the name of it though, it should, if you say I want it in the, in, so is Dire Straits licensed their music to them? Is Bob Seger licensed to them?

SPEAKER_14: Do they have the rights to people's music? Is this trained on, you know, uh, Columbia records and Bob Dylan's catalog?

SPEAKER_00: Will. I am is involved in, you know, he's kind of been at the intersection of tech and music for, uh, you know, a few years. Right.

SPEAKER_49: And there are many years now, but in the article that, you know, I, I pulled.

SPEAKER_150: Bob, they didn't say they didn't confirm or deny if it's been trained on copyright music without permission.

SPEAKER_17: That means it has been. Yeah. All these people are guilty and it's all going to come up.

SPEAKER_54: You might as well all fess it up right now because you're going to get busted because you sent a scraper out and the scraper scraped it. There's a tracker of the scraper. Unless people who are in these LLMs are going back and erasing all the code they did and then telling their employees to lie.

SPEAKER_14: The employees are going to know, and there's some employee who had a conversation, just like when that woman from open AI with Sora was like, yeah, I don't know exactly what it was trained on. And it's like, uh, you work this, I think she's the CTO.

SPEAKER_199: The CTO. Yeah. Yeah. Yeah. Yeah.

SPEAKER_176: You're not like, if you don't know, that's like, it's like going into the back of the restaurant and you're like, who cooked a steak? And there's like 10 chefs. And they're like, we don't know.

SPEAKER_162: It's like, but you're sitting there cooking a steak in front of us that there's the head chef is literally cooking a steak.

SPEAKER_203: And the head chef is like, she's like, I don't know who cooks steak here.

SPEAKER_205: And it's like, she's flipping the steak over as she's saying. And it's like, wow, you know, you're lying.

SPEAKER_21: And you know, the question I have is, I don't believe the music industry has the right to this.

SPEAKER_31: In other words, if you're Columbia records and you have a relationship with Bob Dylan to publish his music, do you have the right to create virtual Bob Dylan?

SPEAKER_54: I don't believe you do. And so I think there's going to be another layer of lawsuits, which is Bob Dylan's going to be able to sue Columbia records.

SPEAKER_31: If they try to license his lyrics to one of these services and his voice to make future music.

SPEAKER_30: And I think, Bob, we should not allow record labels to own people's entire likeness and ability to write the next set of songs without their permission.

SPEAKER_49: The Rolling Stone article is interesting. They, you know, they definitely talk about, they don't say whether they did or not. They do say they have AI to detect if a song sounds too similar to an existing piece of work. And so this is the New York times, uh, lawsuit kind of thing where they got it to spit out like the exact words of the article. And so maybe that's the, um, you know, you remember that famous lawsuit with vanilla ice and ice ice baby. And then he took that, you know, beat from, uh, I think it was like a sting song or wasn't it, or something like that.

SPEAKER_212: Well, no, no, no, no, that was Queen and David Bowie because that was under pressure.

SPEAKER_49: Yeah, you're right. Under pressure. Yeah, yeah, yeah. Sorry. Yeah. I got that wrong. But, and he said, oh no, I, I changed the beat by one. Right. Mm-hmm. Yeah. And then he got in trouble because he didn't change it at all.

SPEAKER_23: Let's put things into two different categories.

SPEAKER_07: There's a human being who's inspired by another human being to create something.

SPEAKER_74: Yeah. Vanilla ice. Right. Yeah.

SPEAKER_54: So whatever you think of him as an artist, he has a collective amount of music he listened to over his lifetime. And he created a new song and either he unconsciously channeled that or he explicitly copied it and edited a little bit. Okay. Yeah. So now that one human has created one piece of output based on some number of pieces of input. Okay. That feels like a certain scale. And then society can debate the output there. And did he steal it or not? Right. And, and how close is it? And there's a test for this, which is, is the audience confused? So if I were to create, if I were to create, um, a Star Wars film and I called myself, you know, Darth J Cal and I made a whole, you know, character around myself as a Sith Lord, et cetera. And I had a lightsaber and I had, you know, two droids who, you know, went around with me and made funny jokes and work, you know, and I was up against a, you know, a Jedi. Um, and then I had a little teacher who was like Yoda, you know, like the audience would be very confused by it. And they would assume that this was in some way. Yeah. Star Wars. And so now we look at wholesale. So now you have two examples there, ice, ice baby. And then me creating my own little short film in both cases, you can sit there and discuss, Hey, how confused is the audience success? Now imagine you take every film ever created, every song ever created, every lyric, every written, every story in the New York times, every book ever published by Harper in the library.

SPEAKER_31: And then you say, now we can create all output at all time. And I, as the person who created this LLM, get all that economic value. These are profoundly different things. And so for people to use the analogy of ice, ice, baby is just so ridiculous because you have but one

SPEAKER_54: artist making, but one song and the courts and society can, you know, debate that taking everything ever created and putting it into an LLM, which is what Sam Wallman and opening eye is doing. This is what they want to do. This is what Google wants to do. They want to take all the content in the world ever created, and they want to get the economic value from it. That is completely different than writing a short story inspired by Stephen King that might use

SPEAKER_31: one of his characters. And even in that case, you probably would have to settle and license, you know, the content or license like the hip hop industry did.

SPEAKER_54: We have a great analogy of this. The hip hop industry has been sampling songs forever, and they just pay a small licensing fee. And the people who have their music licensed by some hip hop artist, which has a tradition of doing these things, they love it. Yeah.

SPEAKER_31: Like for the love of God, please license it more. I I'm sleeping and you take something from queen and under pressure and I get paid royalty.

SPEAKER_227: Yeah. Great.

SPEAKER_14: So, but the, you know, the technology industry doesn't even want to give that inch. That just shows you, you know, I don't want to use a really colorful piece of language here, but it just shows you how unethical our industry is.

SPEAKER_00: Okay. Okay. Well, look, you know, it was nice to see some industry folks involved.

SPEAKER_49: Yes. Um, hopefully they find a path to it. It's really good. Let's grade it. J-Cal. Oh, I give this an A. I give it an A. Okay. Yeah.

SPEAKER_21: Just straight up. It's great.

SPEAKER_14: And if you type in, I mean that at a minimum, if you type in dire straits, it should say, you know, uh, we don't have permission to use dire straits as music and like this in here, you can describe other things about music you like, but it shouldn't tell you that it doesn't

SPEAKER_31: have the rights to it. And if it does have the rights to it, it should say we have the rights to it and explain that. And this is what I think.

SPEAKER_54: What's great about the bill that Adam Schiff is proposing is it's just looking for disclosure. Just be honest about what you're doing here. And our industry could go. The reason our industry is not being honest is because they're liars and they're thieves.

SPEAKER_237: Yeah.

SPEAKER_54: If you weren't stealing and you weren't lying, you would tell the truth. We didn't train on this. When people don't tell you in our industry, what they've trained on, they're liars. Yeah. And they're, and they've stolen it. So just, that's the ultimate tell. Cause if they didn't steal it, they'd say, no, no, no, we trained it on only stuff we've licensed. You really think that these super intelligent people at opening, I don't know what they trained it on. Really? These are the smartest people in the world.

SPEAKER_00: Let me just give you the opposite side, just for the sake of argument.

SPEAKER_49: Yes. When you let something loose onto the internet, like a crawler and you made a crawler and you had it go look at videos everywhere.

SPEAKER_01: Yeah. All right. You know, now the question you could say is like, Hey, did you hit YouTube that you should know because you shouldn't have been there or not. Right?

SPEAKER_242: Yeah. It's a URL. You could just look at the log files.

SPEAKER_01: Yeah. But like, what if. There's YouTube videos embedded on pages, which they are all over the place.

SPEAKER_54: And it was literally in the code. It's literally in the, in the, in the HTML code, you would see it. There would say in bad YouTube.

SPEAKER_171: Yeah. Yeah.

SPEAKER_54: Let's be honest that, you know, you're making, you're, you're actually arguing my side of it, which is these are highly technically sophisticated people. They know what's what an, what an embed is. They actually would know bad content. So all you have to do to bust the open AI stealing is to look at what they excluded and ask them, why did you exclude this? And if you said to them, Hey, why did you exclude this? And they'd say, Oh, those are just like, we, we happened to stumble upon with our crawler or some random.

SPEAKER_248: It's like porn sites most likely. Right. Yeah.

SPEAKER_54: And they said, Oh no, we didn't want to have porn sites in here. We didn't think that was really good content or, you know, although this was a different language and we were doing an English language one. So we didn't need Japanese. So we excluded it or this was just machine language. This was just code. So we, we left out programming code because we, we're not doing code in here. We're doing, you know, whatever content.

SPEAKER_31: This is a, you know, um, an image database. And so you could just ask them, what did they include? Would they not include?

SPEAKER_54: And the crawlers break, the crawlers are optimized. Like Google's crawler is optimized. They're optimizing it all day long. There are, there are probably thousands of people in the last year who have optimized the Google crawler. Yeah. I would say probably a thousand people have sat there and written code to make that crawler more efficient. So to pretend that they just like, Oh, we just set it loose on the internet. And we don't know what came back.

SPEAKER_21: Is the height of arrogance would be the height of arrogance for them to say. Right.

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SPEAKER_108: I mean, this whole idea, just because something's open on the internet, Sonny, doesn't mean it's yours. It does not mean it's yours. My car is parked on the street here.

SPEAKER_74: It's on a public street. You can walk up to it. You can sit on the hood of my car. It doesn't mean you own my car and you take it.

SPEAKER_21: Yeah. Yeah. And that's the position that open AI has taken. We trained on the open internet. Yeah. Society's open. You know, my front door might be open right now. You walk in doesn't mean you can steal from my house.

SPEAKER_267: Yeah.

SPEAKER_45: No, it's, it's interesting times where they are for sure.

SPEAKER_21: I feel like I'm a dad. Why do I sound like a lunatic trying to defend copyright?

SPEAKER_54: It's like, it's so funny in our industry, how people trying to defend.

SPEAKER_269: But you know why?

SPEAKER_54: Because many people in our industry, many people we know have benefited right from

SPEAKER_90: the growth of these companies, whether directly involved or indirectly involved. Right.

SPEAKER_49: And, and look, the flip side is we don't really pay to use Google. We don't pay to use YouTube. We don't pay to use any of these services. And so the company has to sit there and say, well, we have to do some of these things because, um, that's how we make our business work. Right. And so, yeah, it's a really interesting time. Like the whole notion of training a model and the arguments to be said, like, well, that's just like a person going and, you know, watching all the videos of guitar players on YouTube. And then all of a sudden creating a style of their own. That's in the style of Jimi Hendrix. Like do you anything to Jimi Hendrix? Probably not, but it's different if you're doing it as a commercial entity.

SPEAKER_01: But if you went and become famous, it's fine.

SPEAKER_00: That's the, that's the thing I can't really, you know, no one could go to an artist and say, Hey, you were inspired by watching YouTube and you got here and you now owe all these

SPEAKER_49: people royalties because you've become a, you know, a multi platinum or, you know, however the category is.

SPEAKER_10: There is an example of this. If you were to go and take all of Dire Straits records and create a Dire Straits cover band and then go on the road and there's one called the Dire Straits, um, experience.

SPEAKER_14: And I wanted to see them because I would, I mean, they literally do the entire album of Alchemy and they call it Re-Alchemy, which is my favorite album of all time. Yeah. And I've really wanted to see, but they're only touring Europe and I just, there hasn't been in sync, but they have to pay a licensing fee to do that.

SPEAKER_31: So there is literally an analogy here, which is if you do a cover band, you have to pay fees. Every time you perform, you're, you're paying licensing fees through royalty organizations. If you wanted to create, you know, a version like I used before of Star Wars,

SPEAKER_54: if you wanted to make a, a Marvel comic, if I wanted to take one of the X-Men and

SPEAKER_31: make my own series based on Cyclops, I would need permission. Right. And so you could be inspired by, I think we all know what that is. And then when you take the entire corpus, that should give you an indication that, if you took the entire corpus, yeah, maybe you are stealing. Yeah. Yeah. Why would you not get permission?

SPEAKER_21: That's the other thing is like, why wouldn't you get permission? Oh, this is like a reasonable thing to do. You know,

SPEAKER_282: I, I, I, I'm going to show you the shake out. I tried to do this, uh, this one. Maybe I missed it when I did.

SPEAKER_284: What did you give a letter grade, by the way, to this?

SPEAKER_282: I'm going to give it an A, but I, I noticed, uh, I just want to share one thing. Cause I want to get these guys on credit.

SPEAKER_285: U D I O dot com.

SPEAKER_282: I O. Incredible. Yeah. And I was doing this because I thought we would be able to, we could get this song here.

David Sacks: I said, a song in the style diaries, right? Sultans of swing about a friend that is really passionate about copyright protection.

SPEAKER_00: Oh no. Oh no. And when I did create, uh, it, it, it, it did give me an error a second ago saying,

SPEAKER_49: uh, you can't do dire straits. So maybe there.

SPEAKER_188: Huh? Yeah. Fascinating.

SPEAKER_49: Yeah. So.

SPEAKER_21: So maybe they are thinking about these things.

SPEAKER_49: But let's play this one real quick. Guardians of the score.

SPEAKER_21: Guardians of the score. Here we go.

SPEAKER_295: To let it ride patrols the scene with that copyright code in the morning.

SPEAKER_297: So bad.

SPEAKER_295: That's hilarious.

SPEAKER_304: I mean, it's hilarious.

SPEAKER_14: I mean, right now it's super funny. And I think, um, there's going to be great art created with us.

SPEAKER_31: That's what I'll say. I do think we're going to see some great things come out of it. So I don't want to say, I don't want this product in the world.

SPEAKER_307: Yeah.

SPEAKER_31: I just want to see it be fair. So if you're going to do this for dire straits, it would not kill the person who. Is making this piece of software. It would not kill them to pay, get permission from dire straits. And it would not kill them to not have permission from dire straits and have every other artist.

SPEAKER_14: So like, remember what's her name? Um, Taylor Swift didn't want to be on Spotify at some point. And she didn't have her music on there. And I was like, yeah, that's totally fine. I mean, and Daniel was like, well, no, you know, and he kind of got into it on Twitter at some point. And I was like, no artists who don't want to be on your platform don't need to be there. It's their choice. So let them not be there. And, you know, you have to cut a better deal with them if they're a better artist.

SPEAKER_31: And it would not kill this service or any other service to get permission to pay and to pay a term that that person felt was great. The rolling stones might say, yeah, well, the Beatles held out for a long time.

SPEAKER_14: If you remember to be on iTunes, that was like a big deal when the Beatles finally decided.

SPEAKER_309: They had like a whole ad campaign about it.

SPEAKER_31: Yes. And you know what, as it should be, it's their content. They get to negotiate it.

SPEAKER_54: And you know what? All these people are hypocrites because if you stole the, the Google algorithm and made a new search

SPEAKER_311: engine and you sold their index, you could be sure they would be all over you. Absolutely.

SPEAKER_80: Fascinating times. All right, let's, let's go on. J Cal, you're re you're really going to like this next one.

SPEAKER_311: Okay. I love it.

SPEAKER_49: It strikes me as like a J Cal daily. So I think this one might make it into the daily workflow of J Cal. It's called Aqua. And what it is like, sort of like a note taking app that doesn't just do straight transcription.

SPEAKER_00: Okay. And so it lets you talk to it, but then as you're making the edits, it will fix what you're saying. Um, and so you could kind of, you, let's say you're trying to formulate a thought and then you're like, oh no, no, go back and change it. So let's give it a try here. Okay. All right. Um, you know, I've had this meeting with Jason and we were talking about copyright protection.

SPEAKER_49: Actually, actually start all over. Um, um, what really we, we had this meeting and it was about buying, uh, new GPU chips. And I was trying to convince Jason that grok chips were the best chips. Uh, no, no, no. Just start all over again.

SPEAKER_31: So it understands just start all over again. It might understand like when you use notepad or using the transcriber, a new paragraph, that kind of thing. Yeah.

SPEAKER_00: Exactly. But not just that. Right. And so here's the example. You could say, remember that meeting it's on Thursday, Friday, Friday.

SPEAKER_49: And so it knows that you met Friday and you don't have to say start all over again. Or like, you could say imagination is more important than, uh, knowledge. Albert Einstein put quotes on that.

David Sacks: It knows how to go back and do that.

SPEAKER_14: Right. So this is like, it's what I like about this is transcribing has worked pretty well. And we're finally starting to see the gains from it and people use it.

SPEAKER_31: I see people all the time composing text messages. Yeah. By hitting the microphone, it transfers what they're saying and then send it. But you do want to bold something. You want to make something a link. You want to make a new paragraph.

SPEAKER_14: And somebody coming up with a way to do that without ever having to touch the keyboard is great. So, yes, um, I give this a B plus. It sounds like a really smart tool that needs to be made. If you could start talking to say, you know what, can we make a bullet point list out of that

SPEAKER_21: and see how that looks and then make it a table as well. And then show me the bullet point list in the table.

SPEAKER_326: No, no, let's do it. So let's, let's do it. Let's give it a, let's give it a try here. Hold on. Let me bring it back up.

SPEAKER_14: Yeah. Just say, you know, my favorite foods are, you give it a bunch of foods and then say, can you put that in a table with the calorie count and see what happens?

SPEAKER_282: All right. I want to make some pizza tonight.

SPEAKER_49: The items I need are dough, pepperoni, cheese, pizza sauce, and peppers, onions, tomatoes. Actually, can you just put those items in a bulleted list?

SPEAKER_333: Okay. Yeah. Yeah. That's pretty good. That was pretty sweet. I have to say.

SPEAKER_14: So the LLM was listening and for people who are watching, it went from a paragraph just to a bulleted list. I mean, that was pretty nice. I have to say. Right. I think this may transform using natural language.

SPEAKER_338: Yeah. I, I give this a, I give it, I give it a B plus. Yeah. Oh, I thought you were going to upgrade it after the bulleted list.

SPEAKER_54: No, I mean, I think this is what I expected it to do. I feel like this is something that will be a notepad. And so, you know, as a startup, you know, my advice to, with Aqua is to really start

SPEAKER_14: to think about, you know, what's defensible here and who their market is, because I kind of feel like this is something that, um, notepad grammarly are going to be able to add pretty quickly. Uh, but I do think that they've just, you know, they got like, um, a couple of, uh, months ahead of them, like they're six months ahead of them. So I do wonder, you know, who this is for, I'm not, I'm not going to stop using grammarly or my notepad to use this, but I'm going to play with it. I will play with it.

SPEAKER_00: Okay. Okay. I'm going to give this an a, um, and I think what they should go all out on is, and like I said, you know, they, they, um, they should go all out on just dominating this, like becoming the best app that does it like sort of in those early days of apps, because I do think it, look, they have a lead and I think they could create an experience, which the main reason I don't

SPEAKER_49: like using voice, uh, to text, which I noticed a lot of people do now, you know, these errors errors happen, then you're like sitting there editing it. You're like, I wish I just typed this out. But if I wanted to get a thought out and kind of the way that this allows it, this, this would be really, really powerful. I, you know, I'm going to try to put it in my daily work.

SPEAKER_07: Yeah. Give, give it a shot. I mean, I think grammarly works really well for this and that's why I started with grammarly for

SPEAKER_10: I will talk into the grammarly app, or I will talk into the grammarly webpage, or I will use the grammarly keyboard on my phone. And so, yeah, you know, grammarly is just very good at cleaning

SPEAKER_239: up your prose, uh, and giving you some options and ideas of how to make it better. And so I pay for it for my entire team.

SPEAKER_345: It's very good at that. Yeah. Same here. I think it's great.

SPEAKER_346: I find my whole team is like, it's less embarrassing. The idea of being embarrassed by

SPEAKER_31: something somebody sent because they didn't use grammarly is kind of ending now, which is great. Like people would say sometimes I'd see somebody send an email, I'd be like, what the? Work for me and you misspelling this word or you're like, it's so sloppy or it doesn't make sense what you're saying now you use grammarly. It's like really hard to be, to write something

SPEAKER_37: bad in grammarly and get it past grammarly is hard to do. Like to have a mistake in grammarly,

SPEAKER_352: I think it's impossible. I didn't know that they had a, I'm going to try, I have it. They don't have these navigation tools. They don't have that. But I do know in Siri,

SPEAKER_14: you know, when you hit the microphone keyboard, the microphone icon on your keyboard, on your eye, I don't know what they call that dictate. The Apple dictate tool has gotten better. And if you do say new paragraph or strike that last word, like some of those work, but not a lot of, not like this, not like, Hey, start over, not like, Hey, make it a bullet point

SPEAKER_10: list. So I agree. They should keep working on this. Um, and see where it takes notes app. I think

SPEAKER_31: everybody would use it. It'd be awesome. Yeah. Ever. I mean, every couple of years, somebody finds a new vector. Evernote was the one that was a cloud based and mobile, you know, grammarly is based on AI machine learning and making you a better writer. This one is based on formatting. So yeah, there's always an opportunity with no taking it. So I agree. But yeah, I give them a big plus. Keep going.

SPEAKER_282: All right. I'm going to give them an A. I'm going to give them an A. I like it. Great. Keep going. Okay. So the next one on our list is, um, so we, we saw a couple of weeks ago, Devin,

SPEAKER_49: which was the automated coding that had like, uh, sort of multiple workspaces to work on a problem. And, um, this team has basically made an open source version of it. And so if you, um, go to SWE dash agent, this team has come together, it's an open source project and they, uh, you know, compared themselves against some of the bigger models out there, they have just a demo in place here. I didn't want to do this from scratch. And so you can see here, they have like a 30, uh, step demo. So it's like, they have an issue that they pull from their repository. Basically, uh, they have it create like a bug, they reproduce the bug and then they, you know, move the code around. So I'm just going to go through this quickly. This is, you know, and so it reproduces

SPEAKER_00: it and look, it's just, it's open source. And what's awesome about this is they've, um, you know, made this available to everyone very quickly shows the pace of development we're at. And I think folks

SPEAKER_49: are going to take this and really kind of build on top of it, uh, very, very quickly. And, uh, you know, it's kind of just walks through the same workflow that we saw, uh, this project again, this is

SPEAKER_360: called SWE dash agent. Oh, right. So it's a software engineer agent agent. Yeah. And it's now open

SPEAKER_10: source, uh, hosted and you can go play with us at SWE dash agent.com. Yeah. And you can get access to

SPEAKER_49: the code and you can see, you know, what everything is behind it. They're going to release their paper. They show it how they're scoring and the software engineering benchmarks.

SPEAKER_31: So do you see this as being like pair programming where you and a agent are working together eventually? What do you, what do you think this is going to look like when it's finished? You know, when I, when I say finished, it's like reasonably trustworthy enough for you to, to use every day and, and somebody would want to take the time to use it.

SPEAKER_63: Um, and it's not slowing them down. It's actually speeding them up.

SPEAKER_00: Yeah. I think that's a really good analogy. I think it starts out as like a pair. Um, that's kind of giving, you know, someone super powers. And so, you know, I read this really

SPEAKER_49: interesting analogy and I can't find who came up with it. Um, and I'll try to find it in Twitter, but the search is really difficult to use. Well, it has always been terrible.

SPEAKER_282: Yeah. It's always been, it's always been difficult. Uh, you may know someone that can fix it.

SPEAKER_21: Jacob. Um, you know, it's, you know, it's the thing about search. It's like such a rarely used feature compared to all other features that it's always with, with the exception of Google, which

SPEAKER_14: is certain now, but the problem is most people don't. And it really has always been, they, they did like a very advanced Boolean search where you do like from at Jason greater than this many likes

SPEAKER_54: or whatever. That's not what people want. People want a plain language, interpret my search.

SPEAKER_14: Yeah. You know, tell me about the next or people talking about the next playoffs. And that's where I think language models will help something like Twitter X leapfrog, right? Where the language model will be like, Oh, I understand what your intent here. There's a, there's a website called stat muse.

SPEAKER_54: Do you know about stat muse? Yeah. Everybody in the NBA uses this thing constantly. And I guess it's

SPEAKER_31: got some machine learning. So if I said, you know, which point guards have the most 40 point games

SPEAKER_14: this season, um, it should have jail and Brunson or whoever come up with. Yeah, jail and Brunson

SPEAKER_10: number two, Luca, uh, Don chick number one. And so like, that's like a, just a natural language

SPEAKER_31: search, right? And it just nails that. I think that's what people want. And I don't know if this

SPEAKER_10: is a language model doing it, or it's just machine learning. Like, look at this. When I type in,

SPEAKER_101: yeah, for stat muse, which point guards, you know, have 40 years to season. And then if I said,

SPEAKER_48: yeah, this is like seven assists. Yeah. This is like, this is right. Right. This is retrieval

SPEAKER_00: augmented generation. So this is using an LLM to drive a database because this is structured data exists in a database, but the sequel for this is not, you know, trivial. And the LLM is able to take

SPEAKER_49: this, uh, English and turn it into the sequel and go and get the results for it. Yeah. Uh, you know, interestingly, Google just launched this for big query and snowflake just made this available for snowflake powered by Gemini or snow for big query and powered by, I think anthropic for

SPEAKER_31: snowflake, but let me just check the snowflake. It's just amazing when you think about, you know, something simple like being an NBA analyst, right. And you see on my screen here, I said, which point guards have the most points per minute this season, right? Like that's a statistic that, you know, I don't know, you, you would probably do the day before. Yeah. Dallas Mavericks playing the New York Knicks. Right. And then somebody would give that stat to the people who are doing it. Now you can just do it on the fly. Which point guards have the most points per minute this season. And as you can see here, again, Luca and Jalen and Steph are the three best point guards and it's

SPEAKER_239: 0.9 points per minute for Luca. And then Jalen and Steph have 0.81 and then it drops off from there.

SPEAKER_205: But like, how wonderful to be able to pull up this level of detail on the fly. This kind of knowledge

SPEAKER_49: was, you know, pretty amazing. Yeah. I mean, it's, uh, uh, mistraw. Sorry. They did it with text as equal with mixed mistraw on snowflake. Um, yeah, this is going to be the world going forward. So let me, let me share something with you. Just a quick aside, J Cal, and this is not an original thought by me. I read it and I'd like to find the person to give credit to, which is when the world went from pre-industrial revolution to industrial revolution, let's use an example of making cars. We went from

SPEAKER_00: like making cars, like in a bespoke kind of way, which is like, you'd make one a day till, you know, you had the Henry Ford production line and factory. And all of a sudden you make a thousand a day. Similarly in farming, right? We went from people working in farms, having machinery, and all of a sudden you could farm thousands of acres with machinery and feed, um, lots of people. Right. And so that was the industrial revolution impact on our lives. What someone said, what AI is doing, and we're kind of looking at it in different ways, but that person's framework was we are now having the industrial revolution for digital. And so, and their example was like, look, you used to make someone make a mockup or a picture. And then, you know, they'd make like one, you know, a great illustrator, maybe take a couple of days now using mid journey. I can make like, you know, a hundred in a minute, if I really want to, right. Or I can make, you know, a thousand songs

SPEAKER_387: in a minute, right. Similarly, just like a factory can make a thousand buttons in a minute, you know,

SPEAKER_21: or a thousand t-shirts in a minute or a thousand books, but does it mean the book or the book or the button or the t-shirt is going to be high quality or not, but it can spew a lot out.

SPEAKER_45: It can. And so now what we're seeing is for digital and, you know, we've had these eras where

SPEAKER_00: we had digital stuff pre the internet. That was like sort of when you were working in Florida, right. And then you had the internet show up, which, you know, kind of changed distribution. Then we had mobile, which was just like, again, going to like a different form factor. We had cloud, which made it easier, but this gen AI movement is the actual industrialization movement of making more images, making more software, making more mockups, making it easier for us to access data quickly. And so I think with that, when you apply that back to, you know, software engineer agent, right. It's, we can now, you know, the same way we went from making one card eight to 10, we can a hundred X ourselves. And so me as a software engineer, I can work on 10 different

SPEAKER_01: problems at the same time, or 30 different problems with the help of these agents. What do you think about that?

SPEAKER_31: I feel like this existed already where I had writers, no, in a, in a minor way, I had writers who would come to me and they'd have like, I would say, Hey, come to me with like great story ideas. And then, you know, I'd have bloggers or writers come to me in the magazine day and somebody would have seven incredible ideas. Okay. And then other people would have one. And I'm like, how did this person come up with seven really great ideas? Those people came up with one and that person had different resources. They used to come up with story ideas. They had different techniques, right? Yeah. And so they net net were seven times more valuable to the magazine or to the blog. Right. Yeah. And then you had people who had source, it turned out they had sources. In some cases, they might have three or four sources where they would say, you know, to that person, Hey,

SPEAKER_10: tell me what's going on in gadgets this week. And, uh, you know, or they would just be shooting, uh, the issue or chewing the fat with somebody and they would get these ideas for stories.

SPEAKER_21: And it was like, they had some better technique, which just then made them 10 X better than the

SPEAKER_31: next writer or a hundred X. And that's just, but one example in journalism, like your sources matter, right? So some source might get you the content you need some leak that makes you a Pulitzer winner. And then somebody else might be a better technical writer than you. And so I think what's going to happen is there are going to be some people who figure out how to use this pair program or how to use Sora, how to use, you know, whatever the tool is, they're going to find some like little edge and be able to use it in a way to perform at a much higher level. And yes, we will have the same way, some knowledge workers, some developers, some writers with sources, whatever it was or 10 times better than their contemporaries. This could be like a hundred X. And so if you know how to use these tools, you're going to be just so valuable to different companies. And then companies are going to start to learn. If you can't use these tools, then think about how little value you provide to your company. It would be like going, trying to go work at a company in 2024. And you didn't know how to use Microsoft Office or email. Yeah, you didn't know how to use an office suite. You know, how you spreadsheets, word processors, you don't know how to use zoom, you didn't know how to use

SPEAKER_14: Gmail, but you're like, I have a phone. And I have a calendar that I write shit in and have like a

SPEAKER_171: written calendar, maybe like, Okay, short, you can give it a shot. But you would just be like this.

SPEAKER_31: You would be like this Neanderthal, right? And I think that's what's going to happen here is the

SPEAKER_35: people who know how to use these tools are going to be slowly become bionic and outpace their contemporaries. You know, like I said, I

SPEAKER_00: that framework was really interesting for me, you know, I sold my last company to Ford, and I spent a lot of time understanding the history of the company and really just, you know, what they were able to do in terms of industrialization. And when I read that, it just clicked for me. And I said, all the stuff that we spend time thinking about is wrong. Because we are about to undergo this moment that those folks did like 100 years ago, when it was over 100 now, right? Early 1900s, where they were like, you know, people were making like one car a day, and it couldn't change the world. And then all of a sudden, when you took it to 1000 a day, and made it affordable, everything changed, the world fundamentally changed. And I think software is still in that bespoke era, even though we've had all these advancements in making software better programming languages, we're not doing punch cards

SPEAKER_01: anymore. Right? We have great, amazing IDEs. But I think we're about to have the 1000x jump in software development.

SPEAKER_353: I mean, what does that even do? I mean, can we even comprehend? Like, we can comprehend

SPEAKER_54: what a 10x developer does, you ask them to build something, you thought it would take two weeks, it comes back tomorrow. Okay, yeah, instead of 10 days, it took one day. Great, you love that developer, you give them more stuff to do 1000x person, like, okay, we have a roadmap to build something over the next three years. And it came back tomorrow. It's like, wait a second. Okay,

SPEAKER_404: so should we just start another company? Or should we start another product line? Like, that's really

SPEAKER_00: wild when you think about Yeah, or like, or think about like, you know, using like some kind of service. And instead of like, trying to integrate, and you know, maybe even pay someone to use it, you know, this 1000x world, you just get something built bespoke for you. And so it really has a huge impact on SaaS companies, it has an impact on, you know,

SPEAKER_405: Yeah, I mean, it would be like, I'm trying to think of the analogy here. But, you know, I'm envisioning,

SPEAKER_14: I want a car, and I want my car to be a sports car, but it also has a ski rack, or like some way for me to put my skis on it. And, you know, has snow tires and can, you know,

SPEAKER_31: be in four feet of snow. But I also want it to be a sports car because I'm like, all of a sudden, this franking car is on your doorstep, right? You just, you're gonna be able to make very custom things that didn't exist before, which in manufacturing, we actually have now if you wanted to get a the idea that you could get a t shirt made or a pair of sneakers made

SPEAKER_10: to your specification with specific designs, we kind of take that for granted right now. And nobody really does it. But if you want a sweatsuit with, like, I see some people do this, like they'll have a birthday party or something for somebody, and then everybody shows up with like a sweatsuit. That's the person's picture. And you're like, Okay, that's corny, but charming or whatever.

SPEAKER_413: We should do that for our friend.

SPEAKER_414: We should do it for our special friend. Oh my God, if we all wore a Phil Helmuth sweatsuit, which is pictures of Phil blowing up. Oh my God, that'd be hilarious.

SPEAKER_205: We should totally do that for the birthday weekend. Oh my God, if we all showed up in a

SPEAKER_346: sweat, a tracksuit, a Phil Helmuth tracksuit with pictures of Phil Helmuth on it. Oh,

SPEAKER_417: that'd be hilarious. Oh, just the tracksuit with logos at least.

SPEAKER_418: Just the tracksuit with the Phil Helmuth logos on it. Oh my God, so great.

SPEAKER_420: Any other debues, are we ready to wrap? This is a great Saturday show.

SPEAKER_419: Ready? This is a good one.

SPEAKER_171: Ready to wrap it Saturday. All right, listen, everybody go to check out Grock, console.groq.com. Yeah, the cloud. There it is.

SPEAKER_286: We've got 85,000 developers, 25,000 apps. It is really, really awesome.

SPEAKER_10: I love it. I love it. Continued success. And if you have any great demos you want us to do here and give you a lot of great and give you some candid feedback, just at Jason and at Sandeep, S-U-N-D-E-P on x.com slash formerly known as Twitter. And yeah, just reply to us and we'll do it. We'll see you all next time. Bye-bye.