IoT Coffee Talk: Episode 322 - "Let's Get Physical" (What is physical AI anyway?)

Heat. Heat. Heat. Heat. [music] >> Yes. >> Yes. >> Don't stop believing. >> Don't stop believing. >> Don't stop believing. >> Don't stop [cheering] believing. >> Don't stop [cheering] believing. >> Don't stop [cheering] believing. >> Don't stop believing. >> Don't stop believing. >> Don't stop believing. There you go. Keep going. That was like There you go. Keep going. That was like There you go. Keep going. That was like worst. worst. worst. I usually play that perfectly. I usually play that perfectly. I usually play that perfectly. >> Oh well. >> Oh well. >> Oh well. >> Oh wow. >> Oh wow. >> Oh wow. >> It's a nervous to do it in front of the >> It's a nervous to do it in front of the >> It's a nervous to do it in front of the coffee talk audience, right? coffee talk audience, right? coffee talk audience, right? >> Yeah. >> Yeah. >> Yeah. >> Oh, the millions of people you're >> Oh, the millions of people you're >> Oh, the millions of people you're getting stage fright. I can feel it. getting stage fright. I can feel it. getting stage fright. I can feel it. Yeah. Yeah. Yeah. >> Yeah. >> Yeah. >> Yeah. >> Absolutely. >> Absolutely. >> Absolutely. >> Yeah. For all the people who are talking >> Yeah. For all the people who are talking >> Yeah. For all the people who are talking crap about my guitar playing. You do crap about my guitar playing. You do crap about my guitar playing. You do that. [laughter] that. [laughter] that. [laughter] >> I'm not talking crap. >> I'm not talking crap. >> I'm not talking crap. >> You embarrass yourself in front of the >> You embarrass yourself in front of the >> You embarrass yourself in front of the entire internet.

entire internet. entire internet. >> And you're the shred master. You're the >> And you're the shred master. You're the >> And you're the shred master. You're the shred master. shred master. shred master. Yeah. Get a couple of beers at me, man. Yeah. Get a couple of beers at me, man. Yeah. Get a couple of beers at me, man. I'm I'll freaking I'll freaking try. I'm I'll freaking I'll freaking try. I'm I'll freaking I'll freaking try. [laughter] [laughter] [laughter] >> There you go. >> There you go. >> There you go. >> Oh my gosh. >> Oh my gosh. >> Oh my gosh. >> So, welcome to Coffee Talk. >> So, welcome to Coffee Talk. >> So, welcome to Coffee Talk. >> Oh, >> Oh, >> Oh, coffee. coffee. coffee. >> I have coffee. >> I have coffee. >> I have coffee. >> I already drank my coffee. Yeah. >> I already drank my coffee. Yeah. >> I already drank my coffee. Yeah. >> Oh, yeah. >> Oh, yeah. >> Oh, yeah. >> Yeah. So before we get started, remember >> Yeah. So before we get started, remember >> Yeah. So before we get started, remember um we're all about communities, um we're all about communities, um we're all about communities, elevating communities, and there's a lot elevating communities, and there's a lot elevating communities, and there's a lot of crazy crap going on right now. Like of crazy crap going on right now. Like of crazy crap going on right now. Like in San Diego, we had like 95 degree in San Diego, we had like 95 degree in San Diego, we had like 95 degree weather, so we got wamped. Um hey, weather, so we got wamped. Um hey, weather, so we got wamped. Um hey, prayers to the folks in Hill Country and prayers to the folks in Hill Country and prayers to the folks in Hill Country and Texas who are getting flooded. You know, Texas who are getting flooded. You know, Texas who are getting flooded. You know, you heard of Texas floods? Well, you heard of Texas floods? Well, you heard of Texas floods? Well, >> Stevie Rayvon, >> Stevie Rayvon, >> Stevie Rayvon, >> yes, they're real. And >> yes, they're real. And >> yes, they're real. And >> yeah, he told us about that years ago, >> yeah, he told us about that years ago, >> yeah, he told us about that years ago, didn't he? didn't he? didn't he? >> He gave us a heads up. He gave us a >> He gave us a heads up. He gave us a >> He gave us a heads up. He gave us a heads up. That's right. Yes. heads up. That's right. Yes. heads up. That's right. Yes. >> We're still working the details on uh >> We're still working the details on uh >> We're still working the details on uh Elevate Communities, but we hope that Elevate Communities, but we hope that Elevate Communities, but we hope that everyone gets involved because you know, everyone gets involved because you know, everyone gets involved because you know, at some point we have to care about the at some point we have to care about the at some point we have to care about the planet. Um which people seem to not care planet. Um which people seem to not care planet. Um which people seem to not care too much about. Uh but you know, you'll too much about. Uh but you know, you'll too much about. Uh but you know, you'll start to care again because it impacts start to care again because it impacts start to care again because it impacts communities. The Earth impacts communities. The Earth impacts communities. The Earth impacts communities.

communities. communities. >> Yeah. You know, I think a lot of people, >> Yeah. You know, I think a lot of people, >> Yeah. You know, I think a lot of people, we always undermine, you know, IoT we always undermine, you know, IoT we always undermine, you know, IoT Coffee Talk. We've been going on since Coffee Talk. We've been going on since Coffee Talk. We've been going on since 2020. It's actually a charity. That's 2020. It's actually a charity. That's 2020. It's actually a charity. That's that's what this is. We're having fun. that's what this is. We're having fun. that's what this is. We're having fun. We talk about tech every week, but it We talk about tech every week, but it We talk about tech every week, but it it's all about raising money. You know, it's all about raising money. You know, it's all about raising money. You know, we did Elevator Kids during CO and now we did Elevator Kids during CO and now we did Elevator Kids during CO and now we're doing communities and Yeah. We're we're doing communities and Yeah. We're we're doing communities and Yeah. We're going to we're going to go after a lot going to we're going to go after a lot going to we're going to go after a lot of these things we're seeing around the of these things we're seeing around the of these things we're seeing around the world and and do really hands-on help. world and and do really hands-on help. world and and do really hands-on help. >> Yeah. And uh Rob has a book that >> Yeah. And uh Rob has a book that >> Yeah. And uh Rob has a book that provides the recipes for all of that. provides the recipes for all of that. provides the recipes for all of that. So, it's really awesome. And remember to So, it's really awesome. And remember to So, it's really awesome. And remember to take us seriously at your own risk. We take us seriously at your own risk. We take us seriously at your own risk. We highly recommend that you take out highly recommend that you take out highly recommend that you take out insurance if you do. insurance if you do. insurance if you do. >> Are you saying they shouldn't take any >> Are you saying they shouldn't take any >> Are you saying they shouldn't take any stock advice from us or anything like stock advice from us or anything like stock advice from us or anything like that? that? that? >> Yes. Especially from death. [laughter] >> And uh yeah, we're just going to have >> And uh yeah, we're just going to have fun and um you know, fun and um you know, fun and um you know, >> so you mean like when I say things like >> so you mean like when I say things like >> so you mean like when I say things like the NASDAQ sell off today, the NASDAQ sell off today, the NASDAQ sell off today, >> double down on SpaceX, now's the time. >> double down on SpaceX, now's the time. >> double down on SpaceX, now's the time. Now is the [laughter] buy on the dip. Now is the [laughter] buy on the dip. Now is the [laughter] buy on the dip. Buy on the dip.

Buy on the dip. Buy on the dip. >> So, >> So, >> So, >> well, we have a special guest today. We >> well, we have a special guest today. We >> well, we have a special guest today. We got Devin Young here with us. got Devin Young here with us. got Devin Young here with us. >> Welcome to the show. Welcome to the man. >> Welcome to the show. Welcome to the man. >> Welcome to the show. Welcome to the man. >> No, thank you for having me. Hopefully, >> No, thank you for having me. Hopefully, >> No, thank you for having me. Hopefully, I'll be more a permanent installation in I'll be more a permanent installation in I'll be more a permanent installation in your little coffee talk or big coffee your little coffee talk or big coffee your little coffee talk or big coffee talk. talk. talk. >> Absolutely. >> Absolutely. >> Absolutely. >> I see some cool models back there. >> I see some cool models back there. >> I see some cool models back there. >> Look at those planes. >> Look at those planes. >> Look at those planes. >> Yeah. Well, my wife used to be a flight >> Yeah. Well, my wife used to be a flight >> Yeah. Well, my wife used to be a flight attendant for Cath Pacific, so I've attendant for Cath Pacific, so I've attendant for Cath Pacific, so I've always been passioned by aircraft. So, always been passioned by aircraft. So, always been passioned by aircraft. So, had these there for had these there for had these there for >> over a decade. >> over a decade. >> over a decade. >> Wow. Really? You know, I used to fly >> Wow. Really? You know, I used to fly >> Wow. Really? You know, I used to fly Cafe all the time when I was traveling Cafe all the time when I was traveling Cafe all the time when I was traveling to Hong Kong every other week. to Hong Kong every other week. to Hong Kong every other week. >> Great. >> Great. >> Great. >> Yeah. Great. >> Yeah. Great. >> Yeah. Great. >> The pandemic. >> The pandemic. >> The pandemic. >> Yeah. And that was like when first >> Yeah. And that was like when first >> Yeah. And that was like when first class, they actually had a first class. class, they actually had a first class. class, they actually had a first class. [laughter] [laughter] [laughter] Now they like squish you in together Now they like squish you in together Now they like squish you in together right into these right into these right into these >> premium classic. >> premium classic. >> premium classic. >> What you mean you're not lying flat? >> What you mean you're not lying flat? >> What you mean you're not lying flat? What's wrong with you, man? What's wrong with you, man? What's wrong with you, man? >> Really, Leonard? What are you doing? >> Really, Leonard? What are you doing? >> Really, Leonard? What are you doing? >> Do you remember first class back in the >> Do you remember first class back in the >> Do you remember first class back in the day? It was like crazy. You You were up day? It was like crazy. You You were up day? It was like crazy. You You were up on that second level of the 747.

on that second level of the 747. on that second level of the 747. >> Yeah. >> Yeah. >> Yeah. >> And you had your own space. And then in >> And you had your own space. And then in >> And you had your own space. And then in the middle the middle the middle >> uh on cafe a they had like a bar open >> uh on cafe a they had like a bar open >> uh on cafe a they had like a bar open bar and they did uh tableside um salad bar and they did uh tableside um salad bar and they did uh tableside um salad service and service service and service service and service >> and it was >> and it was >> and it was >> that was like real first class and cafe >> that was like real first class and cafe >> that was like real first class and cafe was one of the best. was one of the best. was one of the best. >> They were good >> They were good >> They were good delta one's not bad but delta one's not bad but delta one's not bad but >> Delta's pretty good too. Yeah. >> Delta's pretty good too. Yeah. >> Delta's pretty good too. Yeah. >> Devin, why don't you introduce yourself >> Devin, why don't you introduce yourself >> Devin, why don't you introduce yourself to the audience and kind of what you do, to the audience and kind of what you do, to the audience and kind of what you do, your background, all that kind of stuff. your background, all that kind of stuff. your background, all that kind of stuff. No, absolutely. No, thanks. Thank you No, absolutely. No, thanks. Thank you No, absolutely. No, thanks. Thank you for having me and inviting me. I'm the for having me and inviting me. I'm the for having me and inviting me. I'm the global lead of IoT for NT. Uh my global lead of IoT for NT. Uh my global lead of IoT for NT. Uh my background is I have been a consultant background is I have been a consultant background is I have been a consultant for almost 25 30 years. Um Accenture, for almost 25 30 years. Um Accenture, for almost 25 30 years. Um Accenture, PWC back in the day, you know, when it PWC back in the day, you know, when it PWC back in the day, you know, when it was telemetry and machine to machine. Um was telemetry and machine to machine. Um was telemetry and machine to machine. Um helped a lot of companies do a lot of helped a lot of companies do a lot of helped a lot of companies do a lot of their IoT strategies. You know, I I their IoT strategies. You know, I I their IoT strategies. You know, I I think it just always been something that think it just always been something that think it just always been something that you just fell into. and then you know you just fell into. and then you know you just fell into. and then you know everything of IoT deployments in the everything of IoT deployments in the everything of IoT deployments in the jungles of Colombia to the basement of jungles of Colombia to the basement of jungles of Colombia to the basement of hospitals. Um you know it's just been a hospitals. Um you know it's just been a hospitals. Um you know it's just been a fun ride. So you know thank you for fun ride. So you know thank you for fun ride. So you know thank you for having me and I think I've known Rob for having me and I think I've known Rob for having me and I think I've known Rob for at least a decade or so through you know at least a decade or so through you know at least a decade or so through you know Chatton's event starting off and Chatton's event starting off and Chatton's event starting off and >> yeah just running and sharing things.

>> yeah just running and sharing things. >> yeah just running and sharing things. >> Yeah. I mean you guys all know Chad and >> Yeah. I mean you guys all know Chad and >> Yeah. I mean you guys all know Chad and Sharma. Sharma. Sharma. >> Sharma. Yeah. >> Sharma. Yeah. >> Sharma. Yeah. >> Yeah. Yeah. He and Devin man they're >> Yeah. Yeah. He and Devin man they're >> Yeah. Yeah. He and Devin man they're buds. buds. buds. >> Yeah. I'm still waiting for him to >> Yeah. I'm still waiting for him to >> Yeah. I'm still waiting for him to invite me to an event but Oh. Oh. Do you invite me to an event but Oh. Oh. Do you invite me to an event but Oh. Oh. Do you want to go to Mobile Future Forward? want to go to Mobile Future Forward? want to go to Mobile Future Forward? >> Oh, that's a great event. >> Oh, that's a great event. >> Oh, that's a great event. >> Hey, man. That's his show. I don't I >> Hey, man. That's his show. I don't I >> Hey, man. That's his show. I don't I >> It's a good event, man. >> It's a good event, man. >> It's a good event, man. >> No, I No, I totally get it. But it's his >> No, I No, I totally get it. But it's his >> No, I No, I totally get it. But it's his show. You know what I'm saying? So, show. You know what I'm saying? So, show. You know what I'm saying? So, >> it's He always has it up there at the >> it's He always has it up there at the >> it's He always has it up there at the Freddy Couples Golf Course there, you Freddy Couples Golf Course there, you Freddy Couples Golf Course there, you know, in Belleview. know, in Belleview. know, in Belleview. >> That's a That's always a great show for >> That's a That's always a great show for >> That's a That's always a great show for sure. sure. sure. >> Yeah. No, Cutton's awesome. And >> Yeah. No, Cutton's awesome. And >> Yeah. No, Cutton's awesome. And >> yeah, serious players show that thing. this week. this week. >> I don't think anybody beats chatting >> I don't think anybody beats chatting >> I don't think anybody beats chatting more for slides per second. more for slides per second. more for slides per second. [laughter] [laughter] [laughter] >> Yeah, you're right. You're right. >> Yeah, you're right. You're right. >> Yeah, you're right. You're right. >> I just remember when 5G came out and he >> I just remember when 5G came out and he >> I just remember when 5G came out and he felt the need to dress up like a surgeon felt the need to dress up like a surgeon felt the need to dress up like a surgeon and do remote surgery and do remote surgery and do remote surgery using 5G. I was like, "All right, using 5G. I was like, "All right, using 5G. I was like, "All right, somebody had to get somebody had to get somebody had to get >> on a banana." Was he >> on a banana." Was he >> on a banana." Was he >> somebody had to do it?

>> somebody had to do it? >> somebody had to do it? >> [laughter] >> [laughter] >> [laughter] >> Oh my god. [clears throat] Great stuff. >> Oh my god. [clears throat] Great stuff. >> Oh my god. [clears throat] Great stuff. I love that. Um, so hey man, we got a I love that. Um, so hey man, we got a I love that. Um, so hey man, we got a lot of news going on in the world today. lot of news going on in the world today. lot of news going on in the world today. You know, uh, some of us been talking You know, uh, some of us been talking You know, uh, some of us been talking bag channel about Kimmy 3 bag channel about Kimmy 3 bag channel about Kimmy 3 >> just dropped on the planet and it's >> just dropped on the planet and it's >> just dropped on the planet and it's outperforming all of the models or outperforming all of the models or outperforming all of the models or pretty, you know, or equal or better. pretty, you know, or equal or better. pretty, you know, or equal or better. Uh, the developers like it more. Um, and Uh, the developers like it more. Um, and Uh, the developers like it more. Um, and that price per token per million tokens, that price per token per million tokens, that price per token per million tokens, it's like three bucks input, 30 output, it's like three bucks input, 30 output, it's like three bucks input, 30 output, whatever. whatever. whatever. >> Um, does this totally destroy the whole >> Um, does this totally destroy the whole >> Um, does this totally destroy the whole tokconomics and all our financial tokconomics and all our financial tokconomics and all our financial modeling for all these players in the AI modeling for all these players in the AI modeling for all these players in the AI space? Like, space? Like, space? Like, >> pretty much. >> pretty much. >> pretty much. >> I know that's a that's a I know that's a >> I know that's a that's a I know that's a >> I know that's a that's a I know that's a provocative statement, but it it's real. provocative statement, but it it's real. provocative statement, but it it's real. >> Yeah. I mean, I'd love to get Devon's >> Yeah. I mean, I'd love to get Devon's >> Yeah. I mean, I'd love to get Devon's take on it because, you know, um, you take on it because, you know, um, you take on it because, you know, um, you know, I have my my observations, not know, I have my my observations, not know, I have my my observations, not even a just an opinion, but, uh, what even a just an opinion, but, uh, what even a just an opinion, but, uh, what are you guys seeing at NT in terms of are you guys seeing at NT in terms of are you guys seeing at NT in terms of like these IoT models, you know, how's like these IoT models, you know, how's like these IoT models, you know, how's how's how are LLM andes and all this how's how are LLM andes and all this how's how are LLM andes and all this generative stuff u trickling down into generative stuff u trickling down into generative stuff u trickling down into what you guys are seeing? I mean, um, it what you guys are seeing? I mean, um, it what you guys are seeing? I mean, um, it it'd be cool to compare notes. Do you it'd be cool to compare notes. Do you it'd be cool to compare notes. Do you know what I'm saying? No, I I think know what I'm saying? No, I I think know what I'm saying? No, I I think you're you're absolutely right. One of you're you're absolutely right. One of you're you're absolutely right. One of the biggest challenges especially when the biggest challenges especially when the biggest challenges especially when you're trying to build these models is you're trying to build these models is you're trying to build these models is the lack of compute availability. Yeah.

the lack of compute availability. Yeah. the lack of compute availability. Yeah. >> You know, for example, let's say Cosmos. >> You know, for example, let's say Cosmos. >> You know, for example, let's say Cosmos. Um, you know, just the infrastructure Um, you know, just the infrastructure Um, you know, just the infrastructure alone needed to work on that most people alone needed to work on that most people alone needed to work on that most people don't have access to. But, you know, don't have access to. But, you know, don't have access to. But, you know, this tokconomics is really impact. I this tokconomics is really impact. I this tokconomics is really impact. I think you see it in news at all all the think you see it in news at all all the think you see it in news at all all the time. time. time. >> Are are you talking about wait are you >> Are are you talking about wait are you >> Are are you talking about wait are you talking about Nvidia Cosmos the world talking about Nvidia Cosmos the world talking about Nvidia Cosmos the world model? model? model? >> Sorry. Yes, Nvidia Cosmos, etc. >> Sorry. Yes, Nvidia Cosmos, etc. >> Sorry. Yes, Nvidia Cosmos, etc. >> Sorry, I'm I'm jumping all over the >> Sorry, I'm I'm jumping all over the >> Sorry, I'm I'm jumping all over the path. path. path. >> Not everyone is like you, dude. You >> Not everyone is like you, dude. You >> Not everyone is like you, dude. You know, knows everything. Okay, you have know, knows everything. Okay, you have know, knows everything. Okay, you have to assume that, [laughter] to assume that, [laughter] to assume that, [laughter] >> right? But I but I think you know one of >> right? But I but I think you know one of >> right? But I but I think you know one of the big there's this big dream of how the big there's this big dream of how the big there's this big dream of how what we can do and everyone's excited to what we can do and everyone's excited to what we can do and everyone's excited to do something and then when you realize do something and then when you realize do something and then when you realize how much it's actually costing as far as how much it's actually costing as far as how much it's actually costing as far as compute power and things and that's compute power and things and that's compute power and things and that's where I see you know I think the eastern where I see you know I think the eastern where I see you know I think the eastern world is you know because we in the west world is you know because we in the west world is you know because we in the west have are fat and happy because we have have are fat and happy because we have have are fat and happy because we have all this compute power available to us all this compute power available to us all this compute power available to us all the GPUs so we weren't really all the GPUs so we weren't really all the GPUs so we weren't really focused on efficiency we're just focused focused on efficiency we're just focused focused on efficiency we're just focused on At least it's my opinion on At least it's my opinion on At least it's my opinion >> on creating cool things and getting >> on creating cool things and getting >> on creating cool things and getting everyone started. Now, everyone's everyone started. Now, everyone's everyone started. Now, everyone's jumping on board, but they're jumping jumping on board, but they're jumping jumping on board, but they're jumping and using AI for things that are just and using AI for things that are just and using AI for things that are just >> um rudimentary and so it's sucking up a >> um rudimentary and so it's sucking up a >> um rudimentary and so it's sucking up a lot of the compute power, but in the lot of the compute power, but in the lot of the compute power, but in the east because they have well supposedly east because they have well supposedly east because they have well supposedly they haven't had the access to, you they haven't had the access to, you they haven't had the access to, you know, a plethora of GPU compute. They've know, a plethora of GPU compute. They've know, a plethora of GPU compute. They've had to be efficient.

had to be efficient. had to be efficient. >> Yeah. And so now we're starting to get >> Yeah. And so now we're starting to get >> Yeah. And so now we're starting to get to this disillusionment that there is no to this disillusionment that there is no to this disillusionment that there is no ROI in a lot of these models and these ROI in a lot of these models and these ROI in a lot of these models and these use cases that we're building because it use cases that we're building because it use cases that we're building because it just you know needs so much compute and just you know needs so much compute and just you know needs so much compute and tokens to do it. And then I think the tokens to do it. And then I think the tokens to do it. And then I think the next phase is you know if you don't move next phase is you know if you don't move next phase is you know if you don't move to a new you know um model such as to a new you know um model such as to a new you know um model such as photonix you know and things and make photonix you know and things and make photonix you know and things and make things more efficient it has to be in things more efficient it has to be in things more efficient it has to be in the models. the models. the models. >> Yeah. So, do you have to reinvent the >> Yeah. So, do you have to reinvent the >> Yeah. So, do you have to reinvent the wheel every time you build a model? Are wheel every time you build a model? Are wheel every time you build a model? Are there things that can be learned of, you there things that can be learned of, you there things that can be learned of, you know, do I need to train, for example, a know, do I need to train, for example, a know, do I need to train, for example, a computer vision model every time to computer vision model every time to computer vision model every time to recognize a, you know, recognize a, you know, recognize a, you know, >> a a car versus a truck or are those >> a a car versus a truck or are those >> a a car versus a truck or are those libraries just available to where I libraries just available to where I libraries just available to where I don't have to, you know, rebuild the don't have to, you know, rebuild the don't have to, you know, rebuild the model from scratch? model from scratch? model from scratch? >> And I think economics and efficiency is >> And I think economics and efficiency is >> And I think economics and efficiency is going to be the next phase of where we going to be the next phase of where we going to be the next phase of where we go. And sorry, not to dominate, but go. And sorry, not to dominate, but go. And sorry, not to dominate, but again, processing edge again, processing edge again, processing edge >> proc you know the the edge has become so >> proc you know the the edge has become so >> proc you know the the edge has become so important you know companies back then important you know companies back then important you know companies back then like Fogghorn and others like Fogghorn and others like Fogghorn and others >> of saying do I need to capture every >> of saying do I need to capture every >> of saying do I need to capture every sensor sensor sensor reading and store it in the cloud um reading and store it in the cloud um reading and store it in the cloud um >> you know you don't I think you may know >> you know you don't I think you may know >> you know you don't I think you may know >> um someone told me you know we don't use >> um someone told me you know we don't use >> um someone told me you know we don't use 80 to 90% of the data that we collect 80 to 90% of the data that we collect 80 to 90% of the data that we collect >> but the storage of that data in the data >> but the storage of that data in the data >> but the storage of that data in the data energy. Energy used to store all that energy. Energy used to store all that energy. Energy used to store all that data exceeds that of the commercial

data exceeds that of the commercial data exceeds that of the commercial airline industry. Keeping with the theme airline industry. Keeping with the theme airline industry. Keeping with the theme of airplanes. of airplanes. of airplanes. >> Um, >> Um, >> Um, >> wow. >> wow. >> wow. >> And if you think about sustainability, >> And if you think about sustainability, >> And if you think about sustainability, Rob, and all that, that is kind of a sin Rob, and all that, that is kind of a sin Rob, and all that, that is kind of a sin to just have all that waste. to just have all that waste. to just have all that waste. >> Yeah, it is. >> Yeah, it is. >> Yeah, it is. >> I [laughter] hear that NT is in the data >> I [laughter] hear that NT is in the data >> I [laughter] hear that NT is in the data center business. >> We we we do have we do have quite a you >> We we we do have we do have quite a you know, we do have that little business. know, we do have that little business. know, we do have that little business. No, it's a huge business. Um, you know, No, it's a huge business. Um, you know, No, it's a huge business. Um, you know, I think we're one of the top five data I think we're one of the top five data I think we're one of the top five data center providers. You know, I think a center providers. You know, I think a center providers. You know, I think a lot of people don't realize a lot of lot of people don't realize a lot of lot of people don't realize a lot of things NT does as far as, you know, things NT does as far as, you know, things NT does as far as, you know, photonics, data centers, submarine photonics, data centers, submarine photonics, data centers, submarine cables, um, things that we really enable cables, um, things that we really enable cables, um, things that we really enable behind the scenes. behind the scenes. behind the scenes. >> Are you talking about silicon photonics? >> Are you talking about silicon photonics? >> Are you talking about silicon photonics? When you say photonics or is it a When you say photonics or is it a When you say photonics or is it a product? product? product? >> Right. Photonix all the way down to the >> Right. Photonix all the way down to the >> Right. Photonix all the way down to the chip. So, the ion, chip. So, the ion, chip. So, the ion, >> we can talk about this in a few, right? >> we can talk about this in a few, right? >> we can talk about this in a few, right? But the old ion and all, you know, using But the old ion and all, you know, using But the old ion and all, you know, using light light light for you know that's really where I think for you know that's really where I think for you know that's really where I think everything's going to change you know everything's going to change you know everything's going to change you know first you know first you know first you know >> the intersection of quantum whenever we >> the intersection of quantum whenever we >> the intersection of quantum whenever we get that working and AI is going to get that working and AI is going to get that working and AI is going to really change a lot of things really change a lot of things really change a lot of things >> but it's not scalable so >> but it's not scalable so >> but it's not scalable so >> you know photonics is going to be >> you know photonics is going to be >> you know photonics is going to be another area where another area where another area where >> we we see things becoming more readily >> we we see things becoming more readily >> we we see things becoming more readily affordable for to you know to the masses affordable for to you know to the masses affordable for to you know to the masses >> yeah >> yeah >> yeah is where the hype it's going to be in a is where the hype it's going to be in a is where the hype it's going to be in a couple of years, [laughter] >> right? Well, don't stop believing. You >> right? Well, don't stop believing. You know, we [laughter]

know, we [laughter] know, we [laughter] >> There you go. >> There you go. >> There you go. >> Don't stop believing. Yeah. >> Don't stop believing. Yeah. >> Don't stop believing. Yeah. >> Right. >> Right. >> Right. >> The I was going to say about Kimmy 3 >> The I was going to say about Kimmy 3 >> The I was going to say about Kimmy 3 though is uh you know, I mean Deon's though is uh you know, I mean Deon's though is uh you know, I mean Deon's right, you know, after you sort of right, you know, after you sort of right, you know, after you sort of [clears throat] get it working as we all [clears throat] get it working as we all [clears throat] get it working as we all know, then you try to commercialize it know, then you try to commercialize it know, then you try to commercialize it and that's when cost comes into play. and that's when cost comes into play. and that's when cost comes into play. And I think that's where we're hitting And I think that's where we're hitting And I think that's where we're hitting in the the larger kind of generative AI in the the larger kind of generative AI in the the larger kind of generative AI cycle, right? is now the cycle, right? is now the cycle, right? is now the commercialization and the economics need commercialization and the economics need commercialization and the economics need to start to work. And so, you know, to start to work. And so, you know, to start to work. And so, you know, people are really looking at their P's people are really looking at their P's people are really looking at their P's and Q's about like what is the token and Q's about like what is the token and Q's about like what is the token cost? We've seen this huge backlash now cost? We've seen this huge backlash now cost? We've seen this huge backlash now where, you know, remember we had that where, you know, remember we had that where, you know, remember we had that kind of 18month run of like use as many kind of 18month run of like use as many kind of 18month run of like use as many tokens as you can and there was like tokens as you can and there was like tokens as you can and there was like contests to see how many tokens you can contests to see how many tokens you can contests to see how many tokens you can use. use. use. >> Yeah. >> Yeah. >> Yeah. >> Then at some point someone said, "God, >> Then at some point someone said, "God, >> Then at some point someone said, "God, that is a dumb [ __ ] idea." Like, and that is a dumb [ __ ] idea." Like, and that is a dumb [ __ ] idea." Like, and so stop doing that. [clears throat] And so stop doing that. [clears throat] And so stop doing that. [clears throat] And um um um >> is expensive. [ __ ] is expensive. And um >> is expensive. [ __ ] is expensive. And um >> is expensive. [ __ ] is expensive. And um efficiency is the new currency, right? efficiency is the new currency, right? efficiency is the new currency, right? So So So >> effic efficient models are going to >> effic efficient models are going to >> effic efficient models are going to work. The Kimmy 3 though is interesting work. The Kimmy 3 though is interesting work. The Kimmy 3 though is interesting though because it's like a 2.8 trillion though because it's like a 2.8 trillion though because it's like a 2.8 trillion parameter model. So parameter model. So parameter model. So >> it's not it's not running on it's it's >> it's not it's not running on it's it's >> it's not it's not running on it's it's it requires like a lot of heavy metal to it requires like a lot of heavy metal to it requires like a lot of heavy metal to run, right? It's not running on edge run, right? It's not running on edge run, right? It's not running on edge devices. For edge devices, you need devices. For edge devices, you need devices. For edge devices, you need billion parameter models, right? It's billion parameter models, right? It's billion parameter models, right? It's about a gigabyte of RAM per about a gigabyte of RAM per about a gigabyte of RAM per >> billion parameters or so on the edge. So >> billion parameters or so on the edge. So >> billion parameters or so on the edge. So it's not there yet, but you know, you it's not there yet, but you know, you it's not there yet, but you know, you know, my my hypothesis obviously the know, my my hypothesis obviously the know, my my hypothesis obviously the gravitational pulls toward the edge and gravitational pulls toward the edge and gravitational pulls toward the edge and >> eventually >> eventually >> eventually that's where the efficiency is and you that's where the efficiency is and you that's where the efficiency is and you use the cloud when you need to, but the use the cloud when you need to, but the use the cloud when you need to, but the cloud's kind of going to be pretty cloud's kind of going to be pretty cloud's kind of going to be pretty expensive token costs for probably the

expensive token costs for probably the expensive token costs for probably the the foreseeable the foreseeable the foreseeable >> future. Here's the thing that's really >> future. Here's the thing that's really >> future. Here's the thing that's really disrupt going to be disruptive and I disrupt going to be disruptive and I disrupt going to be disruptive and I think this is what's going we're going think this is what's going we're going think this is what's going we're going to see in the next probably 6 months and to see in the next probably 6 months and to see in the next probably 6 months and it it's going to cause ripples across it it's going to cause ripples across it it's going to cause ripples across the US AI industry not the Chinese one the US AI industry not the Chinese one the US AI industry not the Chinese one because the Chinese have already baked a because the Chinese have already baked a because the Chinese have already baked a lot of this stuff into their approach. lot of this stuff into their approach. lot of this stuff into their approach. You know [clears throat] the thing You know [clears throat] the thing You know [clears throat] the thing that's really startling is a 2.8 8 that's really startling is a 2.8 8 that's really startling is a 2.8 8 trillion parameter model and this is trillion parameter model and this is trillion parameter model and this is what we were kind of like talking about what we were kind of like talking about what we were kind of like talking about uh you know offline uh you know offline uh you know offline sparcity is a dense densely sparse um or sparcity is a dense densely sparse um or sparcity is a dense densely sparse um or a mixture expert where you have only 50 a mixture expert where you have only 50 a mixture expert where you have only 50 billion active parameters. So we're billion active parameters. So we're billion active parameters. So we're talking about less than 1%. So just for talking about less than 1%. So just for talking about less than 1%. So just for reference, a year ago, a year and a half reference, a year ago, a year and a half reference, a year ago, a year and a half ago, Llama was the top open um model, ago, Llama was the top open um model, ago, Llama was the top open um model, right? Um Llama 3 when it came out is a right? Um Llama 3 when it came out is a right? Um Llama 3 when it came out is a mixture of experts 40% sparity. Then mixture of experts 40% sparity. Then mixture of experts 40% sparity. Then Deep Seek came out brought that down to Deep Seek came out brought that down to Deep Seek came out brought that down to 5%. Meaning out of all of the parameters 5%. Meaning out of all of the parameters 5%. Meaning out of all of the parameters for any uh input only five 5% of those for any uh input only five 5% of those for any uh input only five 5% of those parameters are active. Now we're at parameters are active. Now we're at parameters are active. Now we're at below 1%. It's like half a half a below 1%. It's like half a half a below 1%. It's like half a half a percent.

percent. percent. >> This is what the Chinese have done. And >> This is what the Chinese have done. And >> This is what the Chinese have done. And the thing that's incredible is the the thing that's incredible is the the thing that's incredible is the efficiency and the efficacy of their efficiency and the efficacy of their efficiency and the efficacy of their model architecture, right? thee model architecture, right? thee model architecture, right? thee architecture which is kind of like an architecture which is kind of like an architecture which is kind of like an agentic framework within a model. Um and agentic framework within a model. Um and agentic framework within a model. Um and the crazy part I think I don't think the crazy part I think I don't think the crazy part I think I don't think there you're going to be seeing this there you're going to be seeing this there you're going to be seeing this thing run at scale except for certain thing run at scale except for certain thing run at scale except for certain hypers sc like sort of supercomputing hypers sc like sort of supercomputing hypers sc like sort of supercomputing applications like drug discovery applications like drug discovery applications like drug discovery support. It's not going to do the dis support. It's not going to do the dis support. It's not going to do the dis drug discovery itself cuz that requires drug discovery itself cuz that requires drug discovery itself cuz that requires traditional you know high precision um traditional you know high precision um traditional you know high precision um uh supercomputing that you know largely uh supercomputing that you know largely uh supercomputing that you know largely runs off of CPUs and then people don't runs off of CPUs and then people don't runs off of CPUs and then people don't know that supercomputing traditional know that supercomputing traditional know that supercomputing traditional stuff the stuff that quantum is going to stuff the stuff that quantum is going to stuff the stuff that quantum is going to take over that's a different category take over that's a different category take over that's a different category it's not large language models you know it's not large language models you know it's not large language models you know running at like FP4 running at like FP4 running at like FP4 so I think this large model is just so I think this large model is just so I think this large model is just going to be a you know like the teacher going to be a you know like the teacher going to be a you know like the teacher model for a bunch of smaller distilled model for a bunch of smaller distilled model for a bunch of smaller distilled models along the lines of what you were models along the lines of what you were models along the lines of what you were talking about just uh just prior Pete talking about just uh just prior Pete talking about just uh just prior Pete these smaller models that'll be used for these smaller models that'll be used for these smaller models that'll be used for application specific purposes application specific purposes application specific purposes >> and they're just they're going to be >> and they're just they're going to be >> and they're just they're going to be great they'll have 90% of the great they'll have 90% of the great they'll have 90% of the capabilities of the mother or that mama capabilities of the mother or that mama capabilities of the mother or that mama big mama model but big mama model but big mama model but >> you know most of us don't need that >> you know most of us don't need that >> you know most of us don't need that capability developers like what you're capability developers like what you're capability developers like what you're saying Devin saying Devin saying Devin >> they don't give give a crap about the

>> they don't give give a crap about the >> they don't give give a crap about the latest. If they built on a model, it's latest. If they built on a model, it's latest. If they built on a model, it's good enough. They don't want that thing good enough. They don't want that thing good enough. They don't want that thing to change. And if it's being delivered to change. And if it's being delivered to change. And if it's being delivered as a service, they certainly don't want as a service, they certainly don't want as a service, they certainly don't want the service provider, the AI as a the service provider, the AI as a the service provider, the AI as a service guy cutting them off. You know, service guy cutting them off. You know, service guy cutting them off. You know, that pisses them off more than anything. that pisses them off more than anything. that pisses them off more than anything. >> So, and then you have the economics >> So, and then you have the economics >> So, and then you have the economics associated with that. associated with that. associated with that. >> And I I think it's it's just and you >> And I I think it's it's just and you >> And I I think it's it's just and you know, this whole mythos thing, the the know, this whole mythos thing, the the know, this whole mythos thing, the the anthropic moat that everyone was talking anthropic moat that everyone was talking anthropic moat that everyone was talking about, it's gone. about, it's gone. about, it's gone. There is no moat there. There is no moat there. There is no moat there. >> That was two weeks ago, Leonard. >> That was two weeks ago, Leonard. >> That was two weeks ago, Leonard. >> I know, [laughter] you know. Where were >> I know, [laughter] you know. Where were >> I know, [laughter] you know. Where were you? I mean, you know. you? I mean, you know. you? I mean, you know. >> Well, I mean, Stone Age. >> Well, I mean, Stone Age. >> Well, I mean, Stone Age. >> Yeah. Crazy, dude. >> Yeah. Crazy, dude. >> Yeah. Crazy, dude. >> That was June. That was June. >> That was June. That was June. >> That was June. That was June. >> Isn't that interesting how Anthropic >> Isn't that interesting how Anthropic >> Isn't that interesting how Anthropic keeps extending the usage of Fable 5 to keeps extending the usage of Fable 5 to keeps extending the usage of Fable 5 to all their paid customers? They're like, all their paid customers? They're like, all their paid customers? They're like, "Oh, you're only going to get and then "Oh, you're only going to get and then "Oh, you're only going to get and then we're cut off." And it's like they keep we're cut off." And it's like they keep we're cut off." And it's like they keep extending it. exclusive. extending it. exclusive. extending it. exclusive. >> They might. Well, the the other thing is >> They might. Well, the the other thing is >> They might. Well, the the other thing is too when you talk about sparity and too when you talk about sparity and too when you talk about sparity and research, you know, we work with a lot research, you know, we work with a lot research, you know, we work with a lot of academic institutions and all a lot of academic institutions and all a lot of academic institutions and all a lot so much research over the past few years so much research over the past few years so much research over the past few years has all been about model optimization has all been about model optimization has all been about model optimization and sparity and and sparity and and sparity and >> you know doing more with less right >> you know doing more with less right >> you know doing more with less right because ultimately that's the economic because ultimately that's the economic because ultimately that's the economic impact that's the scale impact that's the scale impact that's the scale >> and as you mentioned outside the US >> and as you mentioned outside the US >> and as you mentioned outside the US outside of our Ford F-150s or whatever outside of our Ford F-150s or whatever outside of our Ford F-150s or whatever we have here we have here we have here >> where everyone else is working with you >> where everyone else is working with you >> where everyone else is working with you know less maybe less resources in some know less maybe less resources in some know less maybe less resources in some cases cases cases >> like a Fiat 300 under it.

>> like a Fiat 300 under it. >> like a Fiat 300 under it. >> Yeah. Yeah. [laughter] That's cool, >> Yeah. Yeah. [laughter] That's cool, >> Yeah. Yeah. [laughter] That's cool, right? So, everyone's trying to be more right? So, everyone's trying to be more right? So, everyone's trying to be more sovereign with their AI and have more sovereign with their AI and have more sovereign with their AI and have more control over it and they don't want to control over it and they don't want to control over it and they don't want to write a check to AWS. Although, we love write a check to AWS. Although, we love write a check to AWS. Although, we love AWS. They're on a board of directors AWS. They're on a board of directors AWS. They're on a board of directors now. So, peace. But, [clears throat] you now. So, peace. But, [clears throat] you now. So, peace. But, [clears throat] you know, people don't want to have to write know, people don't want to have to write know, people don't want to have to write a check back to these US hyperscalers a check back to these US hyperscalers a check back to these US hyperscalers all the time just to run AI. It doesn't all the time just to run AI. It doesn't all the time just to run AI. It doesn't make any sense. make any sense. make any sense. >> Yeah. Yeah. Interesting. >> Yeah. Yeah. Interesting. >> Yeah. Yeah. Interesting. >> Very exciting and interesting to see. I >> Very exciting and interesting to see. I >> Very exciting and interesting to see. I saw that Apple is now going to use Quen saw that Apple is now going to use Quen saw that Apple is now going to use Quen in China for their in China for their in China for their >> Y >> Y >> Y >> Apple Intelligence, right? So they're >> Apple Intelligence, right? So they're >> Apple Intelligence, right? So they're And Quinn is probably one of the most And Quinn is probably one of the most And Quinn is probably one of the most popular models out there right now. popular models out there right now. popular models out there right now. >> Well, yeah, that that's the thing. >> Well, yeah, that that's the thing. >> Well, yeah, that that's the thing. They're they're uh in IoT in consumer They're they're uh in IoT in consumer They're they're uh in IoT in consumer IoT, everyone's using it. You talk to IoT, everyone's using it. You talk to IoT, everyone's using it. You talk to any of the Korean companies that are any of the Korean companies that are any of the Korean companies that are coming up with like goofy robotics and coming up with like goofy robotics and coming up with like goofy robotics and physical AI stuff. physical AI stuff. physical AI stuff. >> Physical AI stuff. >> Physical AI stuff. >> Physical AI stuff. >> What's this physical AI you're talking >> What's this physical AI you're talking >> What's this physical AI you're talking about? fiscal. about? fiscal. about? fiscal. >> I think Deon needs to tell us. >> I think Deon needs to tell us. >> I think Deon needs to tell us. >> Either Devin or Mr. >> Either Devin or Mr. >> Either Devin or Mr. >> Mr. Qualcomm over here. >> Mr. Qualcomm over here. >> Mr. Qualcomm over here. >> What's your take, Devin? What's your >> What's your take, Devin? What's your >> What's your take, Devin? What's your take on physical AI? Are you guys having take on physical AI? Are you guys having take on physical AI? Are you guys having to do all that stuff now?

to do all that stuff now? to do all that stuff now? >> Are you guys getting physical? >> Are you guys getting physical? >> Are you guys getting physical? >> Yeah, I think we've had several >> Yeah, I think we've had several >> Yeah, I think we've had several conversations I've had with many conversations I've had with many conversations I've had with many analysts, too. Obviously, it's a analysts, too. Obviously, it's a analysts, too. Obviously, it's a packaging. It's the hot word right now. packaging. It's the hot word right now. packaging. It's the hot word right now. And people are, you know, Jensen said a And people are, you know, Jensen said a And people are, you know, Jensen said a couple times and now everyone's jumping couple times and now everyone's jumping couple times and now everyone's jumping on what is physical AI on what is physical AI on what is physical AI >> and people define it differently. You >> and people define it differently. You >> and people define it differently. You know, the way we look at it is, you know, the way we look at it is, you know, the way we look at it is, you know, the interaction with the the real know, the interaction with the the real know, the interaction with the the real physical world. But if you think about physical world. But if you think about physical world. But if you think about AI, I think Rob, you and I had this AI, I think Rob, you and I had this AI, I think Rob, you and I had this conversation. Most of what you see is conversation. Most of what you see is conversation. Most of what you see is just an evolution of other models that just an evolution of other models that just an evolution of other models that have been around. So for example, all have been around. So for example, all have been around. So for example, all this, you know, a aentic AI we said used this, you know, a aentic AI we said used this, you know, a aentic AI we said used to be called robotic process automation. to be called robotic process automation. to be called robotic process automation. And now it's, you know, robotic process And now it's, you know, robotic process And now it's, you know, robotic process automation on steroids. So this whole automation on steroids. So this whole automation on steroids. So this whole physical AI is, you know, limited to a physical AI is, you know, limited to a physical AI is, you know, limited to a few things that we're doing with like few things that we're doing with like few things that we're doing with like digital twins and stuff, but it's now digital twins and stuff, but it's now digital twins and stuff, but it's now like how do the robots and AGVs interact like how do the robots and AGVs interact like how do the robots and AGVs interact with the physical environment, but some with the physical environment, but some with the physical environment, but some people are saying that, you know, it's people are saying that, you know, it's people are saying that, you know, it's become this hot new thing, but it really become this hot new thing, but it really become this hot new thing, but it really isn't. isn't. isn't. >> Yeah, it's been around a long time. I'll >> Yeah, it's been around a long time. I'll >> Yeah, it's been around a long time. I'll just point to um we had uh this event in just point to um we had uh this event in just point to um we had uh this event in London. I think I mentioned last time. I London. I think I mentioned last time. I London. I think I mentioned last time. I think one of the keynotes was from Max think one of the keynotes was from Max think one of the keynotes was from Max Versace from he's the VP of emerging AI Versace from he's the VP of emerging AI Versace from he's the VP of emerging AI from analog devices which is a really from analog devices which is a really from analog devices which is a really fascinating company by the way been fascinating company by the way been fascinating company by the way been around forever out of Boston everyone's around forever out of Boston everyone's around forever out of Boston everyone's heard of them heard of them heard of them >> and he did a really cool keynote on what >> and he did a really cool keynote on what >> and he did a really cool keynote on what they're working on in terms of um um they're working on in terms of um um they're working on in terms of um um touch uh dexterity and touch and really touch uh dexterity and touch and really touch uh dexterity and touch and really actually detection basically they're actually detection basically they're actually detection basically they're building a neuromorphic AI processor building a neuromorphic AI processor building a neuromorphic AI processor chip into the fingertip of each

chip into the fingertip of each chip into the fingertip of each >> gripper >> gripper >> gripper >> and that thing can actually detect act >> and that thing can actually detect act >> and that thing can actually detect act like am I touching leather or wood or like am I touching leather or wood or like am I touching leather or wood or cement, cement, cement, >> you know, based on all that. And then >> you know, based on all that. And then >> you know, based on all that. And then then the gripper can like he he was then the gripper can like he he was then the gripper can like he he was showing a demo of like how do you want showing a demo of like how do you want showing a demo of like how do you want to like spool cable, you know, like that to like spool cable, you know, like that to like spool cable, you know, like that that motion of spooling cable and things that motion of spooling cable and things that motion of spooling cable and things like that. Being able to use, you know, like that. Being able to use, you know, like that. Being able to use, you know, AI in each kind of gripper tip to detect AI in each kind of gripper tip to detect AI in each kind of gripper tip to detect the the touch and stuff. So for me, the the touch and stuff. So for me, the the touch and stuff. So for me, that's really physical AI, right? That's that's really physical AI, right? That's that's really physical AI, right? That's really that really that really that >> reality digital boundary. And so you see >> reality digital boundary. And so you see >> reality digital boundary. And so you see companies like Analog Devices getting companies like Analog Devices getting companies like Analog Devices getting involved and they were working with a involved and they were working with a involved and they were working with a company called Inotera out of Delft that company called Inotera out of Delft that company called Inotera out of Delft that does the neuromorphic chip. You probably does the neuromorphic chip. You probably does the neuromorphic chip. You probably heard of them heard of them heard of them >> and but it was a really cool like uh >> and but it was a really cool like uh >> and but it was a really cool like uh it's more than just like you know it's more than just like you know it's more than just like you know warehouse robots spinning around and warehouse robots spinning around and warehouse robots spinning around and stuff like that. It's really like stuff like that. It's really like stuff like that. It's really like >> how do you really make these digital >> how do you really make these digital >> how do you really make these digital things interact with the physical world things interact with the physical world things interact with the physical world which is a very as you know messy which is a very as you know messy which is a very as you know messy >> weird unpredictable world. So I think I >> weird unpredictable world. So I think I >> weird unpredictable world. So I think I think there's a lot of cool stuff think there's a lot of cool stuff think there's a lot of cool stuff happening in the space. So I would give happening in the space. So I would give happening in the space. So I would give I would give a you know I would say when I would give a you know I would say when I would give a you know I would say when I think of physical AI like that I think I think of physical AI like that I think I think of physical AI like that I think that is a new thing. I think that is that is a new thing. I think that is that is a new thing. I think that is some new capabilities that we didn't do some new capabilities that we didn't do some new capabilities that we didn't do five years ago. Not even close. Five five years ago. Not even close. Five five years ago. Not even close. Five years ago we were still like had cameras years ago we were still like had cameras years ago we were still like had cameras like we were like is that a human? I like we were like is that a human? I like we were like is that a human? I don't know.

don't know. don't know. >> Yeah. >> Yeah. >> Yeah. >> No but that's that's great because you >> No but that's that's great because you >> No but that's that's great because you know we've you know when I spent all know we've you know when I spent all know we've you know when I spent all that time doing IoT and agriculture and that time doing IoT and agriculture and that time doing IoT and agriculture and I saw startups trying to do robots I saw startups trying to do robots I saw startups trying to do robots picking apples and things like that and picking apples and things like that and picking apples and things like that and they kept squishing them or they bruise they kept squishing them or they bruise they kept squishing them or they bruise them. them. them. >> Yeah. Yeah. Picking strawberries. Right. >> Yeah. Yeah. Picking strawberries. Right. >> Yeah. Yeah. Picking strawberries. Right. You can pick strawberry, a mango, apple, You can pick strawberry, a mango, apple, You can pick strawberry, a mango, apple, like any of this agricultural stuff like any of this agricultural stuff like any of this agricultural stuff requires the this dexterity and requires the this dexterity and requires the this dexterity and intelligence. intelligence. intelligence. >> The stuff I saw in the past, the robot, >> The stuff I saw in the past, the robot, >> The stuff I saw in the past, the robot, I'd see flashing light. It's like chew I'd see flashing light. It's like chew I'd see flashing light. It's like chew ch with computer vision. It would burst ch with computer vision. It would burst ch with computer vision. It would burst this light at the apple. They'd grab it this light at the apple. They'd grab it this light at the apple. They'd grab it and yeah, it was like x percentage of and yeah, it was like x percentage of and yeah, it was like x percentage of them were successfully picked them were successfully picked them were successfully picked >> and then, you know, too many of them >> and then, you know, too many of them >> and then, you know, too many of them were squished or bruised, which made were squished or bruised, which made were squished or bruised, which made >> Nobody buys apples with bruises on them. >> Nobody buys apples with bruises on them. >> Nobody buys apples with bruises on them. >> No, you can't sell them. Yeah. >> No, you can't sell them. Yeah. >> No, you can't sell them. Yeah. >> And you know what those become? >> And you know what those become? >> And you know what those become? Applesauce. Applesauce. Applesauce. >> Yeah. [laughter] >> Yeah. [laughter] >> Yeah. [laughter] >> Yeah. >> Yeah. >> Yeah. >> Yeah. >> They have a thing called strawberry >> They have a thing called strawberry sauce. sauce. sauce. >> That's jam, right? >> That's jam, right? >> That's jam, right? >> That's right. >> That's right. >> That's right. >> Yeah. We call it strawberry sauce.

>> Yeah. We call it strawberry sauce. >> Yeah. We call it strawberry sauce. >> Oh god. Oh. Hey guys, I just have to >> Oh god. Oh. Hey guys, I just have to >> Oh god. Oh. Hey guys, I just have to tell you, all these GMO strawberries tell you, all these GMO strawberries tell you, all these GMO strawberries taste terrible. taste terrible. taste terrible. >> My wife's been buying this crap from >> My wife's been buying this crap from >> My wife's been buying this crap from Costco and watermelons. Wow. What the Costco and watermelons. Wow. What the Costco and watermelons. Wow. What the hell is happening to food? Anyways, I hell is happening to food? Anyways, I hell is happening to food? Anyways, I just wanted to just wanted to just wanted to >> No, grow your own strawberries. That's >> No, grow your own strawberries. That's >> No, grow your own strawberries. That's my recommendation. my recommendation. my recommendation. >> This is what I want to say, you know, >> This is what I want to say, you know, >> This is what I want to say, you know, and you know, kudos to Analog Devices, and you know, kudos to Analog Devices, and you know, kudos to Analog Devices, the only guys that show up at NM show the only guys that show up at NM show the only guys that show up at NM show now. Um, Qualcomm was there last year now. Um, Qualcomm was there last year now. Um, Qualcomm was there last year and they they kind of bailed out. I and they they kind of bailed out. I and they they kind of bailed out. I think that was a bad move because people think that was a bad move because people think that was a bad move because people were really interested in Snapdragon X were really interested in Snapdragon X were really interested in Snapdragon X Elite. They didn't even know that it Elite. They didn't even know that it Elite. They didn't even know that it existed. existed. existed. >> Put that in the suggestion box, Mark. >> Put that in the suggestion box, Mark. >> Put that in the suggestion box, Mark. Yeah, but the the tactile stuff I it's Yeah, but the the tactile stuff I it's Yeah, but the the tactile stuff I it's it's already there in um you know it's already there in um you know it's already there in um you know digital instruments like uh interfaces digital instruments like uh interfaces digital instruments like uh interfaces and stuff, right? Whether it's a and stuff, right? Whether it's a and stuff, right? Whether it's a keyboard, you have like the velocity keyboard, you have like the velocity keyboard, you have like the velocity tracking and tracking and tracking and >> but the here's here's what makes the >> but the here's here's what makes the >> but the here's here's what makes the human human human >> uh incredible is the whole tactile thing >> uh incredible is the whole tactile thing >> uh incredible is the whole tactile thing is birectional. You have to be able to is birectional. You have to be able to is birectional. You have to be able to sense, right? But then also you have to sense, right? But then also you have to sense, right? But then also you have to be able to actuate and and so it goes be able to actuate and and so it goes be able to actuate and and so it goes both ways. And when that tactile stuff both ways. And when that tactile stuff both ways. And when that tactile stuff becomes the next type, the technology is becomes the next type, the technology is becomes the next type, the technology is already there. People have, you know, already there. People have, you know, already there. People have, you know, these researchers have already been these researchers have already been these researchers have already been struggling with the math. So this is the struggling with the math. So this is the struggling with the math. So this is the problem with the hype, right? It it it's problem with the hype, right? It it it's problem with the hype, right? It it it's classic.

classic. classic. people are not aware of a technology people are not aware of a technology people are not aware of a technology they go overboard and then you know they they go overboard and then you know they they go overboard and then you know they get disappointed right and um and that get disappointed right and um and that get disappointed right and um and that that's that's what's going to happen that's that's what's going to happen that's that's what's going to happen there as well the physical AI stuff a there as well the physical AI stuff a there as well the physical AI stuff a lot of things that you know physics lot of things that you know physics lot of things that you know physics models you know simulation models you know simulation models you know simulation >> um a lot of this stuff that has already >> um a lot of this stuff that has already >> um a lot of this stuff that has already existed for a long time like um what existed for a long time like um what existed for a long time like um what ANCIS does the the thermal all these ANCIS does the the thermal all these ANCIS does the the thermal all these things that are used in um you know like things that are used in um you know like things that are used in um you know like engineering um they they've already engineering um they they've already engineering um they they've already existed. You know what I'm saying? And existed. You know what I'm saying? And existed. You know what I'm saying? And and so this is I think really the and so this is I think really the and so this is I think really the problem. People get excited about stuff problem. People get excited about stuff problem. People get excited about stuff before really doing their homework and before really doing their homework and before really doing their homework and understanding understanding understanding um where the technology has uh been and um where the technology has uh been and um where the technology has uh been and where it's going. They overshoot where where it's going. They overshoot where where it's going. They overshoot where they think it's going and that's usually they think it's going and that's usually they think it's going and that's usually complete hyperbole. And this is the the complete hyperbole. And this is the the complete hyperbole. And this is the the thing that I think is kind of comical thing that I think is kind of comical thing that I think is kind of comical about physical AI and Devon's point. You about physical AI and Devon's point. You about physical AI and Devon's point. You ask somebody, oh, what's physical AI?

ask somebody, oh, what's physical AI? ask somebody, oh, what's physical AI? They can't explain it. And then they They can't explain it. And then they They can't explain it. And then they revert to robotics and it's like, what? revert to robotics and it's like, what? revert to robotics and it's like, what? Well, robotics has been around for a Well, robotics has been around for a Well, robotics has been around for a long time. long time. long time. >> Sure. >> Sure. >> Sure. >> And we go to these shows with these >> And we go to these shows with these >> And we go to these shows with these robots doing fancy [ __ ] There's a dude, robots doing fancy [ __ ] There's a dude, robots doing fancy [ __ ] There's a dude, you know, behind a corner with like you know, behind a corner with like you know, behind a corner with like peeppholes. Yeah. with a remote control peeppholes. Yeah. with a remote control peeppholes. Yeah. with a remote control doing like tech and [ __ ] would to make doing like tech and [ __ ] would to make doing like tech and [ __ ] would to make it look like it's doing fancy stuff. it look like it's doing fancy stuff. it look like it's doing fancy stuff. >> Dancing robots. That's the new red flag. >> Dancing robots. That's the new red flag. >> Dancing robots. That's the new red flag. That's a red flag. Anyone at the dancing That's a red flag. Anyone at the dancing That's a red flag. Anyone at the dancing robot? We've actually ban dancing robots robot? We've actually ban dancing robots robot? We've actually ban dancing robots from our events. from our events. from our events. >> What about the dogs? >> What about the dogs? >> What about the dogs? >> You're going to get kicked out. Oh yeah, >> You're going to get kicked out. Oh yeah, >> You're going to get kicked out. Oh yeah, >> the dollars that sort of randomly kick >> the dollars that sort of randomly kick >> the dollars that sort of randomly kick this way and [laughter] this way and [laughter] this way and [laughter] >> but I think a more fundamental question >> but I think a more fundamental question >> but I think a more fundamental question rather than what is what is physical AI rather than what is what is physical AI rather than what is what is physical AI is what is intelligence and is AI the AI is what is intelligence and is AI the AI is what is intelligence and is AI the AI that we have actually intelligent or is that we have actually intelligent or is that we have actually intelligent or is it just a calculator or you know is it it just a calculator or you know is it it just a calculator or you know is it just running a program a prediction I just running a program a prediction I just running a program a prediction I mean what what defines intelligence and mean what what defines intelligence and mean what what defines intelligence and are we there yet thoughts?

are we there yet thoughts? are we there yet thoughts? >> Yeah. Yeah, >> Yeah. Yeah, >> Yeah. Yeah, >> definitely not there yet. Well, you >> definitely not there yet. Well, you >> definitely not there yet. Well, you know, know, know, >> do you know many intelligent >> do you know many intelligent >> do you know many intelligent human or mechanical? human or mechanical? human or mechanical? >> What was that, Mark? >> What was that, Mark? >> What was that, Mark? >> Yeah. >> Yeah. >> Yeah. >> Do you know many intelligent people or >> Do you know many intelligent people or >> Do you know many intelligent people or [laughter] [laughter] [laughter] >> couple? >> couple? >> couple? >> Not here. >> Not here. >> Not here. [laughter] [laughter] [laughter] >> Well, but >> Well, but >> Well, but when you're when you're talking about when you're when you're talking about when you're when you're talking about the intelligence, is it really the intelligence, is it really the intelligence, is it really intelligence? Is it just doing pattern intelligence? Is it just doing pattern intelligence? Is it just doing pattern matching? Is it just is it am I just matching? Is it just is it am I just matching? Is it just is it am I just doing select star against a vector doing select star against a vector doing select star against a vector database? you know, um u whatever the database? you know, um u whatever the database? you know, um u whatever the the guy who had a deep sea, uh not the guy who had a deep sea, uh not the guy who had a deep sea, uh not deepse mind at Google, you know, I love deepse mind at Google, you know, I love deepse mind at Google, you know, I love when he remember he came, oh my god, on when he remember he came, oh my god, on when he remember he came, oh my god, on my as soon as I said that, Gemini came my as soon as I said that, Gemini came my as soon as I said that, Gemini came on. Wow. Um but he said, "All right, how on. Wow. Um but he said, "All right, how on. Wow. Um but he said, "All right, how are we going to test it? We're gonna are we going to test it? We're gonna are we going to test it? We're gonna have the when we have the best model have the when we have the best model have the when we have the best model that we think we have and it's been, you that we think we have and it's been, you that we think we have and it's been, you know, and it has data that goes up to know, and it has data that goes up to know, and it has data that goes up to like 1901 and if it can discover like 1901 and if it can discover like 1901 and if it can discover the theory of relativity, if it can the theory of relativity, if it can the theory of relativity, if it can figure out how to build an atomic bomb figure out how to build an atomic bomb figure out how to build an atomic bomb on its own, then yes, we actually have on its own, then yes, we actually have on its own, then yes, we actually have AGI. If it can't do that, if it can't AGI. If it can't do that, if it can't AGI. If it can't do that, if it can't come up with that stuff on its own, then come up with that stuff on its own, then come up with that stuff on its own, then it's just a pattern matching engine and it's just a pattern matching engine and it's just a pattern matching engine and that's all it is. and he's running he's that's all it is. and he's running he's that's all it is. and he's running he's running deep mind you know and so running deep mind you know and so running deep mind you know and so >> yeah there's a there's a interesting I >> yeah there's a there's a interesting I >> yeah there's a there's a interesting I saw well first of all I saw an article I saw well first of all I saw an article I saw well first of all I saw an article I think it was in the New York Times about think it was in the New York Times about think it was in the New York Times about how a lot of AI companies are now hiring how a lot of AI companies are now hiring how a lot of AI companies are now hiring philosophers right so philosophers I

philosophers right so philosophers I philosophers right so philosophers I went to school my one of my roommates went to school my one of my roommates went to school my one of my roommates had a philosophy degree we used to give had a philosophy degree we used to give had a philosophy degree we used to give him a hard time he was going to go open him a hard time he was going to go open him a hard time he was going to go open a philosophy store when he graduated but a philosophy store when he graduated but a philosophy store when he graduated but >> but they're hiring philosophers now to >> but they're hiring philosophers now to >> but they're hiring philosophers now to sort of kind of ponder these questions sort of kind of ponder these questions sort of kind of ponder these questions about intelligence about intelligence about intelligence >> but I don't know if you know this you've >> but I don't know if you know this you've >> but I don't know if you know this you've heard of Eliza right the member of the heard of Eliza right the member of the heard of Eliza right the member of the program Eliza back in the 70s and ' 80s. program Eliza back in the 70s and ' 80s. program Eliza back in the 70s and ' 80s. This guy Joseph Weisenbal This guy Joseph Weisenbal This guy Joseph Weisenbal [clears throat] um had this he wrote a [clears throat] um had this he wrote a [clears throat] um had this he wrote a whole book had his whole thesis around whole book had his whole thesis around whole book had his whole thesis around how humans we over we kind of overrotate how humans we over we kind of overrotate how humans we over we kind of overrotate on intelligence of mechanical things on intelligence of mechanical things on intelligence of mechanical things like we we have a bias to add to ascribe like we we have a bias to add to ascribe like we we have a bias to add to ascribe too much intelligence to mechanical and too much intelligence to mechanical and too much intelligence to mechanical and digital things like that's just the way digital things like that's just the way digital things like that's just the way we are as humans we are as humans we are as humans >> and uh which kind of explains our >> and uh which kind of explains our >> and uh which kind of explains our fascination with robots and fascination with robots and fascination with robots and anthropomorphic technology and stuff. anthropomorphic technology and stuff. anthropomorphic technology and stuff. But if you do some research on him, he But if you do some research on him, he But if you do some research on him, he wrote a book about that. And uh and it's wrote a book about that. And uh and it's wrote a book about that. And uh and it's true. And so Eliza was his program that true. And so Eliza was his program that true. And so Eliza was his program that he wrote that did a very simple pattern he wrote that did a very simple pattern he wrote that did a very simple pattern matching. It would kind of repeat matching. It would kind of repeat matching. It would kind of repeat phrases back to you. And it fooled a lot phrases back to you. And it fooled a lot phrases back to you. And it fooled a lot of people. They thought, "Oh, wow. This of people. They thought, "Oh, wow. This of people. They thought, "Oh, wow. This is an incredible machine that knows what is an incredible machine that knows what is an incredible machine that knows what I'm talking about." And that's kind of I'm talking about." And that's kind of I'm talking about." And that's kind of what's happening today, too. Like we get what's happening today, too. Like we get what's happening today, too. Like we get these chat bots that are doing mimicking these chat bots that are doing mimicking these chat bots that are doing mimicking and doing this kind of repetitive stuff, and doing this kind of repetitive stuff, and doing this kind of repetitive stuff, and people go gaga over the intelligence and people go gaga over the intelligence and people go gaga over the intelligence of these things. Um of these things. Um of these things. Um >> yeah well you know >> yeah well you know >> yeah well you know >> humans have >> humans have >> humans have >> the problem is is those are all >> the problem is is those are all >> the problem is is those are all distractions there's some really cool distractions there's some really cool distractions there's some really cool stuff that can yeah stuff that can yeah stuff that can yeah >> happen like like for [clears throat] >> happen like like for [clears throat] >> happen like like for [clears throat] instance um coming out of sensors instance um coming out of sensors instance um coming out of sensors converge uh perception edge right the converge uh perception edge right the converge uh perception edge right the idea of perception and it's not just a

idea of perception and it's not just a idea of perception and it's not just a sensor it's just how you can architect a sensor it's just how you can architect a sensor it's just how you can architect a like a perception application or or um like a perception application or or um like a perception application or or um architecture architecture architecture there's a lot of new possibilities and there's a lot of new possibilities and there's a lot of new possibilities and these are like real things. This isn't these are like real things. This isn't these are like real things. This isn't like but it doesn't look that sexy, like but it doesn't look that sexy, like but it doesn't look that sexy, right? And it's not LLM based and it's right? And it's not LLM based and it's right? And it's not LLM based and it's not LLM's down at the sensor level. It's not LLM's down at the sensor level. It's not LLM's down at the sensor level. It's probably a little bit further up. It probably a little bit further up. It probably a little bit further up. It might be on edge uh edge uh might be on edge uh edge uh might be on edge uh edge uh infrastructure, infrastructure, infrastructure, you know, appliance or something like you know, appliance or something like you know, appliance or something like that. But it's it's um you know taking that. But it's it's um you know taking that. But it's it's um you know taking uh information off of sensors and then uh information off of sensors and then uh information off of sensors and then contextualizing them doing the contextualizing them doing the contextualizing them doing the translation much closer um to the translation much closer um to the translation much closer um to the premise and then whatever inside I mean premise and then whatever inside I mean premise and then whatever inside I mean this is like the classical stuff that we this is like the classical stuff that we this is like the classical stuff that we were talking about like 10 years ago were talking about like 10 years ago were talking about like 10 years ago about I you know IoT right industrial about I you know IoT right industrial about I you know IoT right industrial IoT IoT IoT >> well you know I don't think that I >> well you know I don't think that I >> well you know I don't think that I industrial IoT vision is going to industrial IoT vision is going to industrial IoT vision is going to necessarily happen, but there's a lot of necessarily happen, but there's a lot of necessarily happen, but there's a lot of new capabilities that you can deploy on new capabilities that you can deploy on new capabilities that you can deploy on premise, which kind of flips the script premise, which kind of flips the script premise, which kind of flips the script on how we were thinking about industrial on how we were thinking about industrial on how we were thinking about industrial IoT back in the day, right? And then IoT back in the day, right? And then IoT back in the day, right? And then that everyone's going to be sending, you that everyone's going to be sending, you that everyone's going to be sending, you know, the data up to the cloud. No, know, the data up to the cloud. No, know, the data up to the cloud. No, we're starting to build a really we're starting to build a really we're starting to build a really compelling case for uh compute and quote compelling case for uh compute and quote compelling case for uh compute and quote unquote perception intelligence at the e unquote perception intelligence at the e unquote perception intelligence at the e edge. And there can be a lot of benefit.

edge. And there can be a lot of benefit. edge. And there can be a lot of benefit. But you know this question that you're But you know this question that you're But you know this question that you're asking Devin uh you know what is the is asking Devin uh you know what is the is asking Devin uh you know what is the is there intelligence that that may not be there intelligence that that may not be there intelligence that that may not be the relevant question to ask. It's what the relevant question to ask. It's what the relevant question to ask. It's what can we do with whatever can we do with whatever can we do with whatever intelligent kind of technology to um intelligent kind of technology to um intelligent kind of technology to um change the way we do things uh in our change the way we do things uh in our change the way we do things uh in our industrial environments, our enterprise, industrial environments, our enterprise, industrial environments, our enterprise, right? And those are the questions that right? And those are the questions that right? And those are the questions that are quite frankly haven't been asked are quite frankly haven't been asked are quite frankly haven't been asked because most people can't answer that because most people can't answer that because most people can't answer that question. They're distracted by chat question. They're distracted by chat question. They're distracted by chat bots and agentic nonsense. bots and agentic nonsense. bots and agentic nonsense. >> AGI and all >> AGI and all >> AGI and all >> AGI that which nobody talks about >> AGI that which nobody talks about >> AGI that which nobody talks about anymore, right? The Chinese doing AGI. anymore, right? The Chinese doing AGI. anymore, right? The Chinese doing AGI. >> That was back in a we were talking about >> That was back in a we were talking about >> That was back in a we were talking about it [laughter] it [laughter] it [laughter] >> old school. >> old school. >> old school. >> What do you think Kevin? >> What do you think Kevin? >> What do you think Kevin? >> Oh well you know this is this is our >> Oh well you know this is this is our >> Oh well you know this is this is our struggle every day. So the two things struggle every day. So the two things struggle every day. So the two things whenever you have an IoT deployment, one whenever you have an IoT deployment, one whenever you have an IoT deployment, one number one is for security. What number one is for security. What number one is for security. What vulnerabilities are you going to vulnerabilities are you going to vulnerabilities are you going to introduce to my environment? But the introduce to my environment? But the introduce to my environment? But the bigger challenge is this ROI. So bigger challenge is this ROI. So bigger challenge is this ROI. So especially with AI, I'm replacing especially with AI, I'm replacing especially with AI, I'm replacing someone running around with a clipboard someone running around with a clipboard someone running around with a clipboard or a master mechanic that can just slap or a master mechanic that can just slap or a master mechanic that can just slap his hand on a pump and just say, "Hm, his hand on a pump and just say, "Hm, his hand on a pump and just say, "Hm, Betsy is going to have about 10 more Betsy is going to have about 10 more Betsy is going to have about 10 more months of life on them."

months of life on them." months of life on them." A and so the key question from this, you A and so the key question from this, you A and so the key question from this, you know, the CFOs who have to write the know, the CFOs who have to write the know, the CFOs who have to write the check is not only what's the cost of check is not only what's the cost of check is not only what's the cost of implementation, but what's the total implementation, but what's the total implementation, but what's the total cost of ownership? And when I look at cost of ownership? And when I look at cost of ownership? And when I look at the hourly rate of what I'm paying some the hourly rate of what I'm paying some the hourly rate of what I'm paying some of these skills, of these skills, of these skills, >> does AI make sense or IoT with AI, >> does AI make sense or IoT with AI, >> does AI make sense or IoT with AI, physical AI, make sense of replacing physical AI, make sense of replacing physical AI, make sense of replacing them? So, you know, that's them? So, you know, that's them? So, you know, that's [clears throat] the hardest thing right [clears throat] the hardest thing right [clears throat] the hardest thing right now because as you start pulling the now because as you start pulling the now because as you start pulling the bill of material together for any of bill of material together for any of bill of material together for any of these things, aside from the sensor, these things, aside from the sensor, these things, aside from the sensor, then the platform, then the cloud and then the platform, then the cloud and then the platform, then the cloud and the edge compute, then you throw an AI the edge compute, then you throw an AI the edge compute, then you throw an AI model on top of it and then maybe a model on top of it and then maybe a model on top of it and then maybe a layer of security, you know, the license layer of security, you know, the license layer of security, you know, the license costs, the, you know, what's it going to costs, the, you know, what's it going to costs, the, you know, what's it going to cost to run and they run it against cost to run and they run it against cost to run and they run it against someone that is, you know, doing that someone that is, you know, doing that someone that is, you know, doing that that actual work. I'm just going to keep that actual work. I'm just going to keep that actual work. I'm just going to keep my hourly employee, my hourly employee, my hourly employee, >> right? We are running against a labor >> right? We are running against a labor >> right? We are running against a labor shortage of skilled labor, but a lot of shortage of skilled labor, but a lot of shortage of skilled labor, but a lot of these AI these AI these AI things require a whole new set of things require a whole new set of things require a whole new set of support of people that understand support of people that understand support of people that understand robotics or drones and things and those robotics or drones and things and those robotics or drones and things and those skills are harder to find than skills are harder to find than skills are harder to find than true, you know, hiring Bob to run, you true, you know, hiring Bob to run, you true, you know, hiring Bob to run, you know, with just meter reading.

know, with just meter reading. know, with just meter reading. >> Yeah. >> Yeah. >> Yeah. >> One thing though, it's like uh >> One thing though, it's like uh >> One thing though, it's like uh >> it's like it's um I agree with you. >> it's like it's um I agree with you. >> it's like it's um I agree with you. There's a labor shortage in the physical There's a labor shortage in the physical There's a labor shortage in the physical space, right? And there's um and space, right? And there's um and space, right? And there's um and actually D, I don't know if you know, actually D, I don't know if you know, actually D, I don't know if you know, David Randall from AWS is really David Randall from AWS is really David Randall from AWS is really articulate on this. articulate on this. articulate on this. >> But, you know, when you talk about >> But, you know, when you talk about >> But, you know, when you talk about physical AI, sometimes it's not about physical AI, sometimes it's not about physical AI, sometimes it's not about replacing, but it's extending. So, replacing, but it's extending. So, replacing, but it's extending. So, imagine if you had earth moving, you imagine if you had earth moving, you imagine if you had earth moving, you know, someone who's running an earth know, someone who's running an earth know, someone who's running an earth mover and doing some construction, what mover and doing some construction, what mover and doing some construction, what if that was, you know, automated and it if that was, you know, automated and it if that was, you know, automated and it was running during the night, right? So, was running during the night, right? So, was running during the night, right? So, now you've got two two shifts going. now you've got two two shifts going. now you've got two two shifts going. You've got the robotic shift at night You've got the robotic shift at night You've got the robotic shift at night and nobody's going to do that at 2 in and nobody's going to do that at 2 in and nobody's going to do that at 2 in the morning and then you've got human the morning and then you've got human the morning and then you've got human labor doing it during the day. So now labor doing it during the day. So now labor doing it during the day. So now you've kind of, you know, doubled your you've kind of, you know, doubled your you've kind of, you know, doubled your efficiency, right, for the equipment on efficiency, right, for the equipment on efficiency, right, for the equipment on site. The equipment is now on site half site. The equipment is now on site half site. The equipment is now on site half the time because you're now running it the time because you're now running it the time because you're now running it at night. So a lot of times you're going at night. So a lot of times you're going at night. So a lot of times you're going to see physical AI and AI being able to to see physical AI and AI being able to to see physical AI and AI being able to sort of extend the capabilities of an sort of extend the capabilities of an sort of extend the capabilities of an existing workforce as opposed to replace existing workforce as opposed to replace existing workforce as opposed to replace workers. But workers. But workers. But >> yeah, absolutely >> yeah, absolutely >> yeah, absolutely >> my two cents. >> my two cents. >> my two cents. >> No, that's good. >> No, that's good. >> No, that's good. >> Hey, you know what? other news also from >> Hey, you know what? other news also from >> Hey, you know what? other news also from China this week. I read a blurb about China this week. I read a blurb about China this week. I read a blurb about how the Chinese government's trying to how the Chinese government's trying to how the Chinese government's trying to crack down on Chinese people, you know, crack down on Chinese people, you know, crack down on Chinese people, you know, having relationships and girlfriends and having relationships and girlfriends and having relationships and girlfriends and boyfriends with the chat bots boyfriends with the chat bots boyfriends with the chat bots >> because they need to increase the birth >> because they need to increase the birth >> because they need to increase the birth rate in China. the birth rate is so low rate in China. the birth rate is so low rate in China. the birth rate is so low and and so I don't know what they're and and so I don't know what they're and and so I don't know what they're going to do or whatever to prevent you going to do or whatever to prevent you going to do or whatever to prevent you from having a new AI girlfriend um and from having a new AI girlfriend um and from having a new AI girlfriend um and therefore not have babies, but it's a therefore not have babies, but it's a therefore not have babies, but it's a it's a thing. It wrote it bubbled up to, it's a thing. It wrote it bubbled up to, it's a thing. It wrote it bubbled up to, you know, national level.

you know, national level. you know, national level. >> I I I I think that's like the wrong >> I I I I think that's like the wrong >> I I I I think that's like the wrong strategy, man. strategy, man. strategy, man. >> I don't know. I [clears throat] don't >> I don't know. I [clears throat] don't >> I don't know. I [clears throat] don't know. know. know. >> Well, they've had a series of wrong >> Well, they've had a series of wrong >> Well, they've had a series of wrong strategies on birth. strategies on birth. strategies on birth. >> Yeah. Just lean into depopulation. Just >> Yeah. Just lean into depopulation. Just >> Yeah. Just lean into depopulation. Just lean into it. We've talked about it lean into it. We've talked about it lean into it. We've talked about it right on this. just come up with an right on this. just come up with an right on this. just come up with an economic that benefits from less less economic that benefits from less less economic that benefits from less less people. people. people. >> But it's a slippery slope though. So >> But it's a slippery slope though. So >> But it's a slippery slope though. So first relationship. So next, you know, first relationship. So next, you know, first relationship. So next, you know, all of us on this coffee talk is going all of us on this coffee talk is going all of us on this coffee talk is going to be replaced by chat bots. to be replaced by chat bots. to be replaced by chat bots. >> Yeah. [laughter] >> Yeah. [laughter] >> Yeah. [laughter] >> You're probably right. You're probably >> You're probably right. You're probably >> You're probably right. You're probably right. right. right. >> Well, you've probably seen this. We get >> Well, you've probably seen this. We get >> Well, you've probably seen this. We get we get a meeting going and then we get a meeting going and then we get a meeting going and then someone's AI noteaker shows up, but they someone's AI noteaker shows up, but they someone's AI noteaker shows up, but they don't show up, don't show up, don't show up, >> you know. So, I'm like, "Hey, what's the >> you know. So, I'm like, "Hey, what's the >> you know. So, I'm like, "Hey, what's the deal? You can't send your notetaker and deal? You can't send your notetaker and deal? You can't send your notetaker and not show up at the meeting." So, the not show up at the meeting." So, the not show up at the meeting." So, the next step is the the uh like you said, next step is the the uh like you said, next step is the the uh like you said, Devon, the AI avatar will show up Devon, the AI avatar will show up Devon, the AI avatar will show up instead of the human. He'll be like, instead of the human. He'll be like, instead of the human. He'll be like, >> "Yes." >> "Yes." >> "Yes." >> You know what that sounds like, Pete? Do >> You know what that sounds like, Pete? Do >> You know what that sounds like, Pete? Do you remember that movie Back to School you remember that movie Back to School you remember that movie Back to School with Rodney Dangerfield? with Rodney Dangerfield? with Rodney Dangerfield? >> Yes. >> Yes. >> Yes. >> He goes back to college. Do >> He goes back to college. Do >> He goes back to college. Do >> you remember how they showed throughout >> you remember how they showed throughout >> you remember how they showed throughout the semester and that one auditorium the semester and that one auditorium the semester and that one auditorium classroom? Everybody's there. Then a few classroom? Everybody's there. Then a few classroom? Everybody's there. Then a few people with tape recorders and then by people with tape recorders and then by people with tape recorders and then by the end of semester every seat was a the end of semester every seat was a the end of semester every seat was a tape recorder recording the [laughter] tape recorder recording the [laughter] tape recorder recording the [laughter] professor.

professor. professor. Nobody was there. Nobody was there. Nobody was there. >> But but but getting back to what was >> But but but getting back to what was >> But but but getting back to what was said about AI being able to extend. said about AI being able to extend. said about AI being able to extend. >> Yeah. You know, I also, you know, I I >> Yeah. You know, I also, you know, I I >> Yeah. You know, I also, you know, I I read this article out of China that, you read this article out of China that, you read this article out of China that, you know, they're able to take your loved know, they're able to take your loved know, they're able to take your loved one and kind of download their one and kind of download their one and kind of download their personality and voice and looks so that personality and voice and looks so that personality and voice and looks so that after they pass away, after they pass away, after they pass away, >> you could still interact with your loved >> you could still interact with your loved >> you could still interact with your loved one. Is that, one. Is that, one. Is that, >> you know, is that creepy? >> you know, is that creepy? >> you know, is that creepy? >> Those things >> Those things >> Those things >> like it's Do you remember Twilight Zone? >> like it's Do you remember Twilight Zone? >> like it's Do you remember Twilight Zone? They had an episode like that. Uh it They had an episode like that. Uh it They had an episode like that. Uh it sort of like that where um sort of like that where um sort of like that where um no actually it wasn't like that. no actually it wasn't like that. no actually it wasn't like that. >> You know it might feel initially >> You know it might feel initially >> You know it might feel initially >> pretty close. >> pretty close. >> pretty close. >> I I I think it's going to be pervasive. >> I I I think it's going to be pervasive. >> I I I think it's going to be pervasive. >> I think so too. I agree. >> I think so too. I agree. >> I think so too. I agree. >> Totally pervasive. It'll seem creepy. A >> Totally pervasive. It'll seem creepy. A >> Totally pervasive. It'll seem creepy. A good example happening right in the US good example happening right in the US good example happening right in the US of A is a whole bunch of people raised of A is a whole bunch of people raised of A is a whole bunch of people raised money to build a presidential library money to build a presidential library money to build a presidential library for Theodore Roosevelt who didn't have for Theodore Roosevelt who didn't have for Theodore Roosevelt who didn't have one. Uh, I guess they didn't used to one. Uh, I guess they didn't used to one. Uh, I guess they didn't used to have presidential libraries back in the have presidential libraries back in the have presidential libraries back in the old days. That was more of a recent old days. That was more of a recent old days. That was more of a recent phenomenon. Yeah. And so they built it phenomenon. Yeah. And so they built it phenomenon. Yeah. And so they built it in the Badlands in North Dakota and it's in the Badlands in North Dakota and it's in the Badlands in North Dakota and it's a beautiful place. It doesn't look like a beautiful place. It doesn't look like a beautiful place. It doesn't look like any presidential library I've ever seen.

any presidential library I've ever seen. any presidential library I've ever seen. But they've got an avatar of him. But they've got an avatar of him. But they've got an avatar of him. >> Talk to him >> Talk to him >> Talk to him >> and you can and it was on the it was on >> and you can and it was on the it was on >> and you can and it was on the it was on TV last week TV last week TV last week >> uh on a couple of shows I've seen where >> uh on a couple of shows I've seen where >> uh on a couple of shows I've seen where they were talking to the Roosevelt and they were talking to the Roosevelt and they were talking to the Roosevelt and he's actually in it's because they're he's actually in it's because they're he's actually in it's because they're using AI and they've trained him using AI and they've trained him using AI and they've trained him You know, it's a pre-trained model and You know, it's a pre-trained model and You know, it's a pre-trained model and then they fine-tuned his avatar on then they fine-tuned his avatar on then they fine-tuned his avatar on everything he ever said, wrote, everything he ever said, wrote, everything he ever said, wrote, >> did president or or you know, remember >> did president or or you know, remember >> did president or or you know, remember >> at Disney they had those the hall of >> at Disney they had those the hall of >> at Disney they had those the hall of >> Yeah. Automaton. >> Yeah. Automaton. >> Yeah. Automaton. >> Yeah. >> Yeah. >> Yeah. >> Yeah, that's right. It's a small. >> Yeah, that's right. It's a small. >> Yeah, that's right. It's a small. >> It's definitely going to happen. I mean, >> It's definitely going to happen. I mean, >> It's definitely going to happen. I mean, we used to think taking pictures of food we used to think taking pictures of food we used to think taking pictures of food was weird and now everyone does that. was weird and now everyone does that. was weird and now everyone does that. So, [laughter] it's inevitable that we So, [laughter] it's inevitable that we So, [laughter] it's inevitable that we will have this will be a service. Every will have this will be a service. Every will have this will be a service. Every funeral home is now like, hm, funeral home is now like, hm, funeral home is now like, hm, >> let's get the intern working on that. >> let's get the intern working on that. >> let's get the intern working on that. Wow. Wow. Wow. >> Getting a project to like >> Getting a project to like >> Getting a project to like >> line extension. >> line extension. >> line extension. >> We're going to increase our product >> We're going to increase our product >> We're going to increase our product lines, lines, lines, >> you know, >> you know, >> you know, >> for an extra $10,000 >> for an extra $10,000 >> for an extra $10,000 >> as a service, you know, loved one as a >> as a service, you know, loved one as a >> as a service, you know, loved one as a service. I think it's service. I think it's service. I think it's >> You know what's really weird though? It >> You know what's really weird though? It >> You know what's really weird though? It hasn't taken off. I mean, that was like hasn't taken off. I mean, that was like hasn't taken off. I mean, that was like one of the first use cases for a one of the first use cases for a one of the first use cases for a generative AI like LLMs, you know, generative AI like LLMs, you know, generative AI like LLMs, you know, >> was like plugging.

>> was like plugging. >> was like plugging. >> I know. I mean, and and the question is >> I know. I mean, and and the question is >> I know. I mean, and and the question is is like, okay, what kind of societal is like, okay, what kind of societal is like, okay, what kind of societal what what kind of impact will it have on what what kind of impact will it have on what what kind of impact will it have on mental health? mental health? mental health? >> I think it's great. And I want to >> I think it's great. And I want to >> I think it's great. And I want to announce right now I'm launching a new announce right now I'm launching a new announce right now I'm launching a new startup called Tupac.ai. startup called Tupac.ai. startup called Tupac.ai. >> Are you [laughter] serious? >> Are you [laughter] serious? >> Are you [laughter] serious? >> That's We're Are we're using that >> That's We're Are we're using that >> That's We're Are we're using that revolutionary revolutionary revolutionary >> going to be >> going to be >> going to be holograms are extra. holograms are extra. holograms are extra. >> Yeah. >> Yeah. >> Yeah. >> Hologram pre-trained >> Hologram pre-trained >> Hologram pre-trained >> chatbot is silver level. Gold level is >> chatbot is silver level. Gold level is >> chatbot is silver level. Gold level is hologram. hologram. hologram. >> Yeah, that's good point. Loved one as a >> Yeah, that's good point. Loved one as a >> Yeah, that's good point. Loved one as a service. Let's do it. service. Let's do it. service. Let's do it. >> Loved one as a service. >> Loved one as a service. >> Loved one as a service. >> Yes. >> Yes. >> Yes. >> As a service. >> As a service. >> As a service. >> Yeah. >> Yeah. >> Yeah. >> Hey, can I do a little PSA? I wanted to >> Hey, can I do a little PSA? I wanted to >> Hey, can I do a little PSA? I wanted to mention the um I'm going to show my mention the um I'm going to show my mention the um I'm going to show my little sticker. I don't know if you can little sticker. I don't know if you can little sticker. I don't know if you can see that. see that. see that. >> Yeah. >> Yeah. >> Yeah. >> ML and Systems Rising Stars. So, there's >> ML and Systems Rising Stars. So, there's >> ML and Systems Rising Stars. So, there's this program uh every year. They've been this program uh every year. They've been this program uh every year. They've been running it with AMD and Nvidia and ML running it with AMD and Nvidia and ML running it with AMD and Nvidia and ML Commons. and it's like 38 PhD students Commons. and it's like 38 PhD students Commons. and it's like 38 PhD students that go in a cohort for the year and that go in a cohort for the year and that go in a cohort for the year and they're from around the world and uh they're from around the world and uh they're from around the world and uh basically helping support their kind of basically helping support their kind of basically helping support their kind of research. So, we're now a sponsor of research. So, we're now a sponsor of research. So, we're now a sponsor of that and we made these really cool that and we made these really cool that and we made these really cool holographic stickers.

holographic stickers. holographic stickers. >> Wow. >> Wow. >> Wow. >> But if you go to ML Commons or look up >> But if you go to ML Commons or look up >> But if you go to ML Commons or look up ML and Systems Rising Stars, incredible ML and Systems Rising Stars, incredible ML and Systems Rising Stars, incredible students from around the world, uh students from around the world, uh students from around the world, uh Cornell, George Institute, uh you know, Cornell, George Institute, uh you know, Cornell, George Institute, uh you know, ETH in Switzerland. So, I'm going to be ETH in Switzerland. So, I'm going to be ETH in Switzerland. So, I'm going to be down at AMD on the end of July at their down at AMD on the end of July at their down at AMD on the end of July at their they have a big event down there as kind they have a big event down there as kind they have a big event down there as kind of kickoff with the cohort for the year. of kickoff with the cohort for the year. of kickoff with the cohort for the year. But, you know, talking about investing But, you know, talking about investing But, you know, talking about investing in the future, um, you know, we need to in the future, um, you know, we need to in the future, um, you know, we need to train people who are passionate about train people who are passionate about train people who are passionate about this space so that, you know, we can this space so that, you know, we can this space so that, you know, we can keep making improvements. So, we're keep making improvements. So, we're keep making improvements. So, we're pretty psyched to be sponsors now. pretty psyched to be sponsors now. pretty psyched to be sponsors now. >> Are these like forward deployed >> Are these like forward deployed >> Are these like forward deployed engineers that we're talking about here? engineers that we're talking about here? engineers that we're talking about here? >> Yeah. Right. No, not exactly. >> Yeah. Right. No, not exactly. >> Yeah. Right. No, not exactly. >> Devin and I were formerly forward >> Devin and I were formerly forward >> Devin and I were formerly forward deployed engineers, right, Devin? Right. deployed engineers, right, Devin? Right. deployed engineers, right, Devin? Right. Come on, dude. Come on, dude. Come on, dude. >> Is that like >> Is that like >> Is that like >> admit it? >> admit it? >> admit it? >> Yeah. [laughter] >> Field engineers. Field engineers. FAE, >> Field engineers. Field engineers. FAE, right? [laughter] right? [laughter] right? [laughter] >> Don't tell me that you didn't get all >> Don't tell me that you didn't get all >> Don't tell me that you didn't get all those miles not being those miles not being those miles not being >> I was an FA for a long for for few >> I was an FA for a long for for few >> I was an FA for a long for for few years. I was an FAE for Phoenix years. I was an FAE for Phoenix years. I was an FAE for Phoenix Technologies for the BIOS company.

Technologies for the BIOS company. Technologies for the BIOS company. I was, you know, I was I was chatting I was, you know, I was I was chatting I was, you know, I was I was chatting back and forth with a former Microsoft back and forth with a former Microsoft back and forth with a former Microsoft colleague and we were talking about how colleague and we were talking about how colleague and we were talking about how Microsoft is, you know, we're going to Microsoft is, you know, we're going to Microsoft is, you know, we're going to lay off a bunch of people, but then lay off a bunch of people, but then lay off a bunch of people, but then we'll create a whole new company of we'll create a whole new company of we'll create a whole new company of forward deployed engineers um to get forward deployed engineers um to get forward deployed engineers um to get them using co-pilot maybe or whatever. them using co-pilot maybe or whatever. them using co-pilot maybe or whatever. And we talked about, if you remember, And we talked about, if you remember, And we talked about, if you remember, there was a time we had these uh what there was a time we had these uh what there was a time we had these uh what CSAs, cloud solution architects, and at CSAs, cloud solution architects, and at CSAs, cloud solution architects, and at Microsoft. And the whole point of that Microsoft. And the whole point of that Microsoft. And the whole point of that was was was even though our Salesforce could get even though our Salesforce could get even though our Salesforce could get people to sign up for Azure and of people to sign up for Azure and of people to sign up for Azure and of course you know because we had that course you know because we had that course you know because we had that enterprise agreement that most companies enterprise agreement that most companies enterprise agreement that most companies use they got lots of free Azure hours use they got lots of free Azure hours use they got lots of free Azure hours but our problem was people weren't but our problem was people weren't but our problem was people weren't consuming the hours the cloud at all consuming the hours the cloud at all consuming the hours the cloud at all >> and so it's like and they heard that AWS >> and so it's like and they heard that AWS >> and so it's like and they heard that AWS was doing something similar already had was doing something similar already had was doing something similar already had because obviously AWS came before all of because obviously AWS came before all of because obviously AWS came before all of us and so we created that cloud solution us and so we created that cloud solution us and so we created that cloud solution architect deal to literally put these architect deal to literally put these architect deal to literally put these guys bodies in with the customers and guys bodies in with the customers and guys bodies in with the customers and kind of always hold a gun to the kind of always hold a gun to the kind of always hold a gun to the customer's head and say consume Azure customer's head and say consume Azure customer's head and say consume Azure consume Azure and show them but it but consume Azure and show them but it but consume Azure and show them but it but it was the same kind of thing you know it was the same kind of thing you know it was the same kind of thing you know it was like people with skills I want it was like people with skills I want it was like people with skills I want you you're not doing AI enough because you you're not doing AI enough because you you're not doing AI enough because that's been the big thing we're spending that's been the big thing we're spending that's been the big thing we're spending all this money on building AI all this money on building AI all this money on building AI infrastructure and it's all really cool infrastructure and it's all really cool infrastructure and it's all really cool and there's a lot of good slop out there and there's a lot of good slop out there and there's a lot of good slop out there and a lot of good research but and a lot of good research but and a lot of good research but enterprises are just not adopting it the enterprises are just not adopting it the enterprises are just not adopting it the way we thought they would or should and way we thought they would or should and way we thought they would or should and so they think before deployed engineer so they think before deployed engineer so they think before deployed engineer stuff that that you know was originally stuff that that you know was originally stuff that that you know was originally created by Alex Karp and all his buddies created by Alex Karp and all his buddies created by Alex Karp and all his buddies at Palunteer, you know, a long long time

at Palunteer, you know, a long long time at Palunteer, you know, a long long time ago. Just embed them in your customers, ago. Just embed them in your customers, ago. Just embed them in your customers, I guess, for free. You can't I don't I guess, for free. You can't I don't I guess, for free. You can't I don't know if they're charging for them uh know if they're charging for them uh know if they're charging for them uh hourly rate or whatever. I don't know hourly rate or whatever. I don't know hourly rate or whatever. I don't know how that works. how that works. how that works. >> This concept's been around since the >> This concept's been around since the >> This concept's been around since the beginning of time. You know, we used to beginning of time. You know, we used to beginning of time. You know, we used to call them field engineers and call them field engineers and call them field engineers and >> you'd go sit there at Samsung and help >> you'd go sit there at Samsung and help >> you'd go sit there at Samsung and help them with this product you just sold them with this product you just sold them with this product you just sold them and help them deploy it and them and help them deploy it and them and help them deploy it and commercialize it and train them on it so commercialize it and train them on it so commercialize it and train them on it so that they bought more of it. that they bought more of it. that they bought more of it. >> That's a good point. >> That's a good point. >> That's a good point. >> Yeah. >> Yeah. >> Yeah. >> Speaking [clears throat] of nothing new, >> Speaking [clears throat] of nothing new, >> Speaking [clears throat] of nothing new, I mean that's been tech forever, right? I mean that's been tech forever, right? I mean that's been tech forever, right? >> You know, I remember you're right. It's >> You know, I remember you're right. It's >> You know, I remember you're right. It's not anything new. I remember having a not anything new. I remember having a not anything new. I remember having a discussion. Let me do a name drop here. discussion. Let me do a name drop here. discussion. Let me do a name drop here. No one will know that I'm doing a name No one will know that I'm doing a name No one will know that I'm doing a name drop. So, my good friend Rod Canyon, who drop. So, my good friend Rod Canyon, who drop. So, my good friend Rod Canyon, who invented Compact Computer way back when, invented Compact Computer way back when, invented Compact Computer way back when, um, he talked about when after he left um, he talked about when after he left um, he talked about when after he left Compact in the early 90s, he he said Compact in the early 90s, he he said Compact in the early 90s, he he said that was a big problem. He he thought that was a big problem. He he thought that was a big problem. He he thought was we built PCs and we built all this was we built PCs and we built all this was we built PCs and we built all this stuff, but he thought that people stuff, but he thought that people stuff, but he thought that people weren't making good enough use of this weren't making good enough use of this weren't making good enough use of this new computer technology, like not even new computer technology, like not even new computer technology, like not even close. and he I remember having a close. and he I remember having a close. and he I remember having a discussion with him and it's like we discussion with him and it's like we discussion with him and it's like we really need to get out there and really really need to get out there and really really need to get out there and really show these customers how to use these show these customers how to use these show these customers how to use these new computers that we're giving them new computers that we're giving them new computers that we're giving them laptops. I thought I thought it was laptops. I thought I thought it was laptops. I thought I thought it was interesting because I felt like we were interesting because I felt like we were interesting because I felt like we were doing just fine, but he thought we were doing just fine, but he thought we were doing just fine, but he thought we were barely scratching the surface of the barely scratching the surface of the barely scratching the surface of the value we could get from a PC.

value we could get from a PC. value we could get from a PC. >> We used to we used to joke that, you >> We used to we used to joke that, you >> We used to we used to joke that, you know, you don't want to have to ship an know, you don't want to have to ship an know, you don't want to have to ship an engineer in the box engineer in the box engineer in the box >> with your product, right? >> with your product, right? >> with your product, right? >> But I guess now that's the new thing. >> But I guess now that's the new thing. >> But I guess now that's the new thing. It's like, oh, this product comes with It's like, oh, this product comes with It's like, oh, this product comes with an engineer in the box. for the guy pops an engineer in the box. for the guy pops an engineer in the box. for the guy pops out. out. out. [laughter] [laughter] [laughter] >> I'm here. Where do I sit? >> I'm here. Where do I sit? >> I'm here. Where do I sit? >> Yeah. Well, you know, um I mean that's >> Yeah. Well, you know, um I mean that's >> Yeah. Well, you know, um I mean that's the thing. Um like we've said before, it the thing. Um like we've said before, it the thing. Um like we've said before, it it was sold as easy and it's not. Um and it was sold as easy and it's not. Um and it was sold as easy and it's not. Um and then, you know, going back to your then, you know, going back to your then, you know, going back to your little story about compact, Rob, um you little story about compact, Rob, um you little story about compact, Rob, um you know, it's not just about the hardware, know, it's not just about the hardware, know, it's not just about the hardware, it's about the applications, but then it's about the applications, but then it's about the applications, but then you have to have a sense of what the you have to have a sense of what the you have to have a sense of what the solution is. and then what the solution is. and then what the solution is. and then what the supporting application would look like. supporting application would look like. supporting application would look like. And that's pretty much, And that's pretty much, And that's pretty much, you know, non-existent because again, you know, non-existent because again, you know, non-existent because again, it's the distractions. There's things it's the distractions. There's things it's the distractions. There's things you could do. You have to change your you could do. You have to change your you could do. You have to change your mindset. You have to look at like sort mindset. You have to look at like sort mindset. You have to look at like sort of boring looking stuff that's not that of boring looking stuff that's not that of boring looking stuff that's not that sexy that can actually have huge quote sexy that can actually have huge quote sexy that can actually have huge quote unquote exponential impact. But the unquote exponential impact. But the unquote exponential impact. But the thing is is every and it's not going to thing is is every and it's not going to thing is is every and it's not going to have a huge market cap and it's not have a huge market cap and it's not have a huge market cap and it's not going to make you a trillionaire.

going to make you a trillionaire. going to make you a trillionaire. >> Yeah. >> Yeah. >> Yeah. >> It's going to be beneficial for >> It's going to be beneficial for >> It's going to be beneficial for organizations, you know, and people's organizations, you know, and people's organizations, you know, and people's people just can't see it. people just can't see it. people just can't see it. >> You know, Pete, you just kind of >> You know, Pete, you just kind of >> You know, Pete, you just kind of >> Yeah. We gave them PCs. We give we give >> Yeah. We gave them PCs. We give we give >> Yeah. We gave them PCs. We give we give software. We've done this technology and software. We've done this technology and software. We've done this technology and you said the word selfs serve you said the word selfs serve you said the word selfs serve >> word process >> word process >> word process >> and you said self-service. And so we >> and you said self-service. And so we >> and you said self-service. And so we assumed that the customers would just assumed that the customers would just assumed that the customers would just figure it out and there's self-s serve figure it out and there's self-s serve figure it out and there's self-s serve and you do this thing. And I'm having a and you do this thing. And I'm having a and you do this thing. And I'm having a flashback to I don't know maybe 10,000 flashback to I don't know maybe 10,000 flashback to I don't know maybe 10,000 episodes of IoT Coffee Talk ago uh where episodes of IoT Coffee Talk ago uh where episodes of IoT Coffee Talk ago uh where we uh when Mark Post brought on one of we uh when Mark Post brought on one of we uh when Mark Post brought on one of his friends from Spain um who had a his friends from Spain um who had a his friends from Spain um who had a company and remember he gosh I forgot company and remember he gosh I forgot company and remember he gosh I forgot what his name is and they talked about what his name is and they talked about what his name is and they talked about how their company was almost ready to how their company was almost ready to how their company was almost ready to close down their company. It was like close down their company. It was like close down their company. It was like they were doing okay but they were never they were doing okay but they were never they were doing okay but they were never breaking out another IoT platform breaking out another IoT platform breaking out another IoT platform company and all that kind of stuff. And company and all that kind of stuff. And company and all that kind of stuff. And I remember they said they did like some I remember they said they did like some I remember they said they did like some kind of offsite with their executives kind of offsite with their executives kind of offsite with their executives somewhere in Spain and they came back. somewhere in Spain and they came back. somewhere in Spain and they came back. Are we going to shut down the company? Are we going to shut down the company? Are we going to shut down the company? And then someone said maybe we need to And then someone said maybe we need to And then someone said maybe we need to do services. Maybe we need to have our do services. Maybe we need to have our do services. Maybe we need to have our people go in in bed with the customer people go in in bed with the customer people go in in bed with the customer and hold their hand and show them how to and hold their hand and show them how to and hold their hand and show them how to do this IoT stuff.

do this IoT stuff. do this IoT stuff. >> Yeah. >> Yeah. >> Yeah. >> And none of them really wanted to do it. >> And none of them really wanted to do it. >> And none of them really wanted to do it. You know, I I always there's always that You know, I I always there's always that You know, I I always there's always that tension. You know, I'm a product tension. You know, I'm a product tension. You know, I'm a product company. I don't want to be a company. I don't want to be a company. I don't want to be a consultant. I'm not billable. Here's the consultant. I'm not billable. Here's the consultant. I'm not billable. Here's the product. Figured out. product. Figured out. product. Figured out. >> Well, it's low margin business, you >> Well, it's low margin business, you >> Well, it's low margin business, you know. know. know. >> Yeah. But it turned out they they >> Yeah. But it turned out they they >> Yeah. But it turned out they they decided they decided we're going to try decided they decided we're going to try decided they decided we're going to try the professional services and embed the professional services and embed the professional services and embed people people people >> and then they had super success >> and then they had super success >> and then they had super success >> and that changed everything for them. >> and that changed everything for them. >> and that changed everything for them. And so it turns out also a lot of people And so it turns out also a lot of people And so it turns out also a lot of people are really busy. You ever show up at are really busy. You ever show up at are really busy. You ever show up at some company and I'm like, I'm I'm here some company and I'm like, I'm I'm here some company and I'm like, I'm I'm here to you're going to do this digital to you're going to do this digital to you're going to do this digital transformation thing and the guy's like, transformation thing and the guy's like, transformation thing and the guy's like, well, you know, I'm heads down doing my well, you know, I'm heads down doing my well, you know, I'm heads down doing my day job. day job. day job. >> Sit over here. >> Sit over here. >> Sit over here. >> Yeah. >> Yeah. >> Yeah. >> And so sometimes you just need to come >> And so sometimes you just need to come >> And so sometimes you just need to come in and do the thing. in and do the thing. in and do the thing. >> Come do the thing. It's like I always >> Come do the thing. It's like I always >> Come do the thing. It's like I always talk about these IoT platforms. talk about these IoT platforms. talk about these IoT platforms. >> I don't just want the insight. I'm >> I don't just want the insight. I'm >> I don't just want the insight. I'm paying a lot of money for this. I want paying a lot of money for this. I want paying a lot of money for this. I want you to do the thing. you to do the thing. you to do the thing. >> Yeah. >> Yeah. >> Yeah. >> Automation, whatever. >> Automation, whatever. >> Automation, whatever. >> It's like metaverse. industrial >> It's like metaverse. industrial >> It's like metaverse. industrial metaverse, dude. [clears throat] metaverse, dude. [clears throat] metaverse, dude. [clears throat] [laughter] [laughter] [laughter] >> We, you know, we used to always say, you >> We, you know, we used to always say, you >> We, you know, we used to always say, you know, it's a people process technology.

know, it's a people process technology. know, it's a people process technology. Unfortunately, everyone just focuses on Unfortunately, everyone just focuses on Unfortunately, everyone just focuses on technology, but you know, if I swapped technology, but you know, if I swapped technology, but you know, if I swapped out my mom's iPhone for an Android, out my mom's iPhone for an Android, out my mom's iPhone for an Android, she'd just sit there staring at me like, she'd just sit there staring at me like, she'd just sit there staring at me like, "What did you just "What did you just "What did you just >> just do?" So, it's that user experience >> just do?" So, it's that user experience >> just do?" So, it's that user experience that is so important. I get the buy in that is so important. I get the buy in that is so important. I get the buy in of the people that are going to be of the people that are going to be of the people that are going to be adopting this technology. I think we all adopting this technology. I think we all adopting this technology. I think we all have stories about how, you know, change have stories about how, you know, change have stories about how, you know, change is hard and when you throw something in is hard and when you throw something in is hard and when you throw something in front of someone, they just look at front of someone, they just look at front of someone, they just look at like, I'm not going to use it. like, I'm not going to use it. like, I'm not going to use it. >> If you change the process, you get the >> If you change the process, you get the >> If you change the process, you get the people bought in. That's when you start people bought in. That's when you start people bought in. That's when you start seeing the ROI of the technology. seeing the ROI of the technology. seeing the ROI of the technology. >> Yeah. Isn't that weird? After all these >> Yeah. Isn't that weird? After all these >> Yeah. Isn't that weird? After all these years, we're still asking the same years, we're still asking the same years, we're still asking the same questions and evidence. [laughter] questions and evidence. [laughter] questions and evidence. [laughter] >> These are like powerpoints from like >> These are like powerpoints from like >> These are like powerpoints from like >> No, you're right. You you you nailed it. >> No, you're right. You you you nailed it. >> No, you're right. You you you nailed it. We put something different in front of We put something different in front of We put something different in front of them and they're like, "What is this?" them and they're like, "What is this?" them and they're like, "What is this?" Yeah, Pete, why don't we get Steve Zoski Yeah, Pete, why don't we get Steve Zoski Yeah, Pete, why don't we get Steve Zoski on the phone and talk about Windows 8? on the phone and talk about Windows 8? on the phone and talk about Windows 8? >> The start button. Where' the start >> The start button. Where' the start >> The start button. Where' the start button go? button go? button go? >> I don't know what to do. When we got the >> I don't know what to do. When we got the >> I don't know what to do. When we got the feedback when we deployed Windows 8 and feedback when we deployed Windows 8 and feedback when we deployed Windows 8 and we got feedback [clears throat] from our we got feedback [clears throat] from our we got feedback [clears throat] from our enterprise customers that we're that enterprise customers that we're that enterprise customers that we're that every one of them said we're going to every one of them said we're going to every one of them said we're going to have to send our employees to 5day have to send our employees to 5day have to send our employees to 5day training classes to teach them that they training classes to teach them that they training classes to teach them that they have to swipe to find the printer and have to swipe to find the printer and have to swipe to find the printer and all this stuff. And and I remember all this stuff. And and I remember all this stuff. And and I remember arrogant Microsoft people like this is arrogant Microsoft people like this is arrogant Microsoft people like this is you're so stupid. IT'S SO EASY. JUST you're so stupid. IT'S SO EASY. JUST you're so stupid. IT'S SO EASY. JUST FIGURE IT OUT. WHAT'S WRONG WITH YOU?

FIGURE IT OUT. WHAT'S WRONG WITH YOU? FIGURE IT OUT. WHAT'S WRONG WITH YOU? WELL, you know what? You can be arrogant WELL, you know what? You can be arrogant WELL, you know what? You can be arrogant and go out of business real fast. and go out of business real fast. and go out of business real fast. >> Well, you know, and and so, you know, >> Well, you know, and and so, you know, >> Well, you know, and and so, you know, speaking about um uh solutions and speaking about um uh solutions and speaking about um uh solutions and getting to applications that actually getting to applications that actually getting to applications that actually matter. I mean, it just turns out the matter. I mean, it just turns out the matter. I mean, it just turns out the worst people to get advice from or look worst people to get advice from or look worst people to get advice from or look for guidance to for guidance to for guidance to are the likes of Daario and Sam. are the likes of Daario and Sam. are the likes of Daario and Sam. they're the worst because they don't they're the worst because they don't they're the worst because they don't know anything about this stuff. uh they know anything about this stuff. uh they know anything about this stuff. uh they don't and you know it might sound like don't and you know it might sound like don't and you know it might sound like like a shocker but they don't like a shocker but they don't like a shocker but they don't >> that that was never part of the fabric >> that that was never part of the fabric >> that that was never part of the fabric of their experience right I mean they of their experience right I mean they of their experience right I mean they are focused on building models or doing are focused on building models or doing are focused on building models or doing startups they're they haven't been in startups they're they haven't been in startups they're they haven't been in the business of doing like the hard work the business of doing like the hard work the business of doing like the hard work of actually of actually of actually instituting or helping a a a instituting or helping a a a instituting or helping a a a organization go through change right organization go through change right organization go through change right that might that might that might >> be driven off of technologies and the >> be driven off of technologies and the >> be driven off of technologies and the you know sort of the theoretical you know sort of the theoretical you know sort of the theoretical benefits of applying that that benefits of applying that that benefits of applying that that technology technology technology >> there because you know what and they say >> there because you know what and they say >> there because you know what and they say here's the thing people don't listen to here's the thing people don't listen to here's the thing people don't listen to these guys they are the first ones to these guys they are the first ones to these guys they are the first ones to say well you know we're going to just say well you know we're going to just say well you know we're going to just push this [ __ ] out there and someone's push this [ __ ] out there and someone's push this [ __ ] out there and someone's going to figure out developers going to figure out developers going to figure out developers developers developers developers developers developers developers developers developers developers [laughter] right they have no clue so [laughter] right they have no clue so [laughter] right they have no clue so why do we all look toward these guys for why do we all look toward these guys for why do we all look toward these guys for the answers they don't have the answers the answers they don't have the answers the answers they don't have the answers and they told you they don't have the and they told you they don't have the and they told you they don't have the answers. That is the weird weird thing

answers. That is the weird weird thing answers. That is the weird weird thing about these guys because people have about these guys because people have about these guys because people have deified them to the extent where they deified them to the extent where they deified them to the extent where they think that these guys are are think that these guys are are think that these guys are are know-it-alls, right? They have the know-it-alls, right? They have the know-it-alls, right? They have the answers when they clearly tell you that answers when they clearly tell you that answers when they clearly tell you that they don't, whether they intended to or they don't, whether they intended to or they don't, whether they intended to or not. [laughter] >> Don't you find it interesting? Yeah, >> Don't you find it interesting? Yeah, >> there's the people who are in the AI >> there's the people who are in the AI >> there's the people who are in the AI business that are actually doing the business that are actually doing the business that are actually doing the thing and there's what they're saying thing and there's what they're saying thing and there's what they're saying and then there's everyone else who is and then there's everyone else who is and then there's everyone else who is racing forward into the future trying to racing forward into the future trying to racing forward into the future trying to take advantage you know analyst firms take advantage you know analyst firms take advantage you know analyst firms consulting firms product they're jumping consulting firms product they're jumping consulting firms product they're jumping on this AI wave and they're like we're on this AI wave and they're like we're on this AI wave and they're like we're in an AI super cycle and this could be in an AI super cycle and this could be in an AI super cycle and this could be the greatest thing ever and all this the greatest thing ever and all this the greatest thing ever and all this stuff and so they're full speed ahead stuff and so they're full speed ahead stuff and so they're full speed ahead meanwhile the people who are actually meanwhile the people who are actually meanwhile the people who are actually doing the thing are going yeah we might doing the thing are going yeah we might doing the thing are going yeah we might you Oh, it might be an extinction event you Oh, it might be an extinction event you Oh, it might be an extinction event or I don't know what's [laughter] going or I don't know what's [laughter] going or I don't know what's [laughter] going to happen. to happen. to happen. I'm like [clears throat] I'm like [clears throat] I'm like [clears throat] >> weird. >> weird. >> weird. >> Yeah. I mean, >> Yeah. I mean, >> Yeah. I mean, >> it's weird. It's like who do you listen >> it's weird. It's like who do you listen >> it's weird. It's like who do you listen to? You know, you know, people are kind to? You know, you know, people are kind to? You know, you know, people are kind of self-s serving a lot of times. Yeah.

of self-s serving a lot of times. Yeah. of self-s serving a lot of times. Yeah. >> Like don't ask the economist. Ask the AI >> Like don't ask the economist. Ask the AI >> Like don't ask the economist. Ask the AI guy who doesn't even have a economics guy who doesn't even have a economics guy who doesn't even have a economics sub major, you know, or [clears throat] sub major, you know, or [clears throat] sub major, you know, or [clears throat] minor. It just doesn't make sense. You minor. It just doesn't make sense. You minor. It just doesn't make sense. You know, Daario, Daario's clearly a doomer, know, Daario, Daario's clearly a doomer, know, Daario, Daario's clearly a doomer, right? All 24/7. He's he's one of the right? All 24/7. He's he's one of the right? All 24/7. He's he's one of the doomers and and that's fine. You can be doomers and and that's fine. You can be doomers and and that's fine. You can be a doomer. Um, you know, Sam is a doomer. Um, you know, Sam is a doomer. Um, you know, Sam is >> is he really? >> is he really? >> is he really? >> Sam is all in. No, Dario. Every time he >> Sam is all in. No, Dario. Every time he >> Sam is all in. No, Dario. Every time he GETS A CHANCE, PLEASE REGULATE US. WE'RE GETS A CHANCE, PLEASE REGULATE US. WE'RE GETS A CHANCE, PLEASE REGULATE US. WE'RE GOING TO END THE PLANET. GOING TO END THE PLANET. GOING TO END THE PLANET. >> DID YOU see the latest >> DID YOU see the latest >> DID YOU see the latest >> Did you see the latest anthropic TV ads >> Did you see the latest anthropic TV ads >> Did you see the latest anthropic TV ads or ads they had on YouTube? It was like or ads they had on YouTube? It was like or ads they had on YouTube? It was like apocalyptic visions of AI and you know apocalyptic visions of AI and you know apocalyptic visions of AI and you know sort of positioning themselves as like sort of positioning themselves as like sort of positioning themselves as like we're we're going to help keep this from we're we're going to help keep this from we're we're going to help keep this from happening. But they've got good happening. But they've got good happening. But they've got good >> it was a horrible horrible look it up on >> it was a horrible horrible look it up on >> it was a horrible horrible look it up on YouTube. YouTube. YouTube. >> Insane like whoever is in charge of >> Insane like whoever is in charge of >> Insane like whoever is in charge of their marketing. their marketing. their marketing. >> God that is disaster. >> God that is disaster. >> God that is disaster. >> Yeah. So they have this so he Yeah. They >> Yeah. So they have this so he Yeah. They >> Yeah. So they have this so he Yeah. They have this whole doom thing and of course have this whole doom thing and of course have this whole doom thing and of course Sam is probably more like all about Sam is probably more like all about Sam is probably more like all about abundance, you know, abundance.

abundance, you know, abundance. abundance, you know, abundance. Abundance Abundance Abundance >> and Dario is all about doom and gloom. >> and Dario is all about doom and gloom. >> and Dario is all about doom and gloom. But uh But uh But uh >> yeah, somewhere in between. The truth is >> yeah, somewhere in between. The truth is >> yeah, somewhere in between. The truth is somewhere in between, somewhere in between, somewhere in between, >> right? >> right? >> right? >> Yeah. No, >> Yeah. No, >> Yeah. No, >> little bit of doom, little bit of >> little bit of doom, little bit of >> little bit of doom, little bit of abundance. abundance. abundance. >> You know, >> You know, >> You know, >> hey man, I think that perception stuff >> hey man, I think that perception stuff >> hey man, I think that perception stuff has a lot of potential. I mean, again, has a lot of potential. I mean, again, has a lot of potential. I mean, again, it's super boring, but it has huge it's super boring, but it has huge it's super boring, but it has huge potential impact if we can get people potential impact if we can get people potential impact if we can get people paying attention to it, you know? But paying attention to it, you know? But paying attention to it, you know? But look at sensors converge is not the look at sensors converge is not the look at sensors converge is not the hottest conference in the world. Maybe hottest conference in the world. Maybe hottest conference in the world. Maybe it should be it should be it should be or edge. Well, you guys are doing crazy or edge. Well, you guys are doing crazy or edge. Well, you guys are doing crazy stuff. stuff. stuff. >> Yeah, we do have the hottest conferences >> Yeah, we do have the hottest conferences >> Yeah, we do have the hottest conferences in the world. in the world. in the world. >> Yeah, you do. I think >> Yeah, you do. I think >> Yeah, you do. I think you're like freaking killing it, bro. you're like freaking killing it, bro. you're like freaking killing it, bro. >> Thank you. >> Thank you. >> Thank you. >> Edge AI. Hottest conferences. >> Edge AI. Hottest conferences. >> Edge AI. Hottest conferences. >> See you in Singapore. Singapore October >> See you in Singapore. Singapore October >> See you in Singapore. Singapore October 20th. 20th. 20th. >> Oh, you bastard. Are you serious? >> Oh, you bastard. Are you serious? >> Oh, you bastard. Are you serious? >> You're gonna be there, Deon? >> You're gonna be there, Deon? >> You're gonna be there, Deon? >> You got to do Come on, dude. Yeah, Deon, >> You got to do Come on, dude. Yeah, Deon, >> You got to do Come on, dude. Yeah, Deon, come to Singapore. We have We're doing come to Singapore. We have We're doing come to Singapore. We have We're doing Singapore and Taipei, Singapore and Taipei, Singapore and Taipei, >> end of October. >> end of October. >> end of October. [clears throat] [clears throat] [clears throat] >> Oh my god. Taipei, just remember >> Oh my god. Taipei, just remember >> Oh my god. Taipei, just remember >> him out there.

>> him out there. >> him out there. >> Three, they've got three weeks of power. >> Three, they've got three weeks of power. >> Three, they've got three weeks of power. if they get cut off by the Chinese, when if they get cut off by the Chinese, when if they get cut off by the Chinese, when we talk about Taipei and we talk about we talk about Taipei and we talk about we talk about Taipei and we talk about TSMC, there's always that discussion TSMC, there's always that discussion TSMC, there's always that discussion >> around it always pops in my head, you >> around it always pops in my head, you >> around it always pops in my head, you know, that the Chinese love to do drills know, that the Chinese love to do drills know, that the Chinese love to do drills about surrounding the island and about surrounding the island and about surrounding the island and everything. And um, you know, everything. And um, you know, everything. And um, you know, >> I'd only heard just recently that they >> I'd only heard just recently that they >> I'd only heard just recently that they all, you know, Taiwan is totally powered all, you know, Taiwan is totally powered all, you know, Taiwan is totally powered by, you know, liqufied natural gas and by, you know, liqufied natural gas and by, you know, liqufied natural gas and they have three weeks of energy. And so they have three weeks of energy. And so they have three weeks of energy. And so if if it gets cut off, if if it gets cut off, if if it gets cut off, >> I was listening to I guess I was >> I was listening to I guess I was >> I was listening to I guess I was listening to Allin and they were talking listening to Allin and they were talking listening to Allin and they were talking to Pat Gellzinger and they were he's to Pat Gellzinger and they were he's to Pat Gellzinger and they were he's just like just like just like >> he goes if they get cut off and the fabs >> he goes if they get cut off and the fabs >> he goes if they get cut off and the fabs get shut down. He goes it takes 90 days get shut down. He goes it takes 90 days get shut down. He goes it takes 90 days to restart a fab. to restart a fab. to restart a fab. >> Yeah. >> Yeah. >> Yeah. >> It's not like that, >> It's not like that, >> It's not like that, >> right? >> right? >> right? >> They uh that's why TM TSMC upped their >> They uh that's why TM TSMC upped their >> They uh that's why TM TSMC upped their investments in the US, I guess. But it investments in the US, I guess. But it investments in the US, I guess. But it looks like, by the way, people are not looks like, by the way, people are not looks like, by the way, people are not too crazy about AI ran. Just wanted to too crazy about AI ran. Just wanted to too crazy about AI ran. Just wanted to throw that out there real quick.

throw that out there real quick. throw that out there real quick. >> Hey, I ran. >> Hey, I ran. >> Hey, I ran. >> Hey, I ran. Oh, Jesus. >> Hey, I ran. Oh, Jesus. >> Hey, I ran. Oh, Jesus. >> I know. No, it's just another example of >> I know. No, it's just another example of >> I know. No, it's just another example of like hyperbole and ridiculousness that like hyperbole and ridiculousness that like hyperbole and ridiculousness that makes no traction. It It's like, come makes no traction. It It's like, come makes no traction. It It's like, come on, focus on on, focus on on, focus on >> focus on valuable things. And if you >> focus on valuable things. And if you >> focus on valuable things. And if you can't do that, then I mean, can't do that, then I mean, can't do that, then I mean, >> well, you know what? Why don't we get >> well, you know what? Why don't we get >> well, you know what? Why don't we get Chattton on the show and he can tell you Chattton on the show and he can tell you Chattton on the show and he can tell you why AI ran is a good thing. [laughter] why AI ran is a good thing. [laughter] why AI ran is a good thing. [laughter] Yeah, I actually sat down with, you Yeah, I actually sat down with, you Yeah, I actually sat down with, you know, Onosan and Chattton prior to MWC know, Onosan and Chattton prior to MWC know, Onosan and Chattton prior to MWC and we had a long discussion on AI ran, and we had a long discussion on AI ran, and we had a long discussion on AI ran, but I'll keep my mouth shut for my but I'll keep my mouth shut for my but I'll keep my mouth shut for my friends at Nvidia that are trying to friends at Nvidia that are trying to friends at Nvidia that are trying to push it. [laughter] push it. [laughter] push it. [laughter] >> Nvidia, you know, you got to give them >> Nvidia, you know, you got to give them >> Nvidia, you know, you got to give them some they need a little help. Nvidia some they need a little help. Nvidia some they need a little help. Nvidia needs a little help. needs a little help. needs a little help. >> Yeah. Yeah. >> Yeah. Yeah. >> Yeah. Yeah. >> Yeah. Um, anyways, yeah, I'm totally >> Yeah. Um, anyways, yeah, I'm totally >> Yeah. Um, anyways, yeah, I'm totally bought into my theory of disagregated AI bought into my theory of disagregated AI bought into my theory of disagregated AI ran. ran. ran. So anyways, uh hey um I think uh we all So anyways, uh hey um I think uh we all So anyways, uh hey um I think uh we all need to get back to work. So you want to need to get back to work. So you want to need to get back to work. So you want to take us out? take us out? take us out? >> Hey Devin, thanks for coming in, dude. >> Hey Devin, thanks for coming in, dude. >> Hey Devin, thanks for coming in, dude. >> Oh, thanks. Thanks for having me.

>> Oh, thanks. Thanks for having me. >> Oh, thanks. Thanks for having me. >> Really, really great having you. >> Really, really great having you. >> Really, really great having you. >> Awesome. >> Awesome. >> Awesome. >> Absolutely. Yeah, I really appreciate >> Absolutely. Yeah, I really appreciate >> Absolutely. Yeah, I really appreciate coming. coming. coming. >> Anytime. >> Anytime. >> Anytime. >> Anytime. >> Anytime. >> Anytime. >> So thanks everybody for joining us on >> So thanks everybody for joining us on >> So thanks everybody for joining us on another fun episode of IoT Coffee Talk. another fun episode of IoT Coffee Talk. another fun episode of IoT Coffee Talk. This was a lot more serious this time. I This was a lot more serious this time. I This was a lot more serious this time. I feel like we got some serious deep AI feel like we got some serious deep AI feel like we got some serious deep AI conversations. There's a lot going on. conversations. There's a lot going on. conversations. There's a lot going on. There's always a lot going on every There's always a lot going on every There's always a lot going on every week, but there's some serious stuff week, but there's some serious stuff week, but there's some serious stuff going on uh that involves economics and going on uh that involves economics and going on uh that involves economics and tokconomics and token maxing or TJ tokconomics and token maxing or TJ tokconomics and token maxing or TJ Maxxing, if you will. It just depends, Maxxing, if you will. It just depends, Maxxing, if you will. It just depends, you know. Uh but thanks for joining us. you know. Uh but thanks for joining us. you know. Uh but thanks for joining us. Check us out every week. Uh and and Check us out every week. Uh and and Check us out every week. Uh and and definitely look to uh our our main definitely look to uh our our main definitely look to uh our our main charity, Elevate Communities. Uh think charity, Elevate Communities. Uh think charity, Elevate Communities. Uh think about donating. And of course, we have about donating. And of course, we have about donating. And of course, we have some great merch. Look at Leonard. some great merch. Look at Leonard. some great merch. Look at Leonard. >> You can get your own cool IoT. >> You can get your own cool IoT. >> You can get your own cool IoT. >> Pick it up, dude. Okay. >> Pick it up, dude. Okay. >> Pick it up, dude. Okay. >> Your whole whole family and you know >> Your whole whole family and you know >> Your whole whole family and you know you're going to look cool, man. you're going to look cool, man. you're going to look cool, man. >> It looks cool. It looks cool >> It looks cool. It looks cool >> It looks cool. It looks cool >> and people will tap you and when you go >> and people will tap you and when you go >> and people will tap you and when you go to conferences go, "Oh, I to conferences go, "Oh, I to conferences go, "Oh, I >> it's one of the coffee talk people." >> it's one of the coffee talk people." >> it's one of the coffee talk people." Yeah, exactly. That's Yeah, exactly. That's Yeah, exactly. That's >> that's who we are.

>> that's who we are. >> that's who we are. >> So, have a great weekend and we'll see >> So, have a great weekend and we'll see >> So, have a great weekend and we'll see you on the other side. you on the other side. you on the other side. >> Thank you. Byebye.