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Technology's daily show (formerly the Technology Brothers Podcast). Streaming live on X and YouTube from 11 - 2 PM PST Monday - Friday. Available on X, Apple, Spotify, and YouTube.
You're watching TVPN. Today is Thursday, 11/20/2025. We are live from the TVPN UltraDome, the temple of technology, the fortress of finance, the capital of capital. Ramp.com, time is money. Say both.
Speaker 1:Easy use corporate cards, bill pay, accounting, a whole lot more all in one place. Nvidia beat earnings and job, the job numbers came back very positive. Are 119,000 new jobs, and NVIDIA beat earnings. The revenue came in at, 57,000,000,000 for the quarter, up 62% from this quarter last year. Fantastic result for NVIDIA.
Speaker 1:Of course, that's why the stock's selling off and the market's melting down. And Bitcoin's down 10%. Was Bitcoin That
Speaker 2:was my
Speaker 3:prediction that was my prediction after yesterday morning.
Speaker 1:Of your predictions. But I was Yes.
Speaker 3:But I was was wrong on the timeline. Yes. Was interesting.
Speaker 1:Yeah. Yeah. Yeah. It pumped shortly, and now now everything's selling off. Very unclear where we go.
Speaker 1:But I did think it was just interesting that the, I didn't really piece this together until, like, some grand thesis or actually write a piece about it. But, I did think it was funny that we are in a world where, demand for robots is surging and also demand for human labor appears to be surging, like, NVIDIA, you know, the chips that they make sell artificial intelligence. That should be, replacing human labor, and yet the job demand is is surging as well.
Speaker 3:And it's notable they they said they have visibility for a half a trillion dollars Mhmm. In revenue Yeah. Through 2026, which
Speaker 1:I mean, seems crazy, but
Speaker 3:It's not enough anymore.
Speaker 1:They're but, I mean, they're making 57,000,000,000 a quarter. Just for the next quarter, guidance is at 65,000,000,000. Analysts had predicted that revenue guidance would be 62,000,000,000. So everything is trending up. Jensen said, we've entered the virtuous cycle of artificial intelligence.
Speaker 1:AI is going everywhere, doing everything all at once. What a great quote. Tyler's very happy about Jensen. And I'm also happy about Restream, one livestream, 30 plus destinations. If you wanna multistream, go to restream.com.
Speaker 1:So we talked about it a little bit yesterday on the show. There's new product from Travis Kalanick, the founder of Uber, of course. It's called Picnic. We discussed it on the show yesterday, and we got a reply from none other than Travis Kalanick himself. Why don't I read his his reply, and then you could kinda take us through the what you wrote in the newsletter, sign of sort of
Speaker 3:Well, why don't I start why don't I start with a little bit of context
Speaker 1:Please.
Speaker 3:You can add to it. So Please. I wrote in the newsletter this morning, of course, the subject of the newsletter was daddy's home, and that is, of course, Travis Kalanick. Travis is back on the timeline.
Speaker 1:It's so good to see him back on the timeline. Like, he's he's never he's never been not been doing business, but he's been so quiet.
Speaker 3:And he's been scratching like deep alpha on Yes. The market that he's operating
Speaker 1:Yes. In
Speaker 3:Yes. That is, like, non like, in my view, like, I never it makes sense what he what he shared, which we can get into, but I never looked at it exactly like that. But he's obviously back on the timeline with Picnic. Picnic is a new business under City Storage Systems.
Speaker 1:Okay.
Speaker 3:So you don't know the name City Storage Systems, but Cloud Kitchens is actually a subsidiary of City Storage.
Speaker 1:I thought Cloud Kitchens was the top. That's what I
Speaker 3:thought too. No? But it's actually the opposite. City Storage Systems. Great great Oh.
Speaker 1:You want kind
Speaker 3:of an under under the radar Yeah. Holding company to, you know, verticalize food delivery. Sure. So Picnic is kind of a front facing platform focused on meal delivery. The offer sounds too good to be true.
Speaker 3:Mhmm. Meals delivered from 50 restaurants with no tipping and no fees.
Speaker 1:Mhmm.
Speaker 3:They also bundle orders. So a company can order from 10 or so different restaurants, get it all ordered at the same time. Mhmm. He's got a bunch of customers already. Wells Fargo, Live Nation, KPMG, PwC, and a bunch more.
Speaker 3:Sure. And so we were talking yesterday about how, like, broken the tipping experience is. Yeah. You're tipping directly. It's a way to encourage great service by, like, tipping if you're checking into a hotel and you're tipping, you know, somebody on the way in.
Speaker 3:Mhmm. They're incentivized to make your stay great. Mhmm. Same thing, you know, valet tipping on the way in. Yeah.
Speaker 3:They're gonna park your car right up at
Speaker 1:the I saw a viral, maybe Instagram reel or something about a guy who says that whenever he checks a new hotel, he says, you know, we always tip the valet when we're here. We tip the bellman and the person that cleans. But, you know, you folks at the front desk just don't get enough love. And so here's a nice crisp $100 bill. And he says The front
Speaker 3:desk folks really get
Speaker 1:They never get tipped. And he said every time he does it, he gets upgraded to an insane suite. And so he was just like sharing this alpha. I think it's No.
Speaker 3:The I might give it try. Hotel I worked at, like Michael Jordan would stay, and he just actually would carry around, like, $10. Well And just any he was just handing handing it out like candy on the property, and he would have a very nice stay, as you might imagine. Yeah. So, anyways, we were talking about that.
Speaker 3:Travis responded, and you can get into it and kinda give your reaction.
Speaker 1:Yeah. So Travis said, delivery app tipping isn't about feedback feedback mechanisms. It's a tool for maximizing the price paid by consumers. Eaters are economically irrational with tip For every $1 in tip, they economically behave as if it were 80¢. This is just a hypothetical figure, but it's directionally true.
Speaker 1:Because you feel emotionally good about tipping, mentally, it you you give it less it feels less painful to part with
Speaker 3:those dollars. Purchase.
Speaker 1:Yeah. Than, like, gas buying gasoline. Exactly. Like that. So if you the way you look at if you're if there's $10 in taxes and $10 in tip, you'll be like, oh, I feel good about the $10 in in tip.
Speaker 1:That feels like $8. And the taxes, that feels bad. Right? And and and and it happens on the other side. This means that, less price elasticity for the same price.
Speaker 1:So couriers are also economically irrational with tip. For every $1 in tip, they economically be they economically behave as if it were a dollar 20, again, directional. And so you feel good when you're tipped, and so you treat those dollars more as more valuable. And so this is a hack on the human psyche, which apps must implement and maximize or miss out on economic surplus that their competitor will use to defeat them. And so even if you have you know, your whole brand is built around our app doesn't tip.
Speaker 1:Remember this happened with Uber. If your competitor is is using tips, if they implement tips, they will just be making more money than you because of this economic inefficiency that arises from the nature of the human psyche. That is very, very interesting. The app that decides to pay the same net amount to the courier, but as a Square deal via a drop fee plus tip, will lose market share every day to an equal marketplace player that implements and maximizes tip. Now equal marketplace player, that's doing a lot of lifting because it's hard to just spin up.
Speaker 1:Like, you know, I can't just start an Uber
Speaker 3:There's network.
Speaker 1:Right now. It's it's hard. Yeah. But he makes a very good point here. And so what's interesting is that I read this as adding tipping is inevitable.
Speaker 1:Adding tipping is inevitable. We're not doing it right now, but eventually, someone will come to the market, do it. We will have to in order to compete. Is that not the read here?
Speaker 3:So the the differ the difference here is that I I think that one picnic is, like, is already counter positioned.
Speaker 1:Mhmm.
Speaker 3:Right? So it's pricing thing. It's a flexibility standpoint. It's also counter positioning on, like, focusing on one key buyer. Yes.
Speaker 3:Obviously, you know, the DoorDashes, the Uber Eats have Yes. Their kind of, like, corporate offerings. Yes. But I think, like, just creating a a creating a different and more transparent model makes a lot of sense. He's also like I think you have to factor in there's a lot, like, TK's been kind of like secretive about Cloud Kitchens
Speaker 1:Mhmm.
Speaker 3:Secretive about Otter, which is like the the Toast or or Square competitor that he And so, you know, when I when I hear like no fees, no tips, like it just screams like there's been so many attempts at food food delivery
Speaker 1:Yeah.
Speaker 3:And just like new restaurant concepts that have been venture backed. Yeah. And a lot of them haven't worked out. Right? Because it's just like becomes unsustainable.
Speaker 3:And so I think that I think Travis is basically by focusing on a key customer type, trying to make it up with volume and then having this like vertical approach.
Speaker 4:Mhmm.
Speaker 3:I I I just like I wanna I wanna believe that I believe that he's somewhat of a masochist and that like going and trying to win in food delivery
Speaker 1:Yeah.
Speaker 3:Is just, like, the hardest arena, just like food in general. We have David Chang coming on Mhmm. At at noon, which I'm excited to talk with him about. But it's just like the most competitive space. It's low margin all the way down.
Speaker 3:But I think that he I believe, just given the domain expertise, I believe that he's he has a real play here and a real strategy. And I think that I think that already, we were talking with the the person on our team that handles, like, food ordering. Mhmm. He got on the phone with Picnic yesterday, and he was like, this offering is way better than what we're seeing Mhmm. With the with the other delivery apps and wants to switch to it immediately.
Speaker 3:So, again, if if if if if TK can make the model sustainable Mhmm. I think it'll be quite competitive.
Speaker 1:Yeah. I mean, you would you would imagine that vertical integration should allow low true lower prices, like true cost competitiveness. That's like, you know, an age old business adage. If you vertically integrate, you can undercut your competitors and just offer lower prices. Almost like, you know, buying Kirkland brand at Costco is typically, like, sort of like the canonical example of, like, heavy verticalization.
Speaker 1:And there's there's a ton of other examples. But I wonder I still wonder is is this like, the I remember in the early days of Uber. Like, it was amazing because you didn't need to think about the tip. And so there that mental load wasn't there. And there was the star rating system, and it felt like they're actually like, the VCs might have been subsidizing it a little bit, but it felt felt affordable on the on the on the rider side.
Speaker 1:And on the driver side, it felt like people were getting paid pretty well, and everyone was sort of happy, but maybe the VCs weren't. But they wound up getting, you know, a stake in a $200,000,000,000 company. So, you know, I think it all worked out for everyone involved. But, it seems like Travis is reflecting on this idea that, it was tipping was inevitable to come to the Uber ecosystem. Is tipping going to come to the Waymo ecosystem?
Speaker 1:Is tipping going to come to this Picnic ecosystem, the Picnic product eventually?
Speaker 3:I don't know. I just Do
Speaker 1:you think Picnic will have tipping in ten years?
Speaker 3:I just view this more as like a as a as a corporate service Mhmm. In its current positioning True. Than a consumer service. And when a consumer is buying food
Speaker 1:Yes.
Speaker 3:It is if you're ordering food delivery Yes. It is it is not a it is like it is a luxury. Yeah. Right? Like, food delivery has been extremely normalized.
Speaker 3:But if you, you know, re you know, rewind to forty years ago and ask like, oh, how often do you get food delivery? Most people will be like, I never get food delivery. I just go pick it up myself. Right? Yeah.
Speaker 3:So it is a luxury, but this is being positioned like as a corporate offering. And I think that if Picnic can get just like deep relationships with a bunch of these different companies that have, you know, I I listed off some of logos before.
Speaker 1:Yeah.
Speaker 3:If they can if they can just become embedded in these companies and part of their workflows, I think they'll they'll it's possible to, like, make it up in volume.
Speaker 1:Yeah. Yeah. This is so funny the way Shields puts it. Truth bomb from TK. Tipping is a hack to maximize price.
Speaker 1:It's psychology. Consumers are willing to pay more in tips than they are willing to pay in fees for or menu price. So a $16 burrito plus a $4 tip feels far cheaper to people than a $20 burrito that has no a no tip option.
Speaker 3:But but but again, from a from a from a from a business standpoint, I don't I don't know I don't know if it's exactly the same thing. I feel like businesses, like, wanna have, like, more predictable more predictable Yeah. Costs, not have that, like, variability and, like, okay, sometimes the fees are like this, sometimes the fees are like that.
Speaker 1:Yeah.
Speaker 3:I think this will be a better consumer experience. A lot of companies, you know, will give, like, credits to their employees, which is, like, you get $20 of credits
Speaker 1:Yeah.
Speaker 3:Every day. And then it whatever you're kind of, like, spending on top of that, you have to eat. Yeah. And so I think consumers will could very likely, like, picnic more. So we'll see.
Speaker 1:Yeah. The this was one of the original, like, d to c evolutions that happened with a lot of, like, Shopify merchants. I remember looking at I think it was, like, Kylie Cosmetics. And there was a trend for a while that was, like, consumers want transparent pricing. Don't do all the crazy psychological hacks.
Speaker 1:So you'd be like, yeah. I'm just gonna put it's $30, and that's what it is. And it's free shipping. And that tax and shipping is included. And it's just like what we say up front is feels really good.
Speaker 1:Feels really good to say that. And then you go to, like, the high performing stores and all across the board, it would be like $9.99. And then you go in and there's, like, $6.42 added in taxes, and then you add shipping. And it
Speaker 3:it It's like a pop up that says laddering minute to
Speaker 1:add this to your cart. It's laddering you up and just, like, keeping you on the reel reeling you in like a fish, adding adding fees, adding fees until you're like, okay. Well, now I'm, like, entered all my information, and I'm I'm ready to click the button. And so, yeah, okay. You added 2 more bucks.
Speaker 1:Whatever. I'll just deal with it. So these psychological hacks are just, like, somewhat inevitable. But, avoiding them, I think, the short term is a great go to market. I just wonder if there's something, if there's something, like, truly, like, counter position that that will be durable.
Speaker 1:Like, Costco, Kirkland, Costco, like, has not has been, like, the low cost affordable option. Yeah. And that model has held for, like Yeah. Decades. Right?
Speaker 3:The one thing the one thing that we learned from Yeah. Having somebody on the team call Picnic Mhmm. Is that they are focused on higher volume orders. Mhmm. So, like, teams of, like, 25 and up.
Speaker 5:Yeah.
Speaker 3:And so I think that I think that they're just betting, hey, there's we can we can get a lot of volume. We we will be able to Mhmm. Like, handle having it's one person that goes around and picks up every order from all the different restaurants. Right? Yeah.
Speaker 3:And so it's quite a bit less of one individual's time, much higher order volume than when a company is like, hey, we're giving credits to people. Yeah. And then each employee is making individual orders. Mhmm. And then there's like ends up being like 20 drivers on the road Yeah.
Speaker 3:To deliver one lunch Yep. Which like makes no sense.
Speaker 1:I have one more take on this delivery question. But first, I need to tell you about Cognition, the makers of Devon. Devon is the AI software engineer crusher backlog with your personal AI engineering team. My question is, what is TK's drone strategy? What's his autonomous delivery strategy?
Speaker 1:Because he's vertically integrated at the kitchen level. He has the point of sale system. He has the the sort of ordering front end. Yeah. You can interact directly with him.
Speaker 1:He's going he's cutting out several of the middlemen. But is he going to be a logical partner for Zipline? Is he gonna be a logical partner for Coco and Starship and these robotics companies that are delivering, food? Ryan Oskenhorn here says people aren't ready for how much better food tastes when it arrives five x faster. That's a hilarious take because, like, in fact, I have tasted food right when it's made.
Speaker 1:Like, it's not it's not, like, an entirely novel thing.
Speaker 3:Yeah.
Speaker 1:But what he's pointing at here is that Zipline is is getting food delivered in four minutes as opposed to cars that take twenty minutes. And so hot food arrives hot, which is certainly a benefit. But, it just does create more of like a, you know, benchmark to the, to the actual restaurant restaurants.
Speaker 3:I tried I'm trying to find if Travis is an investor in Zipline. The Google AI overview says, yes. Travis Kalanick is an investor Really? Uber. Gemini says Or Uber.
Speaker 3:I could not find definitive evidence that
Speaker 1:Yeah. Travis Okay.
Speaker 3:So so the the AI overview says, yes.
Speaker 1:We will do
Speaker 3:Gemini says, there's no evidence.
Speaker 1:Maybe we can ask him. I I wonder I wonder if that's, like, a logical partner. I mean, on the self driving side, his original vision at Uber, it felt very much like he needed to own that technology. He wanted to be not just a, like, a buyer of it from a different company. It seemed like while he was at Uber, he considered self driving technology as critical path, as something that should be owned by Uber.
Speaker 1:And then once he was out, the company spun down ATG, their advanced Yeah. Autonomy group.
Speaker 3:I mean, but think think about it. So, I mean, right now, if you look at city storage systems, you have cloud kitchens, which is making the food. Mhmm. You have Otter, which is like the payments and ordering infrastructure. Mhmm.
Speaker 3:And then now, you have Picnic, which is like the front end. Yeah. And any type of delivery method actually like fits into that system. Right? Mhmm.
Speaker 3:So I think he's being I I I would imagine, he'll either add a strategy, but potentially more likely, he'll just integrate with a variety of drone delivery and then autonomous vehicle delivery and and and continue to use traditional labor. So we'll see.
Speaker 1:Well, let me tell you about Gemini three Pro. You've probably heard about it, but we're telling you about it anyway. Google's most intelligent model yet with state of the art reasoning, next level of vibe coding, and deep multimodal understanding. I took it for a spin in AI Studio this morning. Had it build a scrollable like, you know, as you scroll, the the the bubbles move around and tries to visualize how DeepMind and Google Brain merged.
Speaker 1:And these these sort of, like, generative UI around, deep research reports, think, are gonna be really, really fun. I need to continue iterating on this particular one.
Speaker 3:Gabe says, how can it be a logical partner if his products is for larger teams? Like Jordy just said, teams of 25 plus don't think a zipline can fit 25 different orders point. It's a good point. Keller said they can fit two full grocery bags worth of food That's still their drones. For 25.
Speaker 3:Yeah. But but I like, what what it'll come down to is the actual cost.
Speaker 1:Your team does a group order and instantly gets swarmed by drones. Yeah. That would happen.
Speaker 3:I mean yeah. I mean, right now, you order on picnic, one delivery driver is, like, driving around to a bunch of different restaurants and getting all that food and bring it to the office.
Speaker 1:You can
Speaker 3:imagine, like, six different drones end up carrying out
Speaker 1:No. I I I mean, I think I think for for this particular for for this particular, like, use, it just feels like they will be much much more a buyer of, a Waymo type autonomy solution as opposed to a zipline autonomy solution, I I would assume.
Speaker 3:Yeah. I just think it I I think the most notable thing about Picnic is Travis doesn't wanna just sit at the infrastructure layer of food delivery. Right? He wants to own the end customer experience and the brands.
Speaker 1:Yep. Yep. Well, we have a beautiful picture of Alex Karp's watch, the Patek Philippe Aquanaut with the orange band. We love to see it.
Speaker 3:We clocked this.
Speaker 1:TJ the wheel Long time ago. Jensen, one of Jensen's leather jackets. Jensen, of course, has many leather jackets. I've only seen Carp in one single Aquanaut. There is a fascinating story of how Carp wound up with this particular watch.
Speaker 1:We'll have to get him to tell it on the show though at some point. We'll also have to tell you about Cognition, the makers of Devon, which I already did. I already did Cognition. I'm out of it today. Adio.
Speaker 1:Adio is the ad. Get a
Speaker 3:lay down. CRM.
Speaker 1:Build scales and grows your company to the next level.
Speaker 3:Yeah. We routine our routine is so dialed in
Speaker 1:Yeah.
Speaker 3:That if we go to bed like two hours later than normal Yeah. Just throws everything off. We had a very very chaotic morning.
Speaker 1:Yeah.
Speaker 3:But we're back.
Speaker 1:We're back. Nano Banana is remarkable. Look at this Golden Gate Bridge image. It generates the image and also all of the diagrams around it.
Speaker 3:This is how Tyler sees the world, by the way.
Speaker 1:Sundar says, you went bananas for nano banana. Now meet nano banana pro. It's state of the art for image generation editing with more advanced world knowledge, text rendering, precision plus controls built on Gemini three. It's really good at complex infographics, which is awesome, much like how engineers see the world. That's very fun.
Speaker 1:You can see that. We were
Speaker 3:playing around with it this morning. It is it is absolutely wild.
Speaker 1:It's really, really good. The text is flawless. There's there's just truly it doesn't make any mistakes with text anymore. It you know, we were we were in the era of, like, the text looked good, but you would still see a double s every once in while. One thing would go wrong.
Speaker 1:And and now we're in a much better spot. There's still one test that it fails. That's the Where's Waldo test. If you have it go generate a Where's Waldo, it will not be it will it like, you you you will clearly
Speaker 3:be You was one you in a real world morning. Did you generate that?
Speaker 1:I had Tyler generate that one. I generated another one.
Speaker 3:This this is like the most funny Where's Waldo ever. John like says like, where's Waldo? And I'm like, are you are you are you messing this with me? Is this a joke? And it's like this massive crowd of people and then Waldo is standing on a stage going like this.
Speaker 1:Yeah. For some reason, it did not hide the Waldo at all. Like, Waldo was just out just perfectly in the center, very obvious.
Speaker 3:Most novice, where's Waldo?
Speaker 1:It was very novice. And then also there were actually two Waldos. And and as you dig in, normally, when you when you're hunting around a Where's Waldo, there's different little sub stories that are happening, and this was more just like a generic crowd. I mean, still remarkably impressive, but but that is currently my go to evaluation, and, and we got a lot a lot closer, but, it's not it's not superhuman. It's not super Waldo yet.
Speaker 1:Anyway, we have Doug O'Laughlin from semi analysis in the restroom waiting room. Let's bring him into the TVPN UltraDome. Doug, how are you doing? Welcome to the show.
Speaker 3:How did you decide to take a vacation in the fall of of twenty twenty five? You were off
Speaker 1:for two days off. We're in the midst of the biggest
Speaker 3:You were like, oh, it's gonna be it's gonna be mellow. No. There's not any any news.
Speaker 1:No days off. So, dude,
Speaker 4:every single time I take a vacation, stocks always drop. But, dude, I did I proposed to my girlfriend in Japan. That's the reason why I went. So yeah. They yeah.
Speaker 4:Well, big deal. Yeah. Woah. We're gonna hit the goal.
Speaker 1:That's massive Anybody can pull together a $200,000,000,000 LOI, but to to find true love is beyond special. So congratulations.
Speaker 4:That's What's a 100,000,000,000 between friends today? I saw the 100,000,000,000 the 100,000,000,000 Brookfield thing. Was like, dude, don't even know. It's just another day, man.
Speaker 3:Another day. Did you did you have somebody did like a semi analysis intern come up during your vacation and and go in your ear like, sir, Sarah Fryer has requested a federal backstop.
Speaker 4:So okay. I mostly consumed it on a twelve hour lag. And the twelve hour lag, I was like, holy shit. And it's just, like, very funny to to get it in, like, slow motion I'm like, okay. That was a bad that was a bad interview, Sam.
Speaker 4:I'm gonna be honest with you. I was like, oh, Sarah Briar? And then I'm just like, oh. Oh. And then I'm like, but I do think and to the Fed, the Fed is what I think people are freaking out on stocks wise, but it's just like this weird thing to witness in, like, slow motion half of on the other side of the world when everyone's asleep and shit.
Speaker 4:It was just really weird.
Speaker 3:Yeah. Totally. Well Okay. Welcome back.
Speaker 1:Well Thank you. What's going on with NVIDIA? Take us through how you're processing the news. We we we've been batting around two takes. One was we're we're we're extra analytical over here.
Speaker 1:The first take we had was Jensen was was seen drinking a beer, and therefore, will
Speaker 3:beat Not just drinking a beer.
Speaker 1:Chugging.
Speaker 3:He was linked linking arms in South Korea. This is Absolutely.
Speaker 1:This is our rigor. Our rigor.
Speaker 3:So I I was confident that they were gonna do quite well.
Speaker 1:And then we just seem to be in the era where things beat on earnings and then immediately sell off for some reason because expectations are so high. And maybe we're in that era now, but how are you processing it?
Speaker 4:I think it's, like, almost a perfect beat. It's very clean. You have, like, almost nothing to complain about.
Speaker 5:Okay.
Speaker 4:Margins, which was like a story last year Mhmm. Doesn't matter. Like, they did a great job. They had a pretty solid like, a meaningfully above buy side consensus. It's it's like a perfect quarter.
Speaker 4:You have no problems with it. But the thing is you're the biggest, most profitable company or not most profitable, but, like, you're one of the biggest companies of all time. Yeah. Perfection is expected every single time you report. So I think it's totally fine, dude.
Speaker 4:I'm I'm being serious. It's just totally fine. Like, stocks do go down. Yeah. People forgot about that.
Speaker 1:People forgot.
Speaker 4:They do go Stocks can't go down. They do go down. They go to town sometimes, man. It's crazy.
Speaker 1:What is the, interpretation of or what should the read be on, Gemini three, the TPU? It it feels like that's like, you know, is NVIDIA still a monopoly if you can train the best model on all the benchmarks without a single NVIDIA chip that seems like maybe a crack in the narrative, but does it matter at all, or is it, just, irrelevant?
Speaker 4:I think it matters a little bit. I think Google being really aggressive is really nice because, like, do they have power and they're, like, waking up and t p v seven is gonna be awesome Yeah. And Anthropic, and we're doing it we're doing, like, actual deals. So, like, that's good shit, I think, because you need like like, Gemini has been, like, I don't know, asleep at the wheel despite inventing all this stuff, and so it's really nice for to see them be back. But I don't think it's I mean, I think it's a big deal.
Speaker 4:Clearly, TPU is number two, and it deserves number two. I think NVIDIA being number one. What I really wanna see is, like, why isn't there a new pretraining run from OpenAI? Like, I gotta I gotta ask that question out loud again. We've seen so many RL scaled up versions, but we know there's no new base to pretrain, and we know that there's Gemini three cooks because it's a new base pre train model.
Speaker 4:So, like, where is that happening? Like, is it because the g b two hundreds aren't stable enough? Is it because, like, they're just totally not, like, dialed in? I think that that's, like, the question to be answered, and OpenAI is just I don't know. They're not cooking.
Speaker 4:I wanna see them cook. So right now,
Speaker 1:Gemini three. The on the on the base pre train, is it still is it still fair to kind of set up that storyline with GPT now called 4.5, now sunset, used to be GPT five potentially, didn't really pan out. There's been a lot of, debate over what went wrong with that pre trained. Is it fair to say that that was, like, an order of magnitude more compute spend, like, cost went into it? Is there anything real about, like, it was expensive to serve it?
Speaker 1:I've heard that Yeah. Bandied about as, like, why a lot of people said, it actually was a better pre trained. It was a better model. It would have been amazing, but we just messed up something about the economics. And so once we tried to deploy it, it wasn't very economical.
Speaker 1:And so it was slow, and that's why we pulled back. Not that it wasn't a good pre train. Not that it wasn't a good model.
Speaker 4:I still think it's a failed run, dude. Okay. I still think it's a failed run. Yeah. I don't think it got quite to where it should have been given its size and something was wrong with that.
Speaker 4:Four point five was decent and, like, a really good creative writer. You talked about the economic side. This is where I have to to to pump InferenceMax.
Speaker 1:Yep.
Speaker 4:Right? Which got shouted out three times. So, yeah, look, man. I think the economics work now Mhmm. Or could work with g b 200 because it's, like, you know, 10 x better performance.
Speaker 4:So you can, in theory, you probably could serve it, but for some reason, they still don't want to. And it's probably something on the RL. Like, it's just a bad base model that is not able to scale with higher chain of chain of order. Like, maybe because of how much compute it takes, it's, you know, the distilled version wasn't doing as well. All this stuff matters and for whatever reason, 4.5 isn't it.
Speaker 4:We know there's been failed training runs, and so it's like, Dude, OpenAI. I wanna see it. And I I think we'll Yeah. I think we'll get it. Right?
Speaker 4:Like, nothing Yeah. Makes them excited Yeah. But, like, competition. Of course.
Speaker 3:Will will there so so OpenAI has a consumer business. They have a front end for AI. It's it's the brand that people think of when they think of AI. At some point, you could imagine them not doing another scaled pretraining run because they're they just like, you know, it's not really worth it to take it from, you know, this IQ to this IQ. It's like the our our average user is just not really gonna care.
Speaker 3:Meanwhile, if you have a company like Anthropic, which is like an API business that like relies on kind of like raw horsepower capability intelligence and and maybe is, like, easier for end platforms to switch in and out of. Like, I don't know that they can afford to not keep doing the bigger and bigger training run. But do you expect OpenAI to, at some point, just say, like, yeah, we're we're kind of good on the core product. Maybe we don't even need to to to do the next run.
Speaker 4:Dude, I think I think at the same time you say all that if they're being a consumer business, but you know OpenAI has massive like, they have coding FOMO, man. They're really, really, really concerned about the coding models. Right? The I'm I'm sure you saw, like, the I can't remember what it's called, but I'm say it's, Project twenty twenty seven or something or no. No.
Speaker 4:Not Project. What is it? The 2027 scenario or something like that?
Speaker 1:Project twenty twenty five was 20 the right way agenda. Yeah. This is this is You're talking about AI 2027, the fast takeoff scenario.
Speaker 4:Yes. Yes.
Speaker 1:Yes. Where OpenAI buys Ford Motor Company to make to make humanoid robots.
Speaker 4:Yeah. To make more widgets, bro. Yeah. No. No.
Speaker 4:So I think I think that while, like, hey. Look. It doesn't seem like it's on track anymore. Sure. I do think the thing about the fast takeoff that people feel very strongly about is better coding models means better AI agent.
Speaker 4:Yep. You know, you know, AI agents and those AI research agents will make better models, and that's there is a recursive loop there. I think that that's where dude, that's where, like you know, they were they had so much CodexFobo. And and despite all this, man, like, Gemini still doesn't have the anthropic lead today. So yeah.
Speaker 4:Yeah. I mean, I think everyone wants that sweet bench. And Mhmm. Yeah. I don't know.
Speaker 4:I I I just think it's it's just like this weird I I think it's just like a perfect vibes time. On the, like, the finance side, dude, people are freaking out about the market, Fed cut. Yeah. It's not gonna happen. And and so it's like we I didn't I learned this yesterday, but this is, the second longest run above the 50 DMA, which is, like, you know, stock chart as men males astrology vibe.
Speaker 4:The the this the second longest run since, like, 1997. And so it's just, like, we've we've been we stocks have been going up for quite some time, and sometimes they can go down or even sideways. Yes. And so I think people are freaking the fuck out, and it's kind of a long, long, long powerful run. And and and, also, I think that people are freaking out because, like, stocks go down, people's vibes get bad, and then they're like, bro, maybe it's actually over.
Speaker 4:Mhmm. Maybe it's actually over. Like, nothing changes sentiment like price, man.
Speaker 1:Totally.
Speaker 3:What did you think about the Financial Times published an article that was pretty I felt pretty misleading. They said Oracle is already underwater on its astonishing 300,000,000,000 OpenAI deal. And they said that because the stock
Speaker 1:This is Elphinville. It's their blog. They're having fun. But they're rage baiting. They're rage baiting.
Speaker 3:They rage baiting.
Speaker 1:What what do you think?
Speaker 4:They they rage rate pretty hard, bro.
Speaker 1:Like, let's be
Speaker 4:let's be clear. Like, I don't think I don't think, you know, making $400,000,000,000 of revenue is being underwater. But I I if you if you're if you're betting on just the stock, then sure. Yes. They are underwater.
Speaker 1:I mean, think you're gonna be okay. Headline today from AlphaVille, it was like was like Who is opening up auditor? Auditor. They're really taking shots at everybody. It's funny.
Speaker 1:I do I do well, yeah. I'm sorry.
Speaker 4:I I was gonna say, man, with AlphaVille, like I don't know. AlphaVille doesn't cook, man. Their alpha is so is it's kinda it's kinda mid. I don't know what to tell you, man.
Speaker 3:Petition to renamed Alphaville Midville.
Speaker 4:To beta
Speaker 3:to to beta.
Speaker 4:Beta beta boys.
Speaker 3:Beta boy. I've enjoyed,
Speaker 1:Alphaville from time to time. I I wanna know about, this Gemini three pre training run. Is there any way for us to understand the rough order of magnitude of compute or dollars that went into it? I I from what I understand, Google has more of a distributed training system. They train across data centers.
Speaker 1:Is that might be right. And so before with, like, the GPT three training run, the GPT four training run, it was like they raise a bunch of money. They go build a data center. They acquire a whole bunch of GPUs, and then you kind of see, like, there was this much energy that went into it. This many GPUs were marshaled for it.
Speaker 1:But it feels like with these Google training runs, they're harder to understand the actual scale of the investment. But do you have a do you have a more solid understanding of how how big the Gemini three project was, like, from a CapEx perspective?
Speaker 4:I have no I mean, I can't tell you because I don't know how big the model is, like, on a parameters basis, but I do I I have a pretty good vibe that it is multi data center. They were first to do that. Yep. Pathways has always been first in terms of, like Mhmm. The OCS, the distributed scale out network.
Speaker 4:Like, they've always or sorry. Scale across. Like, they've always been first in that. Mhmm. Yeah.
Speaker 4:I don't know. I I don't have an actual number, but I don't think the actual pre training of of, like, the final run probably wasn't that much money. Mhmm. Right? But the thing is all the experiments to get up to there, all the other things that that goes into training a really big model cost a lot in r and d.
Speaker 4:And so I think the sync the the final shot or whatever in terms of compute is probably paltry compared to, like, the actual total spent. Right? You probably have a multiplier of, like, 10 x on top of it of what the final number is. But I don't know. It's probably dude, it's probably a billion bucks if I had to guess.
Speaker 4:They're Yep.
Speaker 3:I don't know.
Speaker 4:I'm just gonna throw out a number. I'm the you heard it here
Speaker 1:first That sound that mean, that sounds about
Speaker 3:right. Billion
Speaker 1:Because, I mean, we we we've heard about training runs that were, you know, like, a couple years ago. They were in the 100,000,000 range and and that Yep. The the the billion dollar training run was kind of rumored. I wonder I mean, I wonder if they if we get another 10 x next year or the year after and we're seeing $10,000,000,000 flow into a single training run. Like, from an SEC perspective, does that need to be disclosed at some point?
Speaker 1:Does this wind up going into the filings, into earnings kind of, just as an
Speaker 6:Like an OpEx number?
Speaker 4:Yes. That'd pretty sick, honestly.
Speaker 1:Yeah. I I would just imagine that at a certain point, investors would wanna know. I mean, it's like a mega acquisition. It's a it's a significant slug of
Speaker 4:I guess I guess it goes into cost of goods sold. Like, I don't like, AI accounting is, like, completely made up today, so no who who the hell knows. But it's probably a cost
Speaker 1:I feel like it should be CapEx. I I I liked and I don't know what your take is, but I I liked Dario's framing of each model is individually a profitable company when you you spend a billion dollars and then you make a 100,000,000 a month for, you know, a whole bunch of time, hopefully. But,
Speaker 4:okay, the in order for it to be CapEx, like, to be an accounting brain is it has to have a multi year lifetime. Yeah. And so if you if you train a model every year,
Speaker 3:it's R
Speaker 4:and D.
Speaker 1:Right? So
Speaker 4:that's that's the issue. Right? You can't capitalize it. So I don't know, man. Well I I mean, I think
Speaker 1:Well, I mean, I have a big question about this. This is something that I've been I've been going back and forth with with is, like, we have seen that there is demand for four o chatbots from, like, a group of Redditors potentially forever because those those people are like, four o is my friend. I don't care about Gemini three. I don't care about GPT five. I don't care about o three five thinking max deep reasoning.
Speaker 1:I want four o, and I'm willing to pay for four o. Maybe forever. We don't know. May maybe the churn rate will be very low for a long time. And so you wind up with this weird thing where you can actually amortize four o over years with that cohort.
Speaker 1:Now we don't know how big that cohort is and what the churn will be, but it could be 10,000,000 people for fifty years. It's just like their buddy. And and I'm wondering if the same thing will happen in businesses where you have some company that's like, we have a model that is four o level intelligence or Gemini 2.5, and we have no reason to update to Gemini three because this this model just sits there, and it looks at papers, scans them, summarizes them, and it does that a million times a day. And we're happy with that, and we don't need it we don't need it to be more intelligent ever. So we're just gonna keep that workload going in perpetuity, and we'll leave it on a one hundreds if we need to.
Speaker 1:Like, we we don't need to go to the the latest and greatest. Do you think that's gonna happen in the enterprise?
Speaker 4:I mean, I I don't know if it'll happen. I mean, enterprise just like, let's get, like, more enterprise bullshit y. Yeah. People have to have price raises, and you have to be like, well, why did you raise my price? And they the the single best way to do this is say, we spent more compute.
Speaker 4:We have a better model. We do something like that. But I also wanna say, like, in the consumer side, something that's, like, a good example is, like, dude, RuneScape classic is probably, like, a perfect Yes. Case study of this. People want to play RuneScape classic.
Speaker 4:Yeah. They don't give a shit about, like, the and, like, obviously, RuneScape classic has, like, kinda become a fork universe, and there's, like, a lot of other stuff. But it's run by, like, 10 people, bro. And there's, like, millions of or probably, like, you know, tens of thousands, hundreds of thousands of people who play it. And, like, yeah, I I wouldn't be surprised that we see, like, these long lived little projects that are really stable, and they're like, dude, no notes.
Speaker 4:Do not change it. I don't care. I want to play this one. I want to use this model forever. That's probably like a really good example of like a but I feel like that's like a niche, and it's very hard for you to underwrite everything becoming these, like, weird cohorts.
Speaker 4:That's like a massive fragmentation of the Internet and everything. Like Yeah. Everyone just has their one little, like, you know, you know, freeze all my my memory at this exact point. No new information. You're my favorite version of Gemini 3.5 or whatever.
Speaker 4:I don't know. I I think that that's it's kinda hard for us to be like like and also, dude, that that that's not AGI. Like, I if you're talking about, like, vibes, that's extremely depressing. Like, if we're talking, like, last year to now, that's, like, so depressing. Like, that's why I think this is, like, I think this is why why markets are sad.
Speaker 4:People are sad. They're like, dude, you tell me four o is all I want, then what are we buying? Why are we spending a 100 gigawatts? So I think I think we're just in a weird time, dude. It's in it's, you know, the market didn't go down at all.
Speaker 4:Now the market's going down and everyone's getting sad.
Speaker 3:But but the the the the leverage is still coming in. The the the circular deals haven't actually hit the books yet. They've just been announced. Like, that's some
Speaker 1:we got We we Didn't didn't Jordan from semi analysis say that there was potentially going to be, like, an h 100 index that retail investors could, like, buy into or something like that? Like, I'm just interested in, like, how many different pools of money haven't, like, come online to the AI trade yet. Obviously, private credit is coming online now. There's obviously corporate debt. There's just sucking down all the big tech earnings.
Speaker 1:There's also potentially, like, retail traders getting in on the action one way or another if some of these foundation model companies go out and go public.
Speaker 4:So the last note I had in my team who is sitting in the office right next to me and behind me Yeah. Dude, before I right
Speaker 3:now? It looks like you're in a forest.
Speaker 1:Yeah. I love the walking.
Speaker 4:We are in a forest. Thank you. To be clear, dude, we don't we we are squatting. Thank you to our squatting overlords who let us work here. We're sick.
Speaker 4:We're super happy about that. But, okay, if you just do the math, man. Because here's the thing. I think all the hyperscalers could raise, like, $2,000,000,000,000.
Speaker 5:Yes.
Speaker 4:Like, I really think the number is so large. In fact, I'm trying I'm trying
Speaker 1:free see cash flow, and then you multiply it by 10. If you if they were paying 10% interest, like 10 interest, and it's trillions of dollars because they produce so much. It's hundreds of billions
Speaker 4:of dollars. No? Okay. So I'm I'm gonna I'm gonna be I'm gonna give you the maxed out version of how I think about what we could do. Leverage max.
Speaker 1:Leverage Leverage max. New Leverage max. New report from selling analysis, leverage max.
Speaker 4:Also also, I've been told by I've been told by my corporate overloads, you have star the inference max. That's super important. I've I Okay. You have to star inference max on GitHub. Sorry.
Speaker 4:Before wait. So how
Speaker 1:Go how star inference max on GitHub, please. Thank you. Cool.
Speaker 4:That'll help. Okay. So I think I think they could probably raise something like $6,000,000,000,000
Speaker 1:by 2029. And and is that, like, a 5% interest rate you're assuming on, like, corporate debt, basically, and that's what the that's
Speaker 5:what we're paying?
Speaker 2:We,
Speaker 4:yeah. So we just essentially we're we're doing the current corporate interest. We're just saying, like, hey. The current market rate. There are actual problems with how this is done, but, like, let's use Meta.
Speaker 4:Meta is the most, like, the most aggressive version of this.
Speaker 5:Yep.
Speaker 4:You completely do all your data center CapEx off the balance sheet. Yep. You have BlueOut come in, pay for all that.
Speaker 1:Yep.
Speaker 4:You do a sale leaseback, and then you spend the rest of the money just buying GPUs. And you can probably do, like you could and then they can issue debt in the market that's, like, 50 bps above the government. Yeah. And, also, the rating agencies are like, okay. As long as you don't have more than one turn of debt by 2029 Yeah.
Speaker 4:You're good to go. So the so our we did that number for all all of them. Yeah. We for all the hyperscalers ex Oracle, Oracle's pretty tapped out, $6,000,000,000,000. That's like the that's the the big number.
Speaker 4:That's great.
Speaker 7:So it's yeah. We're
Speaker 3:we're so much the thing. So if if Oracle's tapped out already and they're about to spend four years where free cash flow is gonna be negative, how does that actually work?
Speaker 4:So here's here's the thing about this though is free cash flow doesn't like free cash flow goes negative if you assume there's no revenue growth, but this is like kind of like a a shale well. K? You get a lot of your money on a GPU cluster upfront. Let's say five year economic life, people are gonna fight me about this, but whatever. You you will have your payback for a brand new cluster in something like eighteen months.
Speaker 4:And so after that, on the, like, let's say, on the eighteen to twenty four to thirty six months, which is, like, the two to three year, you're just gonna start now you're gonna start to gather in cash. And that cash, you can go turn around and borrow more against or respend again. And so that's where this like, you know, the shale one of the reasons why shale went so insane in terms of supply is, like, twelve month payback period, which is way more insane than what this is. But, like, twelve month pay so you get all your money back. You could just do it again.
Speaker 4:Do it again. Do it again. So I think next year, Oracle will make a lot more money, and they're
Speaker 3:gonna be
Speaker 4:able to raise a lot more money. Yeah. Okay. But they're they're they're tapped out this year.
Speaker 3:Yeah. Yeah. That makes sense. Okay. I have kind of a lightning round Please.
Speaker 3:Because I know you have a heart out in a few minutes. What is going on with CoreWeave and Core Scientific? Like, CoreWeave is is reliant on Core Scientific. They've they've had, it seems like, some kind of frustrations around getting capabilities delivered from Core Scientific. They tried to buy Core Scientific.
Speaker 3:Core Scientific rejected it. Core Scientific has now traded down 20% or so since the acquisition was attempted. But but can you explain that dynamic and why the Core Scientific shareholders are kind of still holding out at this point?
Speaker 4:Okay. So Core's got offered to be bought in all equity, and this is before all this all the other Bitcoin miner, like, energy names ripped. And then I think a firm called two c's wrote this thing, be like, hey. Look at everyone else's results and how much they've ripped, and you're telling me you're selling out at this price. And so rightfully, if you do the math, you're like, maybe we should just not get sold or we should deal break and we should ask for a higher price.
Speaker 4:And so most of the investors went for a deal break and asking for a higher price. But, you know, people who are like, the stock does go down, and you have a shareholder turnover when when you reject a deal. Because, like, a lot of people are in it for the deal, and then they have to sell. They're like, no more deal. I'm selling.
Speaker 4:And so that's, like, that's pressure. But at the same time, Core Scientific is not delivering their Denton facility on time, and that delay is, like, kind of a big deal for CoreWave. Yeah. So, I mean, that's that's, like, the the the SparkNotes version of it. Mhmm.
Speaker 4:I think the problem is, like, you look at Iron or something like that, and you're like, wait. Wait. Why isn't Core is getting the Iron multiple? YOLO, this is a two x, and that's how the deal broke.
Speaker 3:Mhmm. Got it. That makes sense. Product idea for you guys. Maybe it's something you're thinking about.
Speaker 3:Maybe it's something that doesn't make sense. But I was pitching John last week on an idea for something called a semi analysis product called DiffusionMax, which would be like how what I wanna understand is like how is AI actually diffusing across a bunch of different key industries? So legal, accounting, you can just go on and on at marketing, on and on and on. Actually understanding, like, I would I would want you guys to have phone calls with, like, thousands of business owners and employees in each of these different, like, categories and then give us a read on, okay, are they actually laying people off because of AI? Are they hiring more people because of AI?
Speaker 3:What tools are they actually using? Are they getting a lot of leverage? Are they increasing earnings? Margins. Yeah.
Speaker 3:Is it affecting margins? And I don't feel like that I I I don't know that there's, a definitive data source that I trust on that, and that's something that, like
Speaker 1:I only trust Semi Analysis for everything.
Speaker 4:Thank you. Thank you. It's we're the only source of truth, bro. Thank you. I thank you.
Speaker 4:I I appreciate it.
Speaker 1:I don't think so. Maxi by for psyche analysis.
Speaker 4:Same, dude. So so that sounds like I need an AI agent to call, you know, like a 100,000 people. But Okay. Dude, honestly, kind of survey work is stuff that we're really interested in. You're thinking about it.
Speaker 4:But I don't think we're we're did we dude, there isn't something we haven't thought about. Yeah. But we have a lot on our plate. It's like a throughput problem. Yeah.
Speaker 4:Yeah. Yeah. Yeah. Yeah. Yeah.
Speaker 4:Yeah. We're doing we're really, really interested in the energy side. Like, we're gonna do a grid by grid breakdown. Like, we're very focused on all the I mean, we we like, we're putting a lot of effort and and energy energy into it. And I'm really excited about that, but even that is still probably a little bit far out.
Speaker 4:So we each of these, like, bets take a little bit of time, and you have to reinvest in them and give them time to work out. And so, yeah, man, I would love to do, like, I don't know, diffusion. But problem is diffusion max is a bad name. Yeah. You know, diff it sounds like diffusion.
Speaker 4:Right? But, like, AI penetration max. Who the hell knows?
Speaker 3:Don't don't name it. Don't name it that. That sounds right. Yeah. Another question.
Speaker 3:Another question. XAI and NVIDIA announced like a new data center project in Saudi yesterday. I don't know if you caught that. Do you see X. Do you see x AI just getting into the cloud into the AI cloud business and and helping power, and and helping basically deliver infrastructure for other other companies?
Speaker 4:I you know, no. But that I'm not until this conversation, but they're the best. They're the best at being quick. So if you don't if you have, like, infinite capital and, like, the zoning laws can be whatever the hell they want them to be, I would sign up XAI to put up a cluster as fast as possible. They're the quickest with Colossus.
Speaker 4:Like, I think they literally have the speed run record, and Saudi's want it, dude. They want it so badly. And I think with this new we're, like, allowing to export it. And, I mean, yeah, if they can buy it, bro, I and XAI is gonna be like, give me money in my pocket. I'm gonna make a Saudi Grok, and then I'll make a Grok five.
Speaker 4:So I think that x dude, X is down for business. And I think they like, Tesla's always been supported by a lot of different funders, and I bet you some of the people who took x private probably were Saudis. So, yeah, might as well.
Speaker 1:Makes sense. Truly. Yeah. They actually were. I remember that.
Speaker 3:Quick take on the on the Brookfield deal. You mentioned it early in in after you joined.
Speaker 4:Mhmm. $100,100,000,000,000, but you should look at the actual number. It's 5,000,000,000 committed. So just just No.
Speaker 3:What Not beating the press release economy allegations.
Speaker 4:Yeah. So so so, yeah, dude. This is the press release economy, man. I mean I mean, you can do the you saw the 10 q. Right?
Speaker 1:It's like We gotta do a deal. We gotta do a press release. We'll pay you a $100,000,000,000 if you pay us a $100,000,000,000.
Speaker 4:Dude, I think we can make that work with our accountants.
Speaker 1:I think we can
Speaker 4:do it. And then
Speaker 1:think we'll it. Write we'll write checks to each other. We'll hand them off And we write at the same moment dramatically.
Speaker 4:Exactly. We'll go. Yes.
Speaker 1:Right there. Right there. Right there.
Speaker 3:Yeah. Like, then like
Speaker 4:the economy will just circulate, bro. Exactly. We can use that
Speaker 1:money Exactly.
Speaker 4:To raise capital.
Speaker 8:Yeah. Oh, it'd be beautiful.
Speaker 1:Yeah.
Speaker 4:Holy
Speaker 1:shit. No. No. No. No.
Speaker 1:We we don't wanna we don't wanna take advantage of it and raise capital. We just wanna we just wanna make fun
Speaker 3:of all the aura.
Speaker 1:Yeah. We wanna aura farm everyone who's doing it unironically. That's what he does.
Speaker 5:I mean
Speaker 4:yeah. Well, okay. My most galaxy brain take from NVIDIA, actually, that I feel pretty strongly about is, and this is, you know, in our call with them. They're like, you know, this $10,000,000,000 check is, like, kinda pennies compared to what we're trying to do. The real game we're trying to play.
Speaker 4:And if you think about it, being closer to their customers and understanding what they're doing is probably the number one thing that they have to do as NVIDIA to understand where the technology is going.
Speaker 1:Yeah.
Speaker 4:Right? And so I think if you if you think about it, it's actually just r and d OpEx, bro. It's just a check that you pay in order to make sure that you're closer to OpenAI and Anthropic, and you know exactly what's going on in their data centers. So that's the that's the most, like, bullish take I can think. But realistically, man, the Fed said something, and everyone's freaking the fuck out.
Speaker 1:Yeah. Yeah. That makes sense. Yeah. Yeah.
Speaker 1:I mean, on that, are are you referring to that that line that's getting shared around from the NVIDIA earnings around the quality of the deal with with OpenAI versus the strength of the deal with the Anthropic? Did you read into that? Did you read the same thing that everyone else read into it?
Speaker 4:Yes. We we did read into the same thing that everyone else said. It's actually kind of funny though because in the same language, they're like the opportunity to invest and it's like, yeah. It's just like they glazed Sama. Like, they glazed him a little bit, but then they also were like, yeah.
Speaker 4:This also couldn't happen at all. Yeah. So I think I mean, dude, a lot of the press release and the press release economy, as you know, you can just say the biggest number and you're like, dude, until 2030, you have let's say, we're I'm gonna invest $600,000,000,000 in The United States. I'm gonna be meta. Right?
Speaker 4:I'll invest 200,000,000,000 from here to 2028 and then 400,000,000,000 from 2028 to 2030. Right? You just push it into the back half. Yep. And then, like, if it comes, it comes.
Speaker 4:Right? That's that's how you do
Speaker 1:it. I feel like people could go further here too. I mean, Ray Kurzweil famously said Singularity 2045. You should be doing RPO all the way out to 2045. Why not?
Speaker 4:Yeah. I'll I'll just have my children actually. They'll we'll do a deal with our children.
Speaker 3:My the my children's children. My big number of the week was MBS.
Speaker 1:Oh, yeah.
Speaker 3:Was hanging with Trump and he was like, I said $600,000,000,000 yesterday.
Speaker 1:Let's do it.
Speaker 3:But actually
Speaker 1:Let's round
Speaker 3:it up. Let's actually And and Trump literally like hit like this and like hit him on the knee. Like he was he was so happy to He's so hear that so trillion. He's like
Speaker 4:finally finally someone said the trillion, bro.
Speaker 3:Everybody everybody gets around everybody gets around Trump and they just like detach from reality a little bit and they just start saying, like, Zach Zach had this at the at the AI dinner. Remember? Like, he just threw out Yeah. Threw out a number and had to had to correct it the next day.
Speaker 4:He's like, what what number are we going with? Oh oh, six six six hundred six hundred billion. Yeah. Dude, I mean, it's like it's like his his warp field is that everyone just says the biggest number around him. I love him.
Speaker 4:Like, it's kinda it's, like, kinda ridiculous.
Speaker 3:It's powerful. It's it's real stimulus.
Speaker 4:It's it's stimulating. That's for stimulating.
Speaker 3:I don't know if it's real. It's stimulating.
Speaker 1:Yeah. A semi analysis plan. Like like a a subscription where it's, like, $5 a month for the first twenty years and then $20,000,000,000 in 2045.
Speaker 6:And the
Speaker 1:I will sign up for that, and then you can book it and diff diffuse it back. The revenue. It'd be beautiful, bro. Yeah.
Speaker 3:And, yes, there's an hour. And it can break cancel anytime. We need to bring
Speaker 1:massive POs and press release economy to the to the newsletter analysis. We need we need a Substack feature baked in for this.
Speaker 4:We can do we can do we can do a deal, bro. That'd be great. We'll do yeah. We'll we'll have a a preliminary $1,000,000,000 advertising deal.
Speaker 3:How's this sound?
Speaker 4:We'll do some circular economy.
Speaker 1:Well well, thank you so much for taking the time to hop on. We I I know you you gotta get it.
Speaker 3:Congratulations again. So so happy to here.
Speaker 4:I appreciate
Speaker 1:that. Inference Max. Inference Max. Inference Max. Inference Inference max.
Speaker 1:Inference max. Inference max. Inference max. Go Do you star it on GitHub.
Speaker 4:Okay. Did you know did you sorry. Did you are you familiar with we actually made a video of Dylan at our off-site just screaming analysts. I was like, that's literally it sounded exactly like that. I was like, what the fuck?
Speaker 4:How do you know about that? Like, it sounded
Speaker 1:I have spies and ears.
Speaker 3:Have and organizations. Everywhere.
Speaker 1:Your information is incredible, bro. Holy crap.
Speaker 5:I think it's just
Speaker 1:we were just having fun. Thank you so much for stopping by the show. Of course. Busy day. We'll talk
Speaker 3:to you soon. Yeah.
Speaker 1:Have a good one.
Speaker 4:Yeah. Nice to see you. Take care.
Speaker 1:Bye. Let me tell you about Linear. Meet the system for modern software development. Linear is a purpose built tool for planning and building products. Our next guest is David Chang.
Speaker 1:He is an American chef, restaurateur, author, and TV personality. I believe he is in the Restream waiting room.
Speaker 4:Is
Speaker 1:he? He's the founder of Lomafuku restaurant group.
Speaker 3:Not sure we have him
Speaker 4:quite yet.
Speaker 1:Well, we can also go all over the world. We have lots of content today. We will work on getting him in the studio. In the meantime, I will also tell you about Fall to build and deploy AI video in image models trusted by millions to power generative media at scale. Speaking of generative media, Gemini three Pro image only has an 8% error rate when generating text.
Speaker 1:OpenAI's model is at a 38% rate. And so there's been a very significant quantification of the improvement in Nano Banana Pro, I believe, or Gemini three Pro image. I don't know exactly the difference in the naming conventions. Do you know?
Speaker 8:Yeah. I mean, I I think so originally, Nano Banana was like the the insider, like, that that was the code
Speaker 1:It was the code word that worked.
Speaker 8:And then then they're like, oh, let's just bring Interesting.
Speaker 1:I was wondering how that happened. Yeah. Because it's
Speaker 8:very the same thing.
Speaker 1:It's cool, and it's quirky, and it's actually very on brand for Google, in my opinion, to run with a keyword like this. But, but at the same time, it's added a lot of confusion because they've done so much work just to establish the Gemini brand. And now they're also have a Nano Banana brand, and it's a little bit confusing. But, we can talk about that more after our next guest joins. We have David Chang in the studio.
Speaker 1:Welcome to the show. What's happening? How are
Speaker 5:you doing, guys? Good to
Speaker 1:meet you. Thanks so much for taking the time to talk
Speaker 3:to us. Great. Great fit too. Looking great fit. Go.
Speaker 3:You're ready. Ready for anything. Yes.
Speaker 5:Well, I'm prepping out for a bunch of things right now and then we get on a plane and we'll be in Vegas, by dinner.
Speaker 1:For f one. Right?
Speaker 5:Yes, sir.
Speaker 3:Fantastic. We'll be there Saturday. Can
Speaker 1:you help, everyone in, in the audience understand just the shape of your business between
Speaker 3:The shape of your empire.
Speaker 1:The shape of your empire. Empire is the correct term. Sorry. Not just business. Well,
Speaker 5:it's it's it's restaurants. We have some quick service, fast casual. Yeah. We have casual. We have fine dining, and we have couple spots in Vegas.
Speaker 5:We have a couple places here in Los Angeles. I think it allowed us to pandemic allowed us to sort of refocus exactly our growth strategy
Speaker 1:Yeah.
Speaker 5:Instead of trying to open up all around the world, which we had been doing until 2020. I think, we we we had a lot of sort of plans in place for doing CPG. So Sure. That, like many other things in the world at that time, sort of expedited the plan, and and we, went headfirst into, you know, we had dabbled in making some sauces here or there, but we had always wanted to go into making noodles. And and that's sort of a good part of our business too.
Speaker 5:So it's it it's equal parts restaurants. Even though they're now split out into two completely separate entities, they take up a lot of my time. And then me, it's it's mostly media stuff these days.
Speaker 1:Yeah.
Speaker 5:So I'm I'm I'm dressed up right now because we're doing practice runs for our Netflix show on which we air at 4PM Pacific Standard Time, dinner time line.
Speaker 3:Have you have you ever tried live streaming? We've we've we've wanted somebody to do other version of our show that's just like a live like a daily live cooking show, and I feel like you've got you've certainly got the personality for it. It's it's basically a full time job, but I could see that being a hit.
Speaker 5:We we we tried to do that. You'd be surprised how adverse I think a lot of people that are running network still because, I think if anything, it might have to go on, you know, one of the free streaming platform services. But that's not out of the question. It's certainly on the long term projects list for major known media is to do, just all day. You know, total transparency, what you see is what you get.
Speaker 5:And you you do see some people doing it on Twitch. Yeah.
Speaker 3:That's what I'm saying. You don't need the networks. Just I was wondering Create a Twitch account. Create an X account.
Speaker 5:But, I'm currently pretty preoccupied with Netflix and Amazon important. Yeah. And and Spotify these days, especially since our podcast is moving over to Netflix. Oh, really?
Speaker 3:Oh, no way. You're in that you're in that package.
Speaker 1:Yeah. Talk talk about I mean, obviously, there's a bunch of particulars that are are probably under the under the hood. But what excited you about taking a podcast to Netflix? It's an interesting strategy. It wasn't on my list of predictions for what Netflix would do.
Speaker 1:What have they shared with you about how they'll surface that, what the audience might be like, what the Netflix viewer is looking for in podcast content? I find that whole strategy fascinating.
Speaker 5:I would love to answer all of those questions, but I don't think I'm the
Speaker 1:Okay. Anyway.
Speaker 5:I I would I think if I was the person to answer that Sure. Would be funny to both Spotify and Netflix because there are other people that are
Speaker 1:Okay.
Speaker 5:Yeah. Yeah. Designed to answer that. Have someone from Netflix.
Speaker 1:I'll tell
Speaker 5:you that it's it's it's been something we we you know, we have, what, 600 plus episodes of our podcast, and it certainly changed over the years. When I first did the podcast, it was much more of a insider's take on the restaurant industry and and sort of a sneak peek in terms of the thought process of opening restaurants or, you know, preopening of a a friend's restaurant. Like, my buddy, you know, opened up Angler in San Francisco. We we sort of gave everybody a sneak peek of the the, you know, the the the philosophy behind it. And then the pandemic happened, and then you couldn't travel.
Speaker 5:So it just sort of shifted. And we've been waiting for this moment, quite frankly, where instead of talking heads because food is the one thing which is sort of dumb. Right? It's the one kind of podcast that we can't really react to culturally. I'm very close with Bill Simmons.
Speaker 5:I'm part of the Ringer podcast network. Yeah. And, you know, we can't watch some movie and react to it. We certainly can't watch a Monday night football game and then react to that either. Right?
Speaker 5:So food is so ephemeral and in the moment, also more importantly, not necessarily scalable.
Speaker 1:Yeah.
Speaker 5:So and now with video, right, and more people, you know, with all the data and, like, more people are watching podcasts than actually listening to it. Yeah. You know, there's certainly a lot of people still listening to it. Don't get me wrong, but it's it's certainly, in the near future, going to outpace it if it hasn't already. And now it gives us the opportunity to evolve again and to offer a podcast that is somewhere between a TV show and a podcast.
Speaker 3:Yep.
Speaker 5:And I can do that because cooking is something I can do that, you know, other people that are maybe doing interviews or such as yourselves, know, would you be cooking and doing this interview right now? I don't know if a lot of people would sign up for that.
Speaker 1:It'd be very hard for to spend.
Speaker 3:It's hard to eat and and do a podcast.
Speaker 1:Oh, eating eating on Mike is possible. But even this cooking
Speaker 3:We were we were talking earlier on the show about kind of the the delivery app experience, like the the dynamics of like tipping in in delivery apps. Travis Kalanick was commenting on on on a on a post of ours yesterday. He's got a new product called Picnic, which is like a front end delivery platform, just focused on corporate meals. And like the key value prop is, no tipping and no fees. So they're focused on like higher higher volume orders.
Speaker 3:So like, teams of like 25 people plus. I'm curious like how like, getting I wanted an updated take on from you on like, what it's like working with the delivery platforms today, where you think there's opportunity
Speaker 5:Right.
Speaker 3:And all that stuff.
Speaker 5:Well, I don't know if many people know or remember in 2016. We created the very first to my knowledge, there might have been something called a ghost kitchen, but no one called it a ghost kitchen. We we teamed up with Thrive and Will Gabrick and Josh and Caleb, etcetera.
Speaker 1:Yeah.
Speaker 5:Great team. And we opened up Maple, and we're doing, like, 10,000 meals a day out in New York City as a full stack app. Yeah. We were to do delivery
Speaker 3:I had no idea. I I didn't I didn't realize you were behind that. I remember I remember Maple, but, that's cool.
Speaker 5:And, yeah. Like, it's something that I've been wanting to do and had done for a long time because I saw that's where food was going, particularly because of the the the it's it's been a 30% cut for for some time on delivery fees, and that's just not a sustainable model for the the delivery companies and the restaurants. So, it was a real, opportunity to sort of bridge that and to to do everything oneself. And yeah. And and so much so, I believe in that we started another one called Ondo, which is more of a fast food.
Speaker 5:So, like, maybe you might get a nice kale salad and then butternut squash soup. And Ondo, we started with Garrett Camp's Fun Expo, and we did Ondo, which was like a cheesesteak and fried chicken. So I was all in. I was all in. We were probably just six years too early.
Speaker 1:Yeah.
Speaker 5:It worked, but not enough where it is today because
Speaker 3:But is it so is that is that model is the model like, the the hard thing is, like, Cloud Kitchens is a dominant, like like, company in that space, but they're just incredibly, like, secretive. Right? And so it's hard for me to get a I haven't done, you know, much digging, but it's hard for me to get a read on, like, is the model durable? Is is is there gonna be a lot of value creation there? Or does it ultimately does it ultimately kind of fragment like a lot of the the restaurant industry has as well?
Speaker 5:Listen, I I I think it's like anything else in tech. Right? Remember in the .com bubble, you had like tubesocks.com. Right? Like
Speaker 1:Yeah.
Speaker 5:There's only, like, three or four companies that really Yeah. Came out of Same stuff what I imagine with all this AI shit. Right? You know? Like, I can't even tell you in the food space how many companies are a food logistics company, but now it's AI.
Speaker 5:Right? Like, it reminds me, again, in '99 when at least in New York City, every company and I won't say every company, but a lot of lot of places were now changing their name to sort of pizza two thousand, dry cleaning two thousand. Right? It just is sign of the times, and and I I just I think that in food delivery, you're over you're gonna have about three to four winners, if that. And certainly, I would never bet against Travis and and the team there at CloudKitchen.
Speaker 5:Definitely not I I think what Tony and the team at DoorDash is doing is just unbelievable, and, clearly, you have Uber and the Postmates guys.
Speaker 1:Yeah.
Speaker 5:So the yeah. To me, that's pretty much gonna be the space. And I think when's I'm not sure when, but when they are able to be more open and transparent about everything, people are gonna be like, wow. That's a that's a pretty goddamn huge business. Yeah.
Speaker 3:That makes it interesting. Drinking culture and any restaurants been caught using AI generated imagery for their for their menus or or any of the food delivery apps yet?
Speaker 1:That's a big question.
Speaker 5:No. But you know, like, I think I it's been it's the same shit that's happening. Yeah. Right? And and they're like, AI
Speaker 1:to me
Speaker 5:is like they're getting the lowest common denominator things and sort of like crowdsourcing and just getting something that is not necessarily perfect, but just good enough. And I I just think that in restaurants, that's basically been like consultants. Right? Yeah. They've just been that's just like I can go I I feel like I've going to AI generated restaurants for some time now, you know.
Speaker 5:It just hasn't called AI.
Speaker 1:That's funny.
Speaker 3:It's a good take. Are you excited about drone drone delivery?
Speaker 5:You know, that's one thing where I thought it was gonna be a total zero. I'm dead wrong about that one. I think it's definitely gonna be a thing and and
Speaker 3:Well, isn't it is it exciting as a chef to to know, like, if I make this, it will arrive hot?
Speaker 5:No. It's not gonna arrive well, that's a whole other thing. With this whole the one thing I will tell you on the food delivery space, and I've talked to just about everyone under the sun over the past ten years that's tried to start up a food delivery company because they're like, oh, this guy's done it a few times. Let me just sort of steal all the ideas. And I'll tell them every time, I'm like, unless you've created some kind of new technology to cook the food, it's gonna be hard to really make the food hot ultimately.
Speaker 5:Right? Like well, how should I say this? There's no new technology to make
Speaker 1:the food
Speaker 5:better. None. So the deliberate drone, unless it's cooking the food as it flies, it's always gonna be limited.
Speaker 3:An oven oven in the air, basically.
Speaker 5:Yeah. I mean, like, that's just the truth. Right? Like and also, a lot of these places just have a bottleneck because everybody wants to eat at 06:30, 07:00. So there's just there's not much you can do to make the food go out faster
Speaker 3:Yep.
Speaker 5:Or hotter. And more importantly than not, everything can be delivered well. Like, french fries will never be able to be delivered well. Right? The next step is gonna be whoever makes the food literally right outside the house or apartment.
Speaker 3:Have you heard any pitches on that?
Speaker 5:I have.
Speaker 3:Any No. You don't you don't have to give names, but like I'm I I like it it gets to the point where it's like a street. We just have like like a massive proliferation of like street cards and it's and maybe maybe like
Speaker 5:Well, I mean, I'll I'll tell you this. A lot of these pitch haven't been 200 a couple years because I just don't wanna do it anymore. But a lot of times, there'll be a very like, a a chef that's worked twenty years at a three mission star restaurant making the food, you know, and it comes out. And I'm always like, is this person gonna be making the food? You're gonna get this quality talent making the food at every single sort of satellite location?
Speaker 5:And the answer is they haven't even thought that far. And the other answer is that's not a reality. You know? You might as well be pitching me a unicorn, literally a unicorn with a horse and a a horn on it because it's not gonna ever work because that's the hard part about this business. Right?
Speaker 5:Cooking is still a physical endeavor. And for all the the the VC money and tech money, it can't sort of solve that riddle of how do you make physical labor go away. Or done
Speaker 1:So you're
Speaker 5:not done better.
Speaker 3:So you don't you don't think we're gonna you're you're very bearish on the humanoids chefing up.
Speaker 5:No. No. No. I'm not bearish on that either. I I spoke to somebody.
Speaker 5:Pre pandemic, we did a show, and we did some research and and a robotics expert, and we talked to some people at Caltech and a few other experts. And if somebody was like, oh, maybe forty, fifty years away. I talked to I talked to someone recently, you know, like, yeah, we're probably fifteen years away from getting somebody that has a robot that has the dexterity of a high end best in class sort of chef. So, no, I I it's gonna happen. If anything, I think you're gonna see the next five, ten years, you're gonna see robots.
Speaker 5:You already see it in I mean, I again, like, I I don't think it's been a sudden, oh my god. There's robots. There's robots in the kitchens all the time. Like, if you go to a good restaurant, their dishwasher's a a pretty much a transformer robot. It's amazing.
Speaker 5:Mhmm. And that does the work of, like, 20 people.
Speaker 3:Under pretty underrated.
Speaker 5:Underrated. And and if anything, you're gonna see machines that take the binary movements out. So a fryer that goes up and down Yeah. A bathroom cleaner, things like that.
Speaker 1:And
Speaker 5:already, dishwashers are pretty advanced and that can handle very expensive stemware. So finding somebody that can polish stemware you know, you might get a wine glass that could be $250 per glass. And if you have a 100 of those, that's quite an expensive inventory for a restaurant. You need someone specifically trained to do so.
Speaker 1:Yeah.
Speaker 5:And that's a hard position to find. So, yeah, that that kind of position will be a robot. No questions about it.
Speaker 3:That makes sense. What's the most overrated trend in food right now?
Speaker 5:Oh, man. I'm trying to stay positive these days, guys. I
Speaker 3:I I think I think there's a way to answer the question by just saying like there's things that can be popular now that are not dirt like durable trends. So
Speaker 5:Well, I I would say the the most annoying trend is that everything has to be the best. This hyperbole. Right? Mhmm. I have to have the best x.
Speaker 5:Mhmm. This restaurant has to be, you know, world class, number one. And I hate to tell it to you guys, but I think most people don't wouldn't even know what best is if they ate it.
Speaker 3:Yeah.
Speaker 5:And I think for the most part, I'm just now on this mantra personally of does it bring you joy? Does it bring you happiness? And that's really all that should matter. It's such a relative subjective thing. But more importantly, I'm just trying to tell people, like, good is fucking hard to do.
Speaker 5:Mhmm. Like, just good is hard. And I think we need more people to sort of appreciate just good or, like, even boring good than the world's best, oh my god, this is the greatest thing I've ever had.
Speaker 1:Yeah.
Speaker 5:That to me is the worst trend in the world is you know, and and the media and chefs were all part of the problem too. Right? You know, all these lists and it's all stupid, ultimately.
Speaker 1:Yeah. But
Speaker 3:Do you think do you think we'll ever get to a point where like in in tech in the technology industry, many many products get better with scale for like a variety of reasons. Food has felt almost always the opposite of that where you take an amazing concept in a in a tiny tiny little restaurant. Right? Like, you know, a thousand square feet. And the second you add a second restaurant and the third, it just kind of tends to get it tends to get worse and worse and worse and worse over time.
Speaker 3:And that's just like, there's one there's one you know, maybe it's one chef who had a had an amazing idea and and it and it and it just is very difficult to scale quality. Are you optimistic that there could be any new technology introduced that would kind of change that dynamic? Or is that just kind of an iron law?
Speaker 5:No. I I don't know. I think it's not an iron law. It's not like a law of thermodynamics. I'm sure somebody could figure it out.
Speaker 5:But, you know, the the you just mentioned something, the fact that something that's great is not scalable. And for years, you know, I've certainly tried to do it and scale these things. I I just sort of spoke about this at Reed Hoffman's, masters of scale conference. Right? Like I sort of everybody's at that conference because they wanna scale an idea.
Speaker 5:And I said, you know, the easy ideas are to scale an idea in food that is, you know, wouldn't say cheap, but affordable and mass produced.
Speaker 1:Mhmm.
Speaker 5:Right? The other end is high end experiential dining. Which weirdly has become very scalable because of its inaccessibility. It would it's the equivalent of, like, getting front row tickets at the Knicks or, you know, Chase Center or something like that because you're now eating at, say, the French Laundry. No one else can get there.
Speaker 5:You may not even appreciate the food, but it's now a social flex. It's cultural currency that you can sort of have, and it's a femoral. And because no one can have it, weirdly, now that experience is weirdly I mean, it's scalable because that actually is crazy marketing for the French laundry. Right? And the demand for that kind of restaurant is through the roof.
Speaker 5:And what I mean by that is restaurants, it doesn't have to be super high end in in Napa Valley. It has to be anything that can't be copied immediately. Mhmm. Right? You can't watch a YouTube video and decide I wanna open up a restaurant like this.
Speaker 5:I can't just make an easy facsimile. So it could be barbecue, could be sushi, anything that is best in class that people have a hard time copying. That's like the barbell. So you have really affordable make mass produced stuff on food on one end. On other end, you have things that very few people are gonna be able to experience or eat.
Speaker 5:And, you know, that's been sort of elevated because of technology, right, in different ways. But I chose to sort challenge the audience that you know? Because every I mean, I mean, you guys know. I I I talk to a lot of people in tech and probably a lot of your peers, and they're always wanting to know the next big thing in food. And I'm gonna tell them, like, the hardest thing the the answer that needs to be solved is how do you scale the middle.
Speaker 5:Right? Does that make sense? Like, the mom and pop restaurants, the diner, the the the restaurants that are just, good, again, how do you make it so they can survive? Because they're not like cultural banks. They're great, but they're not they don't have the sizzle.
Speaker 5:They don't have the
Speaker 1:Mhmm.
Speaker 5:Maybe the bottom line that makes it sort of cool for investment. And, again, it's not about creating a company that's, like, the pickaxes and shovels for that middle market restaurant, but there's gotta be something else that can, like, be something that's game changing. I don't know exactly what it is, but I never talk to the people that are trying to make food concepts or invest in food concepts that are actually concerned about the middle. And I'm not talking about credit card processing and shit like that. I'm just saying, like, in general, there's a lot there.
Speaker 5:That's the meatiest part of the food industry right now, but it's just too damn hard and nobody really wants to touch it.
Speaker 1:Interesting. Have you
Speaker 3:seen any interesting experiments on, like, the capital side of of, like, new restaurant creation? Has anybody tried to make, like, a y combinator for for restaurants where there's, you know, a talented, you know, owner operator chefs can get some seed capital and kind of support to go from zero to one? Because I feel like you would have tried that by now.
Speaker 5:We we have definitely tried it. I I I won't say all there there we've tried it. I know a lot of restaurants have tried it. I know there's funds out there, that try to do this, but I would say, you know, Ron Parker created something called HospitalityNX, and that's a website that is a little bit like a job board, legal Zoom, but also a place for people that want to raise funds. So that that's something.
Speaker 5:But I think for the most part, it's not as organized as other you know, at the end of the day, it's because it's hard to create an idea that has a high barrier of entry in food.
Speaker 3:Mhmm. Yeah.
Speaker 5:What Right? And and there's no moat to really create. Right?
Speaker 3:Yeah. Yeah.
Speaker 1:On on the on the topic of, like, you know, starting up and go to market strategies, are there are are there risks to, like, going too viral early? We've experienced a bunch of, like, rage bait in tech recently where people have sent sort of designed products that are that are in designed to the pro the whole product is just designed to enrage and go viral and then get some attention.
Speaker 5:What do you mean by enrage? I I I'm not familiar.
Speaker 3:So so so a company a company made a product that is like a developer tooling. So it's like software to help you make software, and they use AI in it. So there's minute there's time periods where you have a little two minute break. Mhmm. And so they added the ability to gamble with stake while you're while you're making software.
Speaker 3:And that made a lot of people mad for for obvious reasons. So Yeah.
Speaker 1:Or just deliberately picking
Speaker 3:Rage bait in food would be like a product that has like a single meal that has a thousand grams of protein. Like I could see a restaurant doing that just for the just just to try to get people to make TikToks about it.
Speaker 5:I mean yeah. I mean, I don't know what rage me, but like, again, like, this has been happening on for for forever anyway, you know? Doing something that is probably gonna you know, shit, I've opened up restaurants that I guess have been like that too, you know? It just you're not you know? I think if anything, it's just taken to another level because I would say that a lot of chefs now, when they're talking about dish, is it is it something that the younger generation will find appealing to to to to to record?
Speaker 5:And that's what I mean. It's like it's this it's vaguely experiential, but it's very ephemeral at the same time. So I don't know. I wanna be optimistic again. I'm usually mister Eora over here about this, but I do think that with all of this aside, with all of this access, with all this democratization of knowledge, because culinary knowledge with the younger generation is higher than it's ever been.
Speaker 5:I mean, it's never been better to eat in America. It may not have the of the titans of the industry as it used to because things have sort of leveled out. But, eating today, I I talk about this with people a lot in the industry that travel. You can find a great restaurant in every city in America for the most part now. It's pretty remarkable.
Speaker 5:If you just look at that. Right? So maybe New York or San Francisco or other metropolitan cities are not as great. They're still great, but it's really broadened out and flattened out across the country. So Oklahoma City and, you know, places that are tertiary cities to most people are actually might have some of the best restaurants in the in the country.
Speaker 5:And and I think that sort of pattern is what you're gonna see throughout food, and it is a long winded way of sort of answering this sort of rage bait. And I think because of that need to sort of find something that is going to create kinda some kind of spark, in food, that is the catalyst that's gonna cause people in food to get better at their craft. Because at some point, all of that bullshit is just gonna wash away, and you're gonna be left naked with something. And if you wanna be able to have the real goods to show for it. And I think that I really feel strongly that food is about to go into this very specific point of like, a little bit like Japan where you can open up one specific kind of bakery that they that makes one specific type of thing, and you do it better than anybody else.
Speaker 5:And you're gonna see that here in America. I I I feel very strongly about that.
Speaker 3:Yep. I I love that approach. What what advice do you give to kind of emerging chefs on media strategy? I think in in tech, there's like like we tend to see kind of a high low strategy where you wanna be like super online getting a lot of attention or you wanna be kind of the the mysterious dark horse that's kind of going over the radar and there's, a messy middle that's probably a disaster.
Speaker 5:Yeah. I I don't think that that pattern is any different than Mhmm. Than what you see in food. But at the same time, I think, you know, I don't know if apathy is the right word, but I don't care about it as much anymore either. Because it just know I'm not the only chef that feels this way.
Speaker 5:It's just some people are doing it more than ever and getting better at it, but others, I think, are just sort of getting exhausted by the whole thing because I just don't know what that best long term strategy is. And now you have a older generation of you know, I'm I'm 48 years old. I know chefs that
Speaker 2:I won't
Speaker 5:say who, that are, like, clearly gotten a social media strategist or somebody because their content is really fucking good right now. And we we I I still don't know which one works. Right? Because once you feed that beast, you have to do it all the time. Oh, yeah.
Speaker 5:And that's a lot of time. So I I I don't know if the better thing is to just be word-of-mouth because ultimately, all of this is is word-of-mouth.
Speaker 3:Mhmm. Yep.
Speaker 5:Right? And
Speaker 3:And do you build a relationship in that repeatability and and like, there's my favorite restaurants in LA, Like, they don't have to do marketing to me. You know? I don't need to get an email. I don't need to see them on Instagram. I'm just gonna go there, like, when I have the time.
Speaker 3:Right? And so I I I
Speaker 5:think Yeah. Mean, yeah. I think I think that's the zag. Right? Yeah.
Speaker 5:But you can't do that unless you actually have a point of view that resonates with somebody.
Speaker 3:Yep.
Speaker 5:And if you are constantly sort of pandering and figuring out like how to execute other people's dreams, wishes, and visions, and what the hell are you actually making?
Speaker 3:Yeah. And you don't wanna get to a place where your content is better than better than the product. And I'm sure that's, like, a you know, a lot of the more the more you time you spend on content, like, the the more greater there is a likelihood that that it could get to that point, I think.
Speaker 5:Yeah. I mean, but, like, do you guys care about what you see on social media still? Like, I I actually think there's a bifurcation that's happening with what people see versus what actually people are going to eat.
Speaker 1:I do think that there's I don't know. Maybe the the the steel man argument for the the viral over the top, you know, TikTok that gets me to go to a restaurant is that it can, in some ways, create, like, a shelling point and, like, a a a coming together. Like, a if there's something that's trendy and and I and it's an excuse for me to pull my extended family, my friends, different people, and it just gets us an opportunity to kind of come together there and experience that, like, even silly, trendy, over the top thing. I think that there's something that can be good about that, but, but it's certainly not, like, the primary reason why I go to a particular restaurant.
Speaker 5:No. I mean, that's the thing. It's like, I actually I I we're working on a show, and I can't say which or where. But, you know, sort of the thesis is
Speaker 1:Okay. Oh, that's cool. That's very cool.
Speaker 5:And sort of that that principle. Right?
Speaker 1:No. I I like that as a philosophy.
Speaker 5:It's just like the other thing is I I I I sort of mentioned that earlier in this conversation about if somebody was tasting something that was truly good and remarkable, would they actually know what's good and remarkable? And I I think currently, we we, again, have a a knowledge Mhmm. That is greater than it's ever been in terms of food, but and maybe this is the same way in fashion and architecture and film and other arts, but does your audience actually know what good is anymore? Because I don't I don't know. Right?
Speaker 5:And and I'm not it's not trying to be snooty or an artist. I'm just saying, like, let's just talk about wine right now. If if I'm giving somebody, a, like, 1998 Ravano, you know, from bur white burgundy to somebody that has never tasted it before, I know that it might taste good to them, but will they appreciate it? Mhmm. Because this person might be more into natural wines and, you know, oxidization, etcetera, etcetera.
Speaker 5:So it's like, I'm not saying that they're not right, but I always joke, like, can't you know, my friend used to say, you can't you can say that you can never say that Salier was better than Mozart.
Speaker 1:Mhmm.
Speaker 5:Right?
Speaker 3:Yeah.
Speaker 1:Was good, but he
Speaker 5:was not better. Yeah. And that's just sort of unequivocal. Yeah. And you can appreciate Salier, but you can never say that he's better than Mozart.
Speaker 5:My concern is people don't even know who fucking Mozart is right
Speaker 1:now. Yeah.
Speaker 5:And and I and that's sort of my concern when it comes to sort of social media and food is who's deciding what is actually good. Mhmm. Just because something looks good doesn't mean it actually is good. Mhmm. And I know this is getting into a meta sort of philosophical conversation, but this is the shit I think about.
Speaker 3:Mhmm. Oh, I
Speaker 8:love it.
Speaker 1:It makes sense.
Speaker 3:Last question on my side. I'm curious how restaurant operators are planning around America just drinking less than ever.
Speaker 5:Yeah. Well, that is the you know, I I feel like the boy who cried wolf. I've been sort of screaming this flag for a long time. This has been this is the real existential threat. Mhmm.
Speaker 5:Like, for example, LA, the biggest thing that happened in LA over the past ten years in food was really ride sharing because people were getting drunk. And you saw that in revenues. Restaurants are going through the roof. And if anything, restaurants was a bubble. Right?
Speaker 5:Mhmm. Too many restaurants, and I think we're still sort of in this bubble. That's a whole another conversation. But I I think that you can see now, at least in LA, people are drinking much less. Mhmm.
Speaker 5:I think you see a younger generation maybe taking some edibles. They're just not you know, and the crazy thing is I I kids just don't drink anymore. Like, kids start when they start a tab, which is crazy to me, they close it out every time. Yeah. What is going on?
Speaker 5:Like, they're never going to know what it's like to wake up at three in the afternoon and be like, shit. I left my credit card at that bar. I gotta go back and get it.
Speaker 3:Yeah. They're too responsible.
Speaker 5:Closing out every time. There there's a responsibility.
Speaker 3:Hurting small businesses.
Speaker 5:It is. It is hurting small businesses and and but I think that there the the if you look at the sort of the only look at the blended numbers for most restaurants or bev sales, I think that it might look flat or down, but it's actually, I think, way worse because once you split out the 1% of the 1% that are drinking, like, these huge bottles of expensive wine. Yeah. Right? And that is through the roof right now.
Speaker 5:Again, talking about the barbell experiential thing,
Speaker 1:like Totally.
Speaker 5:People that are drinking things that no one else can really afford
Speaker 4:Mhmm.
Speaker 5:That's gone like three x four x for the past five years. It really has.
Speaker 1:And,
Speaker 5:you know, younger people are not drinking cocktails and they don't want mocktails because mocktails are actually way more difficult to make than a regular cocktail with alcohol in it, but nobody wants to drink it for the same or more.
Speaker 3:Why is it why is it more why is it more difficult just to actually deliver something that's
Speaker 5:like Imagine if we were making the alcohol too
Speaker 2:Mhmm.
Speaker 5:Yeah. From scratch. That's hard to do. That's and that you know, a normal restaurant restaurant ratio was 70 to 30% for the most part. You want 70% food.
Speaker 5:I mean, this is not the I
Speaker 1:like Roughly.
Speaker 5:Roughly. 70% food to 30% dev sales, and I think that has completely shifted. And for a good reference, it might have, percent? Like Yeah. I mean, like, if you want 10% of your, you know, profit, for example.
Speaker 5:Right? Like Yeah. Something's gonna give when you're down, like, 18% on dev sales.
Speaker 1:Mhmm. Yeah.
Speaker 5:You know, I think that's the average right now or something like that. 18%. So I don't have an answer. Food needs to get more expensive. I've been saying that for a long time.
Speaker 1:Yeah.
Speaker 5:But that comes across as terrible when people read that as a pull quote.
Speaker 1:Yeah. Yeah.
Speaker 5:Because it's already expensive. So I don't know what the answers are. I will tell you that, like, you know, it's one of the reasons why I invested in athletic brewing Okay. In in 2019. Yeah.
Speaker 5:Because I saw the data within our own restaurants. It was slowly going down year after year, just a little bit, like half a percent, 1%. But, you know, I I and I'm I think that's what we can do is sort of figure out what the alternatives are. I don't have the answer
Speaker 3:But isn't one isn't one of the challenges is, like, these non ALK products? Like, somebody's not, like, there's not the incentive to have the second or third. Like, I I feel like a lot of this stuff, people just have one. They they get a little bit of the taste, but they're not getting, like, a real they're not, like, getting they're not they're they're just like drunk. Right?
Speaker 3:So they're not
Speaker 5:Are you guys drinking as much as you used to?
Speaker 1:Absolutely not. No. Yeah.
Speaker 5:You know? I feel like the way I used to was like Dom Draper and Mad Men, the amount I used to drink.
Speaker 1:Yes. Yes.
Speaker 5:You know? And I you know, part of that is just a generational shift, but I I can assure you, you talk to people under a certain age group, the younger Gen z, they think of drinking like it's smoking cigarettes.
Speaker 1:Oh, yeah.
Speaker 5:It's just not something they want. I I've seen this in kitchens. Like, you finished your twelve, fourteen hour day. All you wanted was that cold beer at the end of their shift.
Speaker 1:Oh.
Speaker 5:And now they don't want that. Yeah. And I just don't I'm just like, what is happening? You know? And I'm not saying they're wrong.
Speaker 5:It's just so so that we're sort of dinosaurs.
Speaker 1:It's a different interpretation. Like, data didn't change, but it was contextualized through podcasts, and there's a lot of health data out there. I I you could maybe call a little bit of the Huberman effect, but there's a whole bunch of there's a long lineage of folks who have been, like, actually ringing the alarm bells on the health consequences of of drinking alcohol even in small amounts. And so that's I feel like that's what's really cascaded.
Speaker 5:Yeah. Maybe what we should do, restaurants should start a lobbyist and just muzzle hoover in
Speaker 1:and eat
Speaker 5:or tea and we'll be open
Speaker 1:to you. Yeah. Yeah. Live life.
Speaker 3:Yeah. I mean, like, the the dual pressure right now from from, like, just labor labor costs on one side and then and then just, like, declining alcohol sales, like, it's just creating mean, I I've seen some the the place we go for breakfast adds like 4% on top of every bill for for benefits. I'm I'm sure that that's helpful. But like, it's a very real cost. Right?
Speaker 3:It it's now 25% between effectively for for 24% for service. At
Speaker 5:at the end of the day, food needs to be more expensive. And and and I'm not it it just sort of has to and it can't be sort of be passed down. I think I've been talking about this for many, many years. I don't know why, but people have a real allergic reaction when it talks to raising prices. For example, I think, you know, it's good.
Speaker 5:I I I'm pro when a restaurant jacks up their prices to, like I'm hoping we see a restaurant where the the the the ability to eat there is basically like going to a Taylor Swift conference on secondary, the mixed secondary market.
Speaker 1:Yeah.
Speaker 5:You know? Like, that's sort of what has to happen. I I do believe there's gonna be innovation. And, again, the problem with the restaurant industry as a whole to sort of mitigating this decline in beverage sales is that we are too slow in prodding to to try new things out, to embrace new technologies. And as my sort of spiel and joke about this, as a whole, we're so goddamn allergic and slow to changing things, we still are using the metric imperial system instead of the metric system.
Speaker 2:Mhmm.
Speaker 5:I mean, that's so dumb. The metric system is scientifically proven to be more accurate and more effective. Why are we still using ounces, pounds? It's so dumb.
Speaker 1:America, baby. It's because we're Americans. We do things the dumb way sometimes.
Speaker 5:Americans don't still do it, but as a industry
Speaker 1:Yeah. I know.
Speaker 5:As restaurant leaders, we
Speaker 1:can just use the metric You can just use metric, but
Speaker 5:that bad when drug dealers use the metric system.
Speaker 1:The drug dealers use the metric system. That's right.
Speaker 5:So what the hell are we
Speaker 1:doing here? What is going on?
Speaker 5:So if we can't adopt the metric system as an industry, what what are we doing here?
Speaker 1:Yeah. Yeah. What a mess. What a mess.
Speaker 3:Last question. Okay. We've got a bunch of people in the chat have asked, who do you think is gonna win the AI race? Hot take.
Speaker 1:What? Really? Okay.
Speaker 3:We had to ask. This is a tech this is a tech show. Just give me your gut gut answer. First reaction. One word.
Speaker 3:One word.
Speaker 1:Google, Anthropic, OpenAI. Who you got?
Speaker 5:Commodore Computers.
Speaker 1:There we go. Commodore There
Speaker 3:you go. More
Speaker 1:coming at you.
Speaker 3:Dark horse in the race.
Speaker 1:I love it.
Speaker 3:Love it.
Speaker 1:Thank you so much.
Speaker 3:Great hanging.
Speaker 5:Well, we'll see you guys at f 1 Yeah. At the Blasio? Yeah. We'll be
Speaker 1:very excited to see you there. Can't wait. Have a great one. We'll talk to
Speaker 5:you soon.
Speaker 1:Be good. Go, guys. The man. Have a great rest of your day. Let me tell you about graphite dot dev code review for the age of AI.
Speaker 1:Graphite helps teams on GitHub ship higher quality software faster. We have been keeping our next guest waiting for far too long. Lord and from Figma, thank you
Speaker 3:so much from for Hello. Holding Look at this look at this background. Man. I couldn't even tell it was I noticed that it was a TV, but it took
Speaker 1:me second. A professional lot of TV hits on CNN and CNBC, people will be sitting right in front of a TV, and they'll put some sort of fake background and work
Speaker 3:so great to have you on the show.
Speaker 9:Thanks for having me.
Speaker 3:We we've been reacting Nana Banana Pro this morning. Very, very impressed on a bunch of different dimensions. But before we get into that, would love an introduction on yourself for the audience and and your background.
Speaker 9:Yeah. Of course. So I joined Figma two months ago as their chief design officer. And before then, I spent close to a decade at Meta, primarily working on messaging, so I led the messenger and Instagram DM teams and more recently leading consumer AI on the product side. And you know, before you ask me, I'll tell you why I joined Figma.
Speaker 9:I did so because in my seat watching all of the AI improvements that that we're seeing with these frontier models, it became very, very clear that product development as a process is going to change drastically. And I truly believe and I saw that Figma has the opportunity and I think the responsibility from my point of view to really build the creative environment that helps people like me, people that really love to live at the intersection. I used to be a musician. I became a designer then a, you know, product leader. I really believe in the thing we're making more than like how different disciplines kind of like line up to to get the product done.
Speaker 9:And so I believe that a creative environment that helps you get that idea from your head into a finished product is what we need right now, and I'm excited to help Figma build this.
Speaker 3:Amazing. So so many different ways that you can integrate AI into Figma. You guys have been doing Figma Make. There's also, like, the core product. What are what's been your priorities kind of in the first couple months?
Speaker 9:Yeah. So for everyone who doesn't know, Figma Make is the place in Figma where you could take your ideas or designs and prompt them into working software. And that's really important because it takes your design and like helps you understand what it looks like, what it feels like in motion. And what we've been looking into with this is an aspect of AI that I think gets overlooked sometimes. As a creative tool, it is important for AI not to box you in.
Speaker 9:So you want to be able to take your design from Design and generate it. But then you want to take those generations back to Canvas and be able to manipulate them as well. And we're moving pretty fast. In the last two months, think we've shipped over 20 major features. And a lot of them have to do with like putting the designer yes.
Speaker 9:Putting the designer in the driver's seat Mhmm. And enabling the designer to take these AI tools, but really wield them as tools that are precise and that go in their direction versus kinda like
Speaker 3:number one frustrating thing with generative AI right now is you generate an asset that's like 98% amazing, And then there's one tiny element. And you try to re prompt it. And you try to say, can you remove that? Could you try again on that?
Speaker 9:Then it all changes.
Speaker 3:Yeah. Yeah. And then it's all changing. And so just making it easier to go back and forth, I think, is probably some of the most important work on the on the creative side.
Speaker 1:Do you have a personal evaluation that you run when a new generative image model drops? I have this, the Where's Waldo test. I try and get it to generate a full Where's Waldo because that's there's a lot of detail in there. It's this whole laddered up image. Do you have a favorite image that you go to as, like, your ground truth just to kinda get the flavor?
Speaker 9:I don't necessarily. I have a a shit ton of styles that I put it through the ringer with Sure. And a number of, like, creative tasks that I wanna see if it does. What's really important with these images and hasn't happened necessarily predictably so far is that they take that certain first scene that they generate or the photo that you give them and then they're dependable in recreating the style and telling the second part of the story. Otherwise, they're helpful.
Speaker 9:An image that doesn't tell a story is not helpful. Yeah. Yeah. This is why I like actually Nano Banana Pro because it's dependable. The the way Weevee, one of our companies, said today, it was like, it's it's a model that behaves.
Speaker 9:You should actually watch the the video that they've put out. It's hilarious, and it's made in movie with with Gemini, three.
Speaker 1:Yeah. Yeah. Apparently, I mean, Prince here on on X is saying, Nano Banana Pro is a reasoning image model, and shares a quote. This enables enhanced image quality, better rendering of long text passages in many languages, improved factuality, which is something like we didn't like, I was never thinking about the factualness of an image generator, but that's actually extremely important. Like, you don't want errors and
Speaker 9:Of course. Is it realistic? Yeah. Is it something that that really connects? Like, our our eyes, right, like, we'll pick up on details Yeah.
Speaker 9:Even before you understand what's going on, and you'll understand that this is an AI generated image. And the truth is that people prefer to look at things that feel human, that a human has put out there in the world. And what's really cool with product products like Nano Banana Pro is that you're able to manipulate that and because it's your creative tool, you could layer all of the different elements. Like as an example, you know Dylan loves to post videos with his like Figma quilt behind him.
Speaker 3:Yeah. Yeah.
Speaker 9:So, I took that. I then generated the quilt directly then I turned it into a sweater and then I put it on, you know, one of Dylan's photos. And all in all of these steps, kept each square of the quilt exact. It did not distort Dylan's face. I could do what I what was in my head versus in in other types of tools like this.
Speaker 9:This has not been possible yet.
Speaker 3:What how how much do you care about, like, leveraging some of these models to help people generate new ideas? Because in in my in in my creative process is just like creativity oftentimes is just like taking two different kind of like random disconnected ideas and bringing them together and sometimes it just hits. And I feel like
Speaker 9:is messy. Yeah. It's like cranking a lot of like disparate things into into the canvas in one way or another and letting them inspire you and like take the taking the net next step with those. That's probably the biggest role of AI in the creative process right now is, like, how much can you explore because these tools exist. Because in the end, what you're trying to create is still the thing in your head.
Speaker 3:Yep. Yep. That makes sense.
Speaker 1:Have there been any internal, memes that have been floating around in within Figma? Like, I'm thinking of the Studio Ghibli moment. That was really big on the Internet broadly. But have there been any, like like, just fun prompts? I I'm seeing people use Nano Banana Pro to make RPG style maps.
Speaker 1:People are are using there's always, a new, like, fun prompt that kind of goes viral on the Internet. I'm wondering if you have any glimmers of what might be the fun prompt from Nano Banana Pro based on what you've seen in, in the internal
Speaker 9:team chat. I haven't seen any in the team chat outside of, like, just broad variations. So, like, none of them, really came up to the repeating, like, patterns so But insane variations, like, you know, taking things that are just sketches and, filling them in with, like, complete three d and, like Yeah. As designers, really, what we love to do is explore. So we pushed this thing pretty hard.
Speaker 1:Yeah. Yeah. I'm I'm excited to get deeper into it. It's such a fun tool.
Speaker 3:What what's your
Speaker 1:We're have fun.
Speaker 3:What's your updated read on just, like, general designer sentiment around AI? Because I feel like it it fluctuates from from fear to excitement. And you have pockets where people are super excited, and you have pockets where people are are kind of not excited about it or or calling it slop. But what what is, like, the most up to date read from your view, specifically with like Yeah.
Speaker 9:I relate to all of those points of view in some way. Right? Because if AI is just about speed and mass production of software and design, like that is very anti what I'm here to put in the world. But at the same time, if design becomes a tool that you could actually control and it starts to inspire you as you you were saying, that's a very different thing. It really widens the canvas and this is why we're so interested in all of the new models and we put them through the wringer because we want to see how in the hands of designers these become clay that they could mold.
Speaker 9:And so I think the different opinions are just really at which point which part of it do you look? Do you look at the potential and what's, you know, kind of what's what's coming up and how it could work? Or do you look at exactly what it produced yesterday, in which case, a lot of times, it is not great.
Speaker 3:Yep. Makes a lot sense.
Speaker 9:I think David Chang was saying something about, like, know, good enough and, you know, producing just something good. Like, have this thing, like, at least at Figma, we believe good enough is not good enough. If all, you know, we're able to do in the future is create the same software a million times, that is just humanity losing.
Speaker 1:Yeah. Yeah. It's interesting. I I I process those two things very, very differently. But, yeah, I I I understand where you're coming from on that.
Speaker 9:Well, say more.
Speaker 1:I I I just I process just this idea of, like I I guess my question is, like, what is the mom and like, what he was getting at was, what is the mom and pop restaurant that's not going to make an awards list that doesn't have the most viral turducken where it's
Speaker 9:Oh, reliable. Yeah. Yeah.
Speaker 1:Yeah. Yeah. It's it's not it's not superlative. It's not the the world's heaviest doughnut, the world's, like, most, you know, gold flakes on a steak possible. Like, it's not viral.
Speaker 1:It's not the best. And even just in terms of fine dining, it's not, oh, it has the 10 Michelin stars. It's the best, the best, the best. There's this demand for the superlative in the restaurant industry. And then there's also the demand for just the cheapest fast casual, just getting get out.
Speaker 1:It's a complete commodity. And I understand what both of those are in the design world a little bit. I I mean, I I feel like we've we've seen design trends, you know, like, from Apple and, you know, where we've all been like, wow. Like, that is truly, like, the best UI possible. Emotional connection.
Speaker 1:Yeah. Absolutely tough. Yeah. And then we've also seen just like, okay. Like, that's just like the bootstrap design library that everyone uses for everything.
Speaker 1:And that's like the the fast food of design. And what's interesting is to think about that messy middle of design. Like, what is the mom and pop shop for of design that's been there for decades, that's reliable, that's not you know, it's not going viral and winning awards, but it's good and you love it. I don't know. It's a hard it's a hard I I don't know enough about enough design to, like, draw an analogy, but maybe you can.
Speaker 2:I don't know.
Speaker 9:Yeah. I think it's it's really use case dependent Yeah. In some way. Like, you want a, you know, Tuesday night restaurant that is not all the bells and whistles, and you wanted to just deliver in some case. And maybe that's your to do app or where you keep your tasks for development, etcetera.
Speaker 9:Like, there is no reason for design to kind of get in the way, like, in in, in those use cases. But then there's moments even in those flows where you wanna feel something. You wanna feel like that developer that thought about, like, the app had you in mind. And those are really the the surprise moments, the delight moments that that make people be loyal to an app.
Speaker 3:Yep. Yeah. I've something something I've been thinking about is, like, what will be the product design equivalent of the Emdash? Or or like when you when you read let's say somebody like publishes an essay and then you start reading it and you get to the second paragraph and you just like immediately close it because you realize like they just fully generated all the text. I feel like we're gonna start to get that with software more and more where you're you'll go to a website or an or an app and and from afar or at least when you first land on it, it looks like, cool.
Speaker 3:This looks like a nice product. And then you start using it, and you realize, like, Okay, they generated a bunch of nice animations, and it looks Okay. But then the second that you actually start using it, you realize there was no real human thought put into the product. It is now you can now make a product that looks like linear in one prompt. Cannot make a product that's going to feel like using linear.
Speaker 9:Exactly.
Speaker 3:And so that's where the human element is just going to continue to be super, super powerful. Taking user feedback and having that empathy with the user and being super thoughtful and using the products to yourself and not just because, yeah, it's never been easier to create any type of application. It still feels just as hard in many ways to create a product that's truly magical to daily drive or rely on. Yeah. Makes sense.
Speaker 9:Yeah. And I think AI will play a role into that. But actually, to go back, I am so pissed about Emdash's. A good tool and every time I write now, I use them and I'm like, whatever. Yeah.
Speaker 1:Think you just have
Speaker 9:to use the minus sign.
Speaker 3:Do you think?
Speaker 1:Just just use the minus sign.
Speaker 9:Be recognizable Yeah. When, you know, a website is just like kind of vibe prompted, vibe coded, and put out there in the world. And you're going to want to feel that the developers spend more time considering that.
Speaker 3:Yeah. Amazing. Well, you so much for joining. Congratulations on the new role as
Speaker 9:Thank you.
Speaker 3:As a Figma DAU for going on a decade now.
Speaker 1:Mhmm.
Speaker 3:I'm very, very happy that you're on board.
Speaker 9:Try out all the all the new toys.
Speaker 3:Yeah. We will.
Speaker 1:Thanks for stopping by. Great. Talk to you soon. Have a great rest of
Speaker 3:your day. Cheers.
Speaker 9:Bye.
Speaker 1:If you want AI to handle your customer support, go to fin.ai, the number one AI agent for customer service. Let's react to some of these Nana Banana prompts. They look fantastic. Here's one where someone took an, a map, a Google Map screenshot, and just turned it into, an RPG style map, an San Francisco monster map. And, it's it's really is that reasoning model.
Speaker 1:Like, you can see the Golden Gate Bridge is there, and what would be logical to have attacking the Golden Gate Bridge? A giant octopus. And then Alcatraz Island is there, and there's this sea monster next to it. And everything kind of, like, fits. Like, you didn't get the dragon is up at Twin Peaks.
Speaker 1:You don't get the dragon in the water. You get the sea monster in the water. And so all these things are, like, pretty logical. There's, of course, some things that are a little bit, like, repetitive. As in stuff.
Speaker 3:Like, ogres in in Golden Gate?
Speaker 1:Yeah. Yeah. It's just very, very cool. I think this is gonna be a lot of fun. Then there's someone else with a with a benchmark here, Angel.
Speaker 1:It says, Nana Banana nailed the burger test. It's the first model to truly do this perfectly. And so the prompt is remove the ingredients, leave just the top bun and the bottom bun in the exact same place and render the rest of the image, just with the prompt. And, previously, this would sort of confuse models a little bit here and there, because, it would be there it it would it would sort of shift the colors or shift the the the the the sections and kind of not not not
Speaker 3:next one wild. The make it Lego.
Speaker 1:The Lego prompt is crazy. This could be the next, like, moment for sure if you can just take a whole bunch of photos and pipe them through. We should take some of the And make
Speaker 3:a take a take a picture of us
Speaker 1:and put it
Speaker 3:through Nana Banana Pro and make it Lego.
Speaker 4:Yeah. I did I I I tried
Speaker 8:doing this earlier. It it works pretty well. Sometimes with the people, it
Speaker 1:it Yeah.
Speaker 8:It it doesn't use, like, the the minifigure, like
Speaker 1:Oh, it doesn't.
Speaker 3:Design. Okay.
Speaker 1:So this is particularly good because it's already because the so this dog one is remarkable, but it's a cartoon character. And so, yeah, is it gonna make us a minifig? I mean, maybe that could be worked into the prompt. Do you wanna take some of the iconic photos that we've used through the press images, through the Wall Street Journal photo? There were the New York Times photos.
Speaker 1:Let's take some of those photos that we have, and let's put those through Nano Banana and ask them to render us as minifigures in this Lego world. I wanna see the UltraDome in four k Lego. I while we're doing that, Pietro Chiron Chirono shares that Nano Banana is wild. Nano Banana Pro, that is. Here's my favorite use case so far.
Speaker 1:Take papers or really long articles and turn them into a detailed whiteboard photo. It's basically the greatest compression algorithm in human history. This is a very cool video where attention is all you need, gets turned into this, image. I I can already imagine, you know, when we got ChatGPT, it was like, oh, wow. You can take, you can take bullet points.
Speaker 1:You can expand it into into an essay. And you can take an essay and expand it down to bullet points. And I imagine that people are gonna be sending these, and then they're not gonna be reading them. And then they're gonna be like, actually, Okay.
Speaker 3:Turn this diagram into an essay and then summarize
Speaker 1:it. And then summarize it. Turn it into two words for me. Just one just turn it into just one word. But it is very cool.
Speaker 1:And I'm I'm excited where people will will play with this. It is it it does look really good. I think we're gonna play with this tomorrow. We're we we have a diagram, a market map of our own coming tomorrow. We're gonna break down the state of AI, from the TBPN perspective.
Speaker 1:Didi shares he that he literally fed Nano Banana Pro raw graph vis of AI compute commits generated by the Gemini three, and it one shotted rendering it with logos perfectly. What in god's name is in this model? That is very, very cool. I've seen, so that's not quite at the level of the elegance that I've been seeing from The Wall Street Journal's visualization of all the circularity in the we we've seen that circular graphic from The Wall Street Journal.
Speaker 3:But it's, like, 70% of the way there?
Speaker 1:Yeah. Yeah. Yeah. I would say it's 70%. I'm I'm just excited that it actually puts it gets logos correctly because with the right direction, it can definitely do some.
Speaker 1:Also, I imagine that sketching a little bit of the ground truth of, like, how you want this laid out would probably give it a lot of, like, scaffolding to build off of. That would be very cool.
Speaker 3:Look at this. Okay.
Speaker 1:Let's see this.
Speaker 3:Made this.
Speaker 1:Let's zoom in on this.
Speaker 6:So we
Speaker 3:are us.
Speaker 1:We're not minifigures. We're just LEGO'd LEGO people. Okay. Color temperature's a little off. I'm not into it.
Speaker 1:Let's move on to the next one. The gong looks cool in the background. I like the LEGO gong. Have we have we done any others? Well This
Speaker 8:is the only one I made so far. Okay. It's pretty slow.
Speaker 1:It's pretty slow.
Speaker 3:Well Wow. This is funny. So on this on this next one
Speaker 1:Yeah. Gemini three Pro image versus GPT. Okay.
Speaker 3:So I didn't read the caption. I just looked straight at the image. I just assumed that the image on the left was an actual image
Speaker 1:Yes.
Speaker 3:And then this was the output on the right.
Speaker 1:That is crazy. Yes.
Speaker 3:But Yes. It's actually the no. This is a real image.
Speaker 1:Same prompt. Two different, two different results. That's pretty pretty remarkable. Crazy. Yeah.
Speaker 1:VO four is gonna be a big, big moment. I'm very excited because VO three, I mean, such a huge leap over, the original Sora. Was it, it was pre Sora. What was what was ChatGPT's video model, not Dolly, but pre Sora app? Was it called Sora?
Speaker 8:Yeah. Was always called Sora.
Speaker 1:It was always called Sora. Okay. Yeah. That because Sora one or whatever, the precursor was really kind of hallucinatory and and and and and crazy. V o three got a ton of the physics down, but it still has this sort of, like, plasticky look that you can just clock.
Speaker 1:But whatever they did with Gemini three Pro image is really pushing the photorealism much farther. Very exciting. What else is going on here? Nano Banana Pro, edit this image and face swap with Sam Altman. Slow show thinking Nano Banana Pro.
Speaker 1:Does that is that a good face swap for Sam?
Speaker 3:It's just okay.
Speaker 1:It's okay. That one's a that one's that's that one's a five out of 10, I think. Let's see. Someone is dropping ChatGPT and saying, I don't wanna play with you anymore. Wait.
Speaker 1:But this is
Speaker 8:No. No. They're dropping GPGemini.
Speaker 1:They're dropping Gemini. They're going back to back. The model wars are really, really heating up constantly. Rota is saying that they're all in on GPT 5.1 Pro because it's rolling out to all Pro users. It delivers clearer, more capable answers for complex work with strong gains in writing help, data science, and business apps.
Speaker 3:What is this?
Speaker 1:That's exciting.
Speaker 3:He just made this one,
Speaker 1:but it only turned you into a LEGO, Jordy. Then zoom out. Did it do to me?
Speaker 3:Look at John. What did it do
Speaker 1:to me?
Speaker 3:Look at John.
Speaker 1:What did it do to me? I'm just a human. It missed me entirely. What's going on here? You got LEGO, Jordy.
Speaker 1:LEGO Jordy. And what is it? It doesn't know what to do with the turbo puffer because the turbo puffer is, like, already LEGO.
Speaker 3:Wait. I know what you are. You're already a LEGO.
Speaker 1:That's funny. That's funny. Well, speaking of turbo puffer, sign up today. Serverless vector and full text search built from first principles in object storage. Fast, 10x cheaper, and extremely scalable.
Speaker 1:So GPT 5.1, have you had a chance to take its first spin, Tyler? What's the latest with GPT 5.1?
Speaker 8:Well, so so the main new thing is the new model is it's not 5.1 because that came out, what, two weeks ago. It's 5.1 Codex.
Speaker 1:5.1 Pro. Well, so there's Codex, but there's also 5.1 Pro for, like, research tasks, I believe.
Speaker 3:Okay.
Speaker 1:But anyway, Codex But so how are we doing on the benchmarks? Oh oh, and you have you have a take. You have a take. Give me your take.
Speaker 8:Yeah. I mean, basically, the so I I think the main graph or the main kind of benchmark that everyone is is now kind of watching
Speaker 5:Yes.
Speaker 8:Is, this one from meter.
Speaker 1:Yeah. Show us where the goalposts have been moved to most recently. Where where do we move the goalposts most
Speaker 3:recently? We still need to get goalposts.
Speaker 1:We do need goalposts. Okay. So we moved the goalposts from, from, like, you know, just surprise me with something that's, remarkably human. And that's
Speaker 8:totally qualitative. You can't measure that at all. I I and I think the reason that people are using this benchmark is because, like, you can't saturate it. It's not like it it's you just can keep measuring. It's not like MMLU.
Speaker 8:Like, there's math questions, and then at some point, you just answer them all correctly.
Speaker 1:Yes. Yes.
Speaker 8:So it's not, like, interesting.
Speaker 1:Okay.
Speaker 8:Where where this is, like, it it this is a benchmark that you could keep doing in in thirty years. Right? Because it it the time just goes up and up.
Speaker 1:Yes.
Speaker 8:Yes. So if we can pull up this graph, it's the time horizon one.
Speaker 3:Mhmm.
Speaker 8:And there's basically what you've seen for the past, like, five years Yep. Is every eight months, the the the the time that a model can do and this is just on coding tasks, but it's kind of generally applicable. Yep. It doubles. Yep.
Speaker 8:And so the I think this is kind of the main thing that we should be looking at. Like, are models stagnating? Are they decelerating? Mhmm. And what you see is that it's basically a straight line.
Speaker 8:If you put it on a log it's exponential, but if you put it on a log scale,
Speaker 5:it's
Speaker 8:a straight line. Yep. And the new model is, like, perfectly, basically on that line.
Speaker 1:Yeah.
Speaker 8:So I I think it's, this is just a great sign. Like, model
Speaker 1:So how long like, what time? What task duration measured in time would you would you say qualifies as AGI?
Speaker 8:Yeah. I mean, I I don't know if it's exactly I don't know if that if that's my definition of AGI because I think there are a lot of tasks that take a long time but don't really require general intelligence.
Speaker 5:Mhmm.
Speaker 8:But I I do think if you're getting into, like, weeks or months, that's like a big kind of project that would take a person it's like a big part of their life. Yes. I think if if we get up to there and I I guess you get I mean, you can just chart it out to see if you follow this path Yeah. How long will that take? But but, I mean, you you said it's, like, basically human lifetime.
Speaker 8:Isn't that your
Speaker 1:That's my that that I think that's my correct benchmark.
Speaker 8:But I I think that's wrong because if you think of, like, build a build a company, that's not your entire lifetime. That's, like for some people, that's only,
Speaker 1:Thirty years.
Speaker 8:Thirty year. Okay.
Speaker 1:Four years maybe? Yeah. I don't know. I the the the the the initiation prompt is the Genesis prompt. It's be fruitful and multiply.
Speaker 1:Like, that is the AGI initialization prompt. Just just replicate. You know? That's your goal, AGI. Just just go create value.
Speaker 1:Just go exist. And then it goes and does whatever it needs to. That's when it's, like, truly, like, you know, embodied, I suppose. I don't know. All I do know is that you can go to ProFound try profound.com, get your brand mentioning, your petite, reach millions of consumers who are using AI to discover new products and brands.
Speaker 1:I guess the question is this this task duration thing is so odd because, like, does time move slower or faster in AI world? You would assume
Speaker 3:Alex car manipulated time.
Speaker 1:You should be able to manipulate time if you're in the computer. Right? So you know what I mean. Right? Okay.
Speaker 1:So so what I'm saying is that, like, is that, like, if if GPT five can do two hours of if it can work for two hours without losing consistency and still complete long tasks, if you get a new chip that speeds that up, you do the same amount of work in half the time. Like, if you just actually speed up the inference, you're you're bringing this curve down. And so you have this weird countervailing force where, like, I would expect a computer to be able to do problems faster than humans. Right?
Speaker 8:Yeah. I mean, so
Speaker 1:so Oh, at least at least over time.
Speaker 8:To, like, how long it takes a human to do it. Right?
Speaker 1:Oh, is that what this is?
Speaker 8:It's like thirty thirty seconds. Answer a question. Sick like, five minutes is count words in a passage. Find fact on web. Yeah.
Speaker 8:It's like compared to how long it takes a human. Interesting.
Speaker 1:Okay. So they have to
Speaker 8:Because, obviously, you could just, if you
Speaker 1:How are they going to how are they gonna benchmark? I I I always thought this was, cut come up with a prompt. Like, do they even have a prompt that can that that theoretically could take months to do? And and and it wouldn't
Speaker 8:be Build a massive company that takes months, years.
Speaker 1:Yeah. Years. Is that where we're gonna be with this meter chart in in, like, what, six more doublings or something like that?
Speaker 3:Yes.
Speaker 1:That's what they're yeah?
Speaker 8:I mean You think so?
Speaker 1:That seems
Speaker 7:That would
Speaker 8:what it would be comparable to, the the time scales.
Speaker 1:Yeah. I just I just I wonder how they're how they're mapping that.
Speaker 3:Just 10 more doublings, sir.
Speaker 1:Yeah. I mean, it certainly does seem like like good progress. And I mean, everyone I I I feel it very much in the sense of like just the the amount of work that a single prompt can kick off feels like it's doubling for
Speaker 3:sure. I think this
Speaker 8:is just a good benchmark. A lot of people it's getting harder and harder to find good prompts Yeah. That show a model is, like, actually better. Yeah. And this is, like, a very kind of, like, objective thing that there's a there's a you know, what we expect it should be and where it actually is, and it actually is where we expect it to be.
Speaker 8:So this is, a good model. Yeah. Like, this is we're on track.
Speaker 1:Well, if you're looking for sales tax AGI, head over to numeral.com. Let numeral worry about sales tax and VAT compliance for you.
Speaker 3:Well said, John.
Speaker 1:Meter says, what is my purpose? All you put new AI models on the graph. Meter. Oh my god. Guys, please, I need to see Sonnet 4.5 on this.
Speaker 1:So Sonnet's not on there. And this is
Speaker 2:Well, no.
Speaker 1:Update the graph.
Speaker 8:A month ago.
Speaker 1:There seems to be there seems to be a mistake. I planned on assessing risks from automated AI r and d. That's funny. They're having fun. What else is going on in in in AI world?
Speaker 1:These, things are looking smoothly exponential for AI over the past several years, and I continue to think this is the best default assumption until the AI r and d automation feedback loop eventually speeds everything up. I we we gotta have the meter folks back on the show and and and understand this a little bit further. I I really I really wonder how they're actually de developing I
Speaker 3:really wanna get their take on protein the amount of protein in in fast casual concepts
Speaker 1:That would be great
Speaker 3:too. And and potentially get them to chart that out
Speaker 1:That'd be
Speaker 3:great too.
Speaker 1:Okay. And and then someone someone took this chart and put it next to the AI twenty twenty seven graph. Is this correct? So there's meters data, GPT five, Codex Max. It looks exponential, but not super exponential.
Speaker 1:Is that what this read is?
Speaker 2:Yes.
Speaker 1:Okay.
Speaker 8:So So this is still on the log graph. You see the blue line is the meter.
Speaker 4:Okay.
Speaker 8:And then the green is the the AI
Speaker 1:So AI 2027 was ex was was expecting, like, even more of an exponential.
Speaker 8:Yeah. And I think that's mostly because they thought agents that would help develop the next AI would come a little bit sooner.
Speaker 3:Okay.
Speaker 8:Interesting. But I I think I I think Gemini three seems to do very well in the kind of computer use stuff Yeah. Which you should imagine should, like, greatly help out kind of agents. Yeah. So maybe they're just maybe they're just a month or two, you know, ahead.
Speaker 1:Oh, so you think we're going back to the green dots there? You're you're optimistic? You think
Speaker 8:I I mean, it's reasonable.
Speaker 1:You think we might jump from one line to the other, from the linear to the super linear or super exponential? From the from the exponential to the super exponential. Daniel says, yep. Things are going somewhat slower than AI 2027 scenario. Our timelines were longer than 2027 when we published, and now they are still a bit longer still.
Speaker 1:Around 2030, lots of uncertainty, though, is what I say these days. Meter, of course, is, evaluating g p t five, point one codex max, triggered drastic AI acceleration or automate autonomously replicate. They concluded this was unlikely. Survey said unlikely, but, obviously, big, big growth in the capabilities. Sweebench, I wanted your reaction to this from vals.ai.
Speaker 1:Different evaluation, different eval. But with this company, they say Gemini three is number one on the independent SWEbench leaderboard. So
Speaker 8:Yes. This is their own SWEbench. It's also they did not test the the, actually, the newest OpenAI
Speaker 2:model. Oh,
Speaker 8:it's Codec Codec
Speaker 1:Max. Okay.
Speaker 8:X high or whatever. Yeah. Yeah. The maxed out.
Speaker 4:Yeah. Yeah.
Speaker 8:I don't know. Yeah. They named it kind
Speaker 1:of poorly, but For the tenth time. Yeah.
Speaker 8:But, yeah, I mean, I I am curious where that where that'll end up. And also, are saying there's, like, rumors of of Gemini three Flash, the small model. Yeah. There's also rumors of Anthropic releasing a model soon, and I assume it would be Opus four five.
Speaker 1:Mhmm.
Speaker 8:Right? Because that that's like they have, three tiers. They have the haiku, sonnet, and Opus.
Speaker 1:Yeah.
Speaker 8:So I'm curious to see where all the or all those end up.
Speaker 3:Very curious to see where Doug landed with his prep
Speaker 1:Oh, we didn't ask
Speaker 3:you He do he got
Speaker 1:He tried it.
Speaker 3:The 100 gram max protein bowls from Sweetgreen yesterday.
Speaker 1:Three of them
Speaker 3:The core the core research team. I don't think
Speaker 1:he No.
Speaker 3:No. No. He didn't do three. But
Speaker 1:wow. Look at this. It's really on there. Chicken, chicken, chicken, chicken, chicken, chicken, chicken. It's insane.
Speaker 3:So So if things go badly, we It's listed out.
Speaker 1:If things go if things go well, we can big bulk on Nvidia. Little update. I ate over a little over half of my tummy hurts. What is this? It's not good.
Speaker 1:Protein Max is tummy hurting. Protein Max is the, is the semi analysis of
Speaker 3:Phil Ehrenstein said yesterday, you're telling me the CEO of Sweetgreen is on TBPN Oh, yeah. That he's a Chad with slick back hair, a golden tan, and a sick leather jacket. And his handle is Johnny Nemo. Annie's a vibes guy disregarding surveys. Annie added seed oil free a 106 gram protein bowl.
Speaker 3:Sweet grand sweet green won me. So That's serious. Sweet. I love it. Doug spoke a little too soon yesterday.
Speaker 3:He said, I survived the great bear market of October 29 to November 19.
Speaker 1:Let me tell you about public.com, investing for those who take it seriously, multi asset investing trusted by millions. NVIDIA emerges successful, and yet the market is still selling off. Nvidia saw its shadow six more months of bull markets as high yield Harry. Although, who knows? The market is tanking still.
Speaker 1:The Nasdaq is down 2.1% now, and Bitcoin is down at $86,000.
Speaker 3:Percent today.
Speaker 1:Significant sell off.
Speaker 3:Let's check-in on the sailor himself. Also down 5%. Yeah. At least he's tracking Meltem. The underlying asset.
Speaker 1:Meltem says, NVIDIA earnings call, first sixty seconds. We have line of sight to half a trillion in revenue in 2026. The bubble hasn't even started yet. Let's go.
Speaker 3:Michael Burry still going incredibly hard.
Speaker 1:This is the circularity chart that I was calling out. It's like
Speaker 3:And I think that Nano Banana could pretty much one shot this.
Speaker 1:I don't know if it would be as as overlapped and nuanced and, like, the it's not You can't just
Speaker 3:say make it more overlapped.
Speaker 1:No. I I don't think you can yet. I mean, this is we're I mean, we're really, really You gotta talk to
Speaker 3:somebody that has been making graphics Yeah. For companies like this
Speaker 1:thirty years. It seems so easy, but if you especially
Speaker 3:It takes a lot of Yeah.
Speaker 1:And especially if it's like
Speaker 3:It takes a lot thinking and reasoning. It takes so much deep thinking and reasoning. A model could never do this.
Speaker 1:No. They will be able to. They will be able to.
Speaker 3:But I'm just Michael Burry says, every company listed below has suspicious revenue recognition. The actual chart with all the give and take deals would be unreadable. The future will regard this this a picture of fraud, not a flywheel. True end damage is ridiculous No. No.
Speaker 3:No. Demand. True end demand is ridiculously small. Almost all customers are funded by their dealers. If you can name OpenAI's auditor in one hour, you win some pride.
Speaker 1:What do you what does he mean true? Hang demand is ridiculously small. It's just not true. Like, there are tons of companies that are paying for subscriptions for all sorts of AI products. And I I don't know.
Speaker 1:I I
Speaker 3:He's a D cell with a crazy p doom.
Speaker 1:He he's a D cell with a zero p doom, I guess. I don't know. I
Speaker 3:mean No. I I I do think he he has yes. If you're looking at the amount of investment happening now Yeah. In comparison to the demand Yeah. And you don't believe that the products will get better at
Speaker 1:all Yeah.
Speaker 3:If you don't believe that
Speaker 1:It just flips so much. Like, there was a moment where it was like, wow. Like, demand for this new thing went from 0 to $10,000,000,000 in just a few years. This is remarkable. Yeah.
Speaker 1:And then people were like, let's invest a trillion dollars in that. And it's like, Okay. Well, at that price, it's like it's actually kind of crazy. I don't know. It's a lot to deal with.
Speaker 3:Yeah. But if you think about any industry on Earth
Speaker 1:Yeah. Yeah.
Speaker 3:Do we think every industry on Earth will be using 50 to a 100 times more tokens within five years, ten years? Strap, you don't even have to be that much of a No. Of a of a
Speaker 1:Of a permeable.
Speaker 3:Of a permeable to believe that.
Speaker 1:Yeah.
Speaker 3:In fact, it's like hard hard to work
Speaker 1:with you. Tyler is permeableing. He's never lost sight at any moment. He's always been long. I love it.
Speaker 1:Let's read this from A Capital. But first, let me tell you about Vanta, automate compliance, and security with the leading AI trust management platform. So A Capital says, of course, that your that's your contention. Of course, this is do you know what movie this is from, Jordy? Top quiz, Hotshot.
Speaker 1:Do know what movie that's from? I do two quizzes.
Speaker 3:This one
Speaker 1:Do know what this
Speaker 3:is from? No. No?
Speaker 1:Goodwill Hunting.
Speaker 3:That's
Speaker 1:it. That's the Goodwill Hunting image. You don't know that. Do you know what Pop quiz Hotshot is from? No.
Speaker 1:That's from Speed. It's issued to Keanu Reeves. He gets on the phone with a pop quiz hotshot.
Speaker 3:Worth seeing.
Speaker 1:Speed is definitely worth seeing. It's a crazy it's a it's a great movie. It's just a thriller. They they crash a bus. It's it's a great, great, great movie.
Speaker 1:I'm in.
Speaker 3:I'm in.
Speaker 1:Back to the goodwill hunting image meme that I love this format. It's a very fun. It's a very fun way to illustrate and, like, kind of tell a whole story. And so a capital says, of course, that is your contention. You're a first year AI skeptic.
Speaker 1:You just finished reading Andrew Ross Sorkin's 1929, and now you think you're reliving the roaring twenties with GPUs. You will cling to that until next month when you hear Jim Chano's talk about unsustainable CapEx, and then you will start parroting that the entire AI ecosystem is about to collapse under the weight of its own spending. That will last until someone posts a CoreWeave CDS chart, and you'll repeat that too without realizing that it was just dealers hedging credit portfolios, not some cosmic warning sign. Then you'll probably start lecturing people about Global Crossing because you heard someone say 1999 fiber bubble, and it made you feel informed. Meanwhile, NVIDIA just printed one of the biggest sequential growth quarters the sector has ever seen and guided higher again.
Speaker 1:The workloads are real. The demand is real, and the CapEx is already contractually locked. None of that came from a cash crash narrative paperback or a chain Chano soundbite. But, sure, keep borrowing other people's takes and pretending they're your own. One day, you might act you might look at the actual numbers and realize this is not a bubble.
Speaker 1:It is the early the early stage of the largest infrastructure build out in decades. Love it. Very fun.
Speaker 3:Let me
Speaker 1:tell you about Figma. Think bigger, build faster. Figma helps design development teams build great products together. Oh, Sunday Robots is coming on the show. Very shortly.
Speaker 1:We will get we we maybe we should
Speaker 3:We should play the video now.
Speaker 1:Just play the video now.
Speaker 3:Little teaser. Pull it up. I understand. Very cool. So this is kind of a combination of the r two d two form factor with a humanoid.
Speaker 1:Yes. Look at that. Picking up two wine glasses is insane.
Speaker 3:I love the way it just bounces around.
Speaker 1:It so this is sped up presumably.
Speaker 3:Yes. I think it's at like a I think it's at like a 10 x
Speaker 5:speed.
Speaker 1:Something about the lighting here leaks it looks CGI to me. I know it's not, but it looks CGI ish. I I'm fascinated with so many questions. Says it's in autonomous mode.
Speaker 2:Sunday is
Speaker 1:says it's five
Speaker 3:past motion.
Speaker 1:Sunday has motion. I think that the I think that the design here is fantastic. I I will have to debate it, and you have to tell me what you think. But definitely beating the, like, creepy, uncanny valley, in my opinion, doesn't feel like, oh, that thing is about to pick up a knife, at least to me. I I'm pretty I'm pretty into this design.
Speaker 1:And I think the Internet was as well since it got over a million views and over 3,000 likes. And Sholto Douglas over at Anthropic says, this is insanely insanely impressive. I agree. It
Speaker 3:is. I'm excited to ask Tony how much they had to spend to get to this point.
Speaker 1:That would be interesting.
Speaker 3:Because I think it I I'm assuming it will be quite a bit less than many of the other players that are kind of competing here.
Speaker 1:The little telescoping pole is very cool. The art
Speaker 3:Taylor says, it's the hat nonthreatening lid. I agree.
Speaker 1:Yeah. Just
Speaker 3:throw a cool hat.
Speaker 1:The hat looks yeah. So, Scott, I think the hat does look kind of dumb, but that's, like, kind of okay. I'd rather it look dumb than scary
Speaker 3:or menacing or or or weird, you know, like Think about how scary, like, some of these humanoid robots would be to a one year old.
Speaker 1:Like, walley looks kind of dumb. R two d two looks kind of dumb, but it's still, like, a friendly, you know? You don't want it to be
Speaker 3:Like, the optimist or or figure would be, like, traumatizing to a one year old.
Speaker 1:Yeah. Yeah. For sure. Well, before we move on to our next post, let me tell you about Julius AI, the AI data analyst that works for you. Join millions who use Julius to connect their data and ask questions and get insights in seconds.
Speaker 1:Andrew Reed says every deal is a special situation if you're enthusiastic enough. Very funny. Let's move on. SAM three video tracking is so good yesterday. Collect data, train custom object detector, use tracker to estimate object motion, days.
Speaker 1:Now track anything with a text prompt in seconds. Who put out Sam three? Is that Google as well? They are
Speaker 8:That's Meta.
Speaker 1:That's Meta? Woah. This is Cygnet anything.
Speaker 8:Yeah. Segment
Speaker 1:Anything. Okay. Okay. Okay. Got it.
Speaker 1:Okay. Wait. Why is it on Google Research then? That's funny. Oh, it's how to segment videos with Segment Anything, Sam three, and they just happen to be hosting this in a Google Colab notebook.
Speaker 1:That makes sense because Meta does not have a a Google Colab competitor that I'm aware of. Interesting. Well, that's very
Speaker 3:Very cool.
Speaker 1:You can track the all of our Gong hits potentially for velocity and understanding That's what actually relative to the We should the audio volume.
Speaker 2:You
Speaker 1:could understand how the production team is doing their job to lower the levels for you.
Speaker 3:So that's like how many out. With the No. But if we had a live, like, speed tracker Yes. Like, as your swing
Speaker 1:at speed. Yeah.
Speaker 3:Very cool.
Speaker 1:We could do it. Diet Coke tracker as well. We could automate all of this.
Speaker 3:Sheel has some great coverage. Grock says Elon is more fit than LeBron and would win a fight against Mike Tyson.
Speaker 1:Fact check. True. You're absolutely right.
Speaker 3:I'm gonna ask Grock if this is true. Is this true? Grock, is this true?
Speaker 1:Did somebody do that? I bet somebody did that in the in the in the replies here. Is this true? Yes. Very funny.
Speaker 1:I wonder how much of this is, like, in the preprompt or just in the x dataset. You know? Elon's obviously like, there's just there's just an incredible amount of Elon fans in the X ecosystem still since a lot of people that weren't Elon fans left. But even the Tesla bulls don't glaze to this level usually. Like, I so I don't know where this would come from.
Speaker 1:This must have been in the preprompt or something, But it's a very silly it's a very silly
Speaker 3:Many people are doing this. I mean, you you went in Sora, and you said depict me as a bodybuilder.
Speaker 1:Yes. That's true.
Speaker 3:And then somebody tried to hack it to to give you small legs.
Speaker 1:They did successfully prompt engineer me.
Speaker 3:They got you good.
Speaker 1:They did they did get me. But, yes, I mean, I feel like I feel like at this point, like, we're we're past we're past this level of, like, novelty being relevant in a purchasing decision for an LLM. In fact, it might work against you, especially in light of Gemini three. Very benchmark driven. They put out the model card.
Speaker 1:They there were a bunch of demos that went out. There were some clear examples of next next value coming from the model. Yep. Sort of a buy the book launch and unclear how much this helps the Grock brand to have something like this Yeah. Leak out, but certainly funny.
Speaker 3:Kevin Weil
Speaker 1:Oh, yeah.
Speaker 3:Friend of the show says, today we say hello world from OpenAI for science. We're releasing a paper across 13 examples of GPT five accelerating scientific research across math, physics, biology, and material science. In four of these examples, GPT-five helped find proofs of previously unsolved problems. A lot of a lot of this type of posting has been heavily contentious in the past, but they are continuing to share their work.
Speaker 1:Yeah. And I think that this stuff will eventually be, you know, fully, you know, peer reviewed. And and also, there's just this interesting dynamic where, like, the other labs, they they won't really let you get away with anything. Like, they'll fact check you so fast. But, if this is if this is seriously, impressive, like, you'll probably see some congrats from
Speaker 3:There's also a dynamic where if you were using ChatGPT Mhmm. To accelerate your own research
Speaker 1:Mhmm.
Speaker 3:Are you gonna or or is everybody gonna stand up and yell, hey. I use ChatGPT for this, or are they gonna be like, my research? Yeah. Yeah. Who you know, I'm not sure that a lot of people that are leveraging the tool are gonna be quick to give OpenAI credit, or an AI credit for for something that Yeah.
Speaker 1:Yeah. But you're doing it something in some sort of controlled environment, go after some specific problem. Before we move on, let me tell you about Privy. Privy makes it easy to build on crypto rails, securely spin up white label wallets, sign transactions, and integrate on chain infrastructure all through one simple API. Bern Hobart says, prescient New Yorker cartoon that saw prediction markets coming more than half a century.
Speaker 3:Wow. 06/27/1970.
Speaker 1:Can't see this. It's, the arrivals at, an airport, and there's flights that are arriving from Chicago, Detroit, Philadelphia, Pittsburgh. They depart at 8AM. They arrive at 10:20AM, and then there's odds listed there because, of course, you'll want to bet on when the plane lands. And now you can maybe you're you're close to being able to with the with the prediction markets, on their relentless march to take over the world.
Speaker 1:Also, before we bring in our next guest, we have to talk about group chats in Chateappity. We mentioned this earlier. It's official. They're rolling out globally. There was a successful pilot with early testers.
Speaker 1:Group chats will now be available for all logged in users on Chateappity free, Go Plus and Propens. I didn't know there's a Chateappity Go plan.
Speaker 3:We got a That was the India plan.
Speaker 1:Okay. That's interesting. And then, also, I mean, it just says it's like, of course, it's rolling out to to the everyone because this one doesn't set the GPUs on fire. This is good old fashioned stuff the text in the database, and, reduce churn in your product. So, makes a ton of sense.
Speaker 1:Very, you know we'll have to test this out and see if it's actually, that useful.
Speaker 3:Yeah. I think this is I mean, this this is the kind of thing that, can, help OpenAI build more of a moat outside of a of a brand and just general distribution moat.
Speaker 1:ChatGPT is turning into a social app. Sam pulled it off. Before Zuck could make the Meta AI app good enough to compete compete with ChatGPT, says Yuchen Jin. Axe and Grok could have a real chance to do it too, but it's rough watching DMs and chat keep breaking. And this is from all the way back in February.
Speaker 1:CNBC said Meta plans to release a standalone Meta AI app in an effort to compete with OpenAI's ChatGPT. And Sam Allman said, okay. Fine. Maybe we'll do a social app. And he did.
Speaker 1:He did Sora, and now he's adding social features to ChatGPT core. I am interested to see, like, how we I I send if I do a deep research report, I'll send it around to people in the organization here at TBPN every once in a while. I'm wondering, how much it makes sense to keep the chat running in ChatGPT. So they certainly do get value out of, like, putting together the queries, sharing that, sharing the whole theory. I I I don't know how much I'll be a DAU of this in a month.
Speaker 1:I'll I'll need to test it out. Yeah. But we have our next guest in the roost room waiting room, Tarek from Stoot here in the studio. Welcome Stoot. How are you doing?
Speaker 3:We're saying it correctly. Right?
Speaker 1:We yes. I mean, first off, we have to say we love the brand because we love Day Job. Legends. Thank you for supporting them and
Speaker 3:They did our brand.
Speaker 1:Excellent, excellent taste in branding agencies, of course.
Speaker 3:The best.
Speaker 1:But please introduce yourself and introduce the business as well.
Speaker 7:Hi. I'm Tarek Uluru, and, you got it right. It is astute. Mhmm. It's actually played rugby after college.
Speaker 1:Oh, cool.
Speaker 7:And it, it means prop
Speaker 4:Yeah.
Speaker 7:In South African.
Speaker 3:Okay. After college, that means you were on the on the pro track? Or
Speaker 7:No. I was I was, like, just guy who wanted to drink some beers every now
Speaker 1:and then.
Speaker 3:Okay. Go. Let's give it up for those guys.
Speaker 7:But anyway
Speaker 3:Underrated guys. Yeah.
Speaker 7:You. I'm I'm here to announce our series a led by Andreessen Horowitz for 29 and a half million dollars. Boom. Also participating is active in Incosa Ventures.
Speaker 3:There we go. There we go.
Speaker 1:Good group. We we actually saw a preview of this, of this brand design, when we were hanging out with the day job folks. And what I thought was interesting was the positioning of how AI comes through in the messaging to the customer. So maybe let's start with, like, the problem, the solution, what you're actually building, and then how you message it to, you know, an an audience of investors or what you're building, but then also how you message it to the actual end consumer who might not care that much about the particular technologies that you're using.
Speaker 7:Yeah. And I think there's a lot of slop in AI or thrown around with brands, and that's why we use day jobs similar to you. Mhmm. You know, our customers just for context, what we do is we help with accounts receivable.
Speaker 1:Mhmm.
Speaker 7:So if you don't know what that is, we help collect what you sold. Mhmm. And most of our customers are the kind of like PerkinElmer, Bishop Lifting, organizations you might not know, but they're flyover states kinda where I'm from, which is Indiana. And what we do is we use AI as a platform, and we help customers collect 40% of their overdue invoices in the first six months of using our our tool.
Speaker 2:Mhmm.
Speaker 7:And it's not like a traditional software where, you know, they're promising you more seats, more people. We're live in three days for large Fortune 100 companies, which is crazy. Most people don't believe us. But then they start seeing the results. And, you know, most of these people we work with, you know, they have a nine to five.
Speaker 7:You know, they're not a startup hustler. They're not grinding. They're not, you know, working in New York on Wall Street. They wanna go see their kids game. And so we plug in at five to nine so you could punch out and go to that game.
Speaker 7:And that's really the you know, what we're about here at Stoop.
Speaker 1:Very cool. And then on the messaging side, do you feel like your customers, want to know details about the technologies that you're implementing? Do they care about that?
Speaker 7:Well, you have everybody claiming AI. Like, I'm not Matthew McConaughey.
Speaker 3:You know, I got a
Speaker 7:bad crush on Matthew McConaughey. Yeah. And I I have to compete with an AI technology versus Matthew McConaughey. It's almost impossible. Yeah.
Speaker 7:And so, you know, our branding reflects our customers.
Speaker 1:Sure.
Speaker 7:You know, think Clippy, which Daydoc did a great job with.
Speaker 1:Yeah. Totally.
Speaker 7:Really helping, yeah, helping them be nostalgic. But it's like what software first promised you. It was gonna automate things. Yeah. But instead, forty years later, you know, you need professional services, consultants.
Speaker 7:It just doesn't do the job.
Speaker 1:What's the best, business model for this type of business these days? Consumption based, seat based Success based. Based, percentage based.
Speaker 7:It's almost like you talk to the team at DayJobs to team me up with these questions.
Speaker 3:But Yeah. We do. We actually didn't
Speaker 1:Oh, yeah. We actually didn't talk about this at all with them.
Speaker 7:You all I mean, you all buy software. It's so confusing. I'm not a smart man. And I go on, and I got multiple spreadsheets. You got a various version.
Speaker 7:You got, like, a pricing guide. My head's spinning. We just charge a monthly fee like you would a coworker. Mhmm. The average accounts receivable person in The United States is paid $60,000 without benefits.
Speaker 7:Mhmm. And it takes three to four months to hire them. Mhmm. We could plug in the next day Mhmm. For a fraction of the cost.
Speaker 1:Sure. That makes sense.
Speaker 3:What what how how does it like, how how is the actual, like, product design work? Is this, an agent that gets integrated into communication channels? Like, what what does it actually look like?
Speaker 7:Yeah. I'm glad you asked. So we do audio, so we'll actually place AI phone calls. We'll we'll do emails. We'll, you know, even do SMS and WhatsApp in different areas of the world.
Speaker 7:Mhmm. So, you know, the way I always tell customers is we have two forms of communication, which is, like, outbound, hey. You need to pay me, or inbound. If you're a bigger customer and somebody calls you and you work in the finance team and you're like, hey. I got a question about invoice 1234.
Speaker 1:Mhmm.
Speaker 7:It's pretty hard. You have to pull up multiple systems. You have to answer questions. AI is great to live behind that IVR tree and just answer it immediately. On the flip side, if we reach out to a customer and they might have, like, their generic invoice template that goes out, they'll have a question.
Speaker 7:Hey. Where do I send the check to? AI can instantly reply without a human being and even sit in the flow of funds where we'll send them a payment hook.
Speaker 1:Yeah. I wanna I wanna dive deeper into your what I think is a hot take about basically sticking with a seat based pricing model. Alex Karp was on the show, and he was saying, like, in the future, all companies will be paid on the value they deliver. And I'm just wondering what the difference is if you wind up going to a company that has a thousand times as many invoices you collect a a thousand times as many payments, you deliver a thousand times as much value. Should you not get paid at least a little bit more?
Speaker 7:Well, I mean, Alex is an amazing entrepreneur, and they're an established brand. Hopefully, someday, if we keep winning each day and executing, we'll be where Palantir is. K. You know, right now, our customers, when we talk to them Mhmm. You have companies that have been around since '97 saying they're AI now.
Speaker 2:Yeah.
Speaker 7:And so, you know, we have to differentiate ourselves. Mhmm. And one of the ways we differentiate ourselves is with something very simple, very easy. It's like if you went to Chipotle for the first time
Speaker 1:Yeah.
Speaker 7:You line up, you get a burrito, you're like, wow. This is amazing back in the day. Not anymore. I know. So we we wanna make things as
Speaker 1:simple as
Speaker 3:our customers. Most brutal falloff of all time.
Speaker 7:I mean, look at me. It's brutal. Right? I love Chipotle.
Speaker 3:Oh, yeah.
Speaker 7:But the the the tough part is a lot of the stuff our customers are looking at isn't simple. Yeah. And they're looking at evaluating multiple days of presentations. They're getting grilled by salespeople.
Speaker 1:Yeah.
Speaker 7:You know, we wanna get in, demonstrate value, and see a really quick ROI with these customers, and that's what we're helping them achieve. So great example is one of our customers, Bishop Lifting, reduced their invoices by 35% past due Mhmm. And have been able to free up that cash flow for other things. Mhmm. It could be, like, the holidays around, bonus time.
Speaker 7:You know? And they have these people across America in locations, and AR is not or receivables isn't their first job. And so being able to offload that and get them a little more money in their pocket is something we try to achieve for our customers.
Speaker 1:Yeah. That makes it so nice.
Speaker 3:That's amazing. Well, I gotta say the chat absolutely loves you.
Speaker 1:They love you.
Speaker 3:They're This guy is a champion. Midwest in Manhattan. People are happy. Powerful. Yeah.
Speaker 3:He's even drinking Yerba Mate. What a freaking legend. True king. I'm liking the sound of this stew. Stew.
Speaker 1:We we let's give it
Speaker 3:up. No. I love it. I mean Collections. I I love an idea that that when you hear it, it's just totally obvious.
Speaker 3:Yeah. It's like applying, you know, the same there's like the capital war happening in, customer experience right now. They're using a lot of the same technology. You're applying it in a very clear way Mhmm. In a different part of the org and I'm bullish.
Speaker 3:Yeah. Thank you so much for joining.
Speaker 7:Really appreciate you having us on and Have fun. On the show.
Speaker 3:Congratulations. We'll see you back for the beef.
Speaker 1:Yeah. We'll talk to you soon. Cheers. Good day. Let me tell you about adquick.com.
Speaker 1:Out of home Matt, out of home advertising made easy and measurable. If you're launching a new company, growing, get on adquick.com. Get some billboards. Our next guest is in the restream waiting room. Let's bring them into the TVP and L for them.
Speaker 3:There he is.
Speaker 1:Nikita from Flexion. Is Uh-huh. Also a day job? Is this a
Speaker 3:day No. No. No. No. We got this is Tony.
Speaker 1:Oh, Tony. Hey. Sorry.
Speaker 3:We got we got mixed around. Tony, so great to have you on the show. I'm sure your twenty four hours last twenty four hours have been absolutely crazy. We played your demo on the show earlier today, and we're absolutely blown away. It's it's really tremendous progress, and we're we're excited to meet you.
Speaker 3:So before we talk, Sunday, would love a intro on yourself, background, and all that good stuff.
Speaker 6:Yeah. Yeah. Absolutely. So excited to be here. So before that, I was actually a PhD student at Stanford working on robotics.
Speaker 6:So some of the works are like aloha. You saw, like, the two robot arms clamped to a table.
Speaker 4:Yeah.
Speaker 6:And it's not just about the hardware, but how it learns. How can we learn from human demonstrations? How can we learn through reinforcement learning? And all these things. And I think last early twenty twenty four is when I have the realization that, like, you know, pumping out more papers and doing more research may not be the most direct way to push robotics forward, but starting a company and working on real product is.
Speaker 6:So this is why I cofounded this company, Sunday, with Chung, who is also a PhD student at Stanford and which leads to memo, act one, and all of these, like, new advances.
Speaker 3:Incredible. What what has it taken to get to this demo that you released yesterday? Because our our I have no idea how much money you've raised up until this point, but it feels like you guys have accomplished a ton in a pretty resource constrained way, at least compared to companies that you're competing with in the sort of, like, helpful humanoid in the home category.
Speaker 6:Yeah. Absolutely. So we're we're functioning in a very efficient way. And I think as a early stage company, we think about it as a blessing that forces us to innovate and finding out, like, these solutions that are orders of magnitude more efficient than, like, 20 efficient and 30% efficient. And I think a big part of it is also about, like, the culture and the team and all the people we have that are, like, really experts and really believes in what we're doing.
Speaker 6:Yeah.
Speaker 1:John? Yeah. I I'd love to know some of the trade offs
Speaker 3:that you're mentioning. You you forgot to mention you worked at DeepMind, Tesla, and and Google. So, sort of a nontraditional background into Robotic. Robotics bit
Speaker 1:a places. Yeah. What are the key trade offs? I mean, there there's a lot of focus right now on teleoperation. Is it is it something just a step in the path towards full autonomy?
Speaker 1:There are obviously some folks that are jumping straight to straight straight to full autonomy, and they say, oh, we never use teleoperation at all. Other folks who say teleoperation is a really useful tool to pull forward some of the capability. Where do you stand on the issue?
Speaker 6:Yeah. So I think teleoperation is a really powerful research tool
Speaker 4:Mhmm.
Speaker 6:But it's not necessarily the best tool to get to a product.
Speaker 1:Mhmm.
Speaker 6:Because if you think about robotics and, you know, put that right next to autonomous driving. Right? Tesla has millions of cars collecting data for them every single
Speaker 4:day. Mhmm.
Speaker 6:And it still took almost a decade to kinda see a light at the end of the tunnel that things are starting to work very well. In robotics, if the only thing we can rely on is teleoperation
Speaker 1:Mhmm.
Speaker 6:To gather the amount of training data, it would take, like, decades for sure, because robotics is a harder problem than self driving.
Speaker 1:Yeah.
Speaker 6:So the way we think about it is that how can we use human data to train the model? We have, like, 8,000,000,000 humans in the world. Like, if you're gonna use, like, 1% of that, that's already huge.
Speaker 3:Yeah.
Speaker 6:So what we designed instead is I actually have it here is called skill transfer bluff. Okay. Skill capture glove. Yeah. That is one to one to Memo's hat.
Speaker 1:Oh, interesting.
Speaker 6:And, yes, the idea here is that if you can wear the glove and do a task, Memo can also do it. Okay. And that essentially decouples this whole, like, you need a robot to be deployed in the wild before you can gather the data to train the AI. We can train AI just by having people wear our glove and cloud data.
Speaker 1:Yes. But, I mean, just to go back to the question of, like, capital intensity, 1% of 8,000,000,000 people, that's eight 80,000,000 gloves. If the glove costs even $10, we're back in you know, you need a billion dollars to get your data set or something like that.
Speaker 3:You don't you don't think Tony can
Speaker 1:can raise your billing? No. I'm not saying you can't do it. I'm just saying, like, is there a smoother path here? How many gloves have you shipped?
Speaker 1:Is is is there a scale thing? And then also, I'd be interested to know about, like, transfer learning. Are you having luck with simulation? Are you having luck with, there's a lot of video, just content out there of people doing tasks. Is there any signal that you can pull from just a YouTube video of someone doing the dishes?
Speaker 1:Or do you need to simulate something in Unreal Engine or use a world model? Like, what are the other tools in the tool chest?
Speaker 6:Yeah. I think robotics is at a point that there are so many of these ideas that we haven't converged to this, like, one single thing, which is, like, pretraining and post training for LMs. Mhmm. And the way we think about it is that out of all these methods, some will be better than others. Mhmm.
Speaker 6:And as a startup, we should focus on that one thing that we believe in and build the best system and stack around it. And what we chose was using human data, like using gloves to gather data.
Speaker 1:Yeah. Yeah.
Speaker 6:And, actually, for all the models that we saw, we, of course, pre trained on, like, Internet scale data, but all the specific behaviors are learned only from the gloves that we make. We don't do tally operation. We don't do simulation, and we don't have role models.
Speaker 1:Woah. Okay. Very opinionated. Then how do you see the the data capture from the glove scaling? Like, do you think that there will be 80,000,000 people in five years using this to create more training data, or do you think it's a little bit more attractable of a problem where, at a certain point okay.
Speaker 1:Yeah. It's been a big operation, but it's more like 10,000 people that you're employing or something like that.
Speaker 6:Yes. So I think this question is more about, like, for us, how can these data generate value so that we can keep this loop going on?
Speaker 4:Mhmm.
Speaker 6:Right? And it is kinda similar to the whole large language model space that we need to spend a lot of money into compute. Yeah. But the model itself is generating, like, tremendous amount of value.
Speaker 1:Mhmm.
Speaker 6:And for us, we don't need to solve robotics to ship a product.
Speaker 3:Mhmm.
Speaker 6:That's the lucky part.
Speaker 1:Sure.
Speaker 6:And in the homes, there are lots of, like it's one of the few places you can do relatively simple tasks
Speaker 1:Yeah.
Speaker 6:But give people a huge amount of value, both emotionally and functionally. Yeah.
Speaker 3:And and it's much more low stakes tasks than self driving. Right? So self driving Yep. Very hard to get You said that home robotics is a harder problem earlier, if I heard that right.
Speaker 1:But at
Speaker 3:least it's yeah, but it but at least it's lower stakes in that if you have a an error, if you drop a dish, it it's annoying and you wanna avoid that, but there's not, like nobody's gonna, like, die.
Speaker 1:Yeah. Yes.
Speaker 6:It's like the north star of the prob like, of the company is to solve robotics. Yeah. But we don't need to solve robotics before we ship a product.
Speaker 3:Yeah. So, yeah, talk talk about timelines.
Speaker 6:Yeah. So we've been around for a year and a half, and our next milestone is the beta program that will run late twenty twenty six. That is when we'll put Memo and, like, tons of them into people's home and actually see how people interact with the robot and what do people want from the robot.
Speaker 1:Mhmm.
Speaker 6:And the general availability of Memo will be either 2027 or 2028 depending on the progress we've made through the whole beta program.
Speaker 3:Yeah. Talk about form factor. Why not why not give it legs? I'm sure you have a a a reason for that, and I and I'm curious because I think I think people's immediate question is, okay. I can see how a wheeled system, it makes a lot more sense in a lot of ways, but what happens if I have stairs?
Speaker 6:Yeah. Absolutely. So the way we designed this robot is to put safety at a really high priority. And the way we define safety is we call it passively safe. That if the robot arm and torso is fully stretched out, and at that point, you cut the power of the robot, can it stay stable or not?
Speaker 3:Interesting. The
Speaker 6:real robot is actually, like, one of the few ways they can
Speaker 1:actually over and crush your dog or or even your foot, basically. Yeah.
Speaker 3:Or rack or just, like, damage the floor.
Speaker 8:All sorts
Speaker 1:of stuff. That makes a ton of sense. And then also, imagine that there's a you can just have more battery power or maybe dock easily, and there just aren't that many tasks that required. I feel like every demo is the I mean, the wine glass demo is remarkable. Holding two wine glasses is hard as a human, let alone as a robot with kind of odd fingers.
Speaker 1:But just the tidying up use case is potentially underrated because that feels like that feels right around the corner even if the like, dealing with all the racks and and different spoons and knives and wine glasses. Doing the full dishwasher feels a little bit harder, but there's a willingness to pay, at least for me, just to go around the house and pick up the ball that's needs to be in the toy basket and pick up the shirt that's on the floor. Like, that's that's valuable. That is actually value Yeah. If you can get the price right.
Speaker 3:What was the, what was the, like, key design inspirations? What matters to you with design? Somebody in the chat was asking if you were influenced by a home star runner or what
Speaker 1:Oh, yeah. I did see of home
Speaker 3:star runner.
Speaker 1:That's hilarious.
Speaker 6:Yeah. So the weird thing about design is we kinda think backwards of what do we want the world to be like if the robots are ubiquitous? If you need to see it, like, every single day, what should it look like? And we lean quite heavily towards building a robot that is friendly but also functional. Mhmm.
Speaker 6:And these two things, there's actually a small overlap in between them. So when we design the robots, one, I think, detail that we decide to do is we do not put camera into the robot's eyes. The camera is actually right underneath its head.
Speaker 3:Yeah. I saw that.
Speaker 6:Yes. So the reasoning is that, like, you're going to make eye contact with the robot. You're going to, like, look at his face. But if you look at someone's face and his eyes, you see, like, a camera watching you, it's a little bit creepy.
Speaker 1:Oh, interesting.
Speaker 6:Intentionally avoided that. And yeah.
Speaker 1:That's very interesting. Yeah. The yeah. The design, I we were talking about earlier. It feels like it it really just it avoided like the uncanny valley, the creepiness.
Speaker 1:Like, there's a lot of risk factors when you're designing humanoid robotics right now. We've seen all sorts of them. Or they can look cool, sci fi, but maybe weirder in certain context.
Speaker 3:Think Yeah.
Speaker 1:This one came across very
Speaker 3:Well, super, super excited for you. Thanks for coming on and breaking it down.
Speaker 1:This is really fun.
Speaker 3:Thank you so much. If if we'd love to be in the in the demo program
Speaker 1:Yeah. We got flat floors here.
Speaker 3:We got flat floors. We have huge messes. And
Speaker 1:we have a team of people that we will make wear these gloves all day long.
Speaker 3:And we will take care of we will take care of Memo. We will. Because he's because he's cute.
Speaker 1:Yes. We
Speaker 3:love And we wanna see him win. So
Speaker 1:Thank you so much.
Speaker 3:Congrats on all the progress.
Speaker 1:Congratulations.
Speaker 6:Thank you, guys.
Speaker 1:We'll talk soon. Goodbye. You know what we gotta do? We gotta get Memo, a watch on getbezel.com.
Speaker 3:Let's shop over with
Speaker 1:6,000. Ice it out. It's luxury wise. Iced out. Fully authenticated RM.
Speaker 1:By Bezels team of experts.
Speaker 3:He needs an RM. He definitely some And some chrome hearts.
Speaker 1:Definitely needs a Richard Mille. Why not? Why not?
Speaker 3:Next up, we got Nikita from Flexion. Excited for this one.
Speaker 1:Thank you so much for taking
Speaker 3:What's going on?
Speaker 1:Time to talk
Speaker 3:to the show.
Speaker 1:Today. Thanks for waiting. Good to meet you. How are you doing?
Speaker 2:Hi. Really excited to be here.
Speaker 5:Thanks so much.
Speaker 2:I'm Nikita. I'm the CEO and cofounder of Flexion. Cool. Where we're building the intelligence layer or the brain that powers all kinds of robots from humanoids to mobile manipulators.
Speaker 1:Yeah. I mean, fantastic. We were just talking about humanoid robotics. How do you see the the the market playing out?
Speaker 3:So so Yeah. So you hopefully, you caught at least the end of our of our conversation with with Sunday Robotics. But something that I was thinking about, like a real challenge, is when Sunday gets good enough at picking up op you know, manipulating objects. What happens if Sunday walks up to our table here after the show? We typically have lunch and Sunday needs to figure out what's trash and what's what like, what should be taken and thrown away and what's actually should just stay there.
Speaker 3:Right? Because that's actually like somewhat of a like, it requires some memory. It's like, okay, this is an item that that is it needs to be able to identify objects, figure out what it what is like what is something that is worthy of just throwing away? What is something that, like, I don't want thrown thrown away? And if it gets thrown away, I'll be frustrated.
Speaker 3:So I feel like there's, like, a lot deeper, more levels of complexity to a lot of these robotic tasks than than just object manipulation and kind of understanding understanding the general environment and and really having intelligence around the environments that it operates. And I feel like that might be something that you're solving, but tell me if I'm wrong or correct.
Speaker 2:Absolutely. Let me just say that Sunday is amazing. I think their videos are really, really impressive, probably the most impressive demo that seen so far. So just start with let's just start with that. I don't know if you're doing it on purpose, but it's a great reference to the video we released this morning where we have a robot walking around and picking up trash and bring it to to a garbage can.
Speaker 1:Yeah.
Speaker 2:And and the way we're doing that is actually splitting the problem into two parts. The first one has nothing to do with robotics. It's about concept and understanding. And the for that part, we don't really need to train a a specific model ourselves because that knowledge is already contained in in large language models. Think of it as GPT five for all of these models.
Speaker 2:If you take a picture of that table in front of you and you ask GPT what is garbage, what is not, those models are already really good at understanding that. And once you have that, then the next part is actually the object manipulation, which we're also solving in a slightly different way compared to Sunday. We we bet that the vast majority of data needed to train those models will come from simulation. Great. You know, can have a look at the video.
Speaker 3:Yeah. So so say more or maybe even just narrate the video.
Speaker 2:Sure. So let me just quickly come back. We're better on on simulations. We train robots using reinforcement learning, not to humiliate humans, but to solve specific tasks and just through trial and error. So we have robots trying millions and millions of times.
Speaker 2:I think it's tens or hundreds of years of simulated data, And then they come up with very specific ways on how to walk across complex terrains, but also use their whole body to manipulate objects.
Speaker 1:Mhmm. But
Speaker 2:And so in this specific video
Speaker 1:problem yeah. Yeah. Isn't there a little bit of a problem there where, to perfectly simulate that forest, path, requires incredible, you know, just like CGI just to I mean, you need, like, Unreal Engine crank to max on every physics calculation because, yes, you can model it like a video game. Like, it's all just one smooth surface, but it's not actually that. In reality, there's tons of different blades of grass.
Speaker 1:There might be slightly more friction over here on this blade of grass versus that one. You have to simulate all of that to actually recreate the real world. Is there not a gap?
Speaker 2:Yeah. Absolutely. That's a great point. Usually, we call it the the gap. Yes.
Speaker 2:And once you train simulation, the whole challenge is to cross that gap. Mhmm. And for example, here in this video, everything is trained in simulation.
Speaker 4:And Mhmm.
Speaker 2:We're actually not even thinking about forests or mountains when we're training the robot. Mhmm. So you don't need to simulate every single piece of grass or every single rock as long as you train on general enough scenarios that somewhat encompass what is happening here Mhmm. Then you can deploy the robot. And and the other thing is that we're not training our policies directly from RGB camera inputs.
Speaker 2:Otherwise, you would actually need to simulate exactly how a forest looks. Mhmm. So we're doing some processing on top, once again, using some other models, but we're actually trained on Internet scale data. Okay. A good example, I think, is if you wanna train a robot to open a door Yeah.
Speaker 2:Either you have to simulate every single possible door that exists in the world with all the textures, the lighting, etcetera. Sure. Or you can use a model, like segment anything. And then and then you paint the door in red and the handle in in, let's say, green. Then all doors kind of start to look the same.
Speaker 1:Look the same. Interesting. And then you're bay you're basically training the motion against the segment anything version of the door of the world.
Speaker 2:Yeah. Something like that.
Speaker 1:Okay. Yeah. What what technologies are are you most excited about across, these generative world models, these Gaussian splats, just Unreal Engine getting better, like traditional three d workflows, Houdini and Cinema four d. Are are of those tools, which ones will be most useful to you, in the future? Or or is everything kind of bespoke in its own world for you?
Speaker 2:No. All of this is super important. It's all about the time frame. Mhmm. So today, we're using physics based simulators Mhmm.
Speaker 2:Just like Unreal Engine. Mhmm. And that's actually, my take is this is enough for for way more than what most people think. We can go a long way with with just those simulators.
Speaker 1:And and
Speaker 2:and But the logic on that
Speaker 1:And and explain explain that. Is it is it that, if you have a physics simulation that's running fine and let let's say, Unreal Engine, you might use something else, but Unreal Engine is do you think we're on a scaling curve where if you had a million GPUs running a million instances of of, Unreal Engine generating simulated data, that that would actually result in better progress on the on the robotics side, on the actual decision making and planning side?
Speaker 2:Yeah. Exactly. That makes with one more thing, which is generative models that can create assets for simulation.
Speaker 1:Okay.
Speaker 2:Gotcha. That you don't need to have humans coming up with a million different versions of all the things that robot needs to interact
Speaker 1:with. Yeah. So previously, that was, programmatically like like, to try and get to something with a varied world like that where there's, you know, a little hill over here and a and a a rock out of place that the robot might trip over, You would have to do all that programmatically, maybe through some node based workflow in Houdini or just kind of, or or just, just inject just randomness, just random number generators, and then rotate this rock over here, change the geometry, etcetera, etcetera. But you're saying that generative AI can can create even more variation. Is that the idea?
Speaker 2:Yeah. Exactly. Mhmm. You actually have two ways to add more variation easily. One is something like Gaussian splats where you go outside, you collect real world data
Speaker 5:Sure.
Speaker 2:And suddenly you have a lot of assets. And the second version is you ask Journey to AI to to do it for you.
Speaker 1:Yeah. Yeah. That makes sense. That's cool. Any news?
Speaker 1:Yeah. What you got? Give us Yeah.
Speaker 2:So we announced this morning that for the first time, they were raised 50,000,000 just a few months ago. Congratulations.
Speaker 3:Who participated?
Speaker 2:Thanks. Bunch of masters, DST Global
Speaker 1:Okay.
Speaker 2:NVIDIA Adventures, participated, ProSys, First Moonfire.
Speaker 3:Awesome. And then where where are you building where are you building the company? You're in Europe, or have you moved over to the West Coast?
Speaker 2:So right now, we're all in Zurich and Switzerland. This is why you see the robot walking in our nice Alps.
Speaker 1:Oh, yeah.
Speaker 2:But, actually, right now, I'm I'm in San Francisco right now for a few days, and and I I'm here to to find the right team to start a second office here.
Speaker 3:Oh, nice.
Speaker 1:That's great. Well, good luck.
Speaker 3:I would if I were you, I wouldn't it'd be tough to leave.
Speaker 1:This looks pretty nice. This is, like, completely
Speaker 3:second favorite country in the world for me after America. So, hopefully, next time next time I'm in Switzerland, I'll I'll definitely I would love to stop by the office and and and meet.
Speaker 2:Yes, please.
Speaker 3:But congratulations on the milestone. Super exciting. And if you ever have hot takes on robotics, feel free to let us know.
Speaker 1:Hop on.
Speaker 2:Thanks. Thanks so much.
Speaker 3:Awesome. Thank you so much. Cheers.
Speaker 1:Oh, what are we to go to the Alps, book a wander with inspiring views, hotel, great amenities, dreamy beds, top tier cleaning, and twenty four seven concierge service.
Speaker 3:Transition. It's a vacation home.
Speaker 1:John. But better. Did you see this that we don't understand how ice is why ice is slippery? I don't know if this is, fully confirmed at this point, but, Massimo Rainmaker 1973 shares new research. So I misspelled that.
Speaker 3:New research shows
Speaker 1:ice is slippery because of electrical charges, not pressure and friction. For almost two hundred years, the prevailing explanation for ice's slipperiness was that friction or pressure from a skate, boot, or tire melted a microscopic film of water on the surface creating a lubricating layer. A new study from Sarland University has overturned that long standing idea. Boris Power here says, who's the head of applied research at OpenAI, says, wow. This is one of the bigger firm beliefs I held that got overturned.
Speaker 1:Like, I I really I like It's like, this is the one thing I knew was
Speaker 3:true. Beliefs.
Speaker 1:I knew that the world is round. The sun rises in the East and it sets in the West, and I know that the reason ice is slippery
Speaker 3:Because of microscopic layer.
Speaker 1:Layer of of of water. But it is a good point. If it's actually about electrical charges, then it begs the question, which he's asking, I wonder how long until we get nonslip shoes for ice. So you could have a shoe that has a battery in there, create some sort of electrical field that cancels out the electrical field or something. Maybe that does something.
Speaker 1:I don't know.
Speaker 3:Ice is is brilliantly humbling, you know. You think you're walking, you're confident, you know. You're like, I I I'm handling this ice and then you just and then and then suddenly it feels like you got a banana under your foot.
Speaker 1:One of my one of my friends was worried about getting canceled because the first tweet he ever posted, like, decades ago when when when he first got on Twitter was, I just slept on some iced. Hashtag f ice. Like, f u c k I I c e. Oh. And he was like, am I being like rude or incooched?
Speaker 1:Should I delete that post?
Speaker 3:Jackson Dahl pulled out 12 lessons from our interview on Dialectic, his podcast. I I don't think we'd ever written down a lot of these ideas. Think we've certainly talked.
Speaker 1:Were talking about the need for principles and the need for some sort of,
Speaker 3:you know, culture. More like operating principles within the company. Yeah. Some of these are relevant.
Speaker 1:Irrelevant. But, yeah, this is more about the style of content.
Speaker 3:Good summary. You can't copy compounding.
Speaker 1:If you wanna know more about us and how we think about the show behind the scenes, you can go listen to Jackson Doll's latest episode with
Speaker 3:That was really fun.
Speaker 1:Other than yours truly and George.
Speaker 3:On our own very set.
Speaker 1:On our own very set. Yeah. We filmed it here. The Dialectic Pod.
Speaker 3:Aiden says, just so we're clear, anti gravity is a Windsurf wrapper. Windsurf is a Versus Code wrapper. Versus Code is an Electron wrapper. Electron is a Chromium wrapper. Chromium is a c plus plus wrapper.
Speaker 3:C plus plus is a c wrapper. C is an assembly wrapper. Assembly is a machine code wrapper. Machine code is a binary wrapper. Binary is a physics wrapper.
Speaker 3:Physics is a wrapper. Math is a logic wrapper. Logic is a philosophy wrapper. Philosophy is a humans wrapper. Humans are a carbon wrapper.
Speaker 3:Carbon is a star forged matter wrapper. Stars are a gravity wrapper. Gravity is definitely not an anti gravity wrapper.
Speaker 1:19 k.
Speaker 3:Like Dang
Speaker 1:people enjoy it. This is very funny.
Speaker 3:Everything's wrapped up. This is funny. Robinhood had a post trade the forecast, weather market predictions, and Augustus says, This is how this is how Augustus can can one way that he can monetize is just, you know Yes. Getting a hedge fund, betting on the weather outcome.
Speaker 1:It's not gonna rain. I'd like to say it's not gonna rain with size. I was like, is it where where is Augustus? Is he in town? Is he betting on?
Speaker 1:He would be I don't know. Would that be in, would that be, investigative? Would that be insider trading? We'll have to figure it out. Satya Nadella had a banger.
Speaker 1:Barely AI says, never forget Satya Nadella in 1993 as a Microsoft technical marketing manager showing how Excel works. We can play this clip. This is funny.
Speaker 10:As you can see, the most important architectural requirement for this piece is to be able to integrate data which exists on a host or a mainframe right now into Excel. Excel being our front end tool and the AS 400, in our case, being the data repository. So what I'm gonna do now is exit out of this environment and show you how we can better integrate this data into Excel. And I'll go ahead and
Speaker 1:Call in questions now.
Speaker 3:No way.
Speaker 1:He's doing a livestream.
Speaker 10:K.
Speaker 1:Basically. I mean, it's on TV.
Speaker 10:But this point, what it did was it talked to the MS Query went ahead and talked to the DRDA driver and got went and connected to the mainframe, brought down the relevant data, and populated my sheet here with the relevant data. Going to using Windows NT SNA server connecting to the database
Speaker 1:It sounds like
Speaker 10:the data back.
Speaker 1:Agentic AI. Sounds like a a workflow that's being
Speaker 3:for numbers.
Speaker 1:This guy's been automating workflows since day one. Now he says he has less hair, but the same love for Excel. And he's, posted a photo making sheet happen since 1985. He's looking great. He's having he's on top of the world.
Speaker 1:Suno raised more money. We had the founder on the show, like, just a week or two before the, the rounds, so we didn't have him back on. But, congrats to everyone over there. And there's the the the the regulatory deals are getting worked out though.
Speaker 3:Yeah. So a company called Clay is the first music AI service to reach a deal with all three major record labels, Universal, Sony, and Warner Music. Mhmm. Clay plans to announce its agreements in the coming days. I guess they kind of front running him there.
Speaker 3:Clay is building a product that will offer the features of a streaming service like Spotify amplified by AI technology that will let users remake songs in different styles. I knew a founder that was working on this exact service and, ultimately thought that it would be impossible to get all these deals done. So I'm glad that somebody, persisted and, built this product because I think it's gonna be pretty fun to play around with. Clay apparently has licensed the rights to thousands of hit songs so that it can train its LLM. The company has positioned itself as a friend of the industry, kind of letting a fox into the hen house, maybe.
Speaker 3:Offering assurances that the artists and labels will have some control over how their work is used. Clay is led by music producer Ari Ade Mhmm. And also employs former executives from Sony Music and Google's DeepMind. And anyways, so this I'm excited to play around with the product when it comes out.
Speaker 1:We should close out with 8sleep.com. Exceptional sleep without exception, fall asleep faster, sleep deeper, and wake up energized. And I want you to tell me which Ferrari do you like because we finally have the Ferrari bench results in a Ferrari in Minecraft. This one's from GPT 5.1 pro with the same prompt as if we scroll down, we can see what Gemini three pro did. Which one do you think is better?
Speaker 1:Which do you think is is more Ferrari?
Speaker 3:I mean, the Minecraft Ferrari Gemini three actually looks something like
Speaker 1:a I think I like the Gemini three one two.
Speaker 3:GPD five one pro doesn't look anything like
Speaker 1:It got red.
Speaker 5:It's
Speaker 1:missing just like the there with the Gemini three pro, you can see, what I like about it is you see that little yellow dot on the hood. It's clearly like, that's where the Ferrari logo goes on an actual Ferrari, and it knew to put that in there just a little bit more. The wing is a little more articulated and opinionated, it feels like. It feels like it's more disconnected from the overall structure, but still an interesting challenge. And, I'm very excited to see where this benchmark goes because it is it's just so visual.
Speaker 1:It's so tangible. Like, okay. I understand, what this should look like, and, it it really illustrates all the hallucinations. Anything else you wanna close out with?
Speaker 3:I will close out by saying it's pouring rain so hard that I'm hearing it through Oh, wow. Through our earbuds. Okay. So if you are in LA
Speaker 1:Yeah. Yeah. Be safe.
Speaker 3:Be safe out there. Wherever you are in the world, we love you. Thank you for tuning in with us today. We will be back tomorrow for a Friday show. We got Sagar coming on.
Speaker 1:It's gonna be a
Speaker 3:fun one. I'm sure he I'm sure he'll have fully one eighty on AI.
Speaker 1:Yeah. Our biggest AI bull. We got semi analysis and then breaking points.
Speaker 5:We're trying
Speaker 3:to bring you diverse perspective.
Speaker 1:We really are.
Speaker 3:We really do care about that. Don't want this to be an echo chamber. We obviously have strong views ourselves. Yeah. Many topics, but, we're here to bring, to to have real conversation.
Speaker 1:So thanks everyone for tuning in.
Speaker 3:Thanks for tuning
Speaker 1:in. Thanks for dealing with the chaos in the chat. Well
Speaker 3:Yeah. Very chaotic day in the chat. That was our first time being like raided.
Speaker 1:Yeah. We got we got raided a
Speaker 3:little It was really funny because they were they made they made like seemingly like 20 fake accounts.
Speaker 1:We had 20 accounts.
Speaker 3:And then, they they were really angry at Ben Yeah. For some reason. And then and then they also were really angry at Merkor. They kept dunking
Speaker 1:on Merkor. Why why are they mad at Merkor? It was very distracting. I I had to wind up turning off chat. But, thank you to everyone who stayed the course, stuck with us for the show and made it through.
Speaker 1:While the chat was getting wild. But we appreciate you all, and we will see you tomorrow.
Speaker 3:Love you. Goodbye. Cheers.