IoT Coffee Talk: Episode 296 - "Predicting Predictive Maintenance" (It's about time!)

Well, that's what you should have done Well, that's what you should have done last night, Rob. You should have saved last night, Rob. You should have saved last night, Rob. You should have saved the night away, right? the night away, right? the night away, right? >> That's what you did. That's why. >> That's what you did. That's why. >> That's what you did. That's why. >> Yes. Can you see? Huh? >> Exactly. Such good stuff. >> Exactly. Such good stuff. >> You know, dude, I'm telling you that was >> You know, dude, I'm telling you that was >> You know, dude, I'm telling you that was it's one of the greatest songs ever it's one of the greatest songs ever it's one of the greatest songs ever written, you know. Yeah. written, you know. Yeah. written, you know. Yeah. >> Pretty good stuff. >> Pretty good stuff. >> Pretty good stuff. >> I'm pretty sure Rob was busy assembling >> I'm pretty sure Rob was busy assembling >> I'm pretty sure Rob was busy assembling his new AI hat on top of his mountains his new AI hat on top of his mountains his new AI hat on top of his mountains of Raspberry Pi. of Raspberry Pi. of Raspberry Pi. >> Yeah, >> Yeah, >> Yeah, >> that song was definitely from a better >> that song was definitely from a better >> that song was definitely from a better time. time. time. >> Yes. >> Yes. >> Yes. a great time. Whereas, a great time. Whereas, a great time. Whereas, >> hey, Yen, what's up, dude? >> hey, Yen, what's up, dude? >> hey, Yen, what's up, dude? >> Hey, guys. >> Hey, guys. >> Hey, guys. >> Happy New Year. >> Happy New Year. >> Happy New Year. >> Good, man. >> Good, man. >> Good, man. >> You're so high. Coming back from uh CES. >> You're so high. Coming back from uh CES. >> You're so high. Coming back from uh CES. >> Uh yeah, but you look like you just got >> Uh yeah, but you look like you just got >> Uh yeah, but you look like you just got back from Australia or something. U back from Australia or something. U back from Australia or something. U >> No, I think my camera is uh is off. I >> No, I think my camera is uh is off. I >> No, I think my camera is uh is off. I think it's u think it's u think it's u Maybe it's not off. Maybe it's giving Maybe it's not off. Maybe it's giving Maybe it's not off. Maybe it's giving you a tan on, you know, you a tan on, you know, you a tan on, you know, >> maybe radiation. Yeah.

>> maybe radiation. Yeah. >> maybe radiation. Yeah. >> Yeah. You have you have to turn on this >> Yeah. You have you have to turn on this >> Yeah. You have you have to turn on this AI filter your camera. AI filter your camera. AI filter your camera. >> Yeah. >> Yeah. >> Yeah. >> Yeah. So, hey Yen, welcome and welcome >> Yeah. So, hey Yen, welcome and welcome >> Yeah. So, hey Yen, welcome and welcome everyone to IoT Coffee Talk. Um, everyone to IoT Coffee Talk. Um, everyone to IoT Coffee Talk. Um, >> we talk just >> we talk just >> we talk just endlessly about all kinds of stuff and endlessly about all kinds of stuff and endlessly about all kinds of stuff and we have no idea what we're going to we have no idea what we're going to we have no idea what we're going to address today. But address today. But address today. But >> wear a hat. Don't take us seriously. >> wear a hat. Don't take us seriously. >> wear a hat. Don't take us seriously. >> I'll wear a hat today. >> I'll wear a hat today. >> I'll wear a hat today. >> Yeah, I have to. Yeah. >> Yeah, I have to. Yeah. >> Yeah, I have to. Yeah. >> Yeah. Look at this hat. >> Yeah. Look at this hat. >> Yeah. Look at this hat. >> It's not just any hat. It's a >> It's not just any hat. It's a >> It's not just any hat. It's a >> Oh, it's a tequila hat. A patron. >> Oh, it's a tequila hat. A patron. >> Oh, it's a tequila hat. A patron. >> Alisco. >> Alisco. >> Alisco. >> Aliscoco. >> Aliscoco. >> Aliscoco. >> Yeah. Awesome, you guys. >> Yeah. Awesome, you guys. >> Yeah. Awesome, you guys. >> There we go. Look at that. I love it. >> There we go. Look at that. I love it. >> There we go. Look at that. I love it. >> Yeah. So, um, yeah, this is >> Yeah. So, um, yeah, this is >> Yeah. So, um, yeah, this is forformational purposes only. You know, forformational purposes only. You know, forformational purposes only. You know, if you listen to Rob and his stock picks if you listen to Rob and his stock picks if you listen to Rob and his stock picks and you lose a [ __ ] ton of money, that's and you lose a [ __ ] ton of money, that's and you lose a [ __ ] ton of money, that's your own freaking fault. Remember, we're your own freaking fault. Remember, we're your own freaking fault. Remember, we're here only to have fun.

here only to have fun. here only to have fun. >> Entertainment purposes only. >> Entertainment purposes only. >> Entertainment purposes only. >> Yeah. >> Yeah. >> Yeah. >> Yeah. >> Yeah. >> Yeah. >> I don't know. We're barely >> I don't know. We're barely >> I don't know. We're barely entertainment. entertainment. entertainment. >> This is not like us. >> This is not like us. >> This is not like us. >> This is not like us being on CNBC and >> This is not like us being on CNBC and >> This is not like us being on CNBC and pumping up pumping up AI stocks or pumping up pumping up AI stocks or pumping up pumping up AI stocks or something, you know. something, you know. something, you know. >> Yeah. We don't do that. Um >> Yeah. We don't do that. Um >> Yeah. We don't do that. Um >> um and what what else say Oh yeah. Um >> um and what what else say Oh yeah. Um >> um and what what else say Oh yeah. Um take it seriously at your own risk. Um take it seriously at your own risk. Um take it seriously at your own risk. Um otherwise there could be dire otherwise there could be dire otherwise there could be dire consequences which is why we uh we consequences which is why we uh we consequences which is why we uh we suggest that you take out insurance. suggest that you take out insurance. suggest that you take out insurance. There's a special policy offered by some There's a special policy offered by some There's a special policy offered by some of the leading insurance companies and of the leading insurance companies and of the leading insurance companies and you should check it out before you you should check it out before you you should check it out before you listen. Otherwise, listen. Otherwise, listen. Otherwise, >> yeah, some serious >> yeah, some serious >> yeah, some serious >> insurance policy of brain derangement. >> insurance policy of brain derangement. >> insurance policy of brain derangement. you know, when we inject weird stuff. you know, when we inject weird stuff. you know, when we inject weird stuff. But the thing is though, But the thing is though, But the thing is though, >> AI derangement syndrome >> AI derangement syndrome >> AI derangement syndrome >> somewhere along the way, you're going to >> somewhere along the way, you're going to >> somewhere along the way, you're going to find some value nugget of information find some value nugget of information find some value nugget of information here. here. here. >> We we have we have a track record of >> We we have we have a track record of >> We we have we have a track record of having a Cassandra complex. You know, we having a Cassandra complex. You know, we having a Cassandra complex. You know, we we things people don't believe me we things people don't believe me we things people don't believe me believe us.

believe us. believe us. >> Yeah. So, in other words, we might sound >> Yeah. So, in other words, we might sound >> Yeah. So, in other words, we might sound really stupid right now, but fast really stupid right now, but fast really stupid right now, but fast forward about two years from now, we forward about two years from now, we forward about two years from now, we won't we won't sound so stupid, right? won't we won't sound so stupid, right? won't we won't sound so stupid, right? >> You sound pretty idiotic right now. So, >> You sound pretty idiotic right now. So, >> You sound pretty idiotic right now. So, >> the world listen >> the world listen >> the world listen >> major decisions are based on what comes >> major decisions are based on what comes >> major decisions are based on what comes out of those. out of those. out of those. >> You could be freaking surprised. >> You could be freaking surprised. >> You could be freaking surprised. >> You'd be surprised. >> You'd be surprised. >> You'd be surprised. >> Well, this this decision in this world, >> Well, this this decision in this world, >> Well, this this decision in this world, I'm not sure it's a good thing, but I'm not sure it's a good thing, but I'm not sure it's a good thing, but >> Oh, I mean, it's even if it's even more >> Oh, I mean, it's even if it's even more >> Oh, I mean, it's even if it's even more important now. You know what I'm saying? important now. You know what I'm saying? important now. You know what I'm saying? Yeah, you know. Yeah, you know. Yeah, you know. >> Yeah, we are five minute in and we only >> Yeah, we are five minute in and we only >> Yeah, we are five minute in and we only talked about disclaimers. So, wow. talked about disclaimers. So, wow. talked about disclaimers. So, wow. >> Well, well, but but we are human. We are >> Well, well, but but we are human. We are >> Well, well, but but we are human. We are human only. It's human only. It's human only. It's >> there's no AI involved. >> there's no AI involved. >> there's no AI involved. >> We are harm AI. But >> We are harm AI. But >> We are harm AI. But >> hey, speak for yourself. You don't know. >> hey, speak for yourself. You don't know. >> hey, speak for yourself. You don't know. >> Yeah. Now, so I so I told you the red >> Yeah. Now, so I so I told you the red >> Yeah. Now, so I so I told you the red thing was AI. thing was AI. thing was AI. >> Yeah. Yeah. Yeah. Okay. So, so I I was >> Yeah. Yeah. Yeah. Okay. So, so I I was >> Yeah. Yeah. Yeah. Okay. So, so I I was all excited that that new Raspberry Pi all excited that that new Raspberry Pi all excited that that new Raspberry Pi hat came out, which is, you know, hat came out, which is, you know, hat came out, which is, you know, accelerated computing for doing an an accelerated computing for doing an an accelerated computing for doing an an LLM, maybe, you know, just six 15 LLM, maybe, you know, just six 15 LLM, maybe, you know, just six 15 billion parameter on a Raspberry Pi. I billion parameter on a Raspberry Pi. I billion parameter on a Raspberry Pi. I was so pumped was so pumped was so pumped >> and then I just get shredded, >> and then I just get shredded, >> and then I just get shredded, you know, by Rick Bada and even even you know, by Rick Bada and even even you know, by Rick Bada and even even Pete was not loving it. I'm like, this Pete was not loving it. I'm like, this Pete was not loving it. I'm like, this is Edge AI. This is Edge AI. It's right is Edge AI. This is Edge AI. It's right is Edge AI. This is Edge AI. It's right here. It's like here. It's like here. It's like >> what what is it they don't like? I >> what what is it they don't like? I >> what what is it they don't like? I didn't didn't didn't >> they don't like >> they don't like >> they don't like >> they're getting religious. Are they

>> they're getting religious. Are they >> they're getting religious. Are they getting techno religious on us? getting techno religious on us? getting techno religious on us? >> Yeah. I mean I I mean I know I um you >> Yeah. I mean I I mean I know I um you >> Yeah. I mean I I mean I know I um you know it's kind of like you know we call know it's kind of like you know we call know it's kind of like you know we call Kevin Oly Mr. Wonderful. I feel like we Kevin Oly Mr. Wonderful. I feel like we Kevin Oly Mr. Wonderful. I feel like we should have a name for Rick Bada like should have a name for Rick Bada like should have a name for Rick Bada like Mr. Positive or Mr. Upbeat Mr. Positive or Mr. Upbeat Mr. Positive or Mr. Upbeat sarcasm sarcasm sarcasm >> you know something like that cuz there's >> you know something like that cuz there's >> you know something like that cuz there's just never any love. Let me let me find just never any love. Let me let me find just never any love. Let me let me find it right here on the post. it right here on the post. it right here on the post. >> I of older minds. >> I of older minds. >> I of older minds. >> Yeah. >> Yeah. >> Yeah. >> Yeah. Because so this new hat came out. >> Yeah. Because so this new hat came out. >> Yeah. Because so this new hat came out. >> Yeah. >> Yeah. >> Yeah. >> And you know, I saw it. You know, cuz >> And you know, I saw it. You know, cuz >> And you know, I saw it. You know, cuz you know, you get your little email spam you know, you get your little email spam you know, you get your little email spam all the time. And so the what is it? The all the time. And so the what is it? The all the time. And so the what is it? The Pi Hut that I buy stuff from. Okay. Pi Hut that I buy stuff from. Okay. Pi Hut that I buy stuff from. Okay. >> It's version two because there was a >> It's version two because there was a >> It's version two because there was a version one which is only version one which is only version one which is only >> Yeah. Version one didn't have any memory >> Yeah. Version one didn't have any memory >> Yeah. Version one didn't have any memory but the new one. but the new one. but the new one. >> Yeah. Version one was really only about >> Yeah. Version one was really only about >> Yeah. Version one was really only about video. video. video. >> Yeah. So AI >> Yeah. So AI >> Yeah. So AI >> plus two. >> plus two. >> plus two. >> So you can do Gen AI, LLM, VLMs, >> So you can do Gen AI, LLM, VLMs, >> So you can do Gen AI, LLM, VLMs, and also maybe VMAs like the, you know, and also maybe VMAs like the, you know, and also maybe VMAs like the, you know, like wouldn't that be like the uh MTV like wouldn't that be like the uh MTV like wouldn't that be like the uh MTV movie awards? Um, so anyway, let's jump movie awards? Um, so anyway, let's jump movie awards? Um, so anyway, let's jump right to the controversy here.

right to the controversy here. right to the controversy here. >> Where is it? Rick Bada. >> Where is it? Rick Bada. >> Where is it? Rick Bada. No love. No love. Yeah, he and he has a No love. No love. Yeah, he and he has a No love. No love. Yeah, he and he has a whole crew of people who uh I don't know whole crew of people who uh I don't know whole crew of people who uh I don't know if he hires them or if they're just uh if he hires them or if they're just uh if he hires them or if they're just uh >> All right, here it is. And this is so >> All right, here it is. And this is so >> All right, here it is. And this is so but this whatever. What do you expect? but this whatever. What do you expect? but this whatever. What do you expect? >> Just because you can doesn't mean you >> Just because you can doesn't mean you >> Just because you can doesn't mean you should. But we say that about lots of should. But we say that about lots of should. But we say that about lots of things. things. things. >> Sure, you can run an LLM here just like >> Sure, you can run an LLM here just like >> Sure, you can run an LLM here just like you can make a Toyota Corolla go 240 you can make a Toyota Corolla go 240 you can make a Toyota Corolla go 240 mph. mph. mph. Good luck with that. You probably Good luck with that. You probably Good luck with that. You probably shouldn't. an LLM performance depends on shouldn't. an LLM performance depends on shouldn't. an LLM performance depends on more on than just an NPU. So, I was more on than just an NPU. So, I was more on than just an NPU. So, I was like, you know what? How about some love like, you know what? How about some love like, you know what? How about some love instead? I was excited because I instead? I was excited because I instead? I was excited because I guarantee you you're not getting this guarantee you you're not getting this guarantee you you're not getting this much horsepower on a microcontroller. much horsepower on a microcontroller. much horsepower on a microcontroller. >> Yeah. >> Yeah. >> Yeah. >> And then Pete Pete etu brute to Rick >> And then Pete Pete etu brute to Rick >> And then Pete Pete etu brute to Rick Bada's point these systems tend to be Bada's point these systems tend to be Bada's point these systems tend to be more RAM bound than topsbound. I am also more RAM bound than topsbound. I am also more RAM bound than topsbound. I am also a big fan of SLMs running on platforms a big fan of SLMs running on platforms a big fan of SLMs running on platforms like the ALF semiconductor like the ALF semiconductor like the ALF semiconductor >> and then you know and so I'm just like >> and then you know and so I'm just like >> and then you know and so I'm just like it I don't just it just sucked all the it I don't just it just sucked all the it I don't just it just sucked all the air out of my excitement air out of my excitement air out of my excitement >> but but you know really give you an >> but but you know really give you an >> but but you know really give you an analogy you know what this reminds me analogy you know what this reminds me analogy you know what this reminds me this reminds me where the Bang radio this reminds me where the Bang radio this reminds me where the Bang radio started to show up started to show up started to show up >> you had all this ham radio dude >> you had all this ham radio dude >> you had all this ham radio dude >> in their 50s or 60s say oh this is crap >> in their 50s or 60s say oh this is crap >> in their 50s or 60s say oh this is crap you this will never work this is blah you this will never work this is blah you this will never work this is blah blah blah blah blah blah, blah blah blah blah blah, blah blah blah blah blah, >> but it actually democratized the whole >> but it actually democratized the whole >> but it actually democratized the whole thing. So, I think it is great. We can

thing. So, I think it is great. We can thing. So, I think it is great. We can democratize. You can you can run on the democratize. You can you can run on the democratize. You can you can run on the local things. I'm going to try it. I local things. I'm going to try it. I local things. I'm going to try it. I don't know if it's don't know if it's don't know if it's >> I'm going to try it. I want to I'm going >> I'm going to try it. I want to I'm going >> I'm going to try it. I want to I'm going to get a hat. to get a hat. to get a hat. >> If I can, >> If I can, >> If I can, >> that's why we are all wearing hats >> that's why we are all wearing hats >> that's why we are all wearing hats today. today. today. >> Yeah. >> Yeah. >> Yeah. >> Yeah. >> Yeah. >> Yeah. >> If I can have a little box that run a >> If I can have a little box that run a >> If I can have a little box that run a large language models that do most of my large language models that do most of my large language models that do most of my assistant work and I know that my data assistant work and I know that my data assistant work and I know that my data is not going to any of this crappy cloud is not going to any of this crappy cloud is not going to any of this crappy cloud and I can run it in the middle of the and I can run it in the middle of the and I can run it in the middle of the mountain with my PC in the little box. mountain with my PC in the little box. mountain with my PC in the little box. I'm happy, you know, I call that I'm happy, you know, I call that I'm happy, you know, I call that freedom. Same thing as the bofang radio. freedom. Same thing as the bofang radio. freedom. Same thing as the bofang radio. You don't have to pay anymore the 2,000 You don't have to pay anymore the 2,000 You don't have to pay anymore the 2,000 to ICO or Kenwood and go through a to ICO or Kenwood and go through a to ICO or Kenwood and go through a freaking certification. You just get the freaking certification. You just get the freaking certification. You just get the 30 bucks. 30 bucks. 30 bucks. >> Oh, there's Pete. You can talk. You can >> Oh, there's Pete. You can talk. You can >> Oh, there's Pete. You can talk. You can talk crap directly to Pete now. talk crap directly to Pete now. talk crap directly to Pete now. >> All right. Thank you. >> All right. Thank you. >> All right. Thank you. >> BEAT HIM UP ON >> BEAT HIM UP ON >> BEAT HIM UP ON >> HERE. He comes, man. >> HERE. He comes, man. >> HERE. He comes, man. >> See, here comes >> See, here comes >> See, here comes >> Okay, >> Okay, >> Okay, >> Pete doesn't know the [ __ ] storm he's >> Pete doesn't know the [ __ ] storm he's >> Pete doesn't know the [ __ ] storm he's about to enter. You know, about to enter. You know, about to enter. You know, >> he's actually hiding himself. See, >> he's actually hiding himself. See, >> he's actually hiding himself. See, >> I know. He's like, "Oh my god, >> I know. He's like, "Oh my god, >> I know. He's like, "Oh my god, >> you know, today is hat day, Pete. And >> you know, today is hat day, Pete. And >> you know, today is hat day, Pete. And I'm just not happy you and Rick Bada I'm just not happy you and Rick Bada I'm just not happy you and Rick Bada were not sending any love to our new AI were not sending any love to our new AI were not sending any love to our new AI hat for the Raspberry Pi."

hat for the Raspberry Pi." hat for the Raspberry Pi." >> Oh, that thing. >> That that hat >> That that hat >> that comes that comes from a Microsoft. >> that comes that comes from a Microsoft. >> that comes that comes from a Microsoft. >> Oh my god. >> Oh my god. >> Oh my god. >> Oh man. And you know, I was so excited >> Oh man. And you know, I was so excited >> Oh man. And you know, I was so excited when I got the email from Pi Hut that when I got the email from Pi Hut that when I got the email from Pi Hut that there's a new version of a hat to do AI there's a new version of a hat to do AI there's a new version of a hat to do AI on my Raspberry Pi. I was super excited on my Raspberry Pi. I was super excited on my Raspberry Pi. I was super excited and it felt kind of edgy to me. and it felt kind of edgy to me. and it felt kind of edgy to me. >> It was, >> It was, >> It was, >> but maybe not edgy enough for Pete or >> but maybe not edgy enough for Pete or >> but maybe not edgy enough for Pete or Rick. Rick. Rick. >> It was good. >> It was good. >> It was good. >> You know, Rick Batada, Mr. Wonder. >> You know, Rick Batada, Mr. Wonder. >> You know, Rick Batada, Mr. Wonder. >> Oh my god. >> Oh my god. >> Oh my god. >> Rick Batada, Mr. Positive. >> Rick Batada, Mr. Positive. >> Rick Batada, Mr. Positive. You're definitely going to have to You're definitely going to have to You're definitely going to have to listen back Pete because it's pretty listen back Pete because it's pretty listen back Pete because it's pretty freaking hilarious. He talks so much freaking hilarious. He talks so much freaking hilarious. He talks so much crap about you and crap about you and crap about you and >> I'm just excited about having, you know, >> I'm just excited about having, you know, >> I'm just excited about having, you know, because when I listen to my the moonshot because when I listen to my the moonshot because when I listen to my the moonshot guys, you know, they're like, you know, guys, you know, they're like, you know, guys, you know, they're like, you know, when people talk about those really when people talk about those really when people talk about those really well-defined small language models, well-defined small language models, well-defined small language models, they're like, "Yeah, pretty soon your they're like, "Yeah, pretty soon your they're like, "Yeah, pretty soon your your whole medical everything will your whole medical everything will your whole medical everything will probably run on something like Raspberry probably run on something like Raspberry probably run on something like Raspberry Pi and it'll be powerful enough to for Pi and it'll be powerful enough to for Pi and it'll be powerful enough to for your health and everything you do for your health and everything you do for your health and everything you do for AI." Yeah, that I'm not not too bullish AI." Yeah, that I'm not not too bullish AI." Yeah, that I'm not not too bullish on. I'm I'm a little bit more bullish on on. I'm I'm a little bit more bullish on on. I'm I'm a little bit more bullish on some of the tiny ML like the tiny ML some of the tiny ML like the tiny ML some of the tiny ML like the tiny ML tiny Gen AI type of applications that tiny Gen AI type of applications that tiny Gen AI type of applications that are very narrow and specific literally are very narrow and specific literally are very narrow and specific literally catering to either a sensor for a catering to either a sensor for a catering to either a sensor for a certain function sensory function or an certain function sensory function or an certain function sensory function or an aggregate right where you're you're aggregate right where you're you're aggregate right where you're you're you're taking the perception you're taking the perception you're taking the perception capabilities a level up the all this

capabilities a level up the all this capabilities a level up the all this reasoning and all this other junk I it reasoning and all this other junk I it reasoning and all this other junk I it it just doesn't have a place so close to it just doesn't have a place so close to it just doesn't have a place so close to the sensor. You know what I'm saying? the sensor. You know what I'm saying? the sensor. You know what I'm saying? And I think that's the problem. And And I think that's the problem. And And I think that's the problem. And right now we have all these folks right now we have all these folks right now we have all these folks starting to get excited about edge AI, starting to get excited about edge AI, starting to get excited about edge AI, but I think they're projecting all this but I think they're projecting all this but I think they're projecting all this crazy data center AI crazy data center AI crazy data center AI um you know neocloud down to the edge. um you know neocloud down to the edge. um you know neocloud down to the edge. And that is the classic mistake. And that is the classic mistake. And that is the classic mistake. It's the classic mistake that people are It's the classic mistake that people are It's the classic mistake that people are repeating. It's not, "Oh, will they do repeating. It's not, "Oh, will they do repeating. It's not, "Oh, will they do it?" It's like, "No, dude. If you don't it?" It's like, "No, dude. If you don't it?" It's like, "No, dude. If you don't know [ __ ] about the edge, step off, stop know [ __ ] about the edge, step off, stop know [ __ ] about the edge, step off, stop pontificating. Listen, and watch IoT pontificating. Listen, and watch IoT pontificating. Listen, and watch IoT Coffee Talk because we already talked Coffee Talk because we already talked Coffee Talk because we already talked about this stuff ages ago. about this stuff ages ago. about this stuff ages ago. >> No, don't pump the brakes. Go full speed >> No, don't pump the brakes. Go full speed >> No, don't pump the brakes. Go full speed ahead with 240 mph Toyota Corolla that ahead with 240 mph Toyota Corolla that ahead with 240 mph Toyota Corolla that Rick Bada talked about Rick Bada talked about Rick Bada talked about >> and dangerously. Wait a second, man. If >> and dangerously. Wait a second, man. If >> and dangerously. Wait a second, man. If anybody's excited about Edge AI anybody's excited about Edge AI anybody's excited about Edge AI somewhere on the planet got some hope somewhere on the planet got some hope somewhere on the planet got some hope that there was a future for them doing that there was a future for them doing that there was a future for them doing that, it came from Pete.

that, it came from Pete. that, it came from Pete. >> That's right. >> That's right. >> That's right. >> And and Leonard, you're just pouring >> And and Leonard, you're just pouring >> And and Leonard, you're just pouring cold water all over that stuff. cold water all over that stuff. cold water all over that stuff. >> No, >> No, >> No, get out of that stuff. You know, get out of that stuff. You know, get out of that stuff. You know, >> not >> not >> not >> Rob is agitating. He's agitating. >> Rob is agitating. He's agitating. >> Rob is agitating. He's agitating. >> No, no. We're we're talking about uh >> No, no. We're we're talking about uh >> No, no. We're we're talking about uh what what makes sense. what is uh you what what makes sense. what is uh you what what makes sense. what is uh you know given the state of the technology know given the state of the technology know given the state of the technology where does it make sense to place where does it make sense to place where does it make sense to place workloads given what silicon can workloads given what silicon can workloads given what silicon can actually do right actually do right actually do right >> yeah I mentioned in the comments that >> yeah I mentioned in the comments that >> yeah I mentioned in the comments that like olive semiconductor has a really like olive semiconductor has a really like olive semiconductor has a really cool MCU that runs small language models cool MCU that runs small language models cool MCU that runs small language models and it's embedded in a wearable in your and it's embedded in a wearable in your and it's embedded in a wearable in your glasses glasses glasses >> and it's just a visual language model >> and it's just a visual language model >> and it's just a visual language model thing so it's basically giving you audio thing so it's basically giving you audio thing so it's basically giving you audio feedback about what the sensor is seeing feedback about what the sensor is seeing feedback about what the sensor is seeing like Leonard was saying when you start like Leonard was saying when you start like Leonard was saying when you start pairing transformer models, small pairing transformer models, small pairing transformer models, small language models with sensors, you get a language models with sensors, you get a language models with sensors, you get a better understanding of what's going on. better understanding of what's going on. better understanding of what's going on. >> And sometimes you get tequila. >> I like that. Well, you know, cuz like >> I like that. Well, you know, cuz like for examples, embedded equipment that understands embedded equipment that understands sensor feedback, right? Complex sensor sensor feedback, right? Complex sensor sensor feedback, right? Complex sensor feedback where the language model can feedback where the language model can feedback where the language model can make sense of a lot of different inputs.

make sense of a lot of different inputs. make sense of a lot of different inputs. things like that make sense, you know, things like that make sense, you know, things like that make sense, you know, that that's that's good application of that that's that's good application of that that's that's good application of it. But you can't just take like a it. But you can't just take like a it. But you can't just take like a >> standard language model and just kind of >> standard language model and just kind of >> standard language model and just kind of cram it into the edge and expect cram it into the edge and expect cram it into the edge and expect anything useful to happen. anything useful to happen. anything useful to happen. >> Yeah. >> Yeah. >> Yeah. >> I want to So we everybody's talking now >> I want to So we everybody's talking now >> I want to So we everybody's talking now about putting data centers in space and about putting data centers in space and about putting data centers in space and I figured that if I had that hat on I figured that if I had that hat on I figured that if I had that hat on >> and then and then I went and bought a >> and then and then I went and bought a >> and then and then I went and bought a Estes model rocket like I used to have Estes model rocket like I used to have Estes model rocket like I used to have when I was a kid and I have that in when I was a kid and I have that in when I was a kid and I have that in there. Maybe I could launch a data there. Maybe I could launch a data there. Maybe I could launch a data center into space made up of Raspberry center into space made up of Raspberry center into space made up of Raspberry Pies. Just thinking out loud here. We're Pies. Just thinking out loud here. We're Pies. Just thinking out loud here. We're spitballing here. spitballing here. spitballing here. >> Yeah, you you you saw my comment on that >> Yeah, you you you saw my comment on that >> Yeah, you you you saw my comment on that on on that post, Rob. We still have on on that post, Rob. We still have on on that post, Rob. We still have actually the Raspberry Pi might be the actually the Raspberry Pi might be the actually the Raspberry Pi might be the solution because the cost of sending 1 solution because the cost of sending 1 solution because the cost of sending 1 kilogram in space is still to a point kilogram in space is still to a point kilogram in space is still to a point that good luck sending your data center that good luck sending your data center that good luck sending your data center right now. right now. right now. >> Exactly. And so we're going to do it >> Exactly. And so we're going to do it >> Exactly. And so we're going to do it with Raspberry Pies orbiting the planet. with Raspberry Pies orbiting the planet. with Raspberry Pies orbiting the planet. That's That might work. Yeah. That's That might work. Yeah. That's That might work. Yeah. >> Actually, you know what makes more sense >> Actually, you know what makes more sense >> Actually, you know what makes more sense is have those things on the moon.

is have those things on the moon. is have those things on the moon. >> Yes. >> Yes. >> Yes. >> Data center on the moon. Yeah. >> Data center on the moon. Yeah. >> Data center on the moon. Yeah. >> Data center on the moon makes a hell of >> Data center on the moon makes a hell of >> Data center on the moon makes a hell of a lot more sense than having it like a lot more sense than having it like a lot more sense than having it like >> But but you know what's the problem with >> But but you know what's the problem with >> But but you know what's the problem with that? that? that? >> Oh, there's a problem. And if there's a >> Oh, there's a problem. And if there's a >> Oh, there's a problem. And if there's a problem, Dimmitri is going to tell us problem, Dimmitri is going to tell us problem, Dimmitri is going to tell us about it. about it. about it. >> Well, guys, there's there's no >> Well, guys, there's there's no >> Well, guys, there's there's no atmosphere on the moon. atmosphere on the moon. atmosphere on the moon. Every random little piece of junk from Every random little piece of junk from Every random little piece of junk from space gonna go directly in the middle of space gonna go directly in the middle of space gonna go directly in the middle of your data center, make a big hole and your data center, make a big hole and your data center, make a big hole and it's going to turn off. it's going to turn off. it's going to turn off. >> I mean, you can't move. I mean, >> I mean, you can't move. I mean, >> I mean, you can't move. I mean, >> so it's going to be inside the moon. >> so it's going to be inside the moon. >> so it's going to be inside the moon. We're going to put We're going to put We're going to put >> inside. So, we have to dig under. Yeah. >> inside. So, we have to dig under. Yeah. >> inside. So, we have to dig under. Yeah. Oh, this is what Oh, this is what Oh, this is what >> lunar. Yeah. >> lunar. Yeah. >> lunar. Yeah. >> And this is why Elon investing in this >> And this is why Elon investing in this >> And this is why Elon investing in this boring company to do, you know. boring company to do, you know. boring company to do, you know. >> Yes. >> Yes. >> Yes. >> Machines. But as Dimmitri well knows, >> Machines. But as Dimmitri well knows, >> Machines. But as Dimmitri well knows, the moon is actually hollow and it's the moon is actually hollow and it's the moon is actually hollow and it's actually possibly some kind of alien actually possibly some kind of alien actually possibly some kind of alien spacecraft. spacecraft. spacecraft. >> Yes. >> Yes. >> Yes. >> Actually, what happened? >> Actually, what happened? >> Actually, what happened? >> Have you ever read that? Have you ever >> Have you ever read that? Have you ever >> Have you ever read that? Have you ever >> There might already be a data center in >> There might already be a data center in >> There might already be a data center in the moon is what you're saying. the moon is what you're saying. the moon is what you're saying. >> It might be like, >> It might be like, >> It might be like, >> have you not ever been on the internet?

>> have you not ever been on the internet? >> have you not ever been on the internet? >> The internet told me that the moon is >> The internet told me that the moon is >> The internet told me that the moon is hollow and it's probably a spaceship. hollow and it's probably a spaceship. hollow and it's probably a spaceship. They weren't trying to deal with the They weren't trying to deal with the They weren't trying to deal with the whole Death Star thing, but could be. whole Death Star thing, but could be. whole Death Star thing, but could be. No, No, No, >> not a Jack Black movie. Rob, the the >> not a Jack Black movie. Rob, the the >> not a Jack Black movie. Rob, the the earth is flat, the sky is a screen, and earth is flat, the sky is a screen, and earth is flat, the sky is a screen, and it's the NC that is projecting the moon it's the NC that is projecting the moon it's the NC that is projecting the moon on it. on it. on it. >> Is that like Capricorn one? >> Is that like Capricorn one? >> Is that like Capricorn one? >> Yeah, exactly. Okay. >> Yeah, exactly. Okay. >> Yeah, exactly. Okay. >> And they can change the color, but they >> And they can change the color, but they >> And they can change the color, but they never wanted to do it. They thought never wanted to do it. They thought never wanted to do it. They thought people would freak out. people would freak out. people would freak out. >> The Truman Show. Yeah, we live in the >> The Truman Show. Yeah, we live in the >> The Truman Show. Yeah, we live in the Truman Show. So, hey, Yen, you haven't Truman Show. So, hey, Yen, you haven't Truman Show. So, hey, Yen, you haven't been on for a long time. What's your been on for a long time. What's your been on for a long time. What's your >> I know. I know. Been crazy timing. >> I know. I know. Been crazy timing. >> I know. I know. Been crazy timing. >> What have you been up to? >> What have you been up to? >> What have you been up to? >> Uh, what? >> Uh, what? >> Uh, what? >> Happy New Year, by the way. >> Happy New Year, by the way. >> Happy New Year, by the way. >> Same to you. Oh, I saw you on industry >> Same to you. Oh, I saw you on industry >> Same to you. Oh, I saw you on industry 4.0 club uh the other day. Yeah. Talking 4.0 club uh the other day. Yeah. Talking 4.0 club uh the other day. Yeah. Talking your favorite topic, AI. your favorite topic, AI. your favorite topic, AI. >> Oh god. >> Oh god. >> Oh god. >> Oh. Oh, you guys were He joined you on >> Oh. Oh, you guys were He joined you on >> Oh. Oh, you guys were He joined you on Clubhouse. >> No, that was >> No, that was >> Is that thing around still? Uh Mark >> Is that thing around still? Uh Mark >> Is that thing around still? Uh Mark Andre's great uh pandemic pet project.

Andre's great uh pandemic pet project. Andre's great uh pandemic pet project. >> Is it still around? >> Is it still around? >> Is it still around? >> It's still around. >> It's still around. >> It's still around. >> Has anybody been on it? >> Has anybody been on it? >> Has anybody been on it? >> I don't know. I'll I'll I'll install it >> I don't know. I'll I'll I'll install it >> I don't know. I'll I'll I'll install it and see. The reason why I'm asking the and see. The reason why I'm asking the and see. The reason why I'm asking the question is because uh the the the there question is because uh the the the there question is because uh the the the there is a question of its uh existence today. is a question of its uh existence today. is a question of its uh existence today. >> So I have obviously not been on. >> So I have obviously not been on. >> So I have obviously not been on. >> Oh, you're right. So Yan, it's been a >> Oh, you're right. So Yan, it's been a >> Oh, you're right. So Yan, it's been a while. What is going on in your while. What is going on in your while. What is going on in your industrial world? industrial world? industrial world? >> Yeah. >> Yeah. >> Yeah. >> How's life? >> How's life? >> How's life? >> Lay it on us. >> Lay it on us. >> Lay it on us. >> That's good. That's good. I mean uh we >> That's good. That's good. I mean uh we >> That's good. That's good. I mean uh we are uh um you know from from an IoT are uh um you know from from an IoT are uh um you know from from an IoT perspective we are uh you know when we perspective we are uh you know when we perspective we are uh you know when we originally started talking about all originally started talking about all originally started talking about all this uh uh IoT and this uh uh IoT and this uh uh IoT and uh putting IoT on our equipment um it uh putting IoT on our equipment um it uh putting IoT on our equipment um it was more or less just to get get data uh was more or less just to get get data uh was more or less just to get get data uh as opposed to not be able to get data as opposed to not be able to get data as opposed to not be able to get data right and and try to be able to help our right and and try to be able to help our right and and try to be able to help our service and over the last four years service and over the last four years service and over the last four years we've now put uh init initially machine we've now put uh init initially machine we've now put uh init initially machine learning uh on on this and we are I mean learning uh on on this and we are I mean learning uh on on this and we are I mean we are just uh it's just amazing at what we are just uh it's just amazing at what we are just uh it's just amazing at what we are able to do now with with uh uh be we are able to do now with with uh uh be we are able to do now with with uh uh be able to tell our customers uh that able to tell our customers uh that able to tell our customers uh that their machine might go down within the their machine might go down within the their machine might go down within the next 21 days. I mean just think about next 21 days. I mean just think about next 21 days. I mean just think about something like this uh in in a something like this uh in in a something like this uh in in a manufacturing environment where where manufacturing environment where where manufacturing environment where where downtime is something you want to avoid downtime is something you want to avoid downtime is something you want to avoid as much as possible at well unscheduled as much as possible at well unscheduled as much as possible at well unscheduled downtime right uh but now be able to downtime right uh but now be able to downtime right uh but now be able to tell customer that there's an issue and tell customer that there's an issue and tell customer that there's an issue and you can resolve it within the next you

you can resolve it within the next you you can resolve it within the next you know week or so and machine will be know week or so and machine will be know week or so and machine will be continue working as as supposed to. So continue working as as supposed to. So continue working as as supposed to. So it's just amazing what what we're doing. it's just amazing what what we're doing. it's just amazing what what we're doing. We are just expanding that uh almost We are just expanding that uh almost We are just expanding that uh almost like every month we are we are finding like every month we are we are finding like every month we are we are finding new types of insights uh that we are new types of insights uh that we are new types of insights uh that we are able to generate through the machine able to generate through the machine able to generate through the machine learning and some of those insight helps learning and some of those insight helps learning and some of those insight helps our customers. Some of those insights our customers. Some of those insights our customers. Some of those insights are more for us that we can tell our are more for us that we can tell our are more for us that we can tell our customer that they're undersized. customer that they're undersized. customer that they're undersized. Um their machine is too too old and we Um their machine is too too old and we Um their machine is too too old and we can see that's not not valuable to to can see that's not not valuable to to can see that's not not valuable to to fix it but but rather replace it. Um fix it but but rather replace it. Um fix it but but rather replace it. Um etc. Right. It's just a just amazing how etc. Right. It's just a just amazing how etc. Right. It's just a just amazing how um this has become probably one of our um this has become probably one of our um this has become probably one of our biggest like technological projects biggest like technological projects biggest like technological projects within the company now. within the company now. within the company now. >> Wow. is the is is is that whole area. Uh >> Wow. is the is is is that whole area. Uh >> Wow. is the is is is that whole area. Uh because I mean everything else we're because I mean everything else we're because I mean everything else we're competing at the same level with our competing at the same level with our competing at the same level with our competitors. I mean it's just about a competitors. I mean it's just about a competitors. I mean it's just about a you know a big big big monster of a of a you know a big big big monster of a of a you know a big big big monster of a of a piece of equipment and there's no real piece of equipment and there's no real piece of equipment and there's no real world differentiation but we're now world differentiation but we're now world differentiation but we're now putting that differentiation into into putting that differentiation into into putting that differentiation into into the information that we can provide our the information that we can provide our the information that we can provide our customers through our IoT and machine customers through our IoT and machine customers through our IoT and machine learning. So yeah, this is this is very learning. So yeah, this is this is very learning. So yeah, this is this is very interesting but but both Rob and I can interesting but but both Rob and I can interesting but but both Rob and I can relate to that because 10 years ago he relate to that because 10 years ago he relate to that because 10 years ago he was at Itachi, I was at G and we were was at Itachi, I was at G and we were was at Itachi, I was at G and we were promising that thing. So what has promising that thing. So what has promising that thing. So what has changed?

changed? changed? >> No, it's true. No, I mean the whole the >> No, it's true. No, I mean the whole the >> No, it's true. No, I mean the whole the whole messaging was no unplanned whole messaging was no unplanned whole messaging was no unplanned downtime machine learning no unplanned downtime machine learning no unplanned downtime machine learning no unplanned down time. It was the whole messaging 10 down time. It was the whole messaging 10 down time. It was the whole messaging 10 years ago. So my my question is why did years ago. So my my question is why did years ago. So my my question is why did it took two years or what has changed so it took two years or what has changed so it took two years or what has changed so that you see such momentum nowadays is that you see such momentum nowadays is that you see such momentum nowadays is what I'm curious about. I think uh when what I'm curious about. I think uh when what I'm curious about. I think uh when when you when you were talking about when you when you were talking about when you when you were talking about that 10 years ago, it was not the OEMs that 10 years ago, it was not the OEMs that 10 years ago, it was not the OEMs that was part of it. It was the you you that was part of it. It was the you you that was part of it. It was the you you went to a manufacturer and says we can went to a manufacturer and says we can went to a manufacturer and says we can put in this big system on all your put in this big system on all your put in this big system on all your equipment and that is just so complex equipment and that is just so complex equipment and that is just so complex because of the complexity of all these because of the complexity of all these because of the complexity of all these individual types of machines, how they individual types of machines, how they individual types of machines, how they work, what the data is. uh it is only work, what the data is. uh it is only work, what the data is. uh it is only within the last I would say 5 to 10 within the last I would say 5 to 10 within the last I would say 5 to 10 years that the OEMs have started going years that the OEMs have started going years that the OEMs have started going down the route because they're really down the route because they're really down the route because they're really the experts on their equipment. Mhm. the experts on their equipment. Mhm. the experts on their equipment. Mhm. >> And so I mean we get uh sometimes we get >> And so I mean we get uh sometimes we get >> And so I mean we get uh sometimes we get uh uh some of our uh key accounts like uh uh some of our uh key accounts like uh uh some of our uh key accounts like really large accounts and when it has really large accounts and when it has really large accounts and when it has their own uh um analytics departments their own uh um analytics departments their own uh um analytics departments and everything like that they say can and everything like that they say can and everything like that they say can you just give us all your uh uh you just give us all your uh uh you just give us all your uh uh algorithms and your machine learning algorithms and your machine learning algorithms and your machine learning your digital twin information and and we your digital twin information and and we your digital twin information and and we will we will want to do the put that will we will want to do the put that will we will want to do the put that into our system. And we tell them that into our system. And we tell them that into our system. And we tell them that you know what it's not going to be that you know what it's not going to be that you know what it's not going to be that beneficial to you because you have five beneficial to you because you have five beneficial to you because you have five pieces of equipment that you're pieces of equipment that you're pieces of equipment that you're connected to. We have a thousand connected to. We have a thousand connected to. We have a thousand >> thousand. Yeah. Yeah. Yeah.

>> thousand. Yeah. Yeah. Yeah. >> thousand. Yeah. Yeah. Yeah. >> And so the information that we can >> And so the information that we can >> And so the information that we can generate and and the logics we can we generate and and the logics we can we generate and and the logics we can we can develop around that is just so much can develop around that is just so much can develop around that is just so much advanced compared to what an individual advanced compared to what an individual advanced compared to what an individual customer even though they're they be customer even though they're they be customer even though they're they be fairly large within that organization fairly large within that organization fairly large within that organization but they're never going to be as large but they're never going to be as large but they're never going to be as large as as as us right from that perspective. as as as us right from that perspective. as as as us right from that perspective. And so so I think that's uh that's where And so so I think that's uh that's where And so so I think that's uh that's where the thing have changed that the OEMs are the thing have changed that the OEMs are the thing have changed that the OEMs are starting to do this. I'm still starting to do this. I'm still starting to do this. I'm still struggling though struggling though struggling though >> with how we then apart from sending SMS >> with how we then apart from sending SMS >> with how we then apart from sending SMS and uh emails and giving them a portal and uh emails and giving them a portal and uh emails and giving them a portal how we integrate back into their systems how we integrate back into their systems how we integrate back into their systems with our advanced knowledge without with our advanced knowledge without with our advanced knowledge without being an integrator because that's what being an integrator because that's what being an integrator because that's what OEMs don't want to be. they don't want OEMs don't want to be. they don't want OEMs don't want to be. they don't want to be an integrator that uh to be an integrator that uh to be an integrator that uh >> you know go to every single v single >> you know go to every single v single >> you know go to every single v single customer and have to develop a solution customer and have to develop a solution customer and have to develop a solution for them. Uh we we're still trying to for them. Uh we we're still trying to for them. Uh we we're still trying to look at that standard that we can look at that standard that we can look at that standard that we can develop to that our customers will just develop to that our customers will just develop to that our customers will just uh uh be able to consume. So this is uh uh be able to consume. So this is uh uh be able to consume. So this is this is really interesting because that this is really interesting because that this is really interesting because that you know in the line of Cassandra you know in the line of Cassandra you know in the line of Cassandra complex that was very early stage when I complex that was very early stage when I complex that was very early stage when I went to this space that was kind of the went to this space that was kind of the went to this space that was kind of the the thing I was I was saying well unless the thing I was I was saying well unless the thing I was I was saying well unless this happen at the level of it's in the this happen at the level of it's in the this happen at the level of it's in the catalog and the equipment you buy catalog and the equipment you buy catalog and the equipment you buy actually ships ships with the minimum actually ships ships with the minimum actually ships ships with the minimum layer of intelligence it's never going layer of intelligence it's never going layer of intelligence it's never going to happen because if you do it post it's to happen because if you do it post it's to happen because if you do it post it's it's too intrusive it's too complicated it's too intrusive it's too complicated it's too intrusive it's too complicated so that's is great actually I'm very so that's is great actually I'm very so that's is great actually I'm very happy to see that finally the ecosystem happy to see that finally the ecosystem happy to see that finally the ecosystem of is actually embracing that vision and of is actually embracing that vision and of is actually embracing that vision and you Clear blade it was very much about

you Clear blade it was very much about you Clear blade it was very much about that as well. I don't know if you worked that as well. I don't know if you worked that as well. I don't know if you worked with them at some point but there also with them at some point but there also with them at some point but there also had really this vision to go after all had really this vision to go after all had really this vision to go after all the different manufacturer and said well the different manufacturer and said well the different manufacturer and said well you have to provide this layer of you have to provide this layer of you have to provide this layer of analytics intelligence analytics intelligence analytics intelligence >> embedded at the source and then we can >> embedded at the source and then we can >> embedded at the source and then we can compose that in larger systems. compose that in larger systems. compose that in larger systems. >> You know what's really interesting is to >> You know what's really interesting is to >> You know what's really interesting is to see how long it's taken to go from those see how long it's taken to go from those see how long it's taken to go from those powerpoints 14 years ago to what you're powerpoints 14 years ago to what you're powerpoints 14 years ago to what you're talking about now talking about now talking about now >> only now. um you know like when I was at >> only now. um you know like when I was at >> only now. um you know like when I was at IBM and I was working with IBM research IBM and I was working with IBM research IBM and I was working with IBM research on some of this advanced analytics and on some of this advanced analytics and on some of this advanced analytics and uh you know a lot of condition uh you know a lot of condition uh you know a lot of condition monitoring so it was like a confluence monitoring so it was like a confluence monitoring so it was like a confluence of uh IoT concepts plus advanced of uh IoT concepts plus advanced of uh IoT concepts plus advanced analytics the all the ML stuff you know analytics the all the ML stuff you know analytics the all the ML stuff you know you had these PowerPoint presentations you had these PowerPoint presentations you had these PowerPoint presentations that were promising you know quite lofty that were promising you know quite lofty that were promising you know quite lofty things but think about that journey that things but think about that journey that things but think about that journey that we've taken and what you're you're we've taken and what you're you're we've taken and what you're you're saying here which is um the reality that saying here which is um the reality that saying here which is um the reality that we've h that the IoT community has had we've h that the IoT community has had we've h that the IoT community has had to live with for more than a decade and to live with for more than a decade and to live with for more than a decade and how there are certain things that were how there are certain things that were how there are certain things that were on that PowerPoint 14 years ago that on that PowerPoint 14 years ago that on that PowerPoint 14 years ago that never came true and can't come true and never came true and can't come true and never came true and can't come true and that I think that's as we look at that I think that's as we look at that I think that's as we look at generative AI it's exactly the same generative AI it's exactly the same generative AI it's exactly the same thing that's going to happen right thing that's going to happen right thing that's going to happen right because back then we were disconnected because back then we were disconnected because back then we were disconnected really from uh a lot of the ground truth really from uh a lot of the ground truth really from uh a lot of the ground truth is that you only learn through a very is that you only learn through a very is that you only learn through a very very sobering

very sobering very sobering um experience trying to translate this um experience trying to translate this um experience trying to translate this technology into value for your technology into value for your technology into value for your customers. You know, and um and for the customers. You know, and um and for the customers. You know, and um and for the longest time you know it's like longest time you know it's like longest time you know it's like predictive maintenance is something predictive maintenance is something predictive maintenance is something we've been talking about forever. we've been talking about forever. we've been talking about forever. You know, think about how hard that has You know, think about how hard that has You know, think about how hard that has been for the industry. And we've talked been for the industry. And we've talked been for the industry. And we've talked about on IoT coffee talk how difficult about on IoT coffee talk how difficult about on IoT coffee talk how difficult it is to sell the value because it's it is to sell the value because it's it is to sell the value because it's literally, hey, you know, we can tell literally, hey, you know, we can tell literally, hey, you know, we can tell the future, trust us, right? And then the future, trust us, right? And then the future, trust us, right? And then there's the dirty laundry. there's the dirty laundry. there's the dirty laundry. >> You're so right. Like when we were in >> You're so right. Like when we were in >> You're so right. Like when we were in the lab in R&D and we got machine the lab in R&D and we got machine the lab in R&D and we got machine learning to work in the lab with a type learning to work in the lab with a type learning to work in the lab with a type of machine and we're all like going, "We of machine and we're all like going, "We of machine and we're all like going, "We could be heroes." could be heroes." could be heroes." But it turned out for just one day But it turned out for just one day But it turned out for just one day because then when we deployed it to the because then when we deployed it to the because then when we deployed it to the field it didn't work. field it didn't work. field it didn't work. That's exciting Yan. Um yeah it's cool That's exciting Yan. Um yeah it's cool That's exciting Yan. Um yeah it's cool and you know and so your next thing is and you know and so your next thing is and you know and so your next thing is you're right it's integration. Um you're right it's integration. Um you're right it's integration. Um because because because baby steps with IoT, it's like here's baby steps with IoT, it's like here's baby steps with IoT, it's like here's this SMS, here's an email, here's a this SMS, here's an email, here's a this SMS, here's an email, here's a Slack message versus I'm going to send a Slack message versus I'm going to send a Slack message versus I'm going to send a message straight to some back-end system message straight to some back-end system message straight to some back-end system that's going to take the action, right?

that's going to take the action, right? that's going to take the action, right? Um whatever it whatever it happens to be Um whatever it whatever it happens to be Um whatever it whatever it happens to be and there there's hundreds or thousands and there there's hundreds or thousands and there there's hundreds or thousands of different things you could integrate of different things you could integrate of different things you could integrate with, but you don't want to be an with, but you don't want to be an with, but you don't want to be an integrator. integrator. integrator. >> Correct. >> Correct. >> Correct. >> And so I have a solution for you. >> And so I have a solution for you. >> And so I have a solution for you. >> Okay? become an integrator. >> Yeah. I mean, if if if you are if if you >> Yeah. I mean, if if if you are if if you are, let's say you're a packaging are, let's say you're a packaging are, let's say you're a packaging vendor, right? And and you you're you're vendor, right? And and you you're you're vendor, right? And and you you're you're you're providing packaging lines and you're providing packaging lines and you're providing packaging lines and they're very very large, very very they're very very large, very very they're very very large, very very expensive, you're doing maybe two, three expensive, you're doing maybe two, three expensive, you're doing maybe two, three a month, right? You can become an a month, right? You can become an a month, right? You can become an integrator. integrator. integrator. >> Yeah. >> Yeah. >> Yeah. >> But if you're if you're selling, you >> But if you're if you're selling, you >> But if you're if you're selling, you know, 10,000 compressors a year know, 10,000 compressors a year know, 10,000 compressors a year >> Yeah. And there's just no way we can't >> Yeah. And there's just no way we can't >> Yeah. And there's just no way we can't scale that. And that that's the that's scale that. And that that's the that's scale that. And that that's the that's the issue we have that there are lots of the issue we have that there are lots of the issue we have that there are lots of OEMs that are integrator or work very OEMs that are integrator or work very OEMs that are integrator or work very closely with closely with closely with >> trying to Yeah. >> trying to Yeah. >> trying to Yeah. >> But but from our perspective, we can't >> But but from our perspective, we can't >> But but from our perspective, we can't we that's just not possible. we that's just not possible. we that's just not possible. >> There's magic. There's magic right now >> There's magic. There's magic right now >> There's magic. There's magic right now though. There's magic out there. Just though. There's magic out there. Just though. There's magic out there. Just like just like you thought you would like just like you thought you would like just like you thought you would have to go to Kepwware to get a driver have to go to Kepwware to get a driver have to go to Kepwware to get a driver to talk to some kind of machine, some to talk to some kind of machine, some to talk to some kind of machine, some kind of protocol kind of protocol kind of protocol >> and then magically because of AI and our >> and then magically because of AI and our >> and then magically because of AI and our friend Claude, friend Claude, friend Claude, you can just say I need a driver, an you can just say I need a driver, an you can just say I need a driver, an adapter for this machine and it will adapter for this machine and it will adapter for this machine and it will give you the code, pick your language to give you the code, pick your language to give you the code, pick your language to do that. So now I don't have to buy do that. So now I don't have to buy do that. So now I don't have to buy anything from Kaplware. So sorry about anything from Kaplware. So sorry about anything from Kaplware. So sorry about that deal going to that private equity that deal going to that private equity that deal going to that private equity Kepler people. And then likewise now you

Kepler people. And then likewise now you Kepler people. And then likewise now you could say I need to integrate with SAP could say I need to integrate with SAP could say I need to integrate with SAP or whatever the system happens to be and or whatever the system happens to be and or whatever the system happens to be and you give it information to your friend you give it information to your friend you give it information to your friend Claude or Chad GBT and it writes the Claude or Chad GBT and it writes the Claude or Chad GBT and it writes the code and it builds that integration for code and it builds that integration for code and it builds that integration for you and then you plug it in. you and then you plug it in. you and then you plug it in. >> You have to test it first but yes >> You have to test it first but yes >> You have to test it first but yes >> what >> what >> what it compiled. We don't need integrators it compiled. We don't need integrators it compiled. We don't need integrators anymore. The integrators are going to anymore. The integrators are going to anymore. The integrators are going to die. die. die. >> No, >> No, >> No, ask Bill. ask Bill. ask Bill. >> That's the gap of the ugly. I was going >> That's the gap of the ugly. I was going >> That's the gap of the ugly. I was going to say it's like what the what the [ __ ] to say it's like what the what the [ __ ] to say it's like what the what the [ __ ] are you guys talking about? You got Bill are you guys talking about? You got Bill are you guys talking about? You got Bill right over here sitting there. I mean, right over here sitting there. I mean, right over here sitting there. I mean, you know, see this is why this guy is you know, see this is why this guy is you know, see this is why this guy is one of the few people who can talk about one of the few people who can talk about one of the few people who can talk about digital twin for in the macro scope of digital twin for in the macro scope of digital twin for in the macro scope of things cuz yeah, you I mean dude, why do things cuz yeah, you I mean dude, why do things cuz yeah, you I mean dude, why do I have to talk for you, man? I have to talk for you, man? I have to talk for you, man? >> You got a big mouth. You got your own >> You got a big mouth. You got your own >> You got a big mouth. You got your own mind, man. You are doing great. mind, man. You are doing great. mind, man. You are doing great. >> No, I'm not your sales guy. I'm not on >> No, I'm not your sales guy. I'm not on >> No, I'm not your sales guy. I'm not on your payroll, dude. your payroll, dude. your payroll, dude. >> Well, you could be. >> Well, you could be. >> Well, you could be. >> Hey, let's talk. >> Hey, let's talk. >> Hey, let's talk. >> Let's talk too.

>> Let's talk too. >> Let's talk too. >> I mean, no, you know, the the thing >> I mean, no, you know, the the thing >> I mean, no, you know, the the thing about it is that everything that about it is that everything that about it is that everything that everything that Jan said is is exactly everything that Jan said is is exactly everything that Jan said is is exactly that. It is that ugly part of making that. It is that ugly part of making that. It is that ugly part of making everything work, everything work, everything work, >> you know, in synergistically. Yeah. Um, >> you know, in synergistically. Yeah. Um, >> you know, in synergistically. Yeah. Um, at the at the end of the day, I mean, at the at the end of the day, I mean, at the at the end of the day, I mean, we're we see it on a regular basis and we're we see it on a regular basis and we're we see it on a regular basis and I'm like, for me, that looks like a I'm like, for me, that looks like a I'm like, for me, that looks like a gourmet meal. I'm like, let's go get it. gourmet meal. I'm like, let's go get it. gourmet meal. I'm like, let's go get it. Um, I, you know, we we do that we do Um, I, you know, we we do that we do Um, I, you know, we we do that we do that kind of stuff on a on a regular that kind of stuff on a on a regular that kind of stuff on a on a regular basis. It's it's completing the the the basis. It's it's completing the the the basis. It's it's completing the the the loop, right? I mean, people are people loop, right? I mean, people are people loop, right? I mean, people are people are always OEMs, companies, system are always OEMs, companies, system are always OEMs, companies, system integrations, all of them will say, integrations, all of them will say, integrations, all of them will say, "Here's the solution." and they draw it "Here's the solution." and they draw it "Here's the solution." and they draw it out for you. Like I said, I mean, out for you. Like I said, I mean, out for you. Like I said, I mean, PowerPoint is a wonderful product. I PowerPoint is a wonderful product. I PowerPoint is a wonderful product. I mean, that is just absolutely amazing. mean, that is just absolutely amazing. mean, that is just absolutely amazing. It works every damn time. It works every damn time. It works every damn time. >> But >> But >> But when you try to take it from PowerPoint when you try to take it from PowerPoint when you try to take it from PowerPoint into the real world, into the real world, into the real world, >> but >> but >> but listen to Rob. We got code now. You put listen to Rob. We got code now. You put listen to Rob. We got code now. You put your PowerPoint into code, you got the your PowerPoint into code, you got the your PowerPoint into code, you got the code, and you're done. You don't have code, and you're done. You don't have code, and you're done. You don't have you didn't add in that extension you didn't add in that extension you didn't add in that extension poweroint with a compile button toolbar.

poweroint with a compile button toolbar. poweroint with a compile button toolbar. No, you will not drop that stuff into No, you will not drop that stuff into No, you will not drop that stuff into claw and all of a sudden magically you claw and all of a sudden magically you claw and all of a sudden magically you got Oh, it's a working prototype. Get got Oh, it's a working prototype. Get got Oh, it's a working prototype. Get the hell out of here. Hey, come close the hell out of here. Hey, come close the hell out of here. Hey, come close everybody. That [ __ ] will never work. everybody. That [ __ ] will never work. everybody. That [ __ ] will never work. Look, Look, Look, that [ __ ] will never work. Don't do that [ __ ] will never work. Don't do that [ __ ] will never work. Don't do that. that. that. >> Oh [ __ ] I am in so much trouble cuz I I >> Oh [ __ ] I am in so much trouble cuz I I >> Oh [ __ ] I am in so much trouble cuz I I do that. do that. do that. Yes, I know. That's But I mean, but but Yes, I know. That's But I mean, but but Yes, I know. That's But I mean, but but look, it's it's a it's a reality. Um I look, it's it's a it's a reality. Um I look, it's it's a it's a reality. Um I mean, mean, mean, >> we you got to go in knowing that you're >> we you got to go in knowing that you're >> we you got to go in knowing that you're you're you're you're in the quagmire. I you're you're you're in the quagmire. I you're you're you're in the quagmire. I mean, you're in the mud, you're in the mean, you're in the mud, you're in the mean, you're in the mud, you're in the sludge, you're in all of that stuff that sludge, you're in all of that stuff that sludge, you're in all of that stuff that you can't really see what what's you can't really see what what's you can't really see what what's happening and you and you just got to go happening and you and you just got to go happening and you and you just got to go do it. do it. do it. >> And and not not many companies want >> And and not not many companies want >> And and not not many companies want that. want to be the pretty shiny object that. want to be the pretty shiny object that. want to be the pretty shiny object as opposed to dealing with um dealing as opposed to dealing with um dealing as opposed to dealing with um dealing with the the the ugliness in order to with the the the ugliness in order to with the the the ugliness in order to get to the golden goblet. get to the golden goblet. get to the golden goblet. >> Actually, you know, when you think about >> Actually, you know, when you think about >> Actually, you know, when you think about it, that that is that sort of lazy it, that that is that sort of lazy it, that that is that sort of lazy Silicon Valley mentality is, you know, Silicon Valley mentality is, you know, Silicon Valley mentality is, you know, everyone's chasing scale and they want everyone's chasing scale and they want everyone's chasing scale and they want their customers to do it themselves. So their customers to do it themselves. So their customers to do it themselves. So there's always been this fixation on no there's always been this fixation on no there's always been this fixation on no code or low code or you know uh you know code or low code or you know uh you know code or low code or you know uh you know selfservice or selfservice or selfservice or you know what I'm saying um you know what I'm saying um you know what I'm saying um >> you nailed it.

>> you nailed it. >> you nailed it. >> Yeah. But you know none of that that >> Yeah. But you know none of that that >> Yeah. But you know none of that that work because a solution is you know work because a solution is you know work because a solution is you know customer specific usually environment customer specific usually environment customer specific usually environment specific and you know like what Rob you specific and you know like what Rob you specific and you know like what Rob you used to bring this up all the time when used to bring this up all the time when used to bring this up all the time when you went out into the Aggie you know you went out into the Aggie you know you went out into the Aggie you know doing the A stuff um each of these you doing the A stuff um each of these you doing the A stuff um each of these you know the availability of wireless know the availability of wireless know the availability of wireless connectivity or connectivity in general connectivity or connectivity in general connectivity or connectivity in general is really tough so you have to work with is really tough so you have to work with is really tough so you have to work with what is available and you can't be what is available and you can't be what is available and you can't be married to any particular particular married to any particular particular married to any particular particular solution. So this is one of the reasons solution. So this is one of the reasons solution. So this is one of the reasons why things don't scale a at a large why things don't scale a at a large why things don't scale a at a large level for a lot of these people that are level for a lot of these people that are level for a lot of these people that are going in with point solutions or hey I going in with point solutions or hey I going in with point solutions or hey I want to sell you a sensor or a module um want to sell you a sensor or a module um want to sell you a sensor or a module um the money is really and you know in the the money is really and you know in the the money is really and you know in the the dirty work right and we've talked the dirty work right and we've talked the dirty work right and we've talked about IoT plumbers and stuff like that about IoT plumbers and stuff like that about IoT plumbers and stuff like that you know you were like in the frigin you you know you were like in the frigin you you know you were like in the frigin you I don't know it was like some you know I don't know it was like some you know I don't know it was like some you know what was it uh you were in a coffee what was it uh you were in a coffee what was it uh you were in a coffee thingy you you did podcasts.

thingy you you did podcasts. thingy you you did podcasts. >> It was hops. Yeah, >> It was hops. Yeah, >> It was hops. Yeah, >> it was in a field of hops. >> it was in a field of hops. >> it was in a field of hops. >> Yeah, that's >> Yeah, that's >> Yeah, that's >> and so and so and you're right. So you >> and so and so and you're right. So you >> and so and so and you're right. So you had to we had to make sure that we could had to we had to make sure that we could had to we had to make sure that we could get the connectivity and so and so get the connectivity and so and so get the connectivity and so and so what's the ugly integration? What would what's the ugly integration? What would what's the ugly integration? What would be the gap of the ugly for that? Cuz be the gap of the ugly for that? Cuz be the gap of the ugly for that? Cuz you're right. You could uh the step baby you're right. You could uh the step baby you're right. You could uh the step baby step one is oh moisture sensor went from step one is oh moisture sensor went from step one is oh moisture sensor went from green to yellow to red. Just keeping it green to yellow to red. Just keeping it green to yellow to red. Just keeping it simple. I need to irrigate this area, simple. I need to irrigate this area, simple. I need to irrigate this area, this block. this block. this block. Baby step one could be, I'm gonna send Baby step one could be, I'm gonna send Baby step one could be, I'm gonna send an SMS message to a human who's going to an SMS message to a human who's going to an SMS message to a human who's going to go open a valve and start irrigating. go open a valve and start irrigating. go open a valve and start irrigating. But what's the better solution? Oh, it But what's the better solution? Oh, it But what's the better solution? Oh, it requires integration. Let me go get the requires integration. Let me go get the requires integration. Let me go get the information from Rainbird or whoever information from Rainbird or whoever information from Rainbird or whoever makes the irrigation system and find a makes the irrigation system and find a makes the irrigation system and find a way to automate that because what I way to automate that because what I way to automate that because what I really want to do is just turn on the really want to do is just turn on the really want to do is just turn on the water and turn it off. water and turn it off. water and turn it off. >> Yeah. Um, and that's that simple example >> Yeah. Um, and that's that simple example >> Yeah. Um, and that's that simple example that we can apply to a million different that we can apply to a million different that we can apply to a million different use cases. That integration, that last use cases. That integration, that last use cases. That integration, that last piece, that's that's what the customer piece, that's that's what the customer piece, that's that's what the customer really wants, you know. Um, really wants, you know. Um, really wants, you know. Um, >> and also I want to react on something >> and also I want to react on something >> and also I want to react on something Leard because I think it is not fair to Leard because I think it is not fair to Leard because I think it is not fair to compare the no code parding to what LLM compare the no code parding to what LLM compare the no code parding to what LLM can do nowadays. And let me explain why can do nowadays. And let me explain why can do nowadays. And let me explain why I'm saying that.

I'm saying that. I'm saying that. >> I'm not talking about LLMs though. So, >> I'm not talking about LLMs though. So, >> I'm not talking about LLMs though. So, but keep going. I I don't know where but keep going. I I don't know where but keep going. I I don't know where you're coming from with that, but go you're coming from with that, but go you're coming from with that, but go ahead. Should be ahead. Should be ahead. Should be >> No, here's what I'm doing. The the >> No, here's what I'm doing. The the >> No, here's what I'm doing. The the reason why no coding approach failed is reason why no coding approach failed is reason why no coding approach failed is because the only way we could remove the because the only way we could remove the because the only way we could remove the coding was by creating kind of an coding was by creating kind of an coding was by creating kind of an abstraction layer which was abstraction layer which was abstraction layer which was fundamentally limited in what you can fundamentally limited in what you can fundamentally limited in what you can describe that you wanted. describe that you wanted. describe that you wanted. >> Yeah. Yeah. >> Yeah. Yeah. >> Yeah. Yeah. Now if you have assuming we solve all Now if you have assuming we solve all Now if you have assuming we solve all the bigger other problems of LLM the bigger other problems of LLM the bigger other problems of LLM generating code but if we reach a point generating code but if we reach a point generating code but if we reach a point where if you describe with a consistency where if you describe with a consistency where if you describe with a consistency and accuracy level in natural language and accuracy level in natural language and accuracy level in natural language what you want and the generation is what you want and the generation is what you want and the generation is predictable and iterable then it can be predictable and iterable then it can be predictable and iterable then it can be a very powerful solution. So I think a very powerful solution. So I think a very powerful solution. So I think that conceptually the idea that you that conceptually the idea that you that conceptually the idea that you actually write specs in language and actually write specs in language and actually write specs in language and then you have an LLM that transfer then you have an LLM that transfer then you have an LLM that transfer methodically and effectively and methodically and effectively and methodically and effectively and predictably that into working code. I predictably that into working code. I predictably that into working code. I think that's a long-term potential. think that's a long-term potential. think that's a long-term potential. We're not there yet but that's a long We're not there yet but that's a long We're not there yet but that's a long you know what I think OpenAI is starting you know what I think OpenAI is starting you know what I think OpenAI is starting to do that. There's a I don't remember to do that. There's a I don't remember to do that. There's a I don't remember the name of the project but there's a the name of the project but there's a the name of the project but there's a concept of you write your prompts for concept of you write your prompts for concept of you write your prompts for specify the code you generate and there specify the code you generate and there specify the code you generate and there is some rigor aspect to it. So I think is some rigor aspect to it. So I think is some rigor aspect to it. So I think there's a there's an interesting there's a there's an interesting there's a there's an interesting direction there.

direction there. direction there. >> Yeah. I mean you know um I I think the >> Yeah. I mean you know um I I think the >> Yeah. I mean you know um I I think the biggest problem with Z no code is you biggest problem with Z no code is you biggest problem with Z no code is you know using LLMs is language. Uh and so know using LLMs is language. Uh and so know using LLMs is language. Uh and so and then and then and then assuming that somebody who doesn't know assuming that somebody who doesn't know assuming that somebody who doesn't know how to code can just describe an outcome how to code can just describe an outcome how to code can just describe an outcome and expectations and expectations and expectations um with sufficient detail and uh you um with sufficient detail and uh you um with sufficient detail and uh you know comprehensiveness. know comprehensiveness. know comprehensiveness. Uh that's a tall order. And so, you Uh that's a tall order. And so, you Uh that's a tall order. And so, you know, who is this human know, who is this human know, who is this human that people are assuming is going to be that people are assuming is going to be that people are assuming is going to be able to safely and reliably able to safely and reliably able to safely and reliably use these tools? And again, you know, I use these tools? And again, you know, I use these tools? And again, you know, I think these are the this is like the think these are the this is like the think these are the this is like the major disconnected major disconnected major disconnected delusional thinking that is being delusional thinking that is being delusional thinking that is being wrapped around this idea of uh no code wrapped around this idea of uh no code wrapped around this idea of uh no code and even to certain well locate code's and even to certain well locate code's and even to certain well locate code's not that delusional. um the no code not that delusional. um the no code not that delusional. um the no code stuff and the vibe coding. I mean, it's stuff and the vibe coding. I mean, it's stuff and the vibe coding. I mean, it's all being proven to be less capable um all being proven to be less capable um all being proven to be less capable um less um let's say usable than people h less um let's say usable than people h less um let's say usable than people h you know these evangelists, these you know these evangelists, these you know these evangelists, these talking heads had suggested, right? It's talking heads had suggested, right? It's talking heads had suggested, right? It's easy for somebody who doesn't know any easy for somebody who doesn't know any easy for somebody who doesn't know any of this [ __ ] to just get really excited of this [ __ ] to just get really excited of this [ __ ] to just get really excited about and say all developers are dead.

about and say all developers are dead. about and say all developers are dead. Nobody should go to computer science um Nobody should go to computer science um Nobody should go to computer science um fields. It's done. You know what I'm fields. It's done. You know what I'm fields. It's done. You know what I'm saying? saying? saying? It's easy for people to say that but no It's easy for people to say that but no It's easy for people to say that but no I'm saying I'm saying I'm saying >> there is no absolutely no disagreement >> there is no absolutely no disagreement >> there is no absolutely no disagreement on that and you said it the key is in on that and you said it the key is in on that and you said it the key is in expressing with language properly but expressing with language properly but expressing with language properly but the reason why I'm saying I believe it's the reason why I'm saying I believe it's the reason why I'm saying I believe it's a potential direction because the a potential direction because the a potential direction because the solution exists there's one thing that's solution exists there's one thing that's solution exists there's one thing that's >> the tool exists >> the tool exists >> the tool exists >> no no no let me say what I'm saying no I >> no no no let me say what I'm saying no I >> no no no let me say what I'm saying no I mean the solution conceptually exist mean the solution conceptually exist mean the solution conceptually exist have you ever heard about TLC plus TLC have you ever heard about TLC plus TLC have you ever heard about TLC plus TLC plus is a way to mathematically express plus is a way to mathematically express plus is a way to mathematically express software a problem and then you have software a problem and then you have software a problem and then you have generators that can actually prove that generators that can actually prove that generators that can actually prove that the system is reliant. the system is reliant. the system is reliant. >> Yeah, but you need to know math. >> Yeah, but you need to know math. >> Yeah, but you need to know math. >> It it is used for some very high-end >> It it is used for some very high-end >> It it is used for some very high-end very super reliable systems at the core very super reliable systems at the core very super reliable systems at the core of the technology. Now, it's extremely of the technology. Now, it's extremely of the technology. Now, it's extremely complex to use because you have to complex to use because you have to complex to use because you have to express in math. But my point is that if express in math. But my point is that if express in math. But my point is that if we train generation of developers to be we train generation of developers to be we train generation of developers to be capable to express as close as math with capable to express as close as math with capable to express as close as math with rigor what the outcome is, then you can rigor what the outcome is, then you can rigor what the outcome is, then you can imagine that LM can generate a code that imagine that LM can generate a code that imagine that LM can generate a code that can work.

can work. can work. I think I think I think I think you hit a nail on the point. If I think you hit a nail on the point. If I think you hit a nail on the point. If we can get programmers to express what we can get programmers to express what we can get programmers to express what they need because I I'm I've been they need because I I'm I've been they need because I I'm I've been thinking about this whole vibe coding. thinking about this whole vibe coding. thinking about this whole vibe coding. If if I go back in time, I've been in If if I go back in time, I've been in If if I go back in time, I've been in product management working with product management working with product management working with developers for the last 20 years and I developers for the last 20 years and I developers for the last 20 years and I can express something in a requirement can express something in a requirement can express something in a requirement spec and still get something out that I spec and still get something out that I spec and still get something out that I didn't expect. didn't expect. didn't expect. >> Yeah. >> Yeah. >> Yeah. >> Right. And that's that's working with >> Right. And that's that's working with >> Right. And that's that's working with people. people. people. >> Yeah. And now now now you now now you're >> Yeah. And now now now you now now you're >> Yeah. And now now now you now now you're doing it to an LLM. Uh and and so I doing it to an LLM. Uh and and so I doing it to an LLM. Uh and and so I think I I think the the right approach think I I think the the right approach think I I think the the right approach is that that you have the same is that that you have the same is that that you have the same environment today but you got developers environment today but you got developers environment today but you got developers utilizing this tool to say okay this is utilizing this tool to say okay this is utilizing this tool to say okay this is how I understand what my user wants. Now how I understand what my user wants. Now how I understand what my user wants. Now I'm going to describe it in some kind of I'm going to describe it in some kind of I'm going to describe it in some kind of I coping environment and get a result I coping environment and get a result I coping environment and get a result out and then I'm going to iterate back out and then I'm going to iterate back out and then I'm going to iterate back and forth until it is what I'm expecting and forth until it is what I'm expecting and forth until it is what I'm expecting to see. to see. to see. >> Right. Right. And but but but if we >> Right. Right. And but but but if we >> Right. Right. And but but but if we don't work on that, how do you specify don't work on that, how do you specify don't work on that, how do you specify properly? And actually this is what properly? And actually this is what properly? And actually this is what happening today. The amount of shitty happening today. The amount of shitty happening today. The amount of shitty code generated is going to go to the code generated is going to go to the code generated is going to go to the roof because it is going through the roof because it is going through the roof because it is going through the roof because you before you this when roof because you before you this when roof because you before you this when Microsoft said 30% of our code generated Microsoft said 30% of our code generated Microsoft said 30% of our code generated >> I'm frightened. I hide in my bedroom and >> I'm frightened. I hide in my bedroom and >> I'm frightened. I hide in my bedroom and I was like are you trying to crack or I was like are you trying to crack or I was like are you trying to crack or you're throwing your company under the you're throwing your company under the you're throwing your company under the bus?

bus? bus? >> No, because I know what crappy code they >> No, because I know what crappy code they >> No, because I know what crappy code they can do today with human. So I'm trying can do today with human. So I'm trying can do today with human. So I'm trying to imagine how much more they're going to imagine how much more they're going to imagine how much more they're going to do with these things. to do with these things. to do with these things. >> Yeah. But so here, let let's back up >> Yeah. But so here, let let's back up >> Yeah. But so here, let let's back up really quick. Here's the really really quick. Here's the really really quick. Here's the really interesting thing about your statement interesting thing about your statement interesting thing about your statement about uh earlier um you you're about uh earlier um you you're about uh earlier um you you're suggesting that people should get down suggesting that people should get down suggesting that people should get down to low-level code to low-level code to low-level code coding language. You know what I'm coding language. You know what I'm coding language. You know what I'm saying? In order to get specific and saying? In order to get specific and saying? In order to get specific and which is kind of a which is kind of a which is kind of a >> kind of you know going in the opposite >> kind of you know going in the opposite >> kind of you know going in the opposite direction and trying you know what I'm direction and trying you know what I'm direction and trying you know what I'm saying. So when we think about these saying. So when we think about these saying. So when we think about these things as being a solution rather than things as being a solution rather than things as being a solution rather than just a tool and then providing just a tool and then providing just a tool and then providing alternative ways of getting something alternative ways of getting something alternative ways of getting something done, this is where where things get done, this is where where things get done, this is where where things get lost. lost. lost. >> Yeah. No, I agree with that. But I'm not >> Yeah. No, I agree with that. But I'm not >> Yeah. No, I agree with that. But I'm not intending that. The the analogy I would intending that. The the analogy I would intending that. The the analogy I would take is I I come from the metadata take is I I come from the metadata take is I I come from the metadata database world. database world. database world. >> So do I. >> So do I. >> So do I. >> So you know what is a conceptual data >> So you know what is a conceptual data >> So you know what is a conceptual data model? You talk about entities, model? You talk about entities, model? You talk about entities, customers place an order, an order as customers place an order, an order as customers place an order, an order as this and that. And once you have this this and that. And once you have this this and that. And once you have this conceptual model, the translation into a conceptual model, the translation into a conceptual model, the translation into a physical set of structure depending on physical set of structure depending on physical set of structure depending on the technology RDBMS sequential can be the technology RDBMS sequential can be the technology RDBMS sequential can be totally automated.

totally automated. totally automated. This is the same idea because at This is the same idea because at This is the same idea because at conceptual level you can describe conceptual level you can describe conceptual level you can describe accurately and well enough what you need accurately and well enough what you need accurately and well enough what you need and what the system should be. So if we and what the system should be. So if we and what the system should be. So if we reach that point of specification then reach that point of specification then reach that point of specification then the generation can be automated and I the generation can be automated and I the generation can be automated and I think that LLMs can do that. But if the think that LLMs can do that. But if the think that LLMs can do that. But if the conceptual model is crap, conceptual model is crap, conceptual model is crap, then the code is crap then the code is crap then the code is crap >> or or the person can't answer the >> or or the person can't answer the >> or or the person can't answer the question and doesn't know math. I mean, question and doesn't know math. I mean, question and doesn't know math. I mean, most people these days can't even count most people these days can't even count most people these days can't even count back change to you. So, I mean, it's back change to you. So, I mean, it's back change to you. So, I mean, it's like they use calculators. They don't like they use calculators. They don't like they use calculators. They don't even know math. I mean, you know, people even know math. I mean, you know, people even know math. I mean, you know, people are getting over reliant on tools. So, are getting over reliant on tools. So, are getting over reliant on tools. So, how are they going to ask good how are they going to ask good how are they going to ask good questions? And you know, the thing is is questions? And you know, the thing is is questions? And you know, the thing is is we've mentioned this before. What's we've mentioned this before. What's we've mentioned this before. What's probably more important than the actual probably more important than the actual probably more important than the actual tool giving you answers is the person tool giving you answers is the person tool giving you answers is the person asking the questions, asking the questions, asking the questions, right? Whether you want to call it right? Whether you want to call it right? Whether you want to call it prompting or what have you. The thing is prompting or what have you. The thing is prompting or what have you. The thing is is um if people can't ask the next best is um if people can't ask the next best is um if people can't ask the next best question from that initial one, question from that initial one, question from that initial one, the tools are are going to create a the tools are are going to create a the tools are are going to create a chain of confusion and nonsense and chain of confusion and nonsense and chain of confusion and nonsense and >> or confusion.

>> or confusion. >> or confusion. >> Ball of confusion. That's what the world >> Ball of confusion. That's what the world >> Ball of confusion. That's what the world is today. >> I mean, you know, you Yeah. And you were >> I mean, you know, you Yeah. And you were like on one one of the first um big Gen like on one one of the first um big Gen like on one one of the first um big Gen AI uh we were on. I mean, think back to AI uh we were on. I mean, think back to AI uh we were on. I mean, think back to that and where we are. Did we get as far that and where we are. Did we get as far that and where we are. Did we get as far as everyone was thinking at that point as everyone was thinking at that point as everyone was thinking at that point and that's a great episode to go back and that's a great episode to go back and that's a great episode to go back and reflect on? No. Actually, a lot of and reflect on? No. Actually, a lot of and reflect on? No. Actually, a lot of the problems we described in that the problems we described in that the problems we described in that episode 3 years ago are the reasons why episode 3 years ago are the reasons why episode 3 years ago are the reasons why these things aren't going as far and the these things aren't going as far and the these things aren't going as far and the industry keeps pivoting, right? There's industry keeps pivoting, right? There's industry keeps pivoting, right? There's a constant pivot. Now, they have to make a constant pivot. Now, they have to make a constant pivot. Now, they have to make an excuse and go into world models. So an excuse and go into world models. So an excuse and go into world models. So that's where something's going to happen that's where something's going to happen that's where something's going to happen where we merge symbolic with all this where we merge symbolic with all this where we merge symbolic with all this like LLM crap and hopefully we'll get to like LLM crap and hopefully we'll get to like LLM crap and hopefully we'll get to ASI which all of a sudden nobody gives a ASI which all of a sudden nobody gives a ASI which all of a sudden nobody gives a crap about you know or AGI. crap about you know or AGI. crap about you know or AGI. >> AGI. Yeah. >> AGI. Yeah. >> AGI. Yeah. >> Yeah. But I I I I personally think I >> Yeah. But I I I I personally think I >> Yeah. But I I I I personally think I think I said that on the show before. I think I said that on the show before. I think I said that on the show before. I think that the it's it's all flown think that the it's it's all flown think that the it's it's all flown because we call that intelligence from because we call that intelligence from because we call that intelligence from the beginning. It is not intelligence.

the beginning. It is not intelligence. the beginning. It is not intelligence. It's cleverness at best. Again, don't It's cleverness at best. Again, don't It's cleverness at best. Again, don't get me wrong. get me wrong. get me wrong. >> Artificial cleverness. >> Artificial cleverness. >> Artificial cleverness. >> No, that's that's what I'm saying. >> No, that's that's what I'm saying. >> No, that's that's what I'm saying. Artificial No, it is it is between Artificial No, it is it is between Artificial No, it is it is between artificial nonsense and artificial artificial nonsense and artificial artificial nonsense and artificial cleverness. Now, don't get me wrong, I cleverness. Now, don't get me wrong, I cleverness. Now, don't get me wrong, I use these tools a lot. If you treat them use these tools a lot. If you treat them use these tools a lot. If you treat them as a system for what they can do, these as a system for what they can do, these as a system for what they can do, these are fantastic tools, but calling these are fantastic tools, but calling these are fantastic tools, but calling these things intelligence is a misconception things intelligence is a misconception things intelligence is a misconception at this point. I haven't seen any of at this point. I haven't seen any of at this point. I haven't seen any of them that is intelligent. And my proof them that is intelligent. And my proof them that is intelligent. And my proof point is always the same. Which one of point is always the same. Which one of point is always the same. Which one of these LLM models has solved one of the these LLM models has solved one of the these LLM models has solved one of the math millennium problem? math millennium problem? math millennium problem? >> Yeah, >> Yeah, >> Yeah, >> none of them. They can they can solve >> none of them. They can they can solve >> none of them. They can they can solve all problem we have solved because all problem we have solved because all problem we have solved because they've been trained for it. But they they've been trained for it. But they they've been trained for it. But they cannot solve a non-human solved problem. cannot solve a non-human solved problem. cannot solve a non-human solved problem. I mean at a conceptual level I'm not I mean at a conceptual level I'm not I mean at a conceptual level I'm not telling at a scale level doing telling at a scale level doing telling at a scale level doing gazillions of multiplication of course gazillions of multiplication of course gazillions of multiplication of course they can do that better than human. But they can do that better than human. But they can do that better than human. But finding a math demonstration that no finding a math demonstration that no finding a math demonstration that no human have found. None of them have done human have found. None of them have done human have found. None of them have done that. that. that. >> Yeah. And you know I have we have seen a >> Yeah. And you know I have we have seen a >> Yeah. And you know I have we have seen a lot of artificial stupidity. So I don't lot of artificial stupidity. So I don't lot of artificial stupidity. So I don't know how that fits in your know how that fits in your know how that fits in your cleverness cleverness cleverness >> because I'm trying to be politically >> because I'm trying to be politically >> because I'm trying to be politically correct due to your resolution. So I I I correct due to your resolution. So I I I correct due to your resolution. So I I I >> when have you ever been politically >> when have you ever been politically >> when have you ever been politically correct?

correct? correct? >> I put I put that into the nonsense >> I put I put that into the nonsense >> I put I put that into the nonsense stupid and nonsense. stupid and nonsense. stupid and nonsense. >> Okay. In nonsense. Okay. >> Okay. In nonsense. Okay. >> Okay. In nonsense. Okay. >> So what do you think Pete? >> So what do you think Pete? >> So what do you think Pete? you've been just you've been just you've been just >> just trying to follow along. >> just trying to follow along. >> just trying to follow along. >> Oh my god. >> Oh my god. >> Oh my god. >> You know, I I saw some stat I saw a stat >> You know, I I saw some stat I saw a stat >> You know, I I saw some stat I saw a stat about uh coding with AI assistance and about uh coding with AI assistance and about uh coding with AI assistance and it was some super high percentage now of it was some super high percentage now of it was some super high percentage now of developers developers developers like almost it's almost like and there's like almost it's almost like and there's like almost it's almost like and there's no developer out there really that's no developer out there really that's no developer out there really that's that's starting to code now without some that's starting to code now without some that's starting to code now without some sort of AI assistant. I mean, it's sort of AI assistant. I mean, it's sort of AI assistant. I mean, it's becoming so standardized. So, you know, becoming so standardized. So, you know, becoming so standardized. So, you know, whether it's good or not or how good it whether it's good or not or how good it whether it's good or not or how good it is, it is it's it's it's part of the is, it is it's it's it's part of the is, it is it's it's it's part of the standard operating procedure at this standard operating procedure at this standard operating procedure at this point. point. point. >> Um, and so, >> Um, and so, >> Um, and so, >> I I think there's a lot of opportunities >> I I think there's a lot of opportunities >> I I think there's a lot of opportunities with with that level at having with with that level at having with with that level at having assistance. So, for example, assistance. So, for example, assistance. So, for example, >> about 10 years ago, the big thing was uh >> about 10 years ago, the big thing was uh >> about 10 years ago, the big thing was uh um what they call pair programming. um what they call pair programming. um what they call pair programming. >> Yeah. >> Yeah. >> Yeah. >> You had two programmers. One was >> You had two programmers. One was >> You had two programmers. One was actually right actually right actually right >> coding and one was actually checking the >> coding and one was actually checking the >> coding and one was actually checking the code and looking up stuff and whatever.

code and looking up stuff and whatever. code and looking up stuff and whatever. >> Programming. Yeah. And that's definitely >> Programming. Yeah. And that's definitely >> Programming. Yeah. And that's definitely something where the pair can be AI. something where the pair can be AI. something where the pair can be AI. Right. Right. Right. >> Right. >> Right. >> Right. >> And even vice >> And even vice >> And even vice >> you know and then then the assistant can >> you know and then then the assistant can >> you know and then then the assistant can be quite powerful. I mean imagine if you be quite powerful. I mean imagine if you be quite powerful. I mean imagine if you have 10 10 20 have 10 10 20 have 10 10 20 >> equivalent programmers helping someone >> equivalent programmers helping someone >> equivalent programmers helping someone then you know it starts to expand then you know it starts to expand then you know it starts to expand expand. So I think that's just that the expand. So I think that's just that the expand. So I think that's just that the standard procedure right now. I don't standard procedure right now. I don't standard procedure right now. I don't expect any developers out there really expect any developers out there really expect any developers out there really uh except maybe the the guy working on uh except maybe the the guy working on uh except maybe the the guy working on the cobalt thing um at the Ford plant the cobalt thing um at the Ford plant the cobalt thing um at the Ford plant >> which is really cool. That's a really >> which is really cool. That's a really >> which is really cool. That's a really >> but you know other than that it's all AI >> but you know other than that it's all AI >> but you know other than that it's all AI assisted coding at this point moving assisted coding at this point moving assisted coding at this point moving forward. forward. forward. >> Yeah. >> Yeah. >> Yeah. >> All ball bearings these days. >> All ball bearings these days. >> All ball bearings these days. >> It's all ball bearing. It's all >> It's all ball bearing. It's all >> It's all ball bearing. It's all plastics. plastics. plastics. >> You know it's amazing how important ball >> You know it's amazing how important ball >> You know it's amazing how important ball bearings are. I never you know like when bearings are. I never you know like when bearings are. I never you know like when you watch those World War II you watch those World War II you watch those World War II documentaries and you you hear about documentaries and you you hear about documentaries and you you hear about like the Allied forces focusing so much like the Allied forces focusing so much like the Allied forces focusing so much on bombing the German factory that on bombing the German factory that on bombing the German factory that produced ball bearings. I always produced ball bearings. I always produced ball bearings. I always wondered why that like freaking ball wondered why that like freaking ball wondered why that like freaking ball bearings. bearings. bearings. Well, actually, if you're going around Well, actually, if you're going around Well, actually, if you're going around the Detroit area, Michigan area, there's the Detroit area, Michigan area, there's the Detroit area, Michigan area, there's there's a lot of familyun manufacturing there's a lot of familyun manufacturing there's a lot of familyun manufacturing that all they do are very specific ball that all they do are very specific ball that all they do are very specific ball bearings for automotive bearings for automotive bearings for automotive >> assembly. Like they've been doing it >> assembly. Like they've been doing it >> assembly. Like they've been doing it forever. And forever. And forever. And >> yeah, it's like the critical part of the >> yeah, it's like the critical part of the >> yeah, it's like the critical part of the supply chain. Like if you don't get the supply chain. Like if you don't get the supply chain. Like if you don't get the ball bearings, like nothing happens. So, ball bearings, like nothing happens. So, ball bearings, like nothing happens. So, it's very interesting.

it's very interesting. it's very interesting. >> I don't do you know how many types of >> I don't do you know how many types of >> I don't do you know how many types of ball bearings there are? ball bearings there are? ball bearings there are? >> I would say two or three. >> I would say two or three. >> I would say two or three. >> Yeah, there's three. >> Yeah, there's three. >> Yeah, there's three. >> One round. It's either bold. It's >> One round. It's either bold. It's >> One round. It's either bold. It's >> square ones don't work well. >> square ones don't work well. >> square ones don't work well. >> No, but I mean I mean in the inside you >> No, but I mean I mean in the inside you >> No, but I mean I mean in the inside you either have little spheres or you have either have little spheres or you have either have little spheres or you have cylinders or you have conic cylinders. cylinders or you have conic cylinders. cylinders or you have conic cylinders. >> This is a whole other level now. >> This is a whole other level now. >> This is a whole other level now. >> Yeah. And it depends on what kind of it >> Yeah. And it depends on what kind of it >> Yeah. And it depends on what kind of it depends on what kind of force are used. depends on what kind of force are used. depends on what kind of force are used. If you have a lot of centrifuge force, If you have a lot of centrifuge force, If you have a lot of centrifuge force, you you use the the cylinders. If you you you use the the cylinders. If you you you use the the cylinders. If you have some side forces, you use the the have some side forces, you use the the have some side forces, you use the the the the kind of the trapeis shaped the the kind of the trapeis shaped the the kind of the trapeis shaped cylinders. Or if you use regular, you cylinders. Or if you use regular, you cylinders. Or if you use regular, you just use the bolts. Yeah, I studied that just use the bolts. Yeah, I studied that just use the bolts. Yeah, I studied that when I was at school. So, when I was at school. So, when I was at school. So, >> really? Wow. There you go. >> really? Wow. There you go. >> really? Wow. There you go. >> I have a I have a good background in >> I have a I have a good background in >> I have a I have a good background in mechanics. Mechanics have a ball bearing mechanics. Mechanics have a ball bearing mechanics. Mechanics have a ball bearing experts. experts. experts. >> That's awesome. That's awesome. That's >> That's awesome. That's awesome. That's >> That's awesome. That's awesome. That's great insight. Good to know. Um, great insight. Good to know. Um, great insight. Good to know. Um, remember remember remember I I tell you an anecdote. So, the place I I tell you an anecdote. So, the place I I tell you an anecdote. So, the place I live right now is rented, so it comes I live right now is rented, so it comes I live right now is rented, so it comes with the the appliance. And during the with the the appliance. And during the with the the appliance. And during the during the the the the Christmas break, during the the the the Christmas break, during the the the the Christmas break, the the the dryer start to make a awful the the the dryer start to make a awful the the the dryer start to make a awful noise. So, I told my daughter, "Oh, that noise. So, I told my daughter, "Oh, that noise. So, I told my daughter, "Oh, that sounds like a bearing noise." And she's sounds like a bearing noise." And she's sounds like a bearing noise." And she's like, and then the boyfriend is here and like, and then the boyfriend is here and like, and then the boyfriend is here and I said, "Hey, do you know where is the I said, "Hey, do you know where is the I said, "Hey, do you know where is the bearing?" And he's like, "Uh, bearing?" And he's like, "Uh, bearing?" And he's like, "Uh, >> what's that?"

>> what's that?" >> what's that?" >> Well, see, that's where we're going to >> Well, see, that's where we're going to >> Well, see, that's where we're going to be in trouble. We're going to have no be in trouble. We're going to have no be in trouble. We're going to have no one that can repair anything. one that can repair anything. one that can repair anything. >> Kids are actually And by the way, it >> Kids are actually And by the way, it >> Kids are actually And by the way, it ended up that this dryer doesn't use ended up that this dryer doesn't use ended up that this dryer doesn't use bearing. It's a piece of crap. But, uh, bearing. It's a piece of crap. But, uh, bearing. It's a piece of crap. But, uh, anyway, anyway, anyway, >> well, no, you need to hold your phone up >> well, no, you need to hold your phone up >> well, no, you need to hold your phone up to the dryer and say, "What is this to the dryer and say, "What is this to the dryer and say, "What is this noise?" And then it will say, "Oh, your noise?" And then it will say, "Oh, your noise?" And then it will say, "Oh, your >> oh, >> oh, >> oh, >> the strap thing here is wearing out and >> the strap thing here is wearing out and >> the strap thing here is wearing out and here's a click here to order another here's a click here to order another here's a click here to order another one." So that's one." So that's one." So that's >> Yeah. In that case, the LLM will >> Yeah. In that case, the LLM will >> Yeah. In that case, the LLM will probably reply, "Hey, you got screwed probably reply, "Hey, you got screwed probably reply, "Hey, you got screwed because you bought this expensive dryer, because you bought this expensive dryer, because you bought this expensive dryer, which is a real piece of crap." It is which is a real piece of crap." It is which is a real piece of crap." It is actually extremely bad. actually extremely bad. actually extremely bad. >> Well, that might actually well in industrial things, Yan, so you well in industrial things, Yan, so you would understand that there's no chassis would understand that there's no chassis would understand that there's no chassis actually. the the the the structure of actually. the the the the structure of actually. the the the the structure of the things. It only holds together when the things. It only holds together when the things. It only holds together when it's assembled with the panels. There's it's assembled with the panels. There's it's assembled with the panels. There's no internal chassis. Wow. no internal chassis. Wow. no internal chassis. Wow. >> It's a piece of crap, but I fixed it. >> It's a piece of crap, but I fixed it. >> It's a piece of crap, but I fixed it. Okay. Okay. Okay. >> Yeah. But, you know, hey, you know, it's >> Yeah. But, you know, hey, you know, it's >> Yeah. But, you know, hey, you know, it's great to have you back on. Yeah. And great to have you back on. Yeah. And great to have you back on. Yeah. And it's been a while. And always bring it's been a while. And always bring it's been a while. And always bring >> good to be on. Good to be on. >> good to be on. Good to be on. >> good to be on. Good to be on. >> Um, well, >> Um, well, >> Um, well, >> I was Yeah, I was thinking you guys are >> I was Yeah, I was thinking you guys are >> I was Yeah, I was thinking you guys are going to be talking about CES and all going to be talking about CES and all going to be talking about CES and all the stuff you learned. And the stuff you learned. And the stuff you learned. And >> that was last week.

>> that was last week. >> that was last week. >> What do you want to know? What do you >> What do you want to know? What do you >> What do you want to know? What do you want to know? want to know? want to know? >> Yes. the past already. >> Yes. the past already. >> Yes. the past already. >> But what's new? What's good? What's uh >> But what's new? What's good? What's uh >> But what's new? What's good? What's uh what's uh revolutionary? Anything? what's uh revolutionary? Anything? what's uh revolutionary? Anything? Nothing. Nothing. Nothing. >> Robots playing pingpong. >> Robots playing pingpong. >> Robots playing pingpong. >> Sorry, the last episode. Yeah, it was >> Sorry, the last episode. Yeah, it was >> Sorry, the last episode. Yeah, it was all robots. Yeah. all robots. Yeah. all robots. Yeah. >> Let me share something with you. I heard >> Let me share something with you. I heard >> Let me share something with you. I heard something really interesting this something really interesting this something really interesting this morning on uh CNBC. had some um morning on uh CNBC. had some um morning on uh CNBC. had some um investment um joker on and they were investment um joker on and they were investment um joker on and they were talking about um the transition of this talking about um the transition of this talking about um the transition of this focus on uh the previous focus on EV and focus on uh the previous focus on EV and focus on uh the previous focus on EV and electric electrification electric electrification electric electrification toward you know the robo taxis and you toward you know the robo taxis and you toward you know the robo taxis and you know uh you know cars as a service or know uh you know cars as a service or know uh you know cars as a service or taxis as a service which are I mean it's taxis as a service which are I mean it's taxis as a service which are I mean it's like yeah taxis or services it's just like yeah taxis or services it's just like yeah taxis or services it's just >> they are already as a service yeah >> they are already as a service yeah >> they are already as a service yeah >> the most ridiculous thing this person >> the most ridiculous thing this person >> the most ridiculous thing this person projected profit margins. She goes, projected profit margins. She goes, projected profit margins. She goes, well, you know, it's kind of like a SAS well, you know, it's kind of like a SAS well, you know, it's kind of like a SAS for cars. for cars. for cars. I'm like, okay, where are we going with I'm like, okay, where are we going with I'm like, okay, where are we going with this? It's like, well, you know, 70 70 this? It's like, well, you know, 70 70 this? It's like, well, you know, 70 70 it's has like a 70% margin.

>> Wait, you're projecting SAS margins, >> Wait, you're projecting SAS margins, software as a service software as a service software as a service >> to taxis, >> to taxis, >> to taxis, >> right? >> right? >> right? >> You got to be effing kidding me, right? >> You got to be effing kidding me, right? >> You got to be effing kidding me, right? The next line was The next line was The next line was >> as a service process. >> as a service process. >> as a service process. >> I hope it works out, Pete. >> I hope it works out, Pete. >> I hope it works out, Pete. >> We're we're we're working on the model. >> We're we're we're working on the model. >> We're we're we're working on the model. >> Good luck. >> Good luck. >> Good luck. >> Very high margin. It's very high margin. >> Very high margin. It's very high margin. >> Very high margin. It's very high margin. >> Leonard as a service means you're paying >> Leonard as a service means you're paying >> Leonard as a service means you're paying every month. That's what they think. every month. That's what they think. every month. That's what they think. >> Well, is that what Tesla just did? They >> Well, is that what Tesla just did? They >> Well, is that what Tesla just did? They announced that they're going to they're announced that they're going to they're announced that they're going to they're going to stop selling the autonomous going to stop selling the autonomous going to stop selling the autonomous driving driving driving >> right now subscription. They're going to >> right now subscription. They're going to >> right now subscription. They're going to move it to a subscription, of course. move it to a subscription, of course. move it to a subscription, of course. And you don't pay, you don't start your And you don't pay, you don't start your And you don't pay, you don't start your car in the morning. And you get an car in the morning. And you get an car in the morning. And you get an advertising, by the way, get this new advertising, by the way, get this new advertising, by the way, get this new credit card to pay for your car today. credit card to pay for your car today. credit card to pay for your car today. >> Oh, they'll have a they'll have an Yeah. >> Oh, they'll have a they'll have an Yeah. >> Oh, they'll have a they'll have an Yeah. an ad ad tier where you see ads on your an ad ad tier where you see ads on your an ad ad tier where you see ads on your screen and pay less per month, right? screen and pay less per month, right? screen and pay less per month, right? >> Oh, no, no, no. Better model, Pete. It >> Oh, no, no, no. Better model, Pete. It >> Oh, no, no, no. Better model, Pete. It drives you to a special place and then drives you to a special place and then drives you to a special place and then it gives you the coupon. it gives you the coupon. it gives you the coupon. >> It drives you to the McDonald's to >> It drives you to the McDonald's to >> It drives you to the McDonald's to redeem your coupon.

redeem your coupon. redeem your coupon. >> What's going to happen? get the food and >> What's going to happen? get the food and >> What's going to happen? get the food and then the food is and you have to say eat then the food is and you have to say eat then the food is and you have to say eat it. it. it. >> What's going to happen though? >> What's going to happen though? >> What's going to happen though? >> They'll watch you eat it. They'll watch >> They'll watch you eat it. They'll watch >> They'll watch you eat it. They'll watch you eat the food. You don't get the you eat the food. You don't get the you eat the food. You don't get the coupon until you consume the food. coupon until you consume the food. coupon until you consume the food. >> It It drives you to a place and you're >> It It drives you to a place and you're >> It It drives you to a place and you're going to walk into this little going to walk into this little going to walk into this little compartment thing. You think you're compartment thing. You think you're compartment thing. You think you're going to be a made man afterwards and going to be a made man afterwards and going to be a made man afterwards and then they just pop you. then they just pop you. then they just pop you. >> That's right. Yeah, that's right. Very >> That's right. Yeah, that's right. Very >> That's right. Yeah, that's right. Very famous scene. I don't know if everyone's famous scene. I don't know if everyone's famous scene. I don't know if everyone's getting your reference. getting your reference. getting your reference. >> Hey, everybody's trying to do things as >> Hey, everybody's trying to do things as >> Hey, everybody's trying to do things as a service. I noticed car washes, which a service. I noticed car washes, which a service. I noticed car washes, which there are very few car washes in the there are very few car washes in the there are very few car washes in the Pacific Northwest, but Pacific Northwest, but Pacific Northwest, but >> Brown Bear. >> Brown Bear. >> Brown Bear. >> Yeah. But there's only It's not like if >> Yeah. But there's only It's not like if >> Yeah. But there's only It's not like if you're in Arizona or Texas where there's you're in Arizona or Texas where there's you're in Arizona or Texas where there's one on every corner, one on every corner, one on every corner, >> right? >> right? >> right? >> And what are the car washes doing? >> And what are the car washes doing? >> And what are the car washes doing? They're trying to get you to do a They're trying to get you to do a They're trying to get you to do a subscription. Just pay us 30 bucks a subscription. Just pay us 30 bucks a subscription. Just pay us 30 bucks a month. You can come as often as you month. You can come as often as you month. You can come as often as you want. want. want. >> Yeah. >> Yeah. >> Yeah. >> But what do we all know? You won't. You >> But what do we all know? You won't. You >> But what do we all know? You won't. You might go once and that's how they're And might go once and that's how they're And might go once and that's how they're And the movie theaters are doing the same the movie theaters are doing the same the movie theaters are doing the same thing. thing. thing. >> Movie theater. Totally. Yeah. You can >> Movie theater. Totally. Yeah. You can >> Movie theater. Totally. Yeah. You can watch four four or five movies a week at watch four four or five movies a week at watch four four or five movies a week at the theater, which who is doing that the theater, which who is doing that the theater, which who is doing that unless you're unless you're unless you're >> right. I know you won't do it.

>> right. I know you won't do it. >> right. I know you won't do it. >> I would do that. That's That's kind of >> I would do that. That's That's kind of >> I would do that. That's That's kind of cool. But cool. But cool. But >> it is cool. >> it is cool. >> it is cool. >> Problem is, uh, have you se Okay. Um, >> Problem is, uh, have you se Okay. Um, >> Problem is, uh, have you se Okay. Um, you know, you guys know I have an Apple you know, you guys know I have an Apple you know, you guys know I have an Apple Vision Pro, right? Vision Pro, right? Vision Pro, right? >> Yeah. Have you you guys have to check >> Yeah. Have you you guys have to check >> Yeah. Have you you guys have to check out the NBA thing? It will out the NBA thing? It will out the NBA thing? It will >> the NBA >> the NBA >> the NBA >> um live games courtside. It It's >> um live games courtside. It It's >> um live games courtside. It It's freaking unbelievable. freaking unbelievable. freaking unbelievable. >> Wow. >> Wow. >> Wow. >> I love that. >> I love that. >> I love that. >> Go go go to Apple store, get like a >> Go go go to Apple store, get like a >> Go go go to Apple store, get like a demo. demo. demo. >> Check it out. Freak out. And um they had >> Check it out. Freak out. And um they had >> Check it out. Freak out. And um they had their first one that they it was u the their first one that they it was u the their first one that they it was u the Bucks versus uh LA. Bucks versus uh LA. Bucks versus uh LA. >> And man, it it's a totally different >> And man, it it's a totally different >> And man, it it's a totally different experience. experience. experience. >> Maybe they need to do like a an NFL >> Maybe they need to do like a an NFL >> Maybe they need to do like a an NFL Sunday ticket Apple Vision Pro combo Sunday ticket Apple Vision Pro combo Sunday ticket Apple Vision Pro combo where you get the Sunday tickets, all where you get the Sunday tickets, all where you get the Sunday tickets, all the football games, and you get the the football games, and you get the the football games, and you get the headset. headset. headset. ticket. ticket. ticket. >> Oh, no. I I I tell you the the sports >> Oh, no. I I I tell you the the sports >> Oh, no. I I I tell you the the sports freaks, they're going to want this cuz freaks, they're going to want this cuz freaks, they're going to want this cuz you can't get you can't even get this you can't get you can't even get this you can't get you can't even get this buying a ticket, going there live. Do buying a ticket, going there live. Do buying a ticket, going there live. Do you know what I'm saying?

you know what I'm saying? you know what I'm saying? >> Right. Right. Right. Yeah. The digital >> Right. Right. Right. Yeah. The digital >> Right. Right. Right. Yeah. The digital experience is actually experience is actually experience is actually >> freaking unbelievable. And some of the >> freaking unbelievable. And some of the >> freaking unbelievable. And some of the angles that they have. angles that they have. angles that they have. >> Yeah. Yeah. And then, you know, the the >> Yeah. Yeah. And then, you know, the the >> Yeah. Yeah. And then, you know, the the thing is is you get such a different thing is is you get such a different thing is is you get such a different vibe from the play, you know, like the vibe from the play, you know, like the vibe from the play, you know, like the um the playing, right? the the um and um the playing, right? the the um and um the playing, right? the the um and you also get a sense of how quick these you also get a sense of how quick these you also get a sense of how quick these guys are. Oh my god. guys are. Oh my god. guys are. Oh my god. >> Yeah. >> Yeah. >> Yeah. >> You know, >> You know, >> You know, >> well then you can overlay your >> well then you can overlay your >> well then you can overlay your DraftKings bets DraftKings bets DraftKings bets >> over the screen as the game's playing >> over the screen as the game's playing >> over the screen as the game's playing and you can see how your bets are doing and you can see how your bets are doing and you can see how your bets are doing in real time. in real time. in real time. >> Exactly. I love that. >> Exactly. I love that. >> Exactly. I love that. >> Against the actual sports action. I >> Against the actual sports action. I >> Against the actual sports action. I think that sounds like a winner. Maybe think that sounds like a winner. Maybe think that sounds like a winner. Maybe DraftKings should give out Apple Vision DraftKings should give out Apple Vision DraftKings should give out Apple Vision Pro cuz when uh when Yan when Yan goes Pro cuz when uh when Yan when Yan goes Pro cuz when uh when Yan when Yan goes into the Apple store today and says he into the Apple store today and says he into the Apple store today and says he wants to do the the 30 minute demo, wants to do the the 30 minute demo, wants to do the the 30 minute demo, everybody there's like they go, "Oh, everybody there's like they go, "Oh, everybody there's like they go, "Oh, 2026. He's the one. 2026. He's the one. 2026. He's the one. That's the one." That's the one." That's the one." >> They'll ring the little bell in the >> They'll ring the little bell in the >> They'll ring the little bell in the back. Ding ding ding ding ding ding. We back. Ding ding ding ding ding ding. We back. Ding ding ding ding ding ding. We got one. We got one.

got one. We got one. got one. We got one. >> All right. So, I have something really >> All right. So, I have something really >> All right. So, I have something really cool to show you guys. Okay. cool to show you guys. Okay. cool to show you guys. Okay. >> Did any pie >> Did any pie >> Did any pie >> Did you No, not this time. Did you ever >> Did you No, not this time. Did you ever >> Did you No, not this time. Did you ever build models when you were a kid? build models when you were a kid? build models when you were a kid? Plastic models, airplanes, whatever. Plastic models, airplanes, whatever. Plastic models, airplanes, whatever. >> Yeah. >> Yeah. >> Yeah. >> All right. A model a rare a rare model >> All right. A model a rare a rare model >> All right. A model a rare a rare model was just delivered to me yesterday. was just delivered to me yesterday. was just delivered to me yesterday. >> What is that? >> What is that? >> What is that? >> Se view. Oh, is that from uh >> Se view. Oh, is that from uh >> Se view. Oh, is that from uh >> Oh, no way. That's um what do you call >> Oh, no way. That's um what do you call >> Oh, no way. That's um what do you call >> to the bottom of the sea? >> to the bottom of the sea? >> to the bottom of the sea? >> Nice. >> Nice. >> Nice. >> The is it are you're going to paint it >> The is it are you're going to paint it >> The is it are you're going to paint it and stuff. Does it come as white plastic and stuff. Does it come as white plastic and stuff. Does it come as white plastic or is it or is it or is it >> I think you do paint it, but I think it >> I think you do paint it, but I think it >> I think you do paint it, but I think it the lights work and it has an opening in the lights work and it has an opening in the lights work and it has an opening in the bottom and the flying sub. The the bottom and the flying sub. The the bottom and the flying sub. The flying sub comes with it. flying sub comes with it. flying sub comes with it. >> Dude, I'm telling you, >> Dude, I'm telling you, >> Dude, I'm telling you, >> like a good project. >> like a good project. >> like a good project. >> Everything was imagined >> Everything was imagined >> Everything was imagined >> in the Yes. >> in the Yes. >> in the Yes. >> Is that some AI you need? >> Is that some AI you need? >> Is that some AI you need? >> No, >> No, >> No, >> there's no AI. They all just It's, you >> there's no AI. They all just It's, you >> there's no AI. They all just It's, you know, know, know, >> you know, I saw a few models the other >> you know, I saw a few models the other >> you know, I saw a few models the other day. Actually, it was at Pike Place day. Actually, it was at Pike Place day. Actually, it was at Pike Place Market. There's a cool store down there Market. There's a cool store down there Market. There's a cool store down there called like Dracula Orange or something called like Dracula Orange or something called like Dracula Orange or something like that.

like that. like that. >> Oh. >> Oh. >> Oh. >> And it's a lot of old kind of pop stuff. >> And it's a lot of old kind of pop stuff. >> And it's a lot of old kind of pop stuff. But remember the model kits with the But remember the model kits with the But remember the model kits with the monsters like the Frankenstein model monsters like the Frankenstein model monsters like the Frankenstein model kit? kit? kit? >> Yeah. >> Yeah. >> Yeah. >> Remember those? And you could build a >> Remember those? And you could build a >> Remember those? And you could build a Frankenstein monster walking had those Frankenstein monster walking had those Frankenstein monster walking had those mint in box. Like you could buy the mint in box. Like you could buy the mint in box. Like you could buy the original monsters. Pretty cool. original monsters. Pretty cool. original monsters. Pretty cool. >> Like the 40-year-old version. >> Like the 40-year-old version. >> Like the 40-year-old version. >> The 40-year-old version >> The 40-year-old version >> The 40-year-old version >> Virgin. Yes. Remember >> Virgin. Yes. Remember >> Virgin. Yes. Remember >> Oh, that's right. Yeah. The movie. The >> Oh, that's right. Yeah. The movie. The >> Oh, that's right. Yeah. The movie. The movie. Right. Carell. movie. Right. Carell. movie. Right. Carell. >> Hey, you got you can't forget Legos, >> Hey, you got you can't forget Legos, >> Hey, you got you can't forget Legos, right? I got I got a little promotion right? I got I got a little promotion right? I got I got a little promotion here from from from my my native here from from from my my native here from from from my my native country, Denmark. So, remember Legos? country, Denmark. So, remember Legos? country, Denmark. So, remember Legos? They're always good. They're always good. They're always good. >> Absolutely. >> Absolutely. >> Absolutely. >> Legos made some news at CES, right? They >> Legos made some news at CES, right? They >> Legos made some news at CES, right? They had the smart Lego bricks and had the smart Lego bricks and had the smart Lego bricks and >> AI. >> AI. >> AI. >> Oh, yeah. That that's going to be >> Oh, yeah. That that's going to be >> Oh, yeah. That that's going to be interesting, too. interesting, too. interesting, too. >> That was pretty cool. >> That was pretty cool. >> That was pretty cool. >> Actually, there's the Enterprise. >> Actually, there's the Enterprise. >> Actually, there's the Enterprise. There's the new one is the uh uh 17 NCC There's the new one is the uh uh 17 NCC There's the new one is the uh uh 17 NCC 1701 enterprise kit from Lego which is 1701 enterprise kit from Lego which is 1701 enterprise kit from Lego which is the the the >> originals a mustave. >> originals a mustave. >> originals a mustave. >> It's Yeah, it's like thousands of >> It's Yeah, it's like thousands of >> It's Yeah, it's like thousands of pieces. It's pieces. It's pieces. It's >> I'm going to build it and then hang it >> I'm going to build it and then hang it >> I'm going to build it and then hang it from the ceiling above my bed.

from the ceiling above my bed. from the ceiling above my bed. >> Yes. There you go. Yeah. >> Yes. There you go. Yeah. >> Yes. There you go. Yeah. >> Oh, yeah. Hey, >> Oh, yeah. Hey, >> Oh, yeah. Hey, >> so crazy. >> so crazy. >> so crazy. >> Yeah. You know what percentage of the >> Yeah. You know what percentage of the >> Yeah. You know what percentage of the the Danish economy or is um attributed the Danish economy or is um attributed the Danish economy or is um attributed to Legos? Is it like 70%? Ah, no. to Legos? Is it like 70%? Ah, no. to Legos? Is it like 70%? Ah, no. >> So mean. >> So mean. >> So mean. >> Dude, you don't understand how big they >> Dude, you don't understand how big they >> Dude, you don't understand how big they are. They have like movies. They have are. They have like movies. They have are. They have like movies. They have like like like >> Oh, yeah. Oh, yeah. >> Oh, yeah. Oh, yeah. >> Oh, yeah. Oh, yeah. >> All kinds of merch like irrelevant like >> All kinds of merch like irrelevant like >> All kinds of merch like irrelevant like non actual building block related merch. non actual building block related merch. non actual building block related merch. Come on, man. Come on, man. Come on, man. >> They're the largest uh family-owned >> They're the largest uh family-owned >> They're the largest uh family-owned company in Denmark. That the largest company in Denmark. That the largest company in Denmark. That the largest corporation is of course Mask. corporation is of course Mask. corporation is of course Mask. >> Yeah. But the Lego is the is the largest >> Yeah. But the Lego is the is the largest >> Yeah. But the Lego is the is the largest familyowned company in Denmark. familyowned company in Denmark. familyowned company in Denmark. >> Oh, really? The the shipping company. >> Oh, really? The the shipping company. >> Oh, really? The the shipping company. Yeah, Yeah, Yeah, >> it's huge. >> it's huge. >> it's huge. >> Yeah, dude. I I did some work with them >> Yeah, dude. I I did some work with them >> Yeah, dude. I I did some work with them long time ago um in you know the dotcom long time ago um in you know the dotcom long time ago um in you know the dotcom era doing uh transport and logistics uh era doing uh transport and logistics uh era doing uh transport and logistics uh B2B. B2B. B2B. >> I did too. >> I did too. >> I did too. >> Yeah, it's actually sounds really really >> Yeah, it's actually sounds really really >> Yeah, it's actually sounds really really boring and like stuff that you wouldn't boring and like stuff that you wouldn't boring and like stuff that you wouldn't want to get into. It's super interesting want to get into. It's super interesting want to get into. It's super interesting >> business. Oh yeah, you look at I think I >> business. Oh yeah, you look at I think I >> business. Oh yeah, you look at I think I mean it's just just my numbers and and mean it's just just my numbers and and mean it's just just my numbers and and just out of my behind here, but like just out of my behind here, but like just out of my behind here, but like half of CEOs in Denmark, they probably half of CEOs in Denmark, they probably half of CEOs in Denmark, they probably came from Mask came from Mask came from Mask >> probably.

>> probably. >> probably. >> Really? >> Really? >> Really? >> So like being being an intern in Mar >> So like being being an intern in Mar >> So like being being an intern in Mar that's how you get into business in that's how you get into business in that's how you get into business in Denmark. Denmark. Denmark. >> Right. Okay. Right. >> Right. Okay. Right. >> Right. Okay. Right. >> Then they send you to some country long >> Then they send you to some country long >> Then they send you to some country long far far away where you there for three far far away where you there for three far far away where you there for three four years then you come back and you're four years then you come back and you're four years then you come back and you're a VP of something. Right. a VP of something. Right. a VP of something. Right. >> Right. That's probably a good thing to >> Right. That's probably a good thing to >> Right. That's probably a good thing to have on your resume there. So, and Yan, have on your resume there. So, and Yan, have on your resume there. So, and Yan, hopefully you don't get drafted and hopefully you don't get drafted and hopefully you don't get drafted and deployed to Greenland. So, we're Frank. deployed to Greenland. So, we're Frank. deployed to Greenland. So, we're Frank. >> Yeah, I know. I know. But I I'm now US >> Yeah, I know. I know. But I I'm now US >> Yeah, I know. I know. But I I'm now US citizen, you know, and now they're citizen, you know, and now they're citizen, you know, and now they're working on removing. working on removing. working on removing. Oh god, dude. Oh god, dude. Oh god, dude. >> Yeah, >> Yeah, >> Yeah, >> you have to bring that up, didn't you? >> you have to bring that up, didn't you? >> you have to bring that up, didn't you? >> All in on Greenland. We're all in on >> All in on Greenland. We're all in on >> All in on Greenland. We're all in on Greenland. Greenland. Greenland. >> Everybody has a price. >> Maybe Lego needs to get involved in >> Maybe Lego needs to get involved in that. that. that. >> How many billions do you need? >> How many billions do you need? >> How many billions do you need? >> Yeah. >> Yeah. >> Yeah. >> Is Or is the price one trillion? Maybe >> Is Or is the price one trillion? Maybe >> Is Or is the price one trillion? Maybe if Lego does like some sort of Donald if Lego does like some sort of Donald if Lego does like some sort of Donald Trump commemorative box set, then he'll Trump commemorative box set, then he'll Trump commemorative box set, then he'll just sort of back off. just sort of back off. just sort of back off. >> Yeah, maybe. Maybe. >> Yeah, maybe. Maybe. >> Yeah, maybe. Maybe. >> Special Nobel Prize edition. >> Special Nobel Prize edition. >> Special Nobel Prize edition. >> Don't give him this idea. >> Don't give him this idea. >> Don't give him this idea. >> Actually, it might be a really good idea >> Actually, it might be a really good idea >> Actually, it might be a really good idea that that that >> it could be. And he's holding like he's >> it could be. And he's holding like he's >> it could be. And he's holding like he's holding someone else's Nobel Prize like holding someone else's Nobel Prize like holding someone else's Nobel Prize like in his hand, you know? Looks like in his hand, you know? Looks like in his hand, you know? Looks like >> I saw that on TV last night. Yeah.

>> I saw that on TV last night. Yeah. >> I saw that on TV last night. Yeah. >> It's going to be a 2 m statue, but >> It's going to be a 2 m statue, but >> It's going to be a 2 m statue, but >> Exactly. Yeah. Yeah. >> Exactly. Yeah. Yeah. >> Exactly. Yeah. Yeah. >> Yeah. >> Yeah. >> Yeah. >> I see potential. They should. >> I see potential. They should. >> I see potential. They should. >> We're just thinking outside the box >> We're just thinking outside the box >> We're just thinking outside the box here. We're thinking you're always doing here. We're thinking you're always doing here. We're thinking you're always doing that. that. that. >> Thinking outside of the Lego box. >> Thinking outside of the Lego box. >> Thinking outside of the Lego box. >> Outside the Lego box. >> Outside the Lego box. >> Outside the Lego box. >> Exactly. Always outside the box. >> Exactly. Always outside the box. >> Exactly. Always outside the box. >> Yeah. >> Yeah. >> Yeah. >> Oh my god. >> Oh my god. >> Oh my god. >> Yeah. >> Yeah. >> Yeah. >> The Saturn 5 in Lego. And >> The Saturn 5 in Lego. And >> The Saturn 5 in Lego. And >> the Saturn 5. Yeah, that was that was >> the Saturn 5. Yeah, that was that was >> the Saturn 5. Yeah, that was that was one of the first big Lego kits. one of the first big Lego kits. one of the first big Lego kits. >> Really? >> Really? >> Really? >> Saturn 5. Yeah. And then the Millennium >> Saturn 5. Yeah. And then the Millennium >> Saturn 5. Yeah. And then the Millennium Falcon, of course, was the other Falcon, of course, was the other Falcon, of course, was the other >> Oh, yeah. Only mega kit. Mega. Have you >> Oh, yeah. Only mega kit. Mega. Have you >> Oh, yeah. Only mega kit. Mega. Have you seen the Star Destroyer one? seen the Star Destroyer one? seen the Star Destroyer one? I've Yes. Yes. I've Yes. Yes. I've Yes. Yes. >> That thing is massive. I think it's like >> That thing is massive. I think it's like >> That thing is massive. I think it's like >> I think they've redone the >> I think they've redone the >> I think they've redone the >> long or something. >> long or something. >> long or something. >> Well, they had a Titanic also that was, >> Well, they had a Titanic also that was, >> Well, they had a Titanic also that was, you know, four feet. Yeah. you know, four feet. Yeah. you know, four feet. Yeah. >> Some some of the mask ships as well. You >> Some some of the mask ships as well. You >> Some some of the mask ships as well. You you can get as a Lego kit. you can get as a Lego kit. you can get as a Lego kit. >> Oh, I'm totally getting those. >> Oh, I'm totally getting those. >> Oh, I'm totally getting those. >> One of my colleagues, he he does uh uh >> One of my colleagues, he he does uh uh >> One of my colleagues, he he does uh uh sports cars and uh race cars. sports cars and uh race cars. sports cars and uh race cars. >> Oh, yeah. I have a Porsche 911 GTR >> Oh, yeah. I have a Porsche 911 GTR >> Oh, yeah. I have a Porsche 911 GTR something GT3 as well in Lego.

something GT3 as well in Lego. something GT3 as well in Lego. >> Awesome. >> Awesome. >> Awesome. >> That's cool. Much love >> That's cool. Much love >> That's cool. Much love >> business. >> business. >> business. >> Yeah. So, you know, it was funny. >> Yeah. So, you know, it was funny. >> Yeah. So, you know, it was funny. >> I was having uh drinks with a >> I was having uh drinks with a >> I was having uh drinks with a undisclosed friend and he dropped not undisclosed friend and he dropped not undisclosed friend and he dropped not but one but two flexes on me. You decide but one but two flexes on me. You decide but one but two flexes on me. You decide which one do you think is a bigger flex. which one do you think is a bigger flex. which one do you think is a bigger flex. The first one he showed me was in The first one he showed me was in The first one he showed me was in Berlin. He finally, he'd been on a Berlin. He finally, he'd been on a Berlin. He finally, he'd been on a 14-year waiting list to get a very rare 14-year waiting list to get a very rare 14-year waiting list to get a very rare 100% platinum Rolex 100% platinum Rolex 100% platinum Rolex uh day just date thing across top. And uh day just date thing across top. And uh day just date thing across top. And he handed it to me and it weighed like he handed it to me and it weighed like he handed it to me and it weighed like 10 Rolexes or 20 Rolexes. It was all 10 Rolexes or 20 Rolexes. It was all 10 Rolexes or 20 Rolexes. It was all platinum and it platinum and it platinum and it >> I think it was $64,000 >> I think it was $64,000 >> I think it was $64,000 or something. or something. or something. >> That's a pretty good flex. >> That's a pretty good flex. >> That's a pretty good flex. >> I go, that's a flex. But then then later >> I go, that's a flex. But then then later >> I go, that's a flex. But then then later on in the conversation, unrelated to on in the conversation, unrelated to on in the conversation, unrelated to this, something comes up. You know the this, something comes up. You know the this, something comes up. You know the you know the Nuremberg ring where you you know the Nuremberg ring where you you know the Nuremberg ring where you can go race your car, right? can go race your car, right? can go race your car, right? >> Yeah. >> Yeah. >> Yeah. >> Okay. He's not just going to go there to >> Okay. He's not just going to go there to >> Okay. He's not just going to go there to race his car. He's shipping his BMW M4 race his car. He's shipping his BMW M4 race his car. He's shipping his BMW M4 Turbo whatever thingy there so that he Turbo whatever thingy there so that he Turbo whatever thingy there so that he can drive it around the ring. And so I can drive it around the ring. And so I can drive it around the ring. And so I was like I don't know what's a bigger was like I don't know what's a bigger was like I don't know what's a bigger flex, the platinum Rolex or that you're flex, the platinum Rolex or that you're flex, the platinum Rolex or that you're going to ship your car from the US to going to ship your car from the US to going to ship your car from the US to Germany just so you can drive the ring Germany just so you can drive the ring Germany just so you can drive the ring in a in a in a >> circle. I I tell you what makes a >> circle. I I tell you what makes a >> circle. I I tell you what makes a difference difference difference >> while wearing your Rolex.

>> while wearing your Rolex. >> while wearing your Rolex. >> Rolex, >> Rolex, >> Rolex, >> which is slowing you down. >> which is slowing you down. >> which is slowing you down. >> I I I tell you what makes a difference. >> I I I tell you what makes a difference. >> I I I tell you what makes a difference. What makes a difference is if he if he What makes a difference is if he if he What makes a difference is if he if he crashes because crashes because crashes because No, but without No, but I meaning a No, but without No, but I meaning a No, but without No, but I meaning a crash, not a crash where he dies or crash, not a crash where he dies or crash, not a crash where he dies or something like that, but just crash. something like that, but just crash. something like that, but just crash. >> Oh, nice. >> Oh, nice. >> Oh, nice. >> When when you when you go in the number >> When when you when you go in the number >> When when you when you go in the number ring, if you crash, you pay the repairs ring, if you crash, you pay the repairs ring, if you crash, you pay the repairs of the ring. of the ring. of the ring. >> Oh, >> Oh, >> Oh, >> and it's not cheap. Oh, you you pay you >> and it's not cheap. Oh, you you pay you >> and it's not cheap. Oh, you you pay you pay for the the truck that comes get pay for the the truck that comes get pay for the the truck that comes get your car. You pay for everything you your car. You pay for everything you your car. You pay for everything you broke. broke. broke. >> Yeah. >> Yeah. >> Yeah. >> Sounds like you have a personal >> Sounds like you have a personal >> Sounds like you have a personal experience. experience. experience. >> Yeah. Yeah. What did you do? You made >> Yeah. Yeah. What did you do? You made >> Yeah. Yeah. What did you do? You made >> I made you crash your car. >> I made you crash your car. >> I made you crash your car. >> I have friends. >> I have friends. >> I have friends. >> Holy >> Holy >> Holy $4,000 Rolex. Yeah. $4,000 Rolex. Yeah. $4,000 Rolex. Yeah. >> Yeah. He's just expanding the scale, >> Yeah. He's just expanding the scale, >> Yeah. He's just expanding the scale, >> the price point. >> the price point. >> the price point. >> But a a BMW M4 even turbo is >> But a a BMW M4 even turbo is >> But a a BMW M4 even turbo is >> Oh my. I mean like you know I mean yeah >> Oh my. I mean like you know I mean yeah >> Oh my. I mean like you know I mean yeah I was going to say if you're going to I was going to say if you're going to I was going to say if you're going to ship a car there BMW M4 I mean no ship a car there BMW M4 I mean no ship a car there BMW M4 I mean no >> no foul of the BMWs but >> no foul of the BMWs but >> no foul of the BMWs but >> it's like some souped up version of it.

>> it's like some souped up version of it. >> it's like some souped up version of it. It's It's It's >> the Mac >> the Mac >> the Mac >> one of a kind. >> one of a kind. >> one of a kind. >> Yeah. >> Yeah. >> Yeah. >> No, you're right. It's obviously not on >> No, you're right. It's obviously not on >> No, you're right. It's obviously not on the level of McLaren or something like the level of McLaren or something like the level of McLaren or something like that. that. that. >> That's pretty big flex though. >> That's pretty big flex though. >> That's pretty big flex though. >> Yeah. Do you get a stamp on your car >> Yeah. Do you get a stamp on your car >> Yeah. Do you get a stamp on your car that it drove in Nberg? You know, open that it drove in Nberg? You know, open that it drove in Nberg? You know, open up the motor and it'll say up the motor and it'll say up the motor and it'll say >> Oh, >> Oh, >> Oh, >> no. Actually, I want a big sticker on >> no. Actually, I want a big sticker on >> no. Actually, I want a big sticker on the side of the car so that when I'm the side of the car so that when I'm the side of the car so that when I'm driving back in the US, people can be driving back in the US, people can be driving back in the US, people can be really impressed. really impressed. really impressed. >> Yeah, that's right. >> Yeah, that's right. >> Yeah, that's right. >> By the way, Pete, you mentioned Ferrari. >> By the way, Pete, you mentioned Ferrari. >> By the way, Pete, you mentioned Ferrari. I know a lot of Ferrari owners, they I know a lot of Ferrari owners, they I know a lot of Ferrari owners, they never drive on the number ring. It's too never drive on the number ring. It's too never drive on the number ring. It's too dangerous for the Ferrari. The Ferrari dangerous for the Ferrari. The Ferrari dangerous for the Ferrari. The Ferrari is not the cars they drive. These is a is not the cars they drive. These is a is not the cars they drive. These is a car they invested. car they invested. car they invested. >> Yeah. >> Yeah. >> Yeah. >> Well, in Redmond, we have lots of people >> Well, in Redmond, we have lots of people >> Well, in Redmond, we have lots of people with their McLarens here. These, you with their McLarens here. These, you with their McLarens here. These, you know, know, know, >> a lot of them. >> a lot of them. >> a lot of them. The only people that really drive their The only people that really drive their The only people that really drive their exotic cars are the Porsche one. exotic cars are the Porsche one. exotic cars are the Porsche one. >> Most of the others >> Most of the others >> Most of the others >> I can see that >> I can see that >> I can see that >> most of the others you you see this >> most of the others you you see this >> most of the others you you see this unique 5 10 years old car and it has unique 5 10 years old car and it has unique 5 10 years old car and it has like 620 miles. like 620 miles. like 620 miles. >> Yeah. >> Yeah. >> Yeah. >> And when I see that I want to kill the >> And when I see that I want to kill the >> And when I see that I want to kill the guy.

guy. guy. >> Oh my god. So violent. If you want to >> Oh my god. So violent. If you want to >> Oh my god. So violent. If you want to make investment, buy art, buy some make investment, buy art, buy some make investment, buy art, buy some freaking AI stock, but don't buy freaking AI stock, but don't buy freaking AI stock, but don't buy exceptional driving machine, you know, exceptional driving machine, you know, exceptional driving machine, you know, designed to be driven and don't drive designed to be driven and don't drive designed to be driven and don't drive them because it's a, you know, them because it's a, you know, them because it's a, you know, >> give it to him. >> give it to him. >> give it to him. >> Exotic car owner. >> Exotic car owner. >> Exotic car owner. >> Give it to him. >> Give it to him. >> Give it to him. >> Oh my god. All right. >> Oh my god. All right. >> Oh my god. All right. >> All right. On that note, >> All right. On that note, >> All right. On that note, >> go Seahawks. Go Seahawks. >> go Seahawks. Go Seahawks. >> go Seahawks. Go Seahawks. >> Go Hawks. >> Go Hawks. >> Go Hawks. >> On that note, um, >> On that note, um, >> On that note, um, >> yes. Rob, you want to take us out or >> yes. Rob, you want to take us out or >> yes. Rob, you want to take us out or should we have should we have should we have >> Go Hawks. Thanks for joining us on this >> Go Hawks. Thanks for joining us on this >> Go Hawks. Thanks for joining us on this uh second episode of 2026, which is more uh second episode of 2026, which is more uh second episode of 2026, which is more episodes than people are going to buy uh episodes than people are going to buy uh episodes than people are going to buy uh the Vision Pro from Apple the Vision Pro from Apple the Vision Pro from Apple this year. this year. this year. >> Ouch. >> Ouch. >> Ouch. >> Ouch. >> Ouch. >> Ouch. >> Unless Unless DraftKings goes with the >> Unless Unless DraftKings goes with the >> Unless Unless DraftKings goes with the idea of including that in there, which I idea of including that in there, which I idea of including that in there, which I think is brilliant. Brilliant. Genius. think is brilliant. Brilliant. Genius. think is brilliant. Brilliant. Genius. >> Wow. You're an idea, man. Wow. I love >> Wow. You're an idea, man. Wow. I love >> Wow. You're an idea, man. Wow. I love you for that. That's good stuff. That's you for that. That's good stuff. That's you for that. That's good stuff. That's a freebie. That's a freebie.

a freebie. That's a freebie. a freebie. That's a freebie. >> That's a freebie. I'm gonna steal that >> That's a freebie. I'm gonna steal that >> That's a freebie. I'm gonna steal that one this afternoon. Um, thanks for one this afternoon. Um, thanks for one this afternoon. Um, thanks for dropping in. It was great to see you on. dropping in. It was great to see you on. dropping in. It was great to see you on. It's been a while. It's been a while. It's been a while. It's been a while. It's been a while. It's been a while. Um, Um, Um, >> and uh, talking about me. >> and uh, talking about me. >> and uh, talking about me. >> Machine learning actually finally >> Machine learning actually finally >> Machine learning actually finally delivering for IoT. How about an delivering for IoT. How about an delivering for IoT. How about an applause? applause? applause? >> I finally delivered FTW. >> I finally delivered FTW. >> I finally delivered FTW. >> There you go. Build plain old machine >> There you go. Build plain old machine >> There you go. Build plain old machine learning for the win. We built a model learning for the win. We built a model learning for the win. We built a model based on a machine working and all its based on a machine working and all its based on a machine working and all its properties and parts and when it was properties and parts and when it was properties and parts and when it was doing well and when it failed and then doing well and when it failed and then doing well and when it failed and then the machine learning model then we send the machine learning model then we send the machine learning model then we send the real live data into it and then it the real live data into it and then it the real live data into it and then it tells us, hey Yan, I think that machine tells us, hey Yan, I think that machine tells us, hey Yan, I think that machine over there is going to fail next over there is going to fail next over there is going to fail next Tuesday. You better go fix it so you Tuesday. You better go fix it so you Tuesday. You better go fix it so you don't have unplanned downtime. So I'm don't have unplanned downtime. So I'm don't have unplanned downtime. So I'm going to send a trophy, an IoT coffee going to send a trophy, an IoT coffee going to send a trophy, an IoT coffee talk trophy to Yan in the mail. Um cuz I talk trophy to Yan in the mail. Um cuz I talk trophy to Yan in the mail. Um cuz I know that um Stephanie has created those know that um Stephanie has created those know that um Stephanie has created those trophies cuz that's the kind of stuff trophies cuz that's the kind of stuff trophies cuz that's the kind of stuff she does in her marketing company. And she does in her marketing company. And she does in her marketing company. And and then maybe we'll have a big and then maybe we'll have a big and then maybe we'll have a big ceremony, you know.

ceremony, you know. ceremony, you know. >> Is it is it going to be my trophy or you >> Is it is it going to be my trophy or you >> Is it is it going to be my trophy or you going to give me your trophy? >> I'm going to give you my Nobel Prize for >> I'm going to give you my Nobel Prize for machine learning. machine learning. machine learning. Sounds Sounds Sounds like a plan. like a plan. like a plan. >> And we're going to do the awards >> And we're going to do the awards >> And we're going to do the awards ceremony in Greenland. And so it's going ceremony in Greenland. And so it's going ceremony in Greenland. And so it's going to be it's going to be something really to be it's going to be something really to be it's going to be something really special. special. special. >> Yeah. >> Yeah. >> Yeah. >> Yeah. >> Yeah. >> Yeah. >> Um anyway, thanks for coming on. Uh >> Um anyway, thanks for coming on. Uh >> Um anyway, thanks for coming on. Uh don't listen to us too much, but there's don't listen to us too much, but there's don't listen to us too much, but there's a few nuggets in here that you're not a few nuggets in here that you're not a few nuggets in here that you're not going to get on any other podcast. You going to get on any other podcast. You going to get on any other podcast. You know, we're the best t YouTubers on know, we're the best t YouTubers on know, we're the best t YouTubers on YouTube and the best podcasters on the YouTube and the best podcasters on the YouTube and the best podcasters on the pod land. And we're not really pod pod land. And we're not really pod pod land. And we're not really pod people like body snatchers. people like body snatchers. people like body snatchers. >> I knew you. >> I knew you. >> I knew you. >> We're still real. We're still real. Uh, >> We're still real. We're still real. Uh, >> We're still real. We're still real. Uh, but it's going to be a great 2026. I'm but it's going to be a great 2026. I'm but it's going to be a great 2026. I'm super excited about it and we will see super excited about it and we will see super excited about it and we will see you next week. you next week. you next week. >> I love those. Is that pagers? I love >> I love those. Is that pagers? I love >> I love those. Is that pagers? I love that. that. that. >> Ah, hey, look. When you do this, you get >> Ah, hey, look. When you do this, you get >> Ah, hey, look. When you do this, you get lasers.

lasers. lasers. >> Lasers. Sharks with laser beams. >> Lasers. Sharks with laser beams. >> Lasers. Sharks with laser beams. >> Really? >> Really? >> Really? >> All I want is sharks with freaking laser >> All I want is sharks with freaking laser >> All I want is sharks with freaking laser beams attached to their heads. Is that beams attached to their heads. Is that beams attached to their heads. Is that asking too much?