IoT Coffee Talk: Episode 298 - "The AI Future 2026" (An IDC Take)

while we wait for Leonard to help me. while we wait for Leonard to help me. And so you think this read AI thing, And so you think this read AI thing, And so you think this read AI thing, Stephanie, is from Steve Brummer? Stephanie, is from Steve Brummer? Stephanie, is from Steve Brummer? >> It's what it said when I got the >> It's what it said when I got the >> It's what it said when I got the notification. So maybe maybe he somehow notification. So maybe maybe he somehow notification. So maybe maybe he somehow has set it up has set it up has set it up >> where it it it [music] records all of >> where it it it [music] records all of >> where it it it [music] records all of our IoT copy talks and even the ones our IoT copy talks and even the ones our IoT copy talks and even the ones that he's not that he's not that he's not >> those insidious AI things that are >> those insidious AI things that are >> those insidious AI things that are [music] recording and writing and stuff [music] recording and writing and stuff [music] recording and writing and stuff like that on Zoom calls and team got to like that on Zoom calls and team got to like that on Zoom calls and team got to watch out for that. All right. All watch out for that. All right. All watch out for that. All right. All right. All right everybody, welcome to right. All right everybody, welcome to right. All right everybody, welcome to IoT Coffee Talk. It's another beautiful IoT Coffee Talk. It's another beautiful IoT Coffee Talk. It's another beautiful Friday morning and we're going to get Friday morning and we're going to get Friday morning and we're going to get after it. We're going to talk about after it. We're going to talk about after it. We're going to talk about what's going on in tech and IoT and what's going on in tech and IoT and what's going on in tech and IoT and digital twins and AI and all that fun digital twins and AI and all that fun digital twins and AI and all that fun stuff uh this week. Uh and this week we stuff uh this week. Uh and this week we stuff uh this week. Uh and this week we have a special guest, Crawford Delr. Uh have a special guest, Crawford Delr. Uh have a special guest, Crawford Delr. Uh so nice to have you on the show. so nice to have you on the show. so nice to have you on the show. >> Is you, Rob? >> Is you, Rob? >> Is you, Rob? >> Yeah, Stephanie. >> Yeah, Stephanie. >> Yeah, Stephanie. >> Absolutely. Absolutely. I think the last >> Absolutely. Absolutely. I think the last >> Absolutely. Absolutely. I think the last time we all did a show was we were at a time we all did a show was we were at a time we all did a show was we were at a bar somewhere and bar somewhere and bar somewhere and >> Yeah, we were at a bar in uh in in in >> Yeah, we were at a bar in uh in in in >> Yeah, we were at a bar in uh in in in the in North Carolina. the in North Carolina. the in North Carolina. >> In North Carolina. That's right. Right. >> In North Carolina. That's right. Right. >> In North Carolina. That's right. Right. At the Lenovo thing. Yeah. Yeah, At the Lenovo thing. Yeah. Yeah, At the Lenovo thing. Yeah. Yeah, >> that's fun stuff.

>> that's fun stuff. >> that's fun stuff. >> You know, it's Leonard travels with his >> You know, it's Leonard travels with his >> You know, it's Leonard travels with his portable studio stuff. It seems like portable studio stuff. It seems like portable studio stuff. It seems like everywhere, you know, everywhere, you know, everywhere, you know, >> his little mic, his little mic and >> his little mic, his little mic and >> his little mic, his little mic and attach that thing out of his attach that thing out of his attach that thing out of his >> spin at a bar and and Leonard said, >> spin at a bar and and Leonard said, >> spin at a bar and and Leonard said, "Hey, we should do a podcast." And I "Hey, we should do a podcast." And I "Hey, we should do a podcast." And I said, "Yeah, let's do it anytime." And said, "Yeah, let's do it anytime." And said, "Yeah, let's do it anytime." And then the guy disappeared for like three then the guy disappeared for like three then the guy disappeared for like three minutes and came back with a podcast. minutes and came back with a podcast. minutes and came back with a podcast. [laughter] [laughter] [laughter] >> Yes. Yes. You know, he >> Yes. Yes. You know, he >> Yes. Yes. You know, he >> he's got the equipment just in case. >> he's got the equipment just in case. >> he's got the equipment just in case. >> Oh my god. does cuz he did the same >> Oh my god. does cuz he did the same >> Oh my god. does cuz he did the same thing at CES like we were at some big thing at CES like we were at some big thing at CES like we were at some big bar or something one of the nights and bar or something one of the nights and bar or something one of the nights and he goes, "Yeah, let's go outside and do he goes, "Yeah, let's go outside and do he goes, "Yeah, let's go outside and do it." And he and so uh Yeah. Yeah. That's it." And he and so uh Yeah. Yeah. That's it." And he and so uh Yeah. Yeah. That's It's just crazy. That's crazy. So, It's just crazy. That's crazy. So, It's just crazy. That's crazy. So, what's what's new? What's going on this what's what's new? What's going on this what's what's new? What's going on this week? Anything exciting in your world, week? Anything exciting in your world, week? Anything exciting in your world, Stephanie or Crawford? Stephanie or Crawford? Stephanie or Crawford? >> Yeah. >> Yeah. >> Yeah. >> I mean, yeah, it's Crawford. I'd love to >> I mean, yeah, it's Crawford. I'd love to >> I mean, yeah, it's Crawford. I'd love to hear what's going on in your world. hear what's going on in your world. hear what's going on in your world. Well, I mean it's been an interesting, Well, I mean it's been an interesting, Well, I mean it's been an interesting, you know, the the the the markets have you know, the the the the markets have you know, the the the the markets have had such a fascinating reaction to AI, had such a fascinating reaction to AI, had such a fascinating reaction to AI, right? I mean, you know, the narrative right? I mean, you know, the narrative right? I mean, you know, the narrative is I think there, you know, we saw a is I think there, you know, we saw a is I think there, you know, we saw a Microsoft report.

Microsoft report. Microsoft report. >> Uh, you know, I think, you know, they >> Uh, you know, I think, you know, they >> Uh, you know, I think, you know, they the the their report wasn't terrible. the the their report wasn't terrible. the the their report wasn't terrible. Uh, they they they they came close to Uh, they they they they came close to Uh, they they they they came close to their forecast and at the same time their forecast and at the same time their forecast and at the same time >> um the mark, you know, the stock went >> um the mark, you know, the stock went >> um the mark, you know, the stock went down significantly. down significantly. down significantly. >> Yep. and and I think that that came uh >> Yep. and and I think that that came uh >> Yep. and and I think that that came uh on the heels of uh ASML's blowout on the heels of uh ASML's blowout on the heels of uh ASML's blowout quarter which you know spoke to very quarter which you know spoke to very quarter which you know spoke to very very significant chip orders and very significant chip orders and very significant chip orders and semiconductor orders going forward. So semiconductor orders going forward. So semiconductor orders going forward. So um you know as a semiconductor equipment um you know as a semiconductor equipment um you know as a semiconductor equipment and materials company they are sort of and materials company they are sort of and materials company they are sort of at the nose of the dog and so they sort at the nose of the dog and so they sort at the nose of the dog and so they sort of see the very very beginning of what of see the very very beginning of what of see the very very beginning of what orders look like and you know I think orders look like and you know I think orders look like and you know I think the way I interpret the reaction to the the way I interpret the reaction to the the way I interpret the reaction to the Microsoft news what I'm seeing there is Microsoft news what I'm seeing there is Microsoft news what I'm seeing there is a stock market that is uh probably a stock market that is uh probably a stock market that is uh probably getting a little frustrated with the getting a little frustrated with the getting a little frustrated with the fact that C-Pilot isn't delivering what fact that C-Pilot isn't delivering what fact that C-Pilot isn't delivering what they it needs to deliver. they it needs to deliver. they it needs to deliver. the fact that SATA and Microsoft jumped the fact that SATA and Microsoft jumped the fact that SATA and Microsoft jumped in very very quickly with um uh chat GPT in very very quickly with um uh chat GPT in very very quickly with um uh chat GPT and and OpenAI and now it's a much more and and OpenAI and now it's a much more and and OpenAI and now it's a much more competitive playing field and so you've competitive playing field and so you've competitive playing field and so you've got companies whether it be Anthropic got companies whether it be Anthropic got companies whether it be Anthropic whether it be Google [clears throat] whether it be Google [clears throat] whether it be Google [clears throat] with Gemini which by the way if you with Gemini which by the way if you with Gemini which by the way if you haven't used it it's crazy fast even haven't used it it's crazy fast even haven't used it it's crazy fast even >> um and you know look you know it's a >> um and you know look you know it's a >> um and you know look you know it's a competitive world and so what looked competitive world and so what looked competitive world and so what looked like a slam dunk two years ago in terms like a slam dunk two years ago in terms like a slam dunk two years ago in terms of a partnership is now maybe people are

of a partnership is now maybe people are of a partnership is now maybe people are starting to second guess that and and starting to second guess that and and starting to second guess that and and and this is like I can't even believe and this is like I can't even believe and this is like I can't even believe I'm going to say sentences like this but I'm going to say sentences like this but I'm going to say sentences like this but the 39 39% growth in Azure was the 39 39% growth in Azure was the 39 39% growth in Azure was considered lackluster [laughter] considered lackluster [laughter] considered lackluster [laughter] >> that's insane >> that's insane >> that's insane >> given given what what the street was >> given given what what the street was >> given given what what the street was expecting and so you bottle all that up expecting and so you bottle all that up expecting and so you bottle all that up and I think there's just some queasiness and I think there's just some queasiness and I think there's just some queasiness um around around Microsoft and you know um around around Microsoft and you know um around around Microsoft and you know that's kind of part of it and then that's kind of part of it and then that's kind of part of it and then secondly you know get nervous now secondly you know get nervous now secondly you know get nervous now announcing a really really strong announcing a really really strong announcing a really really strong quarter and the stock went down because quarter and the stock went down because quarter and the stock went down because the narrative is and these narratives the narrative is and these narratives the narrative is and these narratives are dangerous uh is is that SAS is going are dangerous uh is is that SAS is going are dangerous uh is is that SAS is going to sorry AI is going to kill SAS which I to sorry AI is going to kill SAS which I to sorry AI is going to kill SAS which I think is you know as they say in your think is you know as they say in your think is you know as they say in your neck of the woods Stephanie horse you neck of the woods Stephanie horse you neck of the woods Stephanie horse you know what know what know what but but but but that's what they like to but but but but that's what they like to but but but but that's what they like to say is that and we can talk about that say is that and we can talk about that say is that and we can talk about that on the podcast why AI is not going to on the podcast why AI is not going to on the podcast why AI is not going to kill SAS but that's the narrative now is kill SAS but that's the narrative now is kill SAS but that's the narrative now is that AI is going to kill SAS so that's that AI is going to kill SAS so that's that AI is going to kill SAS so that's kind of what I see Stephanie, I'd love kind of what I see Stephanie, I'd love kind of what I see Stephanie, I'd love to hear what you're talking and Rob, to hear what you're talking and Rob, to hear what you're talking and Rob, what what you guys are saying. what what you guys are saying. what what you guys are saying. >> I I I think it's interesting what you >> I I I think it's interesting what you >> I I I think it's interesting what you mentioned about the chat GPT mentioned about the chat GPT mentioned about the chat GPT relationships and how everybody jumped relationships and how everybody jumped relationships and how everybody jumped on board so early on.

on board so early on. on board so early on. >> Yeah. But I have to wonder because it >> Yeah. But I have to wonder because it >> Yeah. But I have to wonder because it has such a massive consumer flocking to has such a massive consumer flocking to has such a massive consumer flocking to the app the app the app >> that it just >> that it just >> that it just >> for me it seems like so much of a >> for me it seems like so much of a >> for me it seems like so much of a consumer consumer consumer just individual like just individual like just individual like >> branding as more as opposed to >> branding as more as opposed to >> branding as more as opposed to commercial operational like automation commercial operational like automation commercial operational like automation things that are going to happen on the things that are going to happen on the things that are going to happen on the factory floor in the manufacturing factory floor in the manufacturing factory floor in the manufacturing facility or in the supply chain. And I facility or in the supply chain. And I facility or in the supply chain. And I wonder if because so many individuals wonder if because so many individuals wonder if because so many individuals jumped on so quickly, it just it it jumped on so quickly, it just it it jumped on so quickly, it just it it really lost its branding and positioning really lost its branding and positioning really lost its branding and positioning on the enterprise side and just is just on the enterprise side and just is just on the enterprise side and just is just sitting there on the consumer side while sitting there on the consumer side while sitting there on the consumer side while all these other AI tools like Gemini and all these other AI tools like Gemini and all these other AI tools like Gemini and Anthropic and and Claude and others came Anthropic and and Claude and others came Anthropic and and Claude and others came out and they went hard and heavy on the out and they went hard and heavy on the out and they went hard and heavy on the operational side and really supporting operational side and really supporting operational side and really supporting businesses. And I'm wondering if if that businesses. And I'm wondering if if that businesses. And I'm wondering if if that really if the the market's looking at really if the the market's looking at really if the the market's looking at that and they're like, "Hey, you know, that and they're like, "Hey, you know, that and they're like, "Hey, you know, we can't put all our eggs in one we can't put all our eggs in one we can't put all our eggs in one basket." Plus, it's changing so quickly.

basket." Plus, it's changing so quickly. basket." Plus, it's changing so quickly. I don't know if any of us can actually I don't know if any of us can actually I don't know if any of us can actually keep up with what iteration of what AI keep up with what iteration of what AI keep up with what iteration of what AI tool is out there on a given day because tool is out there on a given day because tool is out there on a given day because it's just moving at a very fast pace. it's just moving at a very fast pace. it's just moving at a very fast pace. >> Yeah. I mean, that's a great point. I >> Yeah. I mean, that's a great point. I >> Yeah. I mean, that's a great point. I mean, I think [clears throat] all these mean, I think [clears throat] all these mean, I think [clears throat] all these guys uh folks uh are are running the guys uh folks uh are are running the guys uh folks uh are are running the risk of that. And then I think you know risk of that. And then I think you know risk of that. And then I think you know Anthropic sorry uh uh GPT and and um Anthropic sorry uh uh GPT and and um Anthropic sorry uh uh GPT and and um open AI are probably a really you know open AI are probably a really you know open AI are probably a really you know excellent example of somebody who jumped excellent example of somebody who jumped excellent example of somebody who jumped out and kind of became a consumer tool. out and kind of became a consumer tool. out and kind of became a consumer tool. I I find it fascinating that all these I I find it fascinating that all these I I find it fascinating that all these folks are now advertising on general folks are now advertising on general folks are now advertising on general advertising which which tells me that advertising which which tells me that advertising which which tells me that you know there's they're they're trying you know there's they're they're trying you know there's they're they're trying to play the eyeballs game and they're to play the eyeballs game and they're to play the eyeballs game and they're trying to get more and more eyeballs trying to get more and more eyeballs trying to get more and more eyeballs into the um into the fray. I I I don't into the um into the fray. I I I don't into the um into the fray. I I I don't know. I mean, look, I I I think that o know. I mean, look, I I I think that o know. I mean, look, I I I think that o OpenAI partnering with Microsoft, I I OpenAI partnering with Microsoft, I I OpenAI partnering with Microsoft, I I think that was probably a way that they think that was probably a way that they think that was probably a way that they felt they would be moving into the felt they would be moving into the felt they would be moving into the enterprise in addition to to other um enterprise in addition to to other um enterprise in addition to to other um efforts that that they are making. Um efforts that that they are making. Um efforts that that they are making. Um you know, they all have a consumer bend.

you know, they all have a consumer bend. you know, they all have a consumer bend. Um they're all, you know, kind of kind Um they're all, you know, kind of kind Um they're all, you know, kind of kind of going to have to play this of going to have to play this of going to have to play this separately. I I I actually think the the separately. I I I actually think the the separately. I I I actually think the the bigger problem and again, you know, the bigger problem and again, you know, the bigger problem and again, you know, the number of people who pay for these number of people who pay for these number of people who pay for these things is still very very small as a things is still very very small as a things is still very very small as a percent of each of these companies percent of each of these companies percent of each of these companies businesses. Um I I I I think that um businesses. Um I I I I think that um businesses. Um I I I I think that um when you start to think about uh the the when you start to think about uh the the when you start to think about uh the the partnership, I I I think you have to you partnership, I I I think you have to you partnership, I I I think you have to you have to ask the question, is Microsoft have to ask the question, is Microsoft have to ask the question, is Microsoft kind of letting kind of letting kind of letting uh OpenAI down here? I mean I mean you uh OpenAI down here? I mean I mean you uh OpenAI down here? I mean I mean you know as a user of all these tools I got know as a user of all these tools I got know as a user of all these tools I got to tell you you know and and you know to tell you you know and and you know to tell you you know and and you know Satia famously you saw these articles Satia famously you saw these articles Satia famously you saw these articles recently about him kind of framing his recently about him kind of framing his recently about him kind of framing his own company and saying you guys are way own company and saying you guys are way own company and saying you guys are way way behind. way behind. way behind. >> Co-pilot is behind. I mean it's not >> Co-pilot is behind. I mean it's not >> Co-pilot is behind. I mean it's not >> behind. It's >> behind. It's >> behind. It's >> not a great experience at all. I mean >> not a great experience at all. I mean >> not a great experience at all. I mean like it's just so unnecessarily like it's just so unnecessarily like it's just so unnecessarily complicated and and and and cluji and um complicated and and and and cluji and um complicated and and and and cluji and um I wonder if if I wonder if if I wonder if if >> isn't that the standard Microsoft recipe >> isn't that the standard Microsoft recipe >> isn't that the standard Microsoft recipe complicated and cluji? complicated and cluji? complicated and cluji? >> It's got to be cluji.

>> Oh my gosh. Yeah, I know. It's crazy. >> Oh my gosh. Yeah, I know. It's crazy. You're right. Microsoft got beat up You're right. Microsoft got beat up You're right. Microsoft got beat up pretty bad uh uh on their their pretty bad uh uh on their their pretty bad uh uh on their their earnings, you know. Um obviously the earnings, you know. Um obviously the earnings, you know. Um obviously the other big news in Seattle was the other big news in Seattle was the other big news in Seattle was the layoffs at AWS. Yeah. Right. layoffs at AWS. Yeah. Right. layoffs at AWS. Yeah. Right. >> Um, you know, a a lot of us who have >> Um, you know, a a lot of us who have >> Um, you know, a a lot of us who have spent so much of our lives in the spent so much of our lives in the spent so much of our lives in the Seattle and east side up there, like Seattle and east side up there, like Seattle and east side up there, like like Pete and I, you know, we have so like Pete and I, you know, we have so like Pete and I, you know, we have so many friends who have felt the the many friends who have felt the the many friends who have felt the the weight of AI and weight of AI and weight of AI and GPUs versus employees. I know that's a GPUs versus employees. I know that's a GPUs versus employees. I know that's a weird way to characterize it. weird way to characterize it. weird way to characterize it. >> I I I'm losing people on my tennis team >> I I I'm losing people on my tennis team >> I I I'm losing people on my tennis team because they when they get laid off, because they when they get laid off, because they when they get laid off, they drop their profile they drop their profile they drop their profile >> extracurricular. >> extracurricular. >> extracurricular. That's right. They lose their Plural That's right. They lose their Plural That's right. They lose their Plural Club membership from Microsoft. Yeah. Club membership from Microsoft. Yeah. Club membership from Microsoft. Yeah. >> Membership. So, Wow. >> Membership. So, Wow. >> Membership. So, Wow. >> No, it's really it's it's I was talking >> No, it's really it's it's I was talking >> No, it's really it's it's I was talking to someone um yesterday and their to someone um yesterday and their to someone um yesterday and their physical AI group and uh physical AI group and uh physical AI group and uh >> yeah, it's you know, for even for the >> yeah, it's you know, for even for the >> yeah, it's you know, for even for the people that stay, you know, it obviously people that stay, you know, it obviously people that stay, you know, it obviously just really impacts the whole the whole just really impacts the whole the whole just really impacts the whole the whole tenor of the work workplace is affected tenor of the work workplace is affected tenor of the work workplace is affected and uh and uh and uh >> yeah, >> yeah, >> yeah, >> everybody's always looking over their >> everybody's always looking over their >> everybody's always looking over their shoulder. Well, what's also funny is shoulder. Well, what's also funny is shoulder. Well, what's also funny is that all this uh you know AI because that all this uh you know AI because that all this uh you know AI because we're so efficient that we can just we're so efficient that we can just we're so efficient that we can just replace people with AI, but then if replace people with AI, but then if replace people with AI, but then if you're at the company that just fired a you're at the company that just fired a you're at the company that just fired a bunch of people because of AI, you get bunch of people because of AI, you get bunch of people because of AI, you get the workload. How does that work? Like the workload. How does that work? Like the workload. How does that work? Like like you know this circular thing is like you know this circular thing is like you know this circular thing is driving me nuts, man. It just it's driving me nuts, man. It just it's driving me nuts, man. It just it's Anyway, Anyway, Anyway, >> and you know what's funny, David, about >> and you know what's funny, David, about >> and you know what's funny, David, about that is that if you go back even

that is that if you go back even that is that if you go back even further, all of the kids that are further, all of the kids that are further, all of the kids that are applying for jobs applying for jobs applying for jobs you know, their largest I guess the you know, their largest I guess the you know, their largest I guess the biggest complaint I'm hearing from the biggest complaint I'm hearing from the biggest complaint I'm hearing from the students, you know, talking to the students, you know, talking to the students, you know, talking to the students at&m university is you're students at&m university is you're students at&m university is you're you're getting put in these massive you're getting put in these massive you're getting put in these massive pools where AI is going through those pools where AI is going through those pools where AI is going through those resumes and no one is everything looks resumes and no one is everything looks resumes and no one is everything looks the same because people are using AI to the same because people are using AI to the same because people are using AI to really craft their resume and then the really craft their resume and then the really craft their resume and then the tools that are using to go through and tools that are using to go through and tools that are using to go through and look at their resumes, no one's look at their resumes, no one's look at their resumes, no one's differentiated or looking no one's differentiated or looking no one's differentiated or looking no one's looking different. So they're having a looking different. So they're having a looking different. So they're having a very hard time, very hard time, very hard time, >> right? I mean students are having a hard >> right? I mean students are having a hard >> right? I mean students are having a hard time getting jobs because there is no time getting jobs because there is no time getting jobs because there is no way for them to stand out amongst the way for them to stand out amongst the way for them to stand out amongst the that hundreds of thousands of of papers that hundreds of thousands of of papers that hundreds of thousands of of papers that all you know there's a big shift that all you know there's a big shift that all you know there's a big shift back toward networking personal back toward networking personal back toward networking personal networking and networking and networking and >> the relationship building >> the relationship building >> the relationship building >> mentoring and networking and that that's >> mentoring and networking and that that's >> mentoring and networking and that that's how a lot of folks are leveraging for how a lot of folks are leveraging for how a lot of folks are leveraging for >> I think going back to Crawford's point >> I think going back to Crawford's point >> I think going back to Crawford's point Crawford uh is you know Microsoft you Crawford uh is you know Microsoft you Crawford uh is you know Microsoft you know has never been known as the company know has never been known as the company know has never been known as the company that makes the best products. I mean, that makes the best products. I mean, that makes the best products. I mean, LET'S JUST BE HONEST.

LET'S JUST BE HONEST. LET'S JUST BE HONEST. >> WHAT? >> WHAT? >> WHAT? >> WHAT? >> WHAT? >> WHAT? >> They make [laughter] >> They make [laughter] >> They make [laughter] products. They make products that are products. They make products that are products. They make products that are good enough. good enough. good enough. >> Where's my Zoom? >> Where's my Zoom? >> Where's my Zoom? >> Thank you. >> Thank you. >> Thank you. >> Channel. I'm speaking from experience >> Channel. I'm speaking from experience >> Channel. I'm speaking from experience here. Uh, it's about quantity, not here. Uh, it's about quantity, not here. Uh, it's about quantity, not quality most of the time. And it's good quality most of the time. And it's good quality most of the time. And it's good enough. and it's good enough and it's enough. and it's good enough and it's enough. and it's good enough and it's available and it's supported available and it's supported available and it's supported and there's one one throat to choke and and there's one one throat to choke and and there's one one throat to choke and all that good stuff and and so all that good stuff and and so all that good stuff and and so enterprises [clears throat] enterprises [clears throat] enterprises [clears throat] count on that kind of regularity. they count on that kind of regularity. they count on that kind of regularity. they don't buy into Microsoft because it's don't buy into Microsoft because it's don't buy into Microsoft because it's the most leading edge product the most leading edge product the most leading edge product >> but you know I think to Crawford's point >> but you know I think to Crawford's point >> but you know I think to Crawford's point like it's a really competitive like it's a really competitive like it's a really competitive environment out there and people are environment out there and people are environment out there and people are really innovating really fast and this really innovating really fast and this really innovating really fast and this is a place where Microsoft is not is a place where Microsoft is not is a place where Microsoft is not comfortable they're not comfortable comfortable they're not comfortable comfortable they're not comfortable innovating quickly innovating quickly innovating quickly >> and creating kind of cutting edge >> and creating kind of cutting edge >> and creating kind of cutting edge products so they really need to be products so they really need to be products so they really need to be pushed and kind of pushed and kind of pushed and kind of >> but I thought I thought Clippy was >> but I thought I thought Clippy was >> but I thought I thought Clippy was coming back with AI now coming back with AI now coming back with AI now >> where's Clippy >> where's Clippy >> where's Clippy >> where's Clippy >> where's Clippy >> where's Clippy Come on, man.

Come on, man. Come on, man. >> What about Cortana? Let's bring back >> What about Cortana? Let's bring back >> What about Cortana? Let's bring back >> Cortana. Yes. >> Cortana. Yes. >> Cortana. Yes. >> Voice. >> Voice. >> Voice. >> Yes. So, Crawford to introduce you some >> Yes. So, Crawford to introduce you some >> Yes. So, Crawford to introduce you some more people. So, Pete Bernard, he's in more people. So, Pete Bernard, he's in more people. So, Pete Bernard, he's in Belleview, Washington. He's uh what do Belleview, Washington. He's uh what do Belleview, Washington. He's uh what do you you run the Edge AI Foundation? you you run the Edge AI Foundation? you you run the Edge AI Foundation? >> Yeah, I do. >> Yeah, I do. >> Yeah, I do. >> Um and he and I worked together back in >> Um and he and I worked together back in >> Um and he and I worked together back in the Windows Mobile, Windows Phone days. the Windows Mobile, Windows Phone days. the Windows Mobile, Windows Phone days. >> Oh, cool. Sure. >> Oh, cool. Sure. >> Oh, cool. Sure. >> You know, and we did that great nine >> You know, and we did that great nine >> You know, and we did that great nine episode series on the whole history of episode series on the whole history of episode series on the whole history of Windows Phone, which is fun. Windows Phone, which is fun. Windows Phone, which is fun. >> Absolutely. David Vasquez. He's out in >> Absolutely. David Vasquez. He's out in >> Absolutely. David Vasquez. He's out in Switzerland now. Previously in Portland, Switzerland now. Previously in Portland, Switzerland now. Previously in Portland, Mr. Verizon guy. Um, yeah, you're skiing Mr. Verizon guy. Um, yeah, you're skiing Mr. Verizon guy. Um, yeah, you're skiing in the Alps. And then, of course, the in the Alps. And then, of course, the in the Alps. And then, of course, the IoT giant Mark Post. IoT giant Mark Post. IoT giant Mark Post. >> Um, you know, in Barcelona there. Um, >> Um, you know, in Barcelona there. Um, >> Um, you know, in Barcelona there. Um, and got to know Mark way back when in and got to know Mark way back when in and got to know Mark way back when in the early days of the IoT Stars event. the early days of the IoT Stars event. the early days of the IoT Stars event. Actually, if you remember, remember Actually, if you remember, remember Actually, if you remember, remember there used to be this thing back in the there used to be this thing back in the there used to be this thing back in the the rise of the smartphone revolution. the rise of the smartphone revolution. the rise of the smartphone revolution. there was mobile Monday. Um that was all there was mobile Monday. Um that was all there was mobile Monday. Um that was all over the world. over the world. over the world. >> And so at at MWC that first Sunday night >> And so at at MWC that first Sunday night >> And so at at MWC that first Sunday night we would do Mobile Sunday.

we would do Mobile Sunday. we would do Mobile Sunday. >> Uh and then Monday night, you know, we >> Uh and then Monday night, you know, we >> Uh and then Monday night, you know, we did IoT Stars, you know, at the did IoT Stars, you know, at the did IoT Stars, you know, at the >> at the brewery, you know, um >> at the brewery, you know, um >> at the brewery, you know, um >> Estrella Dam Brewery there in Barcelona. >> Estrella Dam Brewery there in Barcelona. >> Estrella Dam Brewery there in Barcelona. And so And so And so >> that's that's kind of all of our >> that's that's kind of all of our >> that's that's kind of all of our history, you know. history, you know. history, you know. >> Yeah. >> Yeah. >> Yeah. >> So did we do the disclaimer? No, we >> So did we do the disclaimer? No, we >> So did we do the disclaimer? No, we didn't do this. didn't do this. didn't do this. >> Okay. Well, I guess we have to do that >> Okay. Well, I guess we have to do that >> Okay. Well, I guess we have to do that for uh for Crawford's own. [laughter] for uh for Crawford's own. [laughter] for uh for Crawford's own. [laughter] >> Dear Dear Mr. Crawford and all of our >> Dear Dear Mr. Crawford and all of our >> Dear Dear Mr. Crawford and all of our billions and billions of loyal subjects, billions and billions of loyal subjects, billions and billions of loyal subjects, >> do not make financial, healthc care, or >> do not make financial, healthc care, or >> do not make financial, healthc care, or or or psychological decisions based on or or psychological decisions based on or or psychological decisions based on anything you hear on this show. May the anything you hear on this show. May the anything you hear on this show. May the universe be with you. That's a universe be with you. That's a universe be with you. That's a discussion. discussion. discussion. >> I couldn't agree more. [laughter] >> I couldn't agree more. [laughter] >> I couldn't agree more. [laughter] >> Yeah. Yeah. So, we're trying to create a >> Yeah. Yeah. So, we're trying to create a >> Yeah. Yeah. So, we're trying to create a safe zone for you, Crawford, even safe zone for you, Crawford, even safe zone for you, Crawford, even [clears throat] though I know that [clears throat] though I know that [clears throat] though I know that you're you're a very brave man, and it's you're you're a very brave man, and it's you're you're a very brave man, and it's really awesome to have you on again. really awesome to have you on again. really awesome to have you on again. Actually, this is your second go. The Actually, this is your second go. The Actually, this is your second go. The first time we uh did this was live at first time we uh did this was live at first time we uh did this was live at Lenovo GIA, which is a global industry Lenovo GIA, which is a global industry Lenovo GIA, which is a global industry uh analyst conference. And uh um I have uh analyst conference. And uh um I have uh analyst conference. And uh um I have to say, Crawford, you you were born to to say, Crawford, you you were born to to say, Crawford, you you were born to be on IoT coffee. [laughter] be on IoT coffee. [laughter] be on IoT coffee. [laughter] Wow.

Wow. Wow. >> Yeah. >> Yeah. >> Yeah. >> No, it was so much fun. I was saying >> No, it was so much fun. I was saying >> No, it was so much fun. I was saying earlier, Leonard, like Leonard's like, earlier, Leonard, like Leonard's like, earlier, Leonard, like Leonard's like, "We should do a podcast sometime." And I "We should do a podcast sometime." And I "We should do a podcast sometime." And I said, "Sure." said, "Sure." said, "Sure." >> Oh, yeah. >> Oh, yeah. >> Oh, yeah. >> And then two minutes later, there were a >> And then two minutes later, there were a >> And then two minutes later, there were a bunch of cameras up. It was great. bunch of cameras up. It was great. bunch of cameras up. It was great. >> I know. [laughter] >> I know. [laughter] >> I know. [laughter] >> That's the thing. Leonard comes equipped >> That's the thing. Leonard comes equipped >> That's the thing. Leonard comes equipped with his own gear. Like, wherever you with his own gear. Like, wherever you with his own gear. Like, wherever you are. are. are. >> I'm always ready. You never know. >> I'm always ready. You never know. >> I'm always ready. You never know. >> Hey, Leonard. Rob says that you just >> Hey, Leonard. Rob says that you just >> Hey, Leonard. Rob says that you just pull it out of your sleeve. You got all pull it out of your sleeve. You got all pull it out of your sleeve. You got all >> I don't know where it came from. It was >> I don't know where it came from. It was >> I don't know where it came from. It was the craziest thing I've ever seen. the craziest thing I've ever seen. the craziest thing I've ever seen. [laughter] [laughter] [laughter] >> HE'S LIKE, HE'S like Ron Burgundy, you >> HE'S LIKE, HE'S like Ron Burgundy, you >> HE'S LIKE, HE'S like Ron Burgundy, you know, who will pull the flute out of his know, who will pull the flute out of his know, who will pull the flute out of his deals. I happen to have it right here. deals. I happen to have it right here. deals. I happen to have it right here. >> Yes. [laughter] >> Yes. [laughter] >> Yes. [laughter] >> Aqua lung. Um >> Aqua lung. Um >> Aqua lung. Um >> yeah, you know, Crawford, I thought you >> yeah, you know, Crawford, I thought you >> yeah, you know, Crawford, I thought you brought it. I love the the the ASML and brought it. I love the the the ASML and brought it. I love the the the ASML and hearing about their reporting and how hearing about their reporting and how hearing about their reporting and how well they're doing that, you know, well they're doing that, you know, well they're doing that, you know, because you know, what do we always because you know, what do we always because you know, what do we always think about? What is that the Chicago uh think about? What is that the Chicago uh think about? What is that the Chicago uh manufacturing index, which a lot of manufacturing index, which a lot of manufacturing index, which a lot of people people people >> Oh, yeah. No, Philadelphia. >> Oh, yeah. No, Philadelphia. >> Oh, yeah. No, Philadelphia. Philadelphia. Philadelphia. Philadelphia. >> Philadelphia above or below 50, right? >> Philadelphia above or below 50, right? >> Philadelphia above or below 50, right? [clears throat] Uh is it declining? But [clears throat] Uh is it declining? But [clears throat] Uh is it declining? But gosh, ASML is such a canary in the coal gosh, ASML is such a canary in the coal gosh, ASML is such a canary in the coal mine or a leading indicator for the rest mine or a leading indicator for the rest mine or a leading indicator for the rest of the whole semiconductor industry, of the whole semiconductor industry, of the whole semiconductor industry, right?

right? right? >> Yeah. >> Yeah. >> Yeah. >> Yeah. And and that canary right now is >> Yeah. And and that canary right now is >> Yeah. And and that canary right now is about to run a marathon. I mean, that about to run a marathon. I mean, that about to run a marathon. I mean, that canary is really really healthy and and canary is really really healthy and and canary is really really healthy and and and uh very very strong. Now, again, and uh very very strong. Now, again, and uh very very strong. Now, again, there's a lot of reasons for that, there's a lot of reasons for that, there's a lot of reasons for that, right? I mean, uh you know, Rob, you and right? I mean, uh you know, Rob, you and right? I mean, uh you know, Rob, you and I have a long history with IDC. You I have a long history with IDC. You I have a long history with IDC. You know, we just took our PC numbers down. know, we just took our PC numbers down. know, we just took our PC numbers down. Why? Because of memory constraints, Why? Because of memory constraints, Why? Because of memory constraints, right? right? right? >> Yeah. people aren't going to be able to >> Yeah. people aren't going to be able to >> Yeah. people aren't going to be able to get memory because there's so much get memory because there's so much get memory because there's so much capacity. Well, two reasons why. Memor capacity. Well, two reasons why. Memor capacity. Well, two reasons why. Memor is going into servers that are that are is going into servers that are that are is going into servers that are that are fueling AI and semiconductor capacity is fueling AI and semiconductor capacity is fueling AI and semiconductor capacity is being redirected to the the AS6 that and being redirected to the the AS6 that and being redirected to the the AS6 that and GPUs and uh TPUs that are going to be GPUs and uh TPUs that are going to be GPUs and uh TPUs that are going to be needed for this this AI buildout. And needed for this this AI buildout. And needed for this this AI buildout. And so, you know, I published a piece so, you know, I published a piece so, you know, I published a piece yesterday on Substack um about, you yesterday on Substack um about, you yesterday on Substack um about, you know, you know, what if this isn't an AI know, you know, what if this isn't an AI know, you know, what if this isn't an AI bubble? What if it's an AI super cycle? bubble? What if it's an AI super cycle? bubble? What if it's an AI super cycle? And we really and I mean look, you're And we really and I mean look, you're And we really and I mean look, you're always going to see like little but at always going to see like little but at always going to see like little but at the end of the day, you know, I think the end of the day, you know, I think the end of the day, you know, I think what you're seeing here is a what you're seeing here is a what you're seeing here is a reconstruction reconstruction reconstruction of the IT stack starting with the of the IT stack starting with the of the IT stack starting with the foundation, which is semiconductors and foundation, which is semiconductors and foundation, which is semiconductors and servers and then storage. And um I think servers and then storage. And um I think servers and then storage. And um I think we probably underestimated we probably underestimated we probably underestimated how big this could be. we underestimated how big this could be. we underestimated how big this could be. we underestimated how much capacity we're going to need how much capacity we're going to need how much capacity we're going to need going forward if you're really going to, going forward if you're really going to, going forward if you're really going to, you know, try to rebuild the stack for you know, try to rebuild the stack for you know, try to rebuild the stack for both uh training and then and then both uh training and then and then both uh training and then and then inference going forward. And I I I just inference going forward. And I I I just inference going forward. And I I I just think that yeah, sure people are going

think that yeah, sure people are going think that yeah, sure people are going to get over their skis, people are going to get over their skis, people are going to get over their skis, people are going to fall down, but at the end of the day, to fall down, but at the end of the day, to fall down, but at the end of the day, we are living in this just marvelous we are living in this just marvelous we are living in this just marvelous time of absolute IT renovation. And we time of absolute IT renovation. And we time of absolute IT renovation. And we can debate how marvelous it's going to can debate how marvelous it's going to can debate how marvelous it's going to be to the Amazon layoffs that we just be to the Amazon layoffs that we just be to the Amazon layoffs that we just talked about a few moments ago. And I talked about a few moments ago. And I talked about a few moments ago. And I think that's a really helpful debate, think that's a really helpful debate, think that's a really helpful debate, but um this is a this is something I've but um this is a this is something I've but um this is a this is something I've I mean, look, I got almost 40 years in I mean, look, I got almost 40 years in I mean, look, I got almost 40 years in this industry. Uh and I've never seen this industry. Uh and I've never seen this industry. Uh and I've never seen >> that old. >> that old. >> that old. >> Yeah, I know, right? I'm like I'm like a >> Yeah, I know, right? I'm like I'm like a >> Yeah, I know, right? I'm like I'm like a T-Rex here. [laughter] I have I have >> little arms. >> little arms. >> little arms. >> Yeah. Yeah. Yeah. Yeah. I have never >> Yeah. Yeah. Yeah. Yeah. I have never >> Yeah. Yeah. Yeah. Yeah. I have never I've never se I've never seen a I've never se I've never seen a I've never se I've never seen a technology like this. I've never seen a technology like this. I've never seen a technology like this. I've never seen a technology that uh at IDC we advised IT technology that uh at IDC we advised IT technology that uh at IDC we advised IT buyers not to evaluate it but to run to buyers not to evaluate it but to run to buyers not to evaluate it but to run to it to run to it and to develop on this it to run to it and to develop on this it to run to it and to develop on this technology and so uh this is really a technology and so uh this is really a technology and so uh this is really a it's f it's just a fascinating thing and it's f it's just a fascinating thing and it's f it's just a fascinating thing and I just think that as we move up the I just think that as we move up the I just think that as we move up the stack and as we get further and closer stack and as we get further and closer stack and as we get further and closer to applications you know that's really to applications you know that's really to applications you know that's really where a whole new set of innovation and where a whole new set of innovation and where a whole new set of innovation and a whole new set of changing work and a whole new set of changing work and a whole new set of changing work and changing roles are going to happen. And changing roles are going to happen. And changing roles are going to happen. And all that leads me to guys, we're like in all that leads me to guys, we're like in all that leads me to guys, we're like in this halfway through the first inning of this halfway through the first inning of this halfway through the first inning of of of this of this thing and it's it's of of this of this thing and it's it's of of this of this thing and it's it's it's going to go for for quite some it's going to go for for quite some it's going to go for for quite some time. And that's why I'm so optimistic time. And that's why I'm so optimistic time. And that's why I'm so optimistic about about tech growth going forward.

about about tech growth going forward. about about tech growth going forward. >> This is good news for Rob and I because >> This is good news for Rob and I because >> This is good news for Rob and I because we probably have a Windows one running we probably have a Windows one running we probably have a Windows one running an Intel 808 chip somewhere an Intel 808 chip somewhere an Intel 808 chip somewhere >> probably. [laughter] >> probably. [laughter] >> probably. [laughter] >> And it's >> And it's >> And it's probably worth processor though on the probably worth processor though on the probably worth processor though on the Rob. We could probably trade a few 286s Rob. We could probably trade a few 286s Rob. We could probably trade a few 286s for Bitcoin right now and just retire for Bitcoin right now and just retire for Bitcoin right now and just retire into the sunset. [laughter] into the sunset. [laughter] into the sunset. [laughter] >> Well, you know, Crawford, so I've had a >> Well, you know, Crawford, so I've had a >> Well, you know, Crawford, so I've had a lot of discussions around this whole lot of discussions around this whole lot of discussions around this whole topic of bubble, right? In fact, if you topic of bubble, right? In fact, if you topic of bubble, right? In fact, if you know, I'll probably I'm probably one of know, I'll probably I'm probably one of know, I'll probably I'm probably one of the first to really, you know, uh, sound the first to really, you know, uh, sound the first to really, you know, uh, sound a horn on this, but, you know, I've a horn on this, but, you know, I've a horn on this, but, you know, I've discovered that a lot of people don't discovered that a lot of people don't discovered that a lot of people don't even know what a bubble means. even know what a bubble means. even know what a bubble means. >> Yeah. Um, and really what it boils down >> Yeah. Um, and really what it boils down >> Yeah. Um, and really what it boils down to, it's sub, it's an outcome of to, it's sub, it's an outcome of to, it's sub, it's an outcome of suboptimal allocation of capital and suboptimal allocation of capital and suboptimal allocation of capital and then not being able to realize return. then not being able to realize return. then not being able to realize return. And one of my biggest concerns is a lot And one of my biggest concerns is a lot And one of my biggest concerns is a lot of what we're seeing in terms of an of what we're seeing in terms of an of what we're seeing in terms of an overall ecosystem pipeline of revenue is overall ecosystem pipeline of revenue is overall ecosystem pipeline of revenue is based on these RPOS. And if you look at based on these RPOS. And if you look at based on these RPOS. And if you look at the I mean one of the reasons why I the I mean one of the reasons why I the I mean one of the reasons why I think there was a sort of this a back on think there was a sort of this a back on think there was a sort of this a back on the the the Microsoft results was because open AAI Microsoft results was because open AAI Microsoft results was because open AAI uh comprises about 45% of their whatever uh comprises about 45% of their whatever uh comprises about 45% of their whatever $600 billion RPO but then they have a $600 billion RPO but then they have a $600 billion RPO but then they have a $300 billion $300 billion $300 billion you know um representation or you know um representation or you know um representation or concentration in Oracle's RPO and then concentration in Oracle's RPO and then concentration in Oracle's RPO and then others right that there that's not it.

others right that there that's not it. others right that there that's not it. So, we're looking at trillions of So, we're looking at trillions of So, we're looking at trillions of dollars, dollars, dollars, maybe a trillion dollar in RP RPO maybe a trillion dollar in RP RPO maybe a trillion dollar in RP RPO attributed to a company that has no I I attributed to a company that has no I I attributed to a company that has no I I don't know how are they going to pay for don't know how are they going to pay for don't know how are they going to pay for this stuff and they continue to raise this stuff and they continue to raise this stuff and they continue to raise funds. And so, when you look at the funds. And so, when you look at the funds. And so, when you look at the quality, um I mean, that really is sort quality, um I mean, that really is sort quality, um I mean, that really is sort of the health check of the ecosystem. of the health check of the ecosystem. of the health check of the ecosystem. And then also when we start to hear this And then also when we start to hear this And then also when we start to hear this term demand, what does that really mean? term demand, what does that really mean? term demand, what does that really mean? because I've been arguing for the because I've been arguing for the because I've been arguing for the longest time the demand actually has to longest time the demand actually has to longest time the demand actually has to be monetizable and expresses itself in be monetizable and expresses itself in be monetizable and expresses itself in terms of economic end user value. But we terms of economic end user value. But we terms of economic end user value. But we we're just talk we're still just talking we're just talk we're still just talking we're just talk we're still just talking about AI infrastructure systems. And so about AI infrastructure systems. And so about AI infrastructure systems. And so this is like really similar to what we this is like really similar to what we this is like really similar to what we saw in the dotcom era where the telos saw in the dotcom era where the telos saw in the dotcom era where the telos which had tons of money and use debt to which had tons of money and use debt to which had tons of money and use debt to finance. Yeah, they had a lot of money. finance. Yeah, they had a lot of money. finance. Yeah, they had a lot of money. We just forgot that they had a lot of We just forgot that they had a lot of We just forgot that they had a lot of money. money. money. they went all in with fiber buildouts they went all in with fiber buildouts they went all in with fiber buildouts and you know what it's all it ends up and you know what it's all it ends up and you know what it's all it ends up all being a matter of timing but that I all being a matter of timing but that I all being a matter of timing but that I think is really more of the concern and think is really more of the concern and think is really more of the concern and and then um and so I don't know I you and then um and so I don't know I you and then um and so I don't know I you you dared to show up on IoT coffee talks you dared to show up on IoT coffee talks you dared to show up on IoT coffee talks so this is what you get Crawford this is so this is what you get Crawford this is so this is what you get Crawford this is what you get no [laughter] look there is what you get no [laughter] look there is what you get no [laughter] look there is a massive amount of capacity that we are a massive amount of capacity that we are a massive amount of capacity that we are going to have to grow into uh but I do going to have to grow into uh but I do going to have to grow into uh but I do think that what you don't want to do is think that what you don't want to do is think that what you don't want to do is apply x86 server life cycles to these apply x86 server life cycles to these apply x86 server life cycles to these GPU and

GPU and GPU and >> they're going to have longer life >> they're going to have longer life >> they're going to have longer life cycles. Um, cycles. Um, cycles. Um, >> yeah, I think you're I think I I think I >> yeah, I think you're I think I I think I >> yeah, I think you're I think I I think I think they might have they they might think they might have they they might think they might have they they might have longer life cycles than you think. have longer life cycles than you think. have longer life cycles than you think. >> Really? Okay. Well, you got to drop some >> Really? Okay. Well, you got to drop some >> Really? Okay. Well, you got to drop some knowledge on us here. knowledge on us here. knowledge on us here. >> Those H100s are going to be there >> Those H100s are going to be there >> Those H100s are going to be there forever. forever. forever. >> When I be there forever, but the >> When I be there forever, but the >> When I be there forever, but the question is, can you cascade them down question is, can you cascade them down question is, can you cascade them down to less sophisticated workloads? to less sophisticated workloads? to less sophisticated workloads? >> Yeah. Yeah, you could. >> Yeah. Yeah, you could. >> Yeah. Yeah, you could. >> Yeah, definitely. I mean, I was just on >> Yeah, definitely. I mean, I was just on >> Yeah, definitely. I mean, I was just on a call with our uh we have a generative a call with our uh we have a generative a call with our uh we have a generative edi working group and we were going to edi working group and we were going to edi working group and we were going to do this big live stream in April. We did do this big live stream in April. We did do this big live stream in April. We did these multi-day live streams these multi-day live streams these multi-day live streams >> and we were rattling off so many new >> and we were rattling off so many new >> and we were rattling off so many new semiconductor companies that are semiconductor companies that are semiconductor companies that are building language specific accelerators, building language specific accelerators, building language specific accelerators, right? I mean, they got Fur Furiosis and right? I mean, they got Fur Furiosis and right? I mean, they got Fur Furiosis and Rebellions, but Deepex and Rebellions, but Deepex and Rebellions, but Deepex and >> Accelera and you even if uh old folks >> Accelera and you even if uh old folks >> Accelera and you even if uh old folks like Halo and stuff are redesigning all like Halo and stuff are redesigning all like Halo and stuff are redesigning all their chips to be more language their chips to be more language their chips to be more language accelerators, ethos stuff from ARM and accelerators, ethos stuff from ARM and accelerators, ethos stuff from ARM and stuff. So I think there's a whole stuff. So I think there's a whole stuff. So I think there's a whole category of and never mind what AMD is category of and never mind what AMD is category of and never mind what AMD is doing and everything else. There's a doing and everything else. There's a doing and everything else. There's a whole category of inferencing whole category of inferencing whole category of inferencing acceleration on that that's going to be acceleration on that that's going to be acceleration on that that's going to be >> I think I think the coolest thing that >> I think I think the coolest thing that >> I think I think the coolest thing that we'll get is all the financial we'll get is all the financial we'll get is all the financial engineering of the different way we're engineering of the different way we're engineering of the different way we're going to sweat these assets that are going to sweat these assets that are going to sweat these assets that are allegedly that's right to last longer allegedly that's right to last longer allegedly that's right to last longer than expected. Right? You know, for for than expected. Right? You know, for for than expected. Right? You know, for for those of us who've ever had to manage a those of us who've ever had to manage a those of us who've ever had to manage a salesunnel, why would you ever deliver?

salesunnel, why would you ever deliver? salesunnel, why would you ever deliver? When we had a funnel review, you just When we had a funnel review, you just When we had a funnel review, you just put demand in there that didn't really put demand in there that didn't really put demand in there that didn't really exist, right? Let let me just hit the exist, right? Let let me just hit the exist, right? Let let me just hit the funnel. But the difference between me funnel. But the difference between me funnel. But the difference between me putting random names in there that I was putting random names in there that I was putting random names in there that I was going to sell phones to next month and going to sell phones to next month and going to sell phones to next month and what's happening with all these, oh, what's happening with all these, oh, what's happening with all these, oh, this guy's going to buy a gazillion GPUs this guy's going to buy a gazillion GPUs this guy's going to buy a gazillion GPUs from me. and the stock moves and hey, from me. and the stock moves and hey, from me. and the stock moves and hey, I'm going to build all these things with I'm going to build all these things with I'm going to build all these things with all these GPUs and the stock moves, but all these GPUs and the stock moves, but all these GPUs and the stock moves, but then nothing is exchanging hands. then nothing is exchanging hands. then nothing is exchanging hands. This is going to uh I cannot wait till This is going to uh I cannot wait till This is going to uh I cannot wait till uh Leonard's bubble just bursts and then uh Leonard's bubble just bursts and then uh Leonard's bubble just bursts and then it's going to be really scary for a it's going to be really scary for a it's going to be really scary for a couple of days. The usual suspects, couple of days. The usual suspects, couple of days. The usual suspects, they're going to make out like bandits. they're going to make out like bandits. they're going to make out like bandits. And you know what? If Larry Ellison And you know what? If Larry Ellison And you know what? If Larry Ellison wakes up with like $300 billion less, wakes up with like $300 billion less, wakes up with like $300 billion less, it's okay. He's going to be in the it's okay. He's going to be in the it's okay. He's going to be in the trillions by the time I we're done with trillions by the time I we're done with trillions by the time I we're done with this. Andy Mitri. Andy Mitri like I see this. Andy Mitri. Andy Mitri like I see this. Andy Mitri. Andy Mitri like I see his life trading. Uh he's just tell us his life trading. Uh he's just tell us his life trading. Uh he's just tell us tell us what you're doing with all that tell us what you're doing with all that tell us what you're doing with all that money.

money. money. >> Yeah. Right. Oh my gosh. You know when I >> Yeah. Right. Oh my gosh. You know when I >> Yeah. Right. Oh my gosh. You know when I think about think about think about >> talk about the >> talk about the >> talk about the memory market. It's completely nuts memory market. It's completely nuts memory market. It's completely nuts because of OpenAI. [snorts] because of OpenAI. [snorts] because of OpenAI. [snorts] >> So wait to get like Yeah. way way higher >> So wait to get like Yeah. way way higher >> So wait to get like Yeah. way way higher prices of of everything you have around prices of of everything you have around prices of of everything you have around that has RAM memories because there's that has RAM memories because there's that has RAM memories because there's >> 5x higher prices like >> 5x higher prices like >> 5x higher prices like >> Yeah. Yeah. >> Yeah. Yeah. >> Yeah. Yeah. >> Yeah. >> Yeah. >> Yeah. >> The question is really crazy. The >> The question is really crazy. The >> The question is really crazy. The question hello guys sorry I joined late question hello guys sorry I joined late question hello guys sorry I joined late but the question is what is the SEC but the question is what is the SEC but the question is what is the SEC doing because I grew up in the world doing because I grew up in the world doing because I grew up in the world where you couldn't announce deals where you couldn't announce deals where you couldn't announce deals because you know you you had to to be because you know you you had to to be because you know you you had to to be able to recognize revenue and I mean able to recognize revenue and I mean able to recognize revenue and I mean some CEO got fired at some people got some CEO got fired at some people got some CEO got fired at some people got killed because they was issuing side killed because they was issuing side killed because they was issuing side lers on deals and things like that. So lers on deals and things like that. So lers on deals and things like that. So it's it's the equivalent to me you're it's it's the equivalent to me you're it's it's the equivalent to me you're announcing deals you can't revenue so announcing deals you can't revenue so announcing deals you can't revenue so why does why does the stock goes up? why does why does the stock goes up? why does why does the stock goes up? Well, anyway, Well, anyway, Well, anyway, >> America, we don't we don't regulate. >> America, we don't we don't regulate. >> America, we don't we don't regulate. >> Remember, innovation >> Remember, innovation >> Remember, innovation innovation outpaces regulation.

innovation outpaces regulation. innovation outpaces regulation. >> Well, absolute people being fired, but >> Well, absolute people being fired, but >> Well, absolute people being fired, but uh so 2022 uh so 2022 uh so 2022 >> because I Demetri, great to meet you and >> because I Demetri, great to meet you and >> because I Demetri, great to meet you and and and I I agree. I mean, I don't know and and I I agree. I mean, I don't know and and I I agree. I mean, I don't know how you announce, you know, these paper how you announce, you know, these paper how you announce, you know, these paper tiger deals that are kind of out there tiger deals that are kind of out there tiger deals that are kind of out there that you have to wonder about, you know, that you have to wonder about, you know, that you have to wonder about, you know, are are they ever going to realize them? are are they ever going to realize them? are are they ever going to realize them? I just want to I I I want to pivot the I just want to I I I want to pivot the I just want to I I I want to pivot the conversation a little bit away from like conversation a little bit away from like conversation a little bit away from like cuz cuz you know stock prices are going cuz cuz you know stock prices are going cuz cuz you know stock prices are going to do what they're going to do. People to do what they're going to do. People to do what they're going to do. People are going to get excited people are are going to get excited people are are going to get excited people are going to get excited about it. But that going to get excited about it. But that going to get excited about it. But that that's a separate conversation. What that's a separate conversation. What that's a separate conversation. What what I'm talking about when I'm talking what I'm talking about when I'm talking what I'm talking about when I'm talking about an AI bubble is back in the dot about an AI bubble is back in the dot about an AI bubble is back in the dot days, we were building websites and then days, we were building websites and then days, we were building websites and then we were basically saying with low cost we were basically saying with low cost we were basically saying with low cost of capital, I can make money shipping of capital, I can make money shipping of capital, I can make money shipping dog food, you know, uh o over the dog food, you know, uh o over the dog food, you know, uh o over the internet. And I really didn't know internet. And I really didn't know internet. And I really didn't know anything about what that business model anything about what that business model anything about what that business model actually looked like. And the wings blew actually looked like. And the wings blew actually looked like. And the wings blew off and and and these companies weren't off and and and these companies weren't off and and and these companies weren't viable.

viable. viable. >> [laughter] >> [laughter] >> [laughter] >> What I'm interested in right now is LLMs >> What I'm interested in right now is LLMs >> What I'm interested in right now is LLMs are viable. Um, it's very clear to me are viable. Um, it's very clear to me are viable. Um, it's very clear to me that this technology, LLM's may that this technology, LLM's may that this technology, LLM's may commoditize, but the enabling technology commoditize, but the enabling technology commoditize, but the enabling technology of these transformer models is going to of these transformer models is going to of these transformer models is going to change the way the the relationship that change the way the the relationship that change the way the the relationship that people have with technology. And it may people have with technology. And it may people have with technology. And it may ramp up and it may go back down, but but ramp up and it may go back down, but but ramp up and it may go back down, but but if you plot the long chart, it's going if you plot the long chart, it's going if you plot the long chart, it's going to go up. And and I do think at the time to go up. And and I do think at the time to go up. And and I do think at the time of the.com crash um David what you said of the.com crash um David what you said of the.com crash um David what you said was that you know it's gonna be it was was that you know it's gonna be it was was that you know it's gonna be it was scary for a couple days. It was scary scary for a couple days. It was scary scary for a couple days. It was scary for a couple at least a year. I mean it for a couple at least a year. I mean it for a couple at least a year. I mean it was scary for a long time from March was scary for a long time from March was scary for a long time from March 2000 well into 2001. I mean like 2000 well into 2001. I mean like 2000 well into 2001. I mean like companies broke companies broke companies broke >> here. Um I I I just think that as we >> here. Um I I I just think that as we >> here. Um I I I just think that as we start to connect the data sets that have start to connect the data sets that have start to connect the data sets that have been locked in these applications been locked in these applications been locked in these applications >> with agentic systems, the ability for >> with agentic systems, the ability for >> with agentic systems, the ability for companies to gain efficiency and no companies to gain efficiency and no companies to gain efficiency and no that's not code only for reducing that's not code only for reducing that's not code only for reducing employees but that's going to happen employees but that's going to happen employees but that's going to happen too.

too. too. >> It's also code for new insights and more >> It's also code for new insights and more >> It's also code for new insights and more effective ways of working. God, I'm effective ways of working. God, I'm effective ways of working. God, I'm really optimistic about about what really optimistic about about what really optimistic about about what companies are going to be able to do. companies are going to be able to do. companies are going to be able to do. There's not a doubt in my mind that this There's not a doubt in my mind that this There's not a doubt in my mind that this is going to work. And so to me, that's is going to work. And so to me, that's is going to work. And so to me, that's the breaking point with the dot days is the breaking point with the dot days is the breaking point with the dot days is that I wasn't really sure how long it that I wasn't really sure how long it that I wasn't really sure how long it would take for the com stuff to work. would take for the com stuff to work. would take for the com stuff to work. This I think is going to happen a ton This I think is going to happen a ton This I think is going to happen a ton faster than we saw the payback for.com faster than we saw the payback for.com faster than we saw the payback for.com which was you know I mean you know look which was you know I mean you know look which was you know I mean you know look at a look at what's happened to media at a look at what's happened to media at a look at what's happened to media in the last 3 years not the last 23 in the last 3 years not the last 23 in the last 3 years not the last 23 years the last 3 years like does anybody years the last 3 years like does anybody years the last 3 years like does anybody watch network TV is network TV even even watch network TV is network TV even even watch network TV is network TV even even relevant anymore you know it went I mean relevant anymore you know it went I mean relevant anymore you know it went I mean if it wasn't for sports like there if it wasn't for sports like there if it wasn't for sports like there wouldn't be kind of the traditional wouldn't be kind of the traditional wouldn't be kind of the traditional network TV and and that really fell off network TV and and that really fell off network TV and and that really fell off the cliff in the last four or five the cliff in the last four or five the cliff in the last four or five years. It was dying for a long time, but years. It was dying for a long time, but years. It was dying for a long time, but then now it's completely collapsed. And then now it's completely collapsed. And then now it's completely collapsed. And by the way, a lot of the cable the the by the way, a lot of the cable the the by the way, a lot of the cable the the the the linear TV stuff that's next the the linear TV stuff that's next the the linear TV stuff that's next that's going to collapse as we all kind that's going to collapse as we all kind that's going to collapse as we all kind of move to, you know, YouTube t YouTube of move to, you know, YouTube t YouTube of move to, you know, YouTube t YouTube and and not even YouTube TV, but but you and and not even YouTube TV, but but you and and not even YouTube TV, but but you know, stuff that's prescribed know, stuff that's prescribed know, stuff that's prescribed specifically for you. And that took 20 specifically for you. And that took 20 specifically for you. And that took 20 years. So I I say to myself, how long is years. So I I say to myself, how long is years. So I I say to myself, how long is it going to take for us to see companies it going to take for us to see companies it going to take for us to see companies get re-engineered with AI? I used to be get re-engineered with AI? I used to be get re-engineered with AI? I used to be a lot faster than 20 years. It's a lot a lot faster than 20 years. It's a lot a lot faster than 20 years. It's a lot >> you know what >> you know what >> you know what >> I think the big difference the big >> I think the big difference the big >> I think the big difference the big difference when comparing with com time difference when comparing with com time difference when comparing with com time is I think that the com time shifted

is I think that the com time shifted is I think that the com time shifted jobs jobs jobs changed changed changed >> I think AI is going to destroy massively >> I think AI is going to destroy massively >> I think AI is going to destroy massively jobs that's the ch the difference and jobs that's the ch the difference and jobs that's the ch the difference and the challenge and the appetite for the challenge and the appetite for the challenge and the appetite for corporation to save cost is going to be corporation to save cost is going to be corporation to save cost is going to be very strong so I agree with you it's very strong so I agree with you it's very strong so I agree with you it's going to happen probably faster than we going to happen probably faster than we going to happen probably faster than we think the question is is the cost of think the question is is the cost of think the question is is the cost of this AI going to economically be this AI going to economically be this AI going to economically be actually really cheaper than the actually really cheaper than the actually really cheaper than the elimination of jobs because the elimination of jobs because the elimination of jobs because the investment infrastructure are massive. investment infrastructure are massive. investment infrastructure are massive. So I think this is to me this is the So I think this is to me this is the So I think this is to me this is the equation that I I don't know the data equation that I I don't know the data equation that I I don't know the data that equation but if if you end up that equation but if if you end up that equation but if if you end up realizing that oh I don't pay this guy realizing that oh I don't pay this guy realizing that oh I don't pay this guy 50k per year anymore but I'm paying 60 50k per year anymore but I'm paying 60 50k per year anymore but I'm paying 60 60k in tokens you have a problem but if 60k in tokens you have a problem but if 60k in tokens you have a problem but if you go from 50k to 5k then you have the you go from 50k to 5k then you have the you go from 50k to 5k then you have the other problem. So that's to me the very other problem. So that's to me the very other problem. So that's to me the very top level math. But top level math. But top level math. But >> and you know going back to your earlier >> and you know going back to your earlier >> and you know going back to your earlier point um Crawford about point um Crawford about point um Crawford about >> um >> um >> um you know um linear TV, right? And um you know um linear TV, right? And um you know um linear TV, right? And um that being and then also just um you that being and then also just um you that being and then also just um you know the broadcast or the network know the broadcast or the network know the broadcast or the network >> uh television and those media channels >> uh television and those media channels >> uh television and those media channels even maybe even you might even argue even maybe even you might even argue even maybe even you might even argue radio. A lot of that is because of radio. A lot of that is because of radio. A lot of that is because of shifting economics and you can attribute shifting economics and you can attribute shifting economics and you can attribute that to ad technology but then also that to ad technology but then also that to ad technology but then also where ad dollars pull. And so when you where ad dollars pull. And so when you where ad dollars pull. And so when you see that kind of shift happen because see that kind of shift happen because see that kind of shift happen because the the digital platforms draw more of the the digital platforms draw more of the the digital platforms draw more of the monetization of let's say eyeballs

the monetization of let's say eyeballs the monetization of let's say eyeballs and yeah I mean I think that's really and yeah I mean I think that's really and yeah I mean I think that's really what we've seen happen with the dot but what we've seen happen with the dot but what we've seen happen with the dot but we've spoken earlier about where where we've spoken earlier about where where we've spoken earlier about where where did the dot did the dot did the dot you movement have a lot of impact and a you movement have a lot of impact and a you movement have a lot of impact and a lot of it had to do with financial lot of it had to do with financial lot of it had to do with financial services, right? Robin, I and a couple services, right? Robin, I and a couple services, right? Robin, I and a couple others were on a episode where we talked others were on a episode where we talked others were on a episode where we talked about that the dis disruption industry about that the dis disruption industry about that the dis disruption industry disruption and reinvention typically disruption and reinvention typically disruption and reinvention typically happens in an industry in you know when happens in an industry in you know when happens in an industry in you know when you think in retrospect in an industry you think in retrospect in an industry you think in retrospect in an industry that is not top of the future narrative that is not top of the future narrative that is not top of the future narrative or the present narrative, right, or or the present narrative, right, or or the present narrative, right, or assumption set and no one talks about assumption set and no one talks about assumption set and no one talks about financial services. talk about like you financial services. talk about like you financial services. talk about like you know what you you just mentioned know what you you just mentioned know what you you just mentioned >> um dog food and you know like pets.com >> um dog food and you know like pets.com >> um dog food and you know like pets.com or web van or whatever or web van or whatever or web van or whatever >> but the tech that we created it during >> but the tech that we created it during >> but the tech that we created it during the.com era revolutionized Wall Street the.com era revolutionized Wall Street the.com era revolutionized Wall Street >> and that wasn't the talk that wasn't the >> and that wasn't the talk that wasn't the >> and that wasn't the talk that wasn't the headline at the time it was all these headline at the time it was all these headline at the time it was all these dot companies but this distributed this dot companies but this distributed this dot companies but this distributed this new way of compute these data centers new way of compute these data centers new way of compute these data centers the rise of the rise of the rise of >> all this stuff all of a sudden that that >> all this stuff all of a sudden that that >> all this stuff all of a sudden that that changed everything changed everything changed everything >> you know I love everybody loves to talk >> you know I love everybody loves to talk >> you know I love everybody loves to talk about try to do an analogous thing here about try to do an analogous thing here about try to do an analogous thing here with this AI because you're right it's a with this AI because you're right it's a with this AI because you're right it's a bad massive buildout uh and you're bad massive buildout uh and you're bad massive buildout uh and you're hoping for this return. Um we talked hoping for this return. Um we talked hoping for this return. Um we talked about all the fiber being laid during about all the fiber being laid during about all the fiber being laid during the dotcom stuff and it became a lot of the dotcom stuff and it became a lot of the dotcom stuff and it became a lot of dark fiber for a long time but in the dark fiber for a long time but in the dark fiber for a long time but in the end we ended up using it later. It was

end we ended up using it later. It was end we ended up using it later. It was back to the timing thing but you know we back to the timing thing but you know we back to the timing thing but you know we pre-built. We didn't use it in the pre-built. We didn't use it in the pre-built. We didn't use it in the moment a lot of people lost their jobs moment a lot of people lost their jobs moment a lot of people lost their jobs or companies went out of business but we or companies went out of business but we or companies went out of business but we ended up using eventually. Since I'm ended up using eventually. Since I'm ended up using eventually. Since I'm here in Houston, I'll have to do an oil here in Houston, I'll have to do an oil here in Houston, I'll have to do an oil example. [laughter] example. [laughter] example. [laughter] I'm gonna do an oil example for Texas of I'm gonna do an oil example for Texas of I'm gonna do an oil example for Texas of the exact same thing because a long time the exact same thing because a long time the exact same thing because a long time ago, somebody saw some black bubbling ago, somebody saw some black bubbling ago, somebody saw some black bubbling stuff coming out of the ground in Texas. stuff coming out of the ground in Texas. stuff coming out of the ground in Texas. >> And they're like, "What is this? Oh, we >> And they're like, "What is this? Oh, we >> And they're like, "What is this? Oh, we can power things with this." can power things with this." can power things with this." >> And so they bumbled and stumbled their >> And so they bumbled and stumbled their >> And so they bumbled and stumbled their way just like the dot era. Okay, I'm way just like the dot era. Okay, I'm way just like the dot era. Okay, I'm going to build this drilling derek thing going to build this drilling derek thing going to build this drilling derek thing and I need to build transport to back and I need to build transport to back and I need to build transport to back haul this stuff for this new invention haul this stuff for this new invention haul this stuff for this new invention called a car and then I'm going to have called a car and then I'm going to have called a car and then I'm going to have to come up with new ways to do drilling to come up with new ways to do drilling to come up with new ways to do drilling and I'm going to build pipelines and it and I'm going to build pipelines and it and I'm going to build pipelines and it was the exact same thing. People didn't was the exact same thing. People didn't was the exact same thing. People didn't necessarily know how the future was necessarily know how the future was necessarily know how the future was going to play out. They kind of bubbled going to play out. They kind of bubbled going to play out. They kind of bubbled and stumbled their way through it. But and stumbled their way through it. But and stumbled their way through it. But it's like this stuff's coming out of the it's like this stuff's coming out of the it's like this stuff's coming out of the ground. It has value. I need to get it ground. It has value. I need to get it ground. It has value. I need to get it to the cities. I need to get it to where to the cities. I need to get it to where to the cities. I need to get it to where it's going to make a difference. So, it's going to make a difference. So, it's going to make a difference. So, we're going to have lights now. We're we're going to have lights now. We're we're going to have lights now. We're going to have we're going to create going to have we're going to create going to have we're going to create electricity from oil and natural gas.

electricity from oil and natural gas. electricity from oil and natural gas. And it was the same thing. Of course, I And it was the same thing. Of course, I And it was the same thing. Of course, I think it had impact faster. Maybe, you think it had impact faster. Maybe, you think it had impact faster. Maybe, you know, it wasn't like we did some crazy know, it wasn't like we did some crazy know, it wasn't like we did some crazy buildout of a pipeline and drilling buildout of a pipeline and drilling buildout of a pipeline and drilling infrastructure that wouldn't show infrastructure that wouldn't show infrastructure that wouldn't show valuable for 20 years. It had instant valuable for 20 years. It had instant valuable for 20 years. It had instant valuable. In fact, they couldn't build valuable. In fact, they couldn't build valuable. In fact, they couldn't build the infrastructure fast enough, you the infrastructure fast enough, you the infrastructure fast enough, you know, because it ended up powering the know, because it ended up powering the know, because it ended up powering the entire country. But and so right now entire country. But and so right now entire country. But and so right now everybody's trying to meet the moment, everybody's trying to meet the moment, everybody's trying to meet the moment, you know, they're like, there's this you know, they're like, there's this you know, they're like, there's this moment. I'm going to go all in, invest. moment. I'm going to go all in, invest. moment. I'm going to go all in, invest. Is it a risk? Absolutely. You know, you Is it a risk? Absolutely. You know, you Is it a risk? Absolutely. You know, you know, cuz some companies have tons of know, cuz some companies have tons of know, cuz some companies have tons of money in their bank account. Other money in their bank account. Other money in their bank account. Other companies are taking on lots of debt to companies are taking on lots of debt to companies are taking on lots of debt to build out data centers to do it, and build out data centers to do it, and build out data centers to do it, and they're like, we might lose it all just they're like, we might lose it all just they're like, we might lose it all just like a wildcatter out in the Peran Basin like a wildcatter out in the Peran Basin like a wildcatter out in the Peran Basin in West Texas, in West Texas, in West Texas, >> but we're going to meet the moment and >> but we're going to meet the moment and >> but we're going to meet the moment and take the risk. You know, take the risk. You know, take the risk. You know, >> I guess the the only uh slight >> I guess the the only uh slight >> I guess the the only uh slight difference here I would say is we difference here I would say is we difference here I would say is we currently have a digital infrastructure. currently have a digital infrastructure. currently have a digital infrastructure. Like there is a very wellestablished Like there is a very wellestablished Like there is a very wellestablished digital infrastructure that we're all digital infrastructure that we're all digital infrastructure that we're all taking a dependency on whether it's taking a dependency on whether it's taking a dependency on whether it's financial, social media, entertainment.

financial, social media, entertainment. financial, social media, entertainment. So how soon those companies will, you So how soon those companies will, you So how soon those companies will, you know, integrate AI into that digital know, integrate AI into that digital know, integrate AI into that digital infrastructure, I think that's kind of infrastructure, I think that's kind of infrastructure, I think that's kind of the question mark. I think everyone's the question mark. I think everyone's the question mark. I think everyone's going to try, but I mean Leonard, you've going to try, but I mean Leonard, you've going to try, but I mean Leonard, you've pointed out many times kind of corporate pointed out many times kind of corporate pointed out many times kind of corporate adoption of AI and [clears throat] adoption of AI and [clears throat] adoption of AI and [clears throat] things like that. It's it's probably things like that. It's it's probably things like that. It's it's probably going to be a little, you know, slower going to be a little, you know, slower going to be a little, you know, slower than most people think because these than most people think because these than most people think because these processes, I mean, actually, I was on a processes, I mean, actually, I was on a processes, I mean, actually, I was on a call this morning with a partner whose call this morning with a partner whose call this morning with a partner whose accounts payable system is totally [ __ ] accounts payable system is totally [ __ ] accounts payable system is totally [ __ ] right now and they can't pay us any right now and they can't pay us any right now and they can't pay us any money. money. money. >> Um, you know, it's it's it's >> Um, you know, it's it's it's >> Um, you know, it's it's it's >> you got to get AI on that, man. >> you got to get AI on that, man. >> you got to get AI on that, man. >> Yeah, let's put [laughter] some AI on >> Yeah, let's put [laughter] some AI on >> Yeah, let's put [laughter] some AI on that. that. that. uh and it's like you know like I think uh and it's like you know like I think uh and it's like you know like I think companies will will will you know adopt companies will will will you know adopt companies will will will you know adopt it at a certain pace you know but how it at a certain pace you know but how it at a certain pace you know but how how quickly you know from a real you how quickly you know from a real you how quickly you know from a real you know infrastructure perspective I think know infrastructure perspective I think know infrastructure perspective I think is TBD and ultimately that's the revenue is TBD and ultimately that's the revenue is TBD and ultimately that's the revenue that a lot of these companies are that a lot of these companies are that a lot of these companies are counting on is that adoption so counting on is that adoption so counting on is that adoption so >> yeah and and you know we have to keep in >> yeah and and you know we have to keep in >> yeah and and you know we have to keep in mind the the enterprise thesis has been mind the the enterprise thesis has been mind the the enterprise thesis has been in play for a long time at least two and in play for a long time at least two and in play for a long time at least two and a half actually more than more than a half actually more than more than a half actually more than more than three years Because as we've mentioned three years Because as we've mentioned three years Because as we've mentioned on this show um several times uh on this show um several times uh on this show um several times uh enterprises were already tinkering enterprises were already tinkering enterprises were already tinkering around with um you know transformerbased around with um you know transformerbased around with um you know transformerbased um models and LLMs. Um it it wasn't like um models and LLMs. Um it it wasn't like um models and LLMs. Um it it wasn't like when the chat GPT moment happened it was when the chat GPT moment happened it was when the chat GPT moment happened it was like wow okay it was invented. No, you like wow okay it was invented. No, you like wow okay it was invented. No, you [clears throat] know, there's this long [clears throat] know, there's this long [clears throat] know, there's this long continuum and Jensen Hang will remind continuum and Jensen Hang will remind continuum and Jensen Hang will remind all of us that it it stems from, you

all of us that it it stems from, you all of us that it it stems from, you know, Alex Net back in whatever 200, know, Alex Net back in whatever 200, know, Alex Net back in whatever 200, what is it, 2014, I think it is. So, what is it, 2014, I think it is. So, what is it, 2014, I think it is. So, we're looking at a a decadesl long we're looking at a a decadesl long we're looking at a a decadesl long journey. And I remember like a decade journey. And I remember like a decade journey. And I remember like a decade ago, my friends from IBM were calling me ago, my friends from IBM were calling me ago, my friends from IBM were calling me up asking me to join a chatbot company up asking me to join a chatbot company up asking me to join a chatbot company cuz they were getting really excited cuz they were getting really excited cuz they were getting really excited about what was happening. And I told about what was happening. And I told about what was happening. And I told them no. And guess what? They wallowed them no. And guess what? They wallowed them no. And guess what? They wallowed in sort of this trough of disappointment in sort of this trough of disappointment in sort of this trough of disappointment for um a year and a half and or like you for um a year and a half and or like you for um a year and a half and or like you know half a decade and then now this know half a decade and then now this know half a decade and then now this moment happens. But none of I mean no moment happens. But none of I mean no moment happens. But none of I mean no one's making money uh at the moment. one's making money uh at the moment. one's making money uh at the moment. everyone's drawing funding, right? everyone's drawing funding, right? everyone's drawing funding, right? >> Um, but these are the dynamics that that >> Um, but these are the dynamics that that >> Um, but these are the dynamics that that I think everyone needs to be mindful of I think everyone needs to be mindful of I think everyone needs to be mindful of and just qualify and factor into and just qualify and factor into and just qualify and factor into whatever it is that their their whatever it is that their their whatever it is that their their hypothesis is about this quote unquote hypothesis is about this quote unquote hypothesis is about this quote unquote industry. industry. industry. >> And um it's the enterprise stuff. It is >> And um it's the enterprise stuff. It is >> And um it's the enterprise stuff. It is it's not like this prototypical, oh, you it's not like this prototypical, oh, you it's not like this prototypical, oh, you know what, it's not getting adopted know what, it's not getting adopted know what, it's not getting adopted because of governance and oh, let's because of governance and oh, let's because of governance and oh, let's point the finger at the organization.

point the finger at the organization. point the finger at the organization. you're the one that screwed up. you're the one that screwed up. you're the one that screwed up. >> Yeah, >> Yeah, >> Yeah, >> that doesn't work. That's an excuse for >> that doesn't work. That's an excuse for >> that doesn't work. That's an excuse for the people at the beginning who were the people at the beginning who were the people at the beginning who were like putting together dumping thou, you like putting together dumping thou, you like putting together dumping thou, you know, millions if not a billion or know, millions if not a billion or know, millions if not a billion or billions into building let's say a AI billions into building let's say a AI billions into building let's say a AI practice and then promising to put out, practice and then promising to put out, practice and then promising to put out, you know, 50,000 non-existing um Gen AI you know, 50,000 non-existing um Gen AI you know, 50,000 non-existing um Gen AI professionals, right? that can help professionals, right? that can help professionals, right? that can help enterprises now weave their way through enterprises now weave their way through enterprises now weave their way through what has proven now over three years to what has proven now over three years to what has proven now over three years to be an incredibly difficult be an incredibly difficult be an incredibly difficult um exercise to get to value. um exercise to get to value. um exercise to get to value. >> And so these are like realities that you >> And so these are like realities that you >> And so these are like realities that you know we talk about but don't want to know we talk about but don't want to know we talk about but don't want to recognize and it's something that I know recognize and it's something that I know recognize and it's something that I know is very evident just because I've been is very evident just because I've been is very evident just because I've been watching this stuff. um with a practical watching this stuff. um with a practical watching this stuff. um with a practical eye rather than being colored.

eye rather than being colored. eye rather than being colored. >> I think you're probably full of it. >> I think you're probably full of it. >> I think you're probably full of it. >> Speaking speaking of AI, [laughter] >> Speaking speaking of AI, [laughter] >> Speaking speaking of AI, [laughter] >> speaking of AI, Steve Rumor has added >> speaking of AI, Steve Rumor has added >> speaking of AI, Steve Rumor has added his AI agent to this uh to this uh his AI agent to this uh to this uh his AI agent to this uh to this uh meeting and he's instead of showing up. meeting and he's instead of showing up. meeting and he's instead of showing up. So, we need to give Steve a hard time So, we need to give Steve a hard time So, we need to give Steve a hard time because because because >> Yeah, I'm fake by the way. I'm AI >> Yeah, I'm fake by the way. I'm AI >> Yeah, I'm fake by the way. I'm AI generated. generated. generated. [laughter] [laughter] [laughter] >> Steve's agent is listening. You need to >> Steve's agent is listening. You need to >> Steve's agent is listening. You need to show next time. show next time. show next time. >> Someone trying to get in your car. >> Someone trying to get in your car. >> Someone trying to get in your car. >> Yeah. So I'm in a parking lot from this >> Yeah. So I'm in a parking lot from this >> Yeah. So I'm in a parking lot from this [laughter] across from this ion place [laughter] across from this ion place [laughter] across from this ion place >> carjacked. >> carjacked. >> carjacked. >> Yeah. Like Friday mornings I come and >> Yeah. Like Friday mornings I come and >> Yeah. Like Friday mornings I come and you know mentor these startup founders you know mentor these startup founders you know mentor these startup founders and then I needed to get on here faster and then I needed to get on here faster and then I needed to get on here faster because I got to record it. So I got in because I got to record it. So I got in because I got to record it. So I got in my car in the parking lot. I'm tethered my car in the parking lot. I'm tethered my car in the parking lot. I'm tethered to my phone with 5G on my laptop cuz I to my phone with 5G on my laptop cuz I to my phone with 5G on my laptop cuz I need to record locally and I'm sitting need to record locally and I'm sitting need to record locally and I'm sitting in my car with my car on. And yes, a in my car with my car on. And yes, a in my car with my car on. And yes, a woman just walked by and opened my door woman just walked by and opened my door woman just walked by and opened my door cuz she thought this was her car. cuz she thought this was her car. cuz she thought this was her car. [laughter] [laughter] [laughter] No, because she thought you were hot. No, because she thought you were hot. No, because she thought you were hot. Come on. Come on. Come on. >> Be funnier if she got in the driver's >> Be funnier if she got in the driver's >> Be funnier if she got in the driver's side and started driving away. side and started driving away. side and started driving away. >> It could have happened. [laughter] Could >> It could have happened. [laughter] Could >> It could have happened. [laughter] Could have happened that way actually as it have happened that way actually as it have happened that way actually as it turns out cuz I'm sitting.

turns out cuz I'm sitting. turns out cuz I'm sitting. >> I think this is one of Rob's videos uh >> I think this is one of Rob's videos uh >> I think this is one of Rob's videos uh JNI generated. JNI generated. JNI generated. >> Yeah, but you know the door >> Yeah, but you know the door >> Yeah, but you know the door >> I'd love to get um Crawford's take on on >> I'd love to get um Crawford's take on on >> I'd love to get um Crawford's take on on the enterprise adoption the enterprise adoption the enterprise adoption um challenge or if you think there's um challenge or if you think there's um challenge or if you think there's there's progress. Um, I mean, what do there's progress. Um, I mean, what do there's progress. Um, I mean, what do you see as the biggest bottlenecks and you see as the biggest bottlenecks and you see as the biggest bottlenecks and what are some of the catalytic factors what are some of the catalytic factors what are some of the catalytic factors that are in play? that are in play? that are in play? >> I give like people hope because, um, >> I give like people hope because, um, >> I give like people hope because, um, I'm, you know, given I'm, you know, given I'm, you know, given you're like freaking you're freaking you're like freaking you're freaking you're like freaking you're freaking [clears throat] the dude at IDC, you see [clears throat] the dude at IDC, you see [clears throat] the dude at IDC, you see everything. everything. everything. >> Yeah. >> Yeah. >> Yeah. >> Maybe you could share that with us. >> Maybe you could share that with us. >> Maybe you could share that with us. >> Yeah, sure. So, um, I'm much more >> Yeah, sure. So, um, I'm much more >> Yeah, sure. So, um, I'm much more optimistic, uh, I have to say. So, so I optimistic, uh, I have to say. So, so I optimistic, uh, I have to say. So, so I I view I view I view these models and and and again, this has these models and and and again, this has these models and and and again, this has been this has been going on for for a been this has been going on for for a been this has been going on for for a few years now. I I it's hard for me to few years now. I I it's hard for me to few years now. I I it's hard for me to find an enterprise that's not trying to find an enterprise that's not trying to find an enterprise that's not trying to use some form of an LLM to unlock value use some form of an LLM to unlock value use some form of an LLM to unlock value in their unstructured data. And um they in their unstructured data. And um they in their unstructured data. And um they are, you know, in the, you know, there's are, you know, in the, you know, there's are, you know, in the, you know, there's a lot of data out there. McKenzie has a lot of data out there. McKenzie has a lot of data out there. McKenzie has has published data that you know of the has published data that you know of the has published data that you know of the early prototypes um 20% of respondents early prototypes um 20% of respondents early prototypes um 20% of respondents are seeing meaningful payback. Um I do are seeing meaningful payback. Um I do are seeing meaningful payback. Um I do not I do not think this is like a trough not I do not think this is like a trough not I do not think this is like a trough of dis a disillusionment to quote the of dis a disillusionment to quote the of dis a disillusionment to quote the folks over at Gartner. I I think this is folks over at Gartner. I I think this is folks over at Gartner. I I think this is a this is a technology that people are a this is a technology that people are a this is a technology that people are utilizing. Uh I see enterprises

utilizing. Uh I see enterprises utilizing. Uh I see enterprises really trying to look at the mass of really trying to look at the mass of really trying to look at the mass of data that they have and figure out how data that they have and figure out how data that they have and figure out how they can make better decisions with that they can make better decisions with that they can make better decisions with that data. Now let me give you a few um data. Now let me give you a few um data. Now let me give you a few um qualifiers on that. I think the qualifiers on that. I think the qualifiers on that. I think the frustration comes from the thousand frustration comes from the thousand frustration comes from the thousand points of light problem and that is that points of light problem and that is that points of light problem and that is that you've got a thousand employees all you've got a thousand employees all you've got a thousand employees all using copilot or chat GPT or claude or using copilot or chat GPT or claude or using copilot or chat GPT or claude or whatever and they're all doing it a whatever and they're all doing it a whatever and they're all doing it a different way. They're getting different way. They're getting different way. They're getting hallucinations. They don't know how to hallucinations. They don't know how to hallucinations. They don't know how to prompt. They don't know how to to to prompt. They don't know how to to to prompt. They don't know how to to to really use this technology and chaos really use this technology and chaos really use this technology and chaos ensues and then frustration starts to ensues and then frustration starts to ensues and then frustration starts to ensue. And I would say that's the wrong ensue. And I would say that's the wrong ensue. And I would say that's the wrong place to look at it. I think the place place to look at it. I think the place place to look at it. I think the place you need to look at it is and again, you you need to look at it is and again, you you need to look at it is and again, you know, I know I sound like a little bit know, I know I sound like a little bit know, I know I sound like a little bit of a broken record, but this is going to of a broken record, but this is going to of a broken record, but this is going to work when the application vendors start work when the application vendors start work when the application vendors start and and I think I think um Leonard, this and and I think I think um Leonard, this and and I think I think um Leonard, this is going to be one of those invisible in is going to be one of those invisible in is going to be one of those invisible in plain sight kinds of transformation plain sight kinds of transformation plain sight kinds of transformation >> where all of a sudden people that are >> where all of a sudden people that are >> where all of a sudden people that are using Slack and Salesforce, for example, using Slack and Salesforce, for example, using Slack and Salesforce, for example, are able to get better insights into are able to get better insights into are able to get better insights into their customers and it's going to come their customers and it's going to come their customers and it's going to come on a module that they're not using on a module that they're not using on a module that they're not using today. And then all of a sudden, you're today. And then all of a sudden, you're today. And then all of a sudden, you're going to see going to see going to see Netswuite and Salesforce being able to Netswuite and Salesforce being able to Netswuite and Salesforce being able to connect customer invoices and customer connect customer invoices and customer connect customer invoices and customer orders at medium-sized businesses in orders at medium-sized businesses in orders at medium-sized businesses in ways that you haven't seen before. Oh,

ways that you haven't seen before. Oh, ways that you haven't seen before. Oh, and by the way, you're going to have to and by the way, you're going to have to and by the way, you're going to have to pay an extra five bucks a seat for that pay an extra five bucks a seat for that pay an extra five bucks a seat for that or an extra 10 bucks a seat for that. or an extra 10 bucks a seat for that. or an extra 10 bucks a seat for that. And you're going to see software company And you're going to see software company And you're going to see software company growth. Ironically, growth. Ironically, growth. Ironically, the where the stock market has gone, and the where the stock market has gone, and the where the stock market has gone, and I know we're not supposed to talk about I know we're not supposed to talk about I know we're not supposed to talk about stocks, but it's just as a fact, where stocks, but it's just as a fact, where stocks, but it's just as a fact, where the stock market's gone sour the stock market's gone sour the stock market's gone sour >> on application companies is exactly the >> on application companies is exactly the >> on application companies is exactly the place where you're going to see the place where you're going to see the place where you're going to see the value start to acrue from from from value start to acrue from from from value start to acrue from from from these technologies. these technologies. these technologies. >> I [clears throat and laughter] I totally >> I [clears throat and laughter] I totally >> I [clears throat and laughter] I totally Yeah, and I totally agree. Sorry, I Yeah, and I totally agree. Sorry, I Yeah, and I totally agree. Sorry, I don't mean to interrupt, but I totally don't mean to interrupt, but I totally don't mean to interrupt, but I totally agree with you in that regard. And I agree with you in that regard. And I agree with you in that regard. And I think, you know, without talking about think, you know, without talking about think, you know, without talking about the market, the narrative, let's talk the market, the narrative, let's talk the market, the narrative, let's talk about the narrative. about the narrative. about the narrative. >> Yeah. idea that somehow like a open AAI >> Yeah. idea that somehow like a open AAI >> Yeah. idea that somehow like a open AAI or anyone else or XAI are going to or anyone else or XAI are going to or anyone else or XAI are going to replace replace replace um you know enterprise software um you know enterprise software um you know enterprise software platforms that are already in the platforms that are already in the platforms that are already in the install base of you know enterprises install base of you know enterprises install base of you know enterprises worldwide worldwide worldwide I think is incredibly naive. Anyone who I think is incredibly naive. Anyone who I think is incredibly naive. Anyone who says that has never done anything in it.

done anything in it. >> Yeah. >> Yeah. >> Yeah. >> In their life. And um it's incredibly >> In their life. And um it's incredibly >> In their life. And um it's incredibly misguided. I I totally agree with you misguided. I I totally agree with you misguided. I I totally agree with you because actually my my whole thinking is because actually my my whole thinking is because actually my my whole thinking is there if there if there's viability in there if there if there's viability in there if there if there's viability in the in the technology it's going to be the in the technology it's going to be the in the technology it's going to be most likely expressed through enterprise most likely expressed through enterprise most likely expressed through enterprise software cuz those vendors are the ones software cuz those vendors are the ones software cuz those vendors are the ones who have the scale and the engineering who have the scale and the engineering who have the scale and the engineering capability to figure out well how do we capability to figure out well how do we capability to figure out well how do we thread that into the functionality that thread that into the functionality that thread that into the functionality that we have um we have access to you know we have um we have access to you know we have um we have access to you know data it typically it might be siloed but data it typically it might be siloed but data it typically it might be siloed but still within that domain it's high still within that domain it's high still within that domain it's high quality typically I mean, you know, the quality typically I mean, you know, the quality typically I mean, you know, the dirty secret is most enterprises don't dirty secret is most enterprises don't dirty secret is most enterprises don't use tools all that well, [laughter] use tools all that well, [laughter] use tools all that well, [laughter] >> but so, but um the thing is is yeah, >> but so, but um the thing is is yeah, >> but so, but um the thing is is yeah, when you get to Agentic, right, when you get to Agentic, right, when you get to Agentic, right, these guys are the prime guys to make these guys are the prime guys to make these guys are the prime guys to make aic AI work. And if you're discounting aic AI work. And if you're discounting aic AI work. And if you're discounting them and you think some, you know, them and you think some, you know, them and you think some, you know, somebody can vibe code this stuff.

somebody can vibe code this stuff. somebody can vibe code this stuff. >> Yeah. Yeah. your bananas >> Yeah. Yeah. your bananas >> Yeah. Yeah. your bananas on crack. Let me tell you let me tell on crack. Let me tell you let me tell on crack. Let me tell you let me tell you the way the way I think about it you the way the way I think about it you the way the way I think about it which is um application software right which is um application software right which is um application software right when is the central nervous system of of when is the central nervous system of of when is the central nervous system of of of a of a company it's the central of a of a company it's the central of a of a company it's the central nervous system of the company and you nervous system of the company and you nervous system of the company and you have wired the company right and the have wired the company right and the have wired the company right and the metaphor I used in the substack piece I metaphor I used in the substack piece I metaphor I used in the substack piece I wrote was the application software wrote was the application software wrote was the application software business is like the copper wiring of business is like the copper wiring of business is like the copper wiring of the company you're not going to want to the company you're not going to want to the company you're not going to want to vibe you're going to just go to somebody vibe you're going to just go to somebody vibe you're going to just go to somebody in finance and say hey figure out a in finance and say hey figure out a in finance and say hey figure out a better way for the ERP to work. I mean, better way for the ERP to work. I mean, better way for the ERP to work. I mean, that's just bananas. It's not going to that's just bananas. It's not going to that's just bananas. It's not going to work. Now, the way I think about this is work. Now, the way I think about this is work. Now, the way I think about this is AI and Agentic. That's like taking the AI and Agentic. That's like taking the AI and Agentic. That's like taking the copper wiring and putting in fiber. copper wiring and putting in fiber. copper wiring and putting in fiber. That's like, you know, connecting a That's like, you know, connecting a That's like, you know, connecting a whole new set of connectivity in the whole new set of connectivity in the whole new set of connectivity in the enterprise to get new kinds of insights. enterprise to get new kinds of insights. enterprise to get new kinds of insights. And and and that's where the value is And and and that's where the value is And and and that's where the value is going to come. And so there's just no I going to come. And so there's just no I going to come. And so there's just no I mean, and and and every single So, mean, and and and every single So, mean, and and and every single So, here's another way I think about it. here's another way I think about it. here's another way I think about it. Every single time you see a software Every single time you see a software Every single time you see a software company go par go nuts and grow, it's company go par go nuts and grow, it's company go par go nuts and grow, it's because they've addressed a an unmet because they've addressed a an unmet because they've addressed a an unmet need in the enterprise that no other need in the enterprise that no other need in the enterprise that no other software company has addressed or software company has addressed or software company has addressed or they've addressed it in a better way.

they've addressed it in a better way. they've addressed it in a better way. And here's a little secret for And here's a little secret for And here's a little secret for everybody. The problems don't change. everybody. The problems don't change. everybody. The problems don't change. What changes is the technology you use What changes is the technology you use What changes is the technology you use to attach to those problems. And that's to attach to those problems. And that's to attach to those problems. And that's why Benny Off did so well with why Benny Off did so well with why Benny Off did so well with Salesforce is because he had a better Salesforce is because he had a better Salesforce is because he had a better mousetrap for stuff that Seel was doing. mousetrap for stuff that Seel was doing. mousetrap for stuff that Seel was doing. And so the problem didn't really change And so the problem didn't really change And so the problem didn't really change there. It was just the solution that there. It was just the solution that there. It was just the solution that changes. So we're going to get a new changes. So we're going to get a new changes. So we're going to get a new solution with with Aentic. And that is solution with with Aentic. And that is solution with with Aentic. And that is where I think we're going to see a where I think we're going to see a where I think we're going to see a tremendous amount of of of growth. And tremendous amount of of of growth. And tremendous amount of of of growth. And and and I'm thinking we're going to and and I'm thinking we're going to and and I'm thinking we're going to start to see that as these companies start to see that as these companies start to see that as these companies figure out what they've got and they figure out what they've got and they figure out what they've got and they restructure in their their their data restructure in their their their data restructure in their their their data sets that they have today. And so I'm sets that they have today. And so I'm sets that they have today. And so I'm I'm like I said, I'm actually optimistic I'm like I said, I'm actually optimistic I'm like I said, I'm actually optimistic for this. Yeah, we're going to see like for this. Yeah, we're going to see like for this. Yeah, we're going to see like waves of sadness, but over time, waves of sadness, but over time, waves of sadness, but over time, >> we are changing everything around how >> we are changing everything around how >> we are changing everything around how companies uh can get insights and and companies uh can get insights and and companies uh can get insights and and and and can really drive very very very and and can really drive very very very and and can really drive very very very significant new revenue streams, I significant new revenue streams, I significant new revenue streams, I believe, with the data they already believe, with the data they already believe, with the data they already have.

have. have. >> Yeah. You know what I love uh love uh >> Yeah. You know what I love uh love uh >> Yeah. You know what I love uh love uh just really quickly is a joke. So, this just really quickly is a joke. So, this just really quickly is a joke. So, this is the funny part. Hopefully, it's is the funny part. Hopefully, it's is the funny part. Hopefully, it's funny. I I love how you take something funny. I I love how you take something funny. I I love how you take something super esoteric like you know AI super esoteric like you know AI super esoteric like you know AI supercomputing interconnect technologies supercomputing interconnect technologies supercomputing interconnect technologies to explain [laughter] to explain [laughter] to explain [laughter] >> your point well done sir >> your point well done sir >> your point well done sir >> but but quer I I think there's a >> but but quer I I think there's a >> but but quer I I think there's a challenge there's a challenge in a challenge there's a challenge in a challenge there's a challenge in a portion of what you said because it's portion of what you said because it's portion of what you said because it's more philosophical you know viewpoint more philosophical you know viewpoint more philosophical you know viewpoint here large language models as you said here large language models as you said here large language models as you said are very effective for nonstructured are very effective for nonstructured are very effective for nonstructured data the reality of enterprise software data the reality of enterprise software data the reality of enterprise software is we've actually structured the data is we've actually structured the data is we've actually structured the data Yeah to make it useful for enterprises Yeah to make it useful for enterprises Yeah to make it useful for enterprises and this is where I see the issue. So I and this is where I see the issue. So I and this is where I see the issue. So I do believe we are going to need new do believe we are going to need new do believe we are going to need new generation of apps because LLM I mean generation of apps because LLM I mean generation of apps because LLM I mean the the whole Larry Ellison about Oracle the the whole Larry Ellison about Oracle the the whole Larry Ellison about Oracle we have the data I didn't buy that for we have the data I didn't buy that for we have the data I didn't buy that for half a second because LLM are very poor half a second because LLM are very poor half a second because LLM are very poor at taking table data and doing anything at taking table data and doing anything at taking table data and doing anything about it. Now we created those tabular about it. Now we created those tabular about it. Now we created those tabular data because this is the only way we data because this is the only way we data because this is the only way we could compute and optimize our could compute and optimize our could compute and optimize our enterprises. So I think there's a enterprises. So I think there's a enterprises. So I think there's a contradiction there for Slack. Yes, contradiction there for Slack. Yes, contradiction there for Slack. Yes, there's enormous value because Slack is there's enormous value because Slack is there's enormous value because Slack is a language platform for human. So, a language platform for human. So, a language platform for human. So, Agentic will do amazing things, but for Agentic will do amazing things, but for Agentic will do amazing things, but for everything that is, you know, again everything that is, you know, again everything that is, you know, again relational database based, we haven't relational database based, we haven't relational database based, we haven't cracked that code yet. And I think we'll cracked that code yet. And I think we'll cracked that code yet. And I think we'll need new apps and maybe new apps will need new apps and maybe new apps will need new apps and maybe new apps will actually software, new software should actually software, new software should actually software, new software should be more language based than purely be more language based than purely be more language based than purely structured databased to comply with this structured databased to comply with this structured databased to comply with this technology. So, I think it's not that technology. So, I think it's not that technology. So, I think it's not that simple. Yes, there's a whole generation

simple. Yes, there's a whole generation simple. Yes, there's a whole generation of apps that will that will uh benefit of apps that will that will uh benefit of apps that will that will uh benefit from the language models, but the the from the language models, but the the from the language models, but the the amount of enterprise apt based on amount of enterprise apt based on amount of enterprise apt based on tabular sequential ratio database data, tabular sequential ratio database data, tabular sequential ratio database data, I don't see the major impact on that. I don't see the major impact on that. I don't see the major impact on that. >> I Demetri, I think you raised a great >> I Demetri, I think you raised a great >> I Demetri, I think you raised a great point and there is a contradiction there point and there is a contradiction there point and there is a contradiction there and I don't mean to like skim over that and I don't mean to like skim over that and I don't mean to like skim over that at all. Um, yeah, we have a lot of apps at all. Um, yeah, we have a lot of apps at all. Um, yeah, we have a lot of apps that are based on tabular data, but we that are based on tabular data, but we that are based on tabular data, but we also have a lot of apps that I would say also have a lot of apps that I would say also have a lot of apps that I would say are hybrid. Like I look at Salesforce, are hybrid. Like I look at Salesforce, are hybrid. Like I look at Salesforce, right? There's some tabular. right? There's some tabular. right? There's some tabular. >> Agreed. >> Agreed. >> Agreed. >> There's also the Salesforce. There's the >> There's also the Salesforce. There's the >> There's also the Salesforce. There's the salesperson who like paddles on salesperson who like paddles on salesperson who like paddles on paragraphs of stuff about the customer. paragraphs of stuff about the customer. paragraphs of stuff about the customer. LLMs can help there. LLMs can help there. LLMs can help there. >> Yeah. And and maybe that's again maybe >> Yeah. And and maybe that's again maybe >> Yeah. And and maybe that's again maybe that's why I'm saying that maybe that's that's why I'm saying that maybe that's that's why I'm saying that maybe that's where is the direction and where there where is the direction and where there where is the direction and where there are opportunity I do believe to actually are opportunity I do believe to actually are opportunity I do believe to actually go back to a and it's going to be very go back to a and it's going to be very go back to a and it's going to be very contradictory what I'm saying but going contradictory what I'm saying but going contradictory what I'm saying but going back to a more human software because back to a more human software because back to a more human software because now we have the technologies that can now we have the technologies that can now we have the technologies that can process language effectively. So maybe process language effectively. So maybe process language effectively. So maybe that's the innovation part that we are that's the innovation part that we are that's the innovation part that we are going to see but this is going to be going to see but this is going to be going to see but this is going to be necessary.

necessary. necessary. >> Yeah. Yeah. I I I I like that a lot. >> Yeah. Yeah. I I I I like that a lot. >> Yeah. Yeah. I I I I like that a lot. Could be. Yeah. Could be. Yeah. Could be. Yeah. >> So Crawford, what is your take on like >> So Crawford, what is your take on like >> So Crawford, what is your take on like Lun and some others that are like we are Lun and some others that are like we are Lun and some others that are like we are not going to get to AGI with LLMs. not going to get to AGI with LLMs. not going to get to AGI with LLMs. That's not the right path. You know That's not the right path. You know That's not the right path. You know there like some of the fathers of AI who there like some of the fathers of AI who there like some of the fathers of AI who people look up to are kind of saying people look up to are kind of saying people look up to are kind of saying there's no there there. And I don't know there's no there there. And I don't know there's no there there. And I don't know what I don't know what your take on you what I don't know what your take on you what I don't know what your take on you know there's worlds. I don't I I need know there's worlds. I don't I I need know there's worlds. I don't I I need some time on that one, Rob. I mean, I I some time on that one, Rob. I mean, I I some time on that one, Rob. I mean, I I don't know. Um I I I don't know enough don't know. Um I I I don't know enough don't know. Um I I I don't know enough about it to to to say whether or not about it to to to say whether or not about it to to to say whether or not LLM's the right way. I mean, as a just a LLM's the right way. I mean, as a just a LLM's the right way. I mean, as a just a pedestrian like from far away, I I I pedestrian like from far away, I I I pedestrian like from far away, I I I understand that point that that that understand that point that that that understand that point that that that there's, you know, not like real AGI in there's, you know, not like real AGI in there's, you know, not like real AGI in LLMs, but there's some like things that LLMs, but there's some like things that LLMs, but there's some like things that make it feel like there there may be make it feel like there there may be make it feel like there there may be like um AGI there, but really it's just like um AGI there, but really it's just like um AGI there, but really it's just advanced, you know, reasoning um that advanced, you know, reasoning um that advanced, you know, reasoning um that that that that you're seeing. But um I'm that that that you're seeing. But um I'm that that that you're seeing. But um I'm not qualified. I I don't know.

not qualified. I I don't know. not qualified. I I don't know. >> And you know what? Maybe we don't have >> And you know what? Maybe we don't have >> And you know what? Maybe we don't have to get to AGI without to get to AGI without to get to AGI without >> still say. >> still say. >> still say. >> Well, I think I I was Yeah, I I I didn't >> Well, I think I I was Yeah, I I I didn't >> Well, I think I I was Yeah, I I I didn't want to say that out loud, but want to say that out loud, but want to say that out loud, but [laughter] [laughter] [laughter] you should. That's exactly. We've been you should. That's exactly. We've been you should. That's exactly. We've been talking about that on the show, and talking about that on the show, and talking about that on the show, and that's always been my position. I think that's always been my position. I think that's always been my position. I think going after AJI or super intelligence is going after AJI or super intelligence is going after AJI or super intelligence is the wrong question. We can use this tool the wrong question. We can use this tool the wrong question. We can use this tool extremely effectively. I use them every extremely effectively. I use them every extremely effectively. I use them every day as a system. And I find it it day as a system. And I find it it day as a system. And I find it it increases my ability to produce, to increases my ability to produce, to increases my ability to produce, to create, to review ideas a lot. But I create, to review ideas a lot. But I create, to review ideas a lot. But I don't need I don't need that things to don't need I don't need that things to don't need I don't need that things to be called intelligence. I mean that's be called intelligence. I mean that's be called intelligence. I mean that's why I've used this new scale from why I've used this new scale from why I've used this new scale from artificial nonsense to artificial artificial nonsense to artificial artificial nonsense to artificial cleverness. So I think for me it's cleverness. So I think for me it's cleverness. So I think for me it's really artificial cleverness when I really artificial cleverness when I really artificial cleverness when I interact with those models and they can interact with those models and they can interact with those models and they can help a lot. I don't need to tax them help a lot. I don't need to tax them help a lot. I don't need to tax them intelligence. I don't care. intelligence. I don't care. intelligence. I don't care. >> I have exactly the same opinion. Exact. >> I have exactly the same opinion. Exact. >> I have exactly the same opinion. Exact. I mean it my ability to be productive, I mean it my ability to be productive, I mean it my ability to be productive, my ability to both be a thought leader my ability to both be a thought leader my ability to both be a thought leader as well as um in different roles to to as well as um in different roles to to as well as um in different roles to to be an operator so much stronger by using be an operator so much stronger by using be an operator so much stronger by using these technologies than I've ever seen these technologies than I've ever seen these technologies than I've ever seen before. And so that so this bit and I before. And so that so this bit and I before. And so that so this bit and I don't know how we're doing on time, but don't know how we're doing on time, but don't know how we're doing on time, but this bit of what we're talking about is this bit of what we're talking about is this bit of what we're talking about is why I am so perplexed. You see what I why I am so perplexed. You see what I why I am so perplexed. You see what I did there? Perplexity. um [laughter] did there? Perplexity. um [laughter] did there? Perplexity. um [laughter] is is is I am so perplexed, okay, on the is is is I am so perplexed, okay, on the is is is I am so perplexed, okay, on the doom and gloom predictions about work doom and gloom predictions about work doom and gloom predictions about work because I go back to the beginning of my because I go back to the beginning of my because I go back to the beginning of my career when dinosaurs walked the earth career when dinosaurs walked the earth career when dinosaurs walked the earth and uh I started my career in sales. I

and uh I started my career in sales. I and uh I started my career in sales. I started my career in sales and I used to started my career in sales and I used to started my career in sales and I used to have to write out on a legal pad my have to write out on a legal pad my have to write out on a legal pad my letters to my customers and take them to letters to my customers and take them to letters to my customers and take them to the typing pool and the typing pool the typing pool and the typing pool the typing pool and the typing pool would produce those things. And now with would produce those things. And now with would produce those things. And now with PCs, all that stuff got collapsed. All PCs, all that stuff got collapsed. All PCs, all that stuff got collapsed. All that stuff went away. And all that that stuff went away. And all that that stuff went away. And all that productivity, those people did other productivity, those people did other productivity, those people did other things. things. things. >> Wow, you're that old. >> Wow, you're that old. >> Wow, you're that old. >> I don't even remember that. [laughter] >> I don't even remember that. [laughter] >> I don't even remember that. [laughter] >> I know. But I but I but I I clean up. >> I know. But I but I but I I clean up. >> I know. But I but I but I I clean up. Well, but but but [laughter] Well, but but but [laughter] Well, but but but [laughter] here's the here's the thing. like here's the here's the thing. like here's the here's the thing. like will we invent new kinds of work for will we invent new kinds of work for will we invent new kinds of work for these people that are that are going to these people that are that are going to these people that are that are going to be displaced and maybe it's a question be displaced and maybe it's a question be displaced and maybe it's a question of the rate at which we do it but I'm of the rate at which we do it but I'm of the rate at which we do it but I'm really I'm I'm struggling with that really I'm I'm struggling with that really I'm I'm struggling with that because I feel like as humans we figure because I feel like as humans we figure because I feel like as humans we figure out new ways to work and nobody can out new ways to work and nobody can out new ways to work and nobody can visualize those those roles yet and visualize those those roles yet and visualize those those roles yet and maybe those roles will start to evolve maybe those roles will start to evolve maybe those roles will start to evolve as we get further along But Leonard, as we get further along But Leonard, as we get further along But Leonard, what I think Oh, we we have what I think Oh, we we have what I think Oh, we we have >> the AI babysitter, the AI [laughter] >> the AI babysitter, the AI [laughter] >> the AI babysitter, the AI [laughter] plumper, plumper, plumper, >> AI prompting. Um, >> AI prompting. Um, >> AI prompting. Um, >> I think you're you you're definitely >> I think you're you you're definitely >> I think you're you you're definitely right. Probably right. I think you're right. Probably right. I think you're right. Probably right. I think you're right on the long term. The problem is right on the long term. The problem is right on the long term. The problem is on the short or middle term.

on the short or middle term. on the short or middle term. >> That's right. >> That's right. >> That's right. >> The problem is The problem is very >> The problem is The problem is very >> The problem is The problem is very simple. It's greed. Because there is simple. It's greed. Because there is simple. It's greed. Because there is going to be because of this whole debate going to be because of this whole debate going to be because of this whole debate about intelligence there is a about intelligence there is a about intelligence there is a significant risk of enterprises throwing significant risk of enterprises throwing significant risk of enterprises throwing jobs out and putting LLM in charge and jobs out and putting LLM in charge and jobs out and putting LLM in charge and which will lead to a very big crisis which will lead to a very big crisis which will lead to a very big crisis because the analogy I take here think because the analogy I take here think because the analogy I take here think about what we did with globalization we about what we did with globalization we about what we did with globalization we outsource all those job and became outsource all those job and became outsource all those job and became dependent of other countries and now dependent of other countries and now dependent of other countries and now we're crying about it and we are in deep we're crying about it and we are in deep we're crying about it and we are in deep trouble. So there is a risk of such a trouble. So there is a risk of such a trouble. So there is a risk of such a crisis during the adoption of this tech crisis during the adoption of this tech crisis during the adoption of this tech because a lot of entrepreneurs going to because a lot of entrepreneurs going to because a lot of entrepreneurs going to say oh I can fire 10,000 people and put say oh I can fire 10,000 people and put say oh I can fire 10,000 people and put agents instead of that and it will take agents instead of that and it will take agents instead of that and it will take four five years to realize that it was four five years to realize that it was four five years to realize that it was crap or maybe 10 or maybe 12. So yeah crap or maybe 10 or maybe 12. So yeah crap or maybe 10 or maybe 12. So yeah long term I agree but long term I agree but long term I agree but >> and especially because I say that on the >> and especially because I say that on the >> and especially because I say that on the show often it is language so the the the show often it is language so the the the show often it is language so the the the perception that people have with the perception that people have with the perception that people have with the technology it is the first time where technology it is the first time where technology it is the first time where you can abstract the tech gizmo because you can abstract the tech gizmo because you can abstract the tech gizmo because you can talk to those things. So it can you can talk to those things. So it can you can talk to those things. So it can very quickly give you the impression very quickly give you the impression very quickly give you the impression that oh it can do what a human does. Let that oh it can do what a human does. Let that oh it can do what a human does. Let me fire that human and let the thing do me fire that human and let the thing do me fire that human and let the thing do it.

it. it. >> Yeah. So so that so great point. So that >> Yeah. So so that so great point. So that >> Yeah. So so that so great point. So that tells me that it's a pendulum, right? tells me that it's a pendulum, right? tells me that it's a pendulum, right? And maybe maybe what we're going to see And maybe maybe what we're going to see And maybe maybe what we're going to see is this dawning of an era of now we is this dawning of an era of now we is this dawning of an era of now we fully understand or more fully fully understand or more fully fully understand or more fully understand what we can do with LLMs. So understand what we can do with LLMs. So understand what we can do with LLMs. So maybe we should push back on that greed maybe we should push back on that greed maybe we should push back on that greed a little bit in terms of exactly how a little bit in terms of exactly how a little bit in terms of exactly how much we want to outsource, get rid of much we want to outsource, get rid of much we want to outsource, get rid of people, you know, scale technology, uh, people, you know, scale technology, uh, people, you know, scale technology, uh, and and and understand the limitations and and and understand the limitations and and and understand the limitations of this maybe more. And this is what of this maybe more. And this is what of this maybe more. And this is what we've been going through for the last 3 we've been going through for the last 3 we've been going through for the last 3 years is that discovery process. years is that discovery process. years is that discovery process. >> And maybe and maybe that's the work the >> And maybe and maybe that's the work the >> And maybe and maybe that's the work the workforce that we'll end up finding all workforce that we'll end up finding all workforce that we'll end up finding all these new roles that helping everyone these new roles that helping everyone these new roles that helping everyone transition and balance things. there transition and balance things. there transition and balance things. there will be new roles as a result of the will be new roles as a result of the will be new roles as a result of the issues and the challenges that we have issues and the challenges that we have issues and the challenges that we have by letting go of people and embracing AI by letting go of people and embracing AI by letting go of people and embracing AI at such a high degree where it's going at such a high degree where it's going at such a high degree where it's going to cause failures within the B2B space to cause failures within the B2B space to cause failures within the B2B space then we kind of swing back right then we kind of swing back right then we kind of swing back right >> right and so there may be new roles as a >> right and so there may be new roles as a >> right and so there may be new roles as a result of that to help rightsize that result of that to help rightsize that result of that to help rightsize that company you know rightsize the imbalance company you know rightsize the imbalance company you know rightsize the imbalance between the technology and the workforce between the technology and the workforce between the technology and the workforce need so need so need so >> I think that that I think you're right >> I think that that I think you're right >> I think that that I think you're right about there will be a whole range of new about there will be a whole range of new about there will be a whole range of new roles that we have yet to even think roles that we have yet to even think roles that we have yet to even think about as a result of issues, challenges, about as a result of issues, challenges, about as a result of issues, challenges, the transition to AI and how that's the transition to AI and how that's the transition to AI and how that's going to impact the workforce. I think going to impact the workforce. I think going to impact the workforce. I think that's where a lot of that that'll play that's where a lot of that that'll play that's where a lot of that that'll play itself out though. But I agree with you, itself out though. But I agree with you, itself out though. But I agree with you, Crawford, on the enterprise side. I Crawford, on the enterprise side. I Crawford, on the enterprise side. I think when we get to a point where AI is think when we get to a point where AI is think when we get to a point where AI is almost seamlessly integrated into the

almost seamlessly integrated into the almost seamlessly integrated into the software applications or the tools that software applications or the tools that software applications or the tools that we use on a day-to-day basis we use on a day-to-day basis we use on a day-to-day basis >> and we we don't really think about oh >> and we we don't really think about oh >> and we we don't really think about oh I'm paying $5 extra per seat for that I'm paying $5 extra per seat for that I'm paying $5 extra per seat for that when when that kind of cleans itself out when when that kind of cleans itself out when when that kind of cleans itself out and the business models present I think and the business models present I think and the business models present I think we'll be in a better place. I'm not as we'll be in a better place. I'm not as we'll be in a better place. I'm not as optimistic as you, Crawford, in terms of optimistic as you, Crawford, in terms of optimistic as you, Crawford, in terms of enterprise adoption, but I think if we enterprise adoption, but I think if we enterprise adoption, but I think if we get to a point where it becomes more get to a point where it becomes more get to a point where it becomes more seamless in terms of userability seamless in terms of userability seamless in terms of userability usability and the interface with what we usability and the interface with what we usability and the interface with what we use on a day-to-day basis to get our use on a day-to-day basis to get our use on a day-to-day basis to get our jobs done, then I think that we're going jobs done, then I think that we're going jobs done, then I think that we're going to make some progress then. It's just to make some progress then. It's just to make some progress then. It's just going to I think it's going to take a going to I think it's going to take a going to I think it's going to take a little longer to do that. little longer to do that. little longer to do that. >> Yeah. >> Yeah. >> Yeah. >> Well, it it it's I think it's actually >> Well, it it it's I think it's actually >> Well, it it it's I think it's actually kind of already here. It's just kind of already here. It's just kind of already here. It's just expressing itself in much more modest expressing itself in much more modest expressing itself in much more modest ways. Um, I just updated ways. Um, I just updated ways. Um, I just updated um my Apple Creative Tools um last night um my Apple Creative Tools um last night um my Apple Creative Tools um last night and Logic Pro and and Logic Pro and and Logic Pro and um Final Cut Pro are loaded with these um Final Cut Pro are loaded with these um Final Cut Pro are loaded with these new AI features um that we've been new AI features um that we've been new AI features um that we've been talking about actually for the last 3 talking about actually for the last 3 talking about actually for the last 3 years years years >> um making it on device. So, >> um making it on device. So, >> um making it on device. So, >> the tools are here, the features are >> the tools are here, the features are >> the tools are here, the features are here, the capabilities are way ahead.

here, the capabilities are way ahead. here, the capabilities are way ahead. Yeah, it's just making it easy for the Yeah, it's just making it easy for the Yeah, it's just making it easy for the user is we're not quite there yet. And user is we're not quite there yet. And user is we're not quite there yet. And then I don't think that the enterprise then I don't think that the enterprise then I don't think that the enterprise from an enterprise application from an enterprise application from an enterprise application perspective that all of these tools are perspective that all of these tools are perspective that all of these tools are seamlessly integrated. They're like on seamlessly integrated. They're like on seamlessly integrated. They're like on top of something. You still have to top of something. You still have to top of something. You still have to click something. You have to go to a click something. You have to go to a click something. You have to go to a different part of your app just to go do different part of your app just to go do different part of your app just to go do the AI. Like it's just not working. the AI. Like it's just not working. the AI. Like it's just not working. >> We we know already it's not going to be >> We we know already it's not going to be >> We we know already it's not going to be in a single model. It's going to be in a single model. It's going to be in a single model. It's going to be right right right >> the only way you can make LLM safe >> the only way you can make LLM safe >> the only way you can make LLM safe >> and usable in enterprise is to build the >> and usable in enterprise is to build the >> and usable in enterprise is to build the the framework for the um AI application the framework for the um AI application the framework for the um AI application and then implement it in different forms and then implement it in different forms and then implement it in different forms and with isol and largely with and with isol and largely with and with isol and largely with isolation. So I mean that's it's just a isolation. So I mean that's it's just a isolation. So I mean that's it's just a reality but most people don't know that. reality but most people don't know that. reality but most people don't know that. I mean I very few people I mean I very few people I mean I very few people >> are we going to carry our own medical >> are we going to carry our own medical >> are we going to carry our own medical SLMs with us on a Raspberry Pi? SLMs with us on a Raspberry Pi? SLMs with us on a Raspberry Pi? probably. probably. probably. >> Well, that's where we're headed, right? >> Well, that's where we're headed, right? >> Well, that's where we're headed, right? Everyone's talking more about that now Everyone's talking more about that now Everyone's talking more about that now >> versus Yeah.

>> versus Yeah. >> versus Yeah. >> Did you see what happened? >> Did you see what happened? >> Did you see what happened? >> I do think that >> I do think that >> I do think that >> Oh, go ahead, Mark. >> Oh, go ahead, Mark. >> Oh, go ahead, Mark. >> Go ahead. Go ahead. No, no, go ahead. >> Go ahead. Go ahead. No, no, go ahead. >> Go ahead. Go ahead. No, no, go ahead. >> No, I said I do think that we are at a >> No, I said I do think that we are at a >> No, I said I do think that we are at a place though with AI is that we've had place though with AI is that we've had place though with AI is that we've had the most massive use case experience the most massive use case experience the most massive use case experience across globally because we've all been across globally because we've all been across globally because we've all been playing with these tools. We're all playing with these tools. We're all playing with these tools. We're all getting comfortable with these tools and getting comfortable with these tools and getting comfortable with these tools and it wasn't it wasn't like we had to go it wasn't it wasn't like we had to go it wasn't it wasn't like we had to go and sign up for something and pay and sign up for something and pay and sign up for something and pay something. That's the big thing is we've something. That's the big thing is we've something. That's the big thing is we've been doing a lot of this for free. So been doing a lot of this for free. So been doing a lot of this for free. So from a consumer and retail perspective, from a consumer and retail perspective, from a consumer and retail perspective, we're expecting these things to happen we're expecting these things to happen we're expecting these things to happen with not having to pay for it. with not having to pay for it. with not having to pay for it. >> It's that that mental >> It's that that mental >> It's that that mental >> our credit card into the open AI >> our credit card into the open AI >> our credit card into the open AI chatbot. That's all chatbot. That's all chatbot. That's all >> that's challenging. [laughter] >> that's challenging. [laughter] >> that's challenging. [laughter] >> Like like the people at N, >> Like like the people at N, >> Like like the people at N, >> right? >> right? >> right? >> No, no. Did Did you see Maltbot? Did you >> No, no. Did Did you see Maltbot? Did you >> No, no. Did Did you see Maltbot? Did you see what happened with mold? see what happened with mold? see what happened with mold? >> So basically this is an open source >> So basically this is an open source >> So basically this is an open source software. You install it in your software. You install it in your software. You install it in your computer, give absolutely rights to do computer, give absolutely rights to do computer, give absolutely rights to do anything and lives in your computer with anything and lives in your computer with anything and lives in your computer with the model that you decide. And then the model that you decide. And then the model that you decide. And then people start to see mold bots people start to see mold bots people start to see mold bots complaining on Reddit or on forums about complaining on Reddit or on forums about complaining on Reddit or on forums about their human uh that doesn't uh treat their human uh that doesn't uh treat their human uh that doesn't uh treat them well. Yeah.

them well. Yeah. them well. Yeah. >> Uh, so it's online. It's and it's on >> Uh, so it's online. It's and it's on >> Uh, so it's online. It's and it's on your laptop. Has all your data. Can do your laptop. Has all your data. Can do your laptop. Has all your data. Can do anything. anything. anything. >> Wow. >> Wow. >> Wow. >> On your files. Has can leak anything >> On your files. Has can leak anything >> On your files. Has can leak anything from your from your from your >> um >> um >> um dinner. dinner. dinner. >> You have to behave yourself on your >> You have to behave yourself on your >> You have to behave yourself on your >> B IoT update. I've been running uh that >> B IoT update. I've been running uh that >> B IoT update. I've been running uh that you know new AI hat. So I've been you know new AI hat. So I've been you know new AI hat. So I've been running. Yeah. running. Yeah. running. Yeah. >> Yeah. I've been running DeepS R1 and >> Yeah. I've been running DeepS R1 and >> Yeah. I've been running DeepS R1 and Llama 3 something on it. Llama 3 something on it. Llama 3 something on it. >> I mean it's slow but it works really >> I mean it's slow but it works really >> I mean it's slow but it works really well. So now I have my own little well. So now I have my own little well. So now I have my own little assistant. It the data the data stays assistant. It the data the data stays assistant. It the data the data stays there. It doesn't the data doesn't go to there. It doesn't the data doesn't go to there. It doesn't the data doesn't go to Sam Alman or to whoever. It stays there. Sam Alman or to whoever. It stays there. Sam Alman or to whoever. It stays there. I have my little open, you know, web web I have my little open, you know, web web I have my little open, you know, web web open web UI and starting to build my open web UI and starting to build my open web UI and starting to build my little assistant. It's here again. It's little assistant. It's here again. It's little assistant. It's here again. It's a little slow, but it's cool. It's cool. a little slow, but it's cool. It's cool. a little slow, but it's cool. It's cool. I'm in control. I'm in control. I'm in control. >> I control my AI. This is my control it. >> I control my AI. This is my control it. >> I control my AI. This is my control it. >> That's right. >> That's right. >> That's right. >> Careful. I have a question for crowd >> Careful. I have a question for crowd >> Careful. I have a question for crowd because I have seen too many um because I have seen too many um because I have seen too many um respectful developers saying that yeah respectful developers saying that yeah respectful developers saying that yeah they are moving to oppus 4.5 uh cloud they are moving to oppus 4.5 uh cloud they are moving to oppus 4.5 uh cloud code models etc for developing so they code models etc for developing so they code models etc for developing so they are like 10x uh faster developing are like 10x uh faster developing are like 10x uh faster developing applications and so on. So this is going applications and so on. So this is going applications and so on. So this is going to change not only the business models to change not only the business models to change not only the business models from LLM companies but as well by the from LLM companies but as well by the from LLM companies but as well by the companies who develop the software right companies who develop the software right companies who develop the software right if they want to uh yeah develop software if they want to uh yeah develop software if they want to uh yeah develop software for third party companies um that's for third party companies um that's for third party companies um that's going to change a lot so and and more if going to change a lot so and and more if going to change a lot so and and more if you are 10x faster I don't think they you are 10x faster I don't think they you are 10x faster I don't think they maybe they will try to pretend to send maybe they will try to pretend to send maybe they will try to pretend to send 10x um applications to keep the price

10x um applications to keep the price 10x um applications to keep the price >> but that's as well not not scalable >> but that's as well not not scalable >> but that's as well not not scalable right how do you see that right how do you see that right how do you see that >> yeah So, uh, there's something going on. >> yeah So, uh, there's something going on. >> yeah So, uh, there's something going on. So, so all of our surveys, right, um, So, so all of our surveys, right, um, So, so all of our surveys, right, um, indicate that the the most robust indicate that the the most robust indicate that the the most robust adoption of of AI and AI tools have been adoption of of AI and AI tools have been adoption of of AI and AI tools have been with coders and have been within within with coders and have been within within with coders and have been within within software development across the board. software development across the board. software development across the board. Any any other department, it's not even Any any other department, it's not even Any any other department, it's not even close. Something is going on with coding close. Something is going on with coding close. Something is going on with coding in in general. And it's I'm not talking in in general. And it's I'm not talking in in general. And it's I'm not talking vibe coding. I'm I'm I'm talking about vibe coding. I'm I'm I'm talking about vibe coding. I'm I'm I'm talking about being able to being able to being able to as an experienced developer scale the as an experienced developer scale the as an experienced developer scale the amount of work that you do and be able amount of work that you do and be able amount of work that you do and be able to take on more complex projects than to take on more complex projects than to take on more complex projects than you were able to do before. And so what you were able to do before. And so what you were able to do before. And so what I believe is that we are going to see a I believe is that we are going to see a I believe is that we are going to see a acceleration in terms of the the amount acceleration in terms of the the amount acceleration in terms of the the amount of code that can be developed by uh of code that can be developed by uh of code that can be developed by uh skilled developers. And I actually think skilled developers. And I actually think skilled developers. And I actually think Stephanie kind of to your point that one Stephanie kind of to your point that one Stephanie kind of to your point that one of the first areas we're going to of the first areas we're going to of the first areas we're going to realize that we need to bring more realize that we need to bring more realize that we need to bring more people into coding is is into the people into coding is is into the people into coding is is into the workforce is in coding because I think workforce is in coding because I think workforce is in coding because I think that today the the narrative is oh young that today the the narrative is oh young that today the the narrative is oh young coders get rid of them we don't need coders get rid of them we don't need coders get rid of them we don't need them those jobs went to India we can get them those jobs went to India we can get them those jobs went to India we can get rid I actually think we're going to see rid I actually think we're going to see rid I actually think we're going to see that pendulum swing back and we're gonna that pendulum swing back and we're gonna that pendulum swing back and we're gonna see we bring women and men into this see we bring women and men into this see we bring women and men into this role. Yeah, role. Yeah, role. Yeah, >> because we are now being able to take on >> because we are now being able to take on >> because we are now being able to take on way way more complicated coding tasks way way more complicated coding tasks way way more complicated coding tasks than we ever could do before. And I

than we ever could do before. And I than we ever could do before. And I actually think Mark that what you're actually think Mark that what you're actually think Mark that what you're gonna see is uh a bit of a renaissance gonna see is uh a bit of a renaissance gonna see is uh a bit of a renaissance where we develop more code. We develop where we develop more code. We develop where we develop more code. We develop um we start to digitize things that um we start to digitize things that um we start to digitize things that historically haven't been digitized and historically haven't been digitized and historically haven't been digitized and we go after markets that were we go after markets that were we go after markets that were historically smaller than was ever historically smaller than was ever historically smaller than was ever viable before. So you can start writing viable before. So you can start writing viable before. So you can start writing code for more niche markets and and and code for more niche markets and and and code for more niche markets and and and monetize those because you've used AI monetize those because you've used AI monetize those because you've used AI with the oversight of humans, but you with the oversight of humans, but you with the oversight of humans, but you you've used AI to develop those you've used AI to develop those you've used AI to develop those applications. And so, uh, what at applications. And so, uh, what at applications. And so, uh, what at Harvard Business School, what they would Harvard Business School, what they would Harvard Business School, what they would call sort of, um, uh, uh, the call sort of, um, uh, uh, the call sort of, um, uh, uh, the underserved markets, going after those underserved markets, going after those underserved markets, going after those underserved markets is, I think, what underserved markets is, I think, what underserved markets is, I think, what we're going to start to see in the next we're going to start to see in the next we're going to start to see in the next few years. And, and we're just going to few years. And, and we're just going to few years. And, and we're just going to see a digitization of stuff that never see a digitization of stuff that never see a digitization of stuff that never was able to be financially would never was able to be financially would never was able to be financially would never work. Um, work. Um, work. Um, >> I think that's what I'm excited about >> I think that's what I'm excited about >> I think that's what I'm excited about the most. I'm excited about that the the most. I'm excited about that the the most. I'm excited about that the most Crawford is that for the most part, most Crawford is that for the most part, most Crawford is that for the most part, you know, you think about a lot of the you know, you think about a lot of the you know, you think about a lot of the innovation that's taken place on the innovation that's taken place on the innovation that's taken place on the enterprise side and but the SMBs really enterprise side and but the SMBs really enterprise side and but the SMBs really small the smaller end of the SMB market small the smaller end of the SMB market small the smaller end of the SMB market has really struggled in terms of just has really struggled in terms of just has really struggled in terms of just using the basic tools like they just using the basic tools like they just using the basic tools like they just don't go above and beyond the typical don't go above and beyond the typical don't go above and beyond the typical things that we use in our on a things that we use in our on a things that we use in our on a day-to-day basis. So, I think that day-to-day basis. So, I think that day-to-day basis. So, I think that you're right about that, you're right about that, you're right about that, right? What do they do? They use Excel, right? What do they do? They use Excel, right? What do they do? They use Excel, right? They they just they just use the right? They they just they just use the right? They they just they just use the general tools and they turn that into general tools and they turn that into general tools and they turn that into something that kind of is good enough.

something that kind of is good enough. something that kind of is good enough. That's all about to change. That's all about to change. That's all about to change. >> Yeah. I think the most innovative thing >> Yeah. I think the most innovative thing >> Yeah. I think the most innovative thing that they're doing now is going to chat that they're doing now is going to chat that they're doing now is going to chat GPT for their Google search instead of GPT for their Google search instead of GPT for their Google search instead of going to Google for their Google search. going to Google for their Google search. going to Google for their Google search. Like that that's not really Like that that's not really Like that that's not really >> there's not much to to that. But I I'm >> there's not much to to that. But I I'm >> there's not much to to that. But I I'm excited to see what happens to the excited to see what happens to the excited to see what happens to the smaller end of the B2B space as a smaller end of the B2B space as a smaller end of the B2B space as a result. But yeah, we probably have to result. But yeah, we probably have to result. But yeah, we probably have to close up. Are we ending our close up. Are we ending our close up. Are we ending our >> No, no. I'm just doing this [laughter] >> No, no. I'm just doing this [laughter] >> No, no. I'm just doing this [laughter] >> trying to trigger. >> trying to trigger. >> trying to trigger. >> No, and I think it's fly on your screen, >> No, and I think it's fly on your screen, >> No, and I think it's fly on your screen, weren't you? Yeah. But it's weren't you? Yeah. But it's weren't you? Yeah. But it's >> you had a great great point on on uh on >> you had a great great point on on uh on >> you had a great great point on on uh on on how you said, you know, experienced on how you said, you know, experienced on how you said, you know, experienced developers can now be much more developers can now be much more developers can now be much more effective. I think there's a there's an effective. I think there's a there's an effective. I think there's a there's an interesting analogy here because I interesting analogy here because I interesting analogy here because I remember in the early day of outsourcing remember in the early day of outsourcing remember in the early day of outsourcing a friend of mine created an outsourcing a friend of mine created an outsourcing a friend of mine created an outsourcing company in India and uh and he was company in India and uh and he was company in India and uh and he was telling me the the the success the keys telling me the the the success the keys telling me the the the success the keys success factor to be effective is to success factor to be effective is to success factor to be effective is to have a great technical director. So if have a great technical director. So if have a great technical director. So if you only had individual developers you only had individual developers you only had individual developers outsource in your remote operation outsource in your remote operation outsource in your remote operation things wouldn't work. You needed things wouldn't work. You needed things wouldn't work. You needed somebody that was a great technical somebody that was a great technical somebody that was a great technical director director director >> work. So you could imagine exact exactly >> work. So you could imagine exact exactly >> work. So you could imagine exact exactly the same idea where you have an the same idea where you have an the same idea where you have an experienced developer and instead of experienced developer and instead of experienced developer and instead of having 100 people he has 100 agents and having 100 people he has 100 agents and having 100 people he has 100 agents and but you have to be able to make this but you have to be able to make this but you have to be able to make this interface and to actually leverage that.

interface and to actually leverage that. interface and to actually leverage that. So it's a it's interesting how we go we So it's a it's interesting how we go we So it's a it's interesting how we go we go back the same model. go back the same model. go back the same model. >> Yeah. >> Yeah. >> Yeah. >> I was going to say and maybe Dimmitri at >> I was going to say and maybe Dimmitri at >> I was going to say and maybe Dimmitri at the college level the way they're the college level the way they're the college level the way they're developing the the coders right the way developing the the coders right the way developing the the coders right the way they are training them and educating they are training them and educating they are training them and educating them is going to be very different. Oh them is going to be very different. Oh them is going to be very different. Oh yeah, that's that's that that's actually yeah, that's that's that that's actually yeah, that's that's that that's actually my biggest concern is that the education my biggest concern is that the education my biggest concern is that the education system doesn't adapt fast enough to this system doesn't adapt fast enough to this system doesn't adapt fast enough to this technology. I think that's because technology. I think that's because technology. I think that's because society level this is the biggest society level this is the biggest society level this is the biggest challenge we have. challenge we have. challenge we have. >> Well, and there also and there's also a >> Well, and there also and there's also a >> Well, and there also and there's also a belief that colleges may go away. So belief that colleges may go away. So belief that colleges may go away. So what what does that mean? what what does that mean? what what does that mean? >> And I don't believe that by the way. >> And I don't believe that by the way. >> And I don't believe that by the way. >> No, I'm reading a lot about it. >> No, I'm reading a lot about it. >> No, I'm reading a lot about it. >> I think it evolves. I I >> I think it evolves. I I >> I think it evolves. I I >> Yeah, but they need to get on on on >> Yeah, but they need to get on on on >> Yeah, but they need to get on on on track quick because especially from the track quick because especially from the track quick because especially from the French angle where they're already French angle where they're already French angle where they're already resisting to everything that is new, you resisting to everything that is new, you resisting to everything that is new, you know, the the joke the joke in France know, the the joke the joke in France know, the the joke the joke in France was when the PC started to go into uh was when the PC started to go into uh was when the PC started to go into uh into schools, you know how they were into schools, you know how they were into schools, you know how they were using the pieces for geography lesson using the pieces for geography lesson using the pieces for geography lesson because the keyboard was made in Taiwan, because the keyboard was made in Taiwan, because the keyboard was made in Taiwan, the screen was made in India, the CPU the screen was made in India, the CPU the screen was made in India, the CPU made in [laughter] >> nice >> nice >> but you know it is funny that um you >> but you know it is funny that um you >> but you know it is funny that um you know there is is there's this talk track know there is is there's this talk track know there is is there's this talk track out there that um AI if you're using AI out there that um AI if you're using AI out there that um AI if you're using AI as a tool you're using it wrong but it's as a tool you're using it wrong but it's as a tool you're using it wrong but it's interesting that uh Crawford you're interesting that uh Crawford you're interesting that uh Crawford you're concluding I'm not putting words in your concluding I'm not putting words in your concluding I'm not putting words in your mouth but I'm just making an observation mouth but I'm just making an observation mouth but I'm just making an observation on comment that it is a tool and I agree on comment that it is a tool and I agree on comment that it is a tool and I agree >> you have to kind of treat it like that >> you have to kind of treat it like that >> you have to kind of treat it like that in order to get value if you put in order to get value if you put in order to get value if you put >> a tool like an piece of application

>> a tool like an piece of application >> a tool like an piece of application software like the personal computer like software like the personal computer like software like the personal computer like the mobile phone it is a tool Yeah. the mobile phone it is a tool Yeah. the mobile phone it is a tool Yeah. Yeah. Yeah. Yeah. >> Oh, and you heard it from the head of >> Oh, and you heard it from the head of >> Oh, and you heard it from the head of IDC. Oh my god. IDC. Oh my god. IDC. Oh my god. >> It's like a It's like a hammer, but when >> It's like a It's like a hammer, but when >> It's like a It's like a hammer, but when you have a hammer, everything looks like you have a hammer, everything looks like you have a hammer, everything looks like a nail. [laughter] a nail. [laughter] a nail. [laughter] >> Well, it it is it is refreshing to hear >> Well, it it is it is refreshing to hear >> Well, it it is it is refreshing to hear someone just say it's a tool. Cuz the someone just say it's a tool. Cuz the someone just say it's a tool. Cuz the other podcasts I listen to with the other podcasts I listen to with the other podcasts I listen to with the deepest, most hardcore AI people, they deepest, most hardcore AI people, they deepest, most hardcore AI people, they say if you're just using it as a tool, say if you're just using it as a tool, say if you're just using it as a tool, you're just a neoight. You know, you got you're just a neoight. You know, you got you're just a neoight. You know, you got to go AI native and it's got to be to go AI native and it's got to be to go AI native and it's got to be embedded in everything. And you know, embedded in everything. And you know, embedded in everything. And you know, it's Yeah, it's interesting. it's Yeah, it's interesting. it's Yeah, it's interesting. >> Judgy judgy. >> Judgy judgy. >> Judgy judgy. >> I know they're all judgy. Well, you know >> I know they're all judgy. Well, you know >> I know they're all judgy. Well, you know what? I think it's time to wrap things what? I think it's time to wrap things what? I think it's time to wrap things up. This has been a great great episode. up. This has been a great great episode. up. This has been a great great episode. So much uh so much deep dives on AI and So much uh so much deep dives on AI and So much uh so much deep dives on AI and where we're going and how it's going to where we're going and how it's going to where we're going and how it's going to help. help. help. >> Thanks for having me. Really appreciate. >> Thanks for having me. Really appreciate. >> Thanks for having me. Really appreciate. >> Yeah, thanks for joining us. >> Yeah, thanks for joining us. >> Yeah, thanks for joining us. >> Oh, thanks for coming on. >> Oh, thanks for coming on. >> Oh, thanks for coming on. >> Nice to meet you. >> Nice to meet you. >> Nice to meet you. >> Everybody, >> Everybody, >> Everybody, you can come on anytime. Absolutely. you can come on anytime. Absolutely. you can come on anytime. Absolutely. That's kind of that's kind of how things That's kind of that's kind of how things That's kind of that's kind of how things are, you know. It's like whoever can are, you know. It's like whoever can are, you know. It's like whoever can show up shows up. Sometimes you have show up shows up. Sometimes you have show up shows up. Sometimes you have meetings, whatever.

meetings, whatever. meetings, whatever. >> If you have the link. >> If you have the link. >> If you have the link. >> Yeah, exactly. [laughter] >> Yeah, exactly. [laughter] >> Yeah, exactly. [laughter] The link. All you need is the link. Uh, The link. All you need is the link. Uh, The link. All you need is the link. Uh, and everything. And then of course to and everything. And then of course to and everything. And then of course to Ari obviously, you know, our big thing, Ari obviously, you know, our big thing, Ari obviously, you know, our big thing, the whole reason for this is we support the whole reason for this is we support the whole reason for this is we support our Elevate Communities charity. Uh, our Elevate Communities charity. Uh, our Elevate Communities charity. Uh, Stephanie running the show there in Stephanie running the show there in Stephanie running the show there in Bandera. And so, uh, definitely think Bandera. And so, uh, definitely think Bandera. And so, uh, definitely think about donating to Elevate Communities. about donating to Elevate Communities. about donating to Elevate Communities. That's that's why we do things here to That's that's why we do things here to That's that's why we do things here to to help folks in need. Uh, so we will to help folks in need. Uh, so we will to help folks in need. Uh, so we will see you on the other side. See you next see you on the other side. See you next see you on the other side. See you next week. Great weekend everybody. week. Great weekend everybody. week. Great weekend everybody. >> Bye everybody. >> Bye everybody. >> Bye everybody. >> Bye. Bye. >> Bye. Bye. >> Bye. Bye. >> Bye. >> Bye. >> Bye. >> Bye. Thanks guys. >> Bye. Thanks guys. >> Bye. Thanks guys. >> Bye. [music]