IoT Coffee Talk: Episode 283 - "The AI Database" (The Oracle RDBMS Revolution)

All right, everybody. Welcome to IoT All right, everybody. Welcome to IoT Coffee Talk. We don't have an electric Coffee Talk. We don't have an electric Coffee Talk. We don't have an electric guitar at the moment, so you know, I guitar at the moment, so you know, I guitar at the moment, so you know, I guess Demetri and I could do air guitar. guess Demetri and I could do air guitar. guess Demetri and I could do air guitar. I'm not sure. Um, but it's another big I'm not sure. Um, but it's another big I'm not sure. Um, but it's another big week. You know, uh, as as you all well week. You know, uh, as as you all well week. You know, uh, as as you all well know, you know, October is, you know, know, you know, October is, you know, know, you know, October is, you know, we're right back in the middle of we're right back in the middle of we're right back in the middle of conference season. You know, we kind of conference season. You know, we kind of conference season. You know, we kind of left off conference season and, you left off conference season and, you left off conference season and, you know, April, May, June and took a break know, April, May, June and took a break know, April, May, June and took a break in the summer. Now we're back and in the summer. Now we're back and in the summer. Now we're back and everybody's traveling all over the everybody's traveling all over the everybody's traveling all over the place. Um but you know I think very place. Um but you know I think very place. Um but you know I think very topical and I know Demetri has a lot to topical and I know Demetri has a lot to topical and I know Demetri has a lot to talk about this today is we had the talk about this today is we had the talk about this today is we had the Oracle event uh formerly Oracle World Oracle event uh formerly Oracle World Oracle event uh formerly Oracle World then it became Oracle Cloud World and it then it became Oracle Cloud World and it then it became Oracle Cloud World and it actually even started this year has even actually even started this year has even actually even started this year has even started to be Oracle Cloud World and started to be Oracle Cloud World and started to be Oracle Cloud World and then halfway through they changed the then halfway through they changed the then halfway through they changed the marketing people obviously got to them marketing people obviously got to them marketing people obviously got to them and changed it to Oracle AI world. So um and changed it to Oracle AI world. So um and changed it to Oracle AI world. So um you know even the biggest companies in you know even the biggest companies in you know even the biggest companies in the world have to keep pivoting right the world have to keep pivoting right the world have to keep pivoting right AI got to be cooler than cloud I guess.

AI got to be cooler than cloud I guess. AI got to be cooler than cloud I guess. So uh and so yeah that was a big thing So uh and so yeah that was a big thing So uh and so yeah that was a big thing and um you know you had uh a lot of a and um you know you had uh a lot of a and um you know you had uh a lot of a lot of talk about their Oracle cloud lot of talk about their Oracle cloud lot of talk about their Oracle cloud infrastructure OCI and and you know infrastructure OCI and and you know infrastructure OCI and and you know what's going on with them. Um but they what's going on with them. Um but they what's going on with them. Um but they did release an update to their database. did release an update to their database. did release an update to their database. So I think it's Oracle 26 So I think it's Oracle 26 So I think it's Oracle 26 AI AI AI >> 26 >> 26 >> 26 >> where they have AI in it and um I mean I >> where they have AI in it and um I mean I >> where they have AI in it and um I mean I know most of our audience here you know know most of our audience here you know know most of our audience here you know you've been database people probably you've been database people probably you've been database people probably since the 80s or '9s you know you since the 80s or '9s you know you since the 80s or '9s you know you learned at the feet of Mr. Cod and um learned at the feet of Mr. Cod and um learned at the feet of Mr. Cod and um you know I don't know what what database you know I don't know what what database you know I don't know what what database did you get started with Demetri do you did you get started with Demetri do you did you get started with Demetri do you remember what your first database was? remember what your first database was? remember what your first database was? Yeah. Well, yeah. Yeah. Well, yeah. Yeah. Well, yeah. >> Go ahead. Don't be embarrassed. >> Go ahead. Don't be embarrassed. >> Go ahead. Don't be embarrassed. >> No, no, it's not embarrassing. It was >> No, no, it's not embarrassing. It was >> No, no, it's not embarrassing. It was actually pre-SQL and there was actually actually pre-SQL and there was actually actually pre-SQL and there was actually two generation that I started there. I two generation that I started there. I two generation that I started there. I mean, I started with sequential files on mean, I started with sequential files on mean, I started with sequential files on the main frame system using assembly the main frame system using assembly the main frame system using assembly language. So that was kind of the first language. So that was kind of the first language. So that was kind of the first actually I was not really a master at actually I was not really a master at actually I was not really a master at that but a friend of mine I developed that but a friend of mine I developed that but a friend of mine I developed mostly him actually I have to give his mostly him actually I have to give his mostly him actually I have to give his credit guys Bernard and um he built um credit guys Bernard and um he built um credit guys Bernard and um he built um with a little bit of my help a u we we with a little bit of my help a u we we with a little bit of my help a u we we had a comics books library in the had a comics books library in the had a comics books library in the engineer school yeah engineer schools in engineer school yeah engineer schools in engineer school yeah engineer schools in France have tons of money so this is France have tons of money so this is France have tons of money so this is kind of thing we we buy comics books so kind of thing we we buy comics books so kind of thing we we buy comics books so we did a management system that actually we did a management system that actually we did a management system that actually run on the miniel so you could connect run on the miniel so you could connect run on the miniel so you could connect to the main frame from the miniel and to the main frame from the miniel and to the main frame from the miniel and you could actually manage the the the you could actually manage the the the you could actually manage the the the because we were we were actually uh

because we were we were actually uh because we were we were actually uh renting those comingings book to to the renting those comingings book to to the renting those comingings book to to the students of the of the of the school. So students of the of the of the school. So students of the of the of the school. So that was a sequence based but then I that was a sequence based but then I that was a sequence based but then I started working for a small startup and started working for a small startup and started working for a small startup and it was a p system. So I spent a lot of it was a p system. So I spent a lot of it was a p system. So I spent a lot of time on the p system which was a kind of time on the p system which was a kind of time on the p system which was a kind of a pre- relational with some relational a pre- relational with some relational a pre- relational with some relational concept later on. concept later on. concept later on. >> Yeah. And um and we actually built a >> Yeah. And um and we actually built a >> Yeah. And um and we actually built a whole business small business in France whole business small business in France whole business small business in France out of those P system because I think I out of those P system because I think I out of those P system because I think I might have mentioned that on the show might have mentioned that on the show might have mentioned that on the show but uh we used to the French well I but uh we used to the French well I but uh we used to the French well I don't want to get political but if you don't want to get political but if you don't want to get political but if you look at what we're doing in the US right look at what we're doing in the US right look at what we're doing in the US right now it looks like what the French were now it looks like what the French were now it looks like what the French were doing in the 70s and 80s where if you doing in the 70s and 80s where if you doing in the 70s and 80s where if you were a uh a public company like you know were a uh a public company like you know were a uh a public company like you know a ministry or an agency you had to buy a ministry or an agency you had to buy a ministry or an agency you had to buy your computing software and hardware your computing software and hardware your computing software and hardware from a French company. Oh, okay. from a French company. Oh, okay. from a French company. Oh, okay. >> Yeah, they had no choice. You couldn't >> Yeah, they had no choice. You couldn't >> Yeah, they had no choice. You couldn't buy any. Well, there was some exception buy any. Well, there was some exception buy any. Well, there was some exception for IBM obviously because there was for IBM obviously because there was for IBM obviously because there was actually not so much because this is why actually not so much because this is why actually not so much because this is why we actually had IBM clones in France. we actually had IBM clones in France. we actually had IBM clones in France. >> And um there was a a big company in in >> And um there was a a big company in in >> And um there was a a big company in in aviation called enter technique. There aviation called enter technique. There aviation called enter technique. There was kind of a Loid Martin or you know was kind of a Loid Martin or you know was kind of a Loid Martin or you know components type of stuff components type of stuff components type of stuff >> and uh the the sounds founder was a >> and uh the the sounds founder was a >> and uh the the sounds founder was a young guy and he was interesting in young guy and he was interesting in young guy and he was interesting in computing. So he created in 2 Inter computing. So he created in 2 Inter computing. So he created in 2 Inter technique 2 and he got a license from technique 2 and he got a license from technique 2 and he got a license from Mr. richer pic the pig systems in the US Mr. richer pic the pig systems in the US Mr. richer pic the pig systems in the US and he actually the company were and he actually the company were and he actually the company were building hardware and the software that building hardware and the software that building hardware and the software that goes with it. So there was p system goes with it. So there was p system goes with it. So there was p system boxes being sold in France and they were boxes being sold in France and they were boxes being sold in France and they were actually very pretty pretty pretty actually very pretty pretty pretty actually very pretty pretty pretty competitive. So they were everywhere in

competitive. So they were everywhere in competitive. So they were everywhere in the in the governments and type of the in the governments and type of the in the governments and type of places. And in the mid80s uh the places. And in the mid80s uh the places. And in the mid80s uh the actually the the constraint of being actually the the constraint of being actually the the constraint of being forced to use only uh only uh French forced to use only uh only uh French forced to use only uh only uh French hardware was starting to be lifted and hardware was starting to be lifted and hardware was starting to be lifted and there was a US company that had a um a there was a US company that had a um a there was a US company that had a um a kind of emulation of the p system on on kind of emulation of the p system on on kind of emulation of the p system on on Unix and actually resent those things. Unix and actually resent those things. Unix and actually resent those things. So and you know we did that for a few So and you know we did that for a few So and you know we did that for a few years and then we started to see this years and then we started to see this years and then we started to see this guy Larry Ellison coming up with Oracle. guy Larry Ellison coming up with Oracle. guy Larry Ellison coming up with Oracle. So my first relational database was So my first relational database was So my first relational database was actually Oracle. actually Oracle. actually Oracle. >> Yeah. >> Yeah. >> Yeah. >> A long long answer to a short question >> A long long answer to a short question >> A long long answer to a short question makes me feel my gray hair is totally makes me feel my gray hair is totally makes me feel my gray hair is totally deserved. deserved. deserved. >> We've been around for sure. Um, I always >> We've been around for sure. Um, I always >> We've been around for sure. Um, I always found it interesting, found it interesting, found it interesting, you know, how Oracle was able to jump you know, how Oracle was able to jump you know, how Oracle was able to jump out ahead of IBM, you know, if because out ahead of IBM, you know, if because out ahead of IBM, you know, if because it seemed like a lot of the the brain it seemed like a lot of the the brain it seemed like a lot of the the brain power at the time way back then around power at the time way back then around power at the time way back then around the relational theory was with them and the relational theory was with them and the relational theory was with them and yet Larry put together something and yet Larry put together something and yet Larry put together something and Oracle quickly took the lead, you know.

Oracle quickly took the lead, you know. Oracle quickly took the lead, you know. Yeah, I think you're definitely right Yeah, I think you're definitely right Yeah, I think you're definitely right because the the research paper from SQL because the the research paper from SQL because the the research paper from SQL if I remember well were actually if I remember well were actually if I remember well were actually initially created by IBM. initially created by IBM. initially created by IBM. >> Yeah. >> Yeah. >> Yeah. >> But I think that uh that Alison >> But I think that uh that Alison >> But I think that uh that Alison definitely you know he had the vision definitely you know he had the vision definitely you know he had the vision that the standard will you know had the that the standard will you know had the that the standard will you know had the potential to actually become a standard. potential to actually become a standard. potential to actually become a standard. >> Yeah. >> Yeah. >> Yeah. >> But also I think the big difference is >> But also I think the big difference is >> But also I think the big difference is that because I I I so we we worked with that because I I I so we we worked with that because I I I so we we worked with Oracle and later on we the company Oracle and later on we the company Oracle and later on we the company actually got acquired by Caiase that I actually got acquired by Caiase that I actually got acquired by Caiase that I work for. work for. work for. >> Oh yeah. the the I think the and even >> Oh yeah. the the I think the and even >> Oh yeah. the the I think the and even Infformomix was it was Infomix I think Infformomix was it was Infomix I think Infformomix was it was Infomix I think at that time was one of the contender as at that time was one of the contender as at that time was one of the contender as well. well. well. >> I remember Informics. Yeah. >> I remember Informics. Yeah. >> I remember Informics. Yeah. >> But uh but Oracle won because the their >> But uh but Oracle won because the their >> But uh but Oracle won because the their saves engine and strategy was extremely saves engine and strategy was extremely saves engine and strategy was extremely effective. They were basically they were effective. They were basically they were effective. They were basically they were and you have to to give credit to to and you have to to give credit to to and you have to to give credit to to Edison on that. It was it was conquer Edison on that. It was it was conquer Edison on that. It was it was conquer there was they basically and I remember there was they basically and I remember there was they basically and I remember it's very interesting because we we were it's very interesting because we we were it's very interesting because we we were doing services around Oracle and uh they doing services around Oracle and uh they doing services around Oracle and uh they when they started going extremely fast when they started going extremely fast when they started going extremely fast there was all these long tail of Oracle there was all these long tail of Oracle there was all these long tail of Oracle customer they were actually pretty customer they were actually pretty customer they were actually pretty pissed because Oracle had closed the pissed because Oracle had closed the pissed because Oracle had closed the deal put the software and then you on deal put the software and then you on deal put the software and then you on your own to implement it. Oh, your own to implement it. Oh, your own to implement it. Oh, >> and uh now Oracle add services for you >> and uh now Oracle add services for you >> and uh now Oracle add services for you know tuning of the database but I mean know tuning of the database but I mean know tuning of the database but I mean we were doing the same but literally I we were doing the same but literally I we were doing the same but literally I remember being going to uh accounts remember being going to uh accounts remember being going to uh accounts where you know Oracle we say oh we're where you know Oracle we say oh we're where you know Oracle we say oh we're sending you the consultant which will sending you the consultant which will sending you the consultant which will probably charge like $600 an hour probably charge like $600 an hour probably charge like $600 an hour >> right >> right >> right >> the guy would basically come to the >> the guy would basically come to the >> the guy would basically come to the meeting sit and he had this little book meeting sit and he had this little book meeting sit and he had this little book that was Oracle press that was you know that was Oracle press that was you know that was Oracle press that was you know how to tune the database and he was

how to tune the database and he was how to tune the database and he was basically flipping the book and reading basically flipping the book and reading basically flipping the book and reading the book. Oh my god, that's the book. Oh my god, that's the book. Oh my god, that's embarrassing. embarrassing. embarrassing. >> And I had read the book and I knew hit >> And I had read the book and I knew hit >> And I had read the book and I knew hit by heart. I was like, are you paying by heart. I was like, are you paying by heart. I was like, are you paying this dude to read the thing number five? this dude to read the thing number five? this dude to read the thing number five? >> Oh my god. >> Oh my god. >> Oh my god. >> And everybody was happy because you know >> And everybody was happy because you know >> And everybody was happy because you know they had committed million dollars in they had committed million dollars in they had committed million dollars in Oracle database and they had to make it Oracle database and they had to make it Oracle database and they had to make it work now. work now. work now. >> Wow. So but again the their their >> Wow. So but again the their their >> Wow. So but again the their their selling strategy was so aggressive. So selling strategy was so aggressive. So selling strategy was so aggressive. So it was actually the combination of the it was actually the combination of the it was actually the combination of the vision that SQL would impose itself vision that SQL would impose itself vision that SQL would impose itself because it was actually and I keep on because it was actually and I keep on because it was actually and I keep on saying that you it's funny you you saying that you it's funny you you saying that you it's funny you you probably seen some of my early article probably seen some of my early article probably seen some of my early article on digital twins Rob where actually the on digital twins Rob where actually the on digital twins Rob where actually the parallel between SQL and digital twins parallel between SQL and digital twins parallel between SQL and digital twins >> because what people don't realize that >> because what people don't realize that >> because what people don't realize that what was very powerful in SQL was not what was very powerful in SQL was not what was very powerful in SQL was not just the query language but the whole just the query language but the whole just the query language but the whole construct had the the DDL the data construct had the the DDL the data construct had the the DDL the data description language which was you know description language which was you know description language which was you know what is the metadata to create design what is the metadata to create design what is the metadata to create design and implement the schema and implement the schema and implement the schema And then you had the query language and And then you had the query language and And then you had the query language and what we were actually doing you you were what we were actually doing you you were what we were actually doing you you were doing it better itachi but what we were doing it better itachi but what we were doing it better itachi but what we were doing at G digital is oh we had this doing at G digital is oh we had this doing at G digital is oh we had this vision of you know how to talk to the vision of you know how to talk to the vision of you know how to talk to the digital twin so it's kind of the API digital twin so it's kind of the API digital twin so it's kind of the API interface interface interface >> right >> right >> right >> nobody had no vision in terms of >> nobody had no vision in terms of >> nobody had no vision in terms of designing it designing it designing it >> so Microsoft came in with DTML with all >> so Microsoft came in with DTML with all >> so Microsoft came in with DTML with all its limitation later on but my my pitch its limitation later on but my my pitch its limitation later on but my my pitch inside was hey this is what we need to inside was hey this is what we need to inside was hey this is what we need to define that take the same analogy if we define that take the same analogy if we define that take the same analogy if we give people the tool to design the give people the tool to design the give people the tool to design the digital twins the data structures or the digital twins the data structures or the digital twins the data structures or the the construct now object orientize the construct now object orientize the construct now object orientize because it's not just data it's also all because it's not just data it's also all because it's not just data it's also all the methods and then gives them a qu

the methods and then gives them a qu the methods and then gives them a qu language then you have the potential for language then you have the potential for language then you have the potential for a standard and if a standard is adopted a standard and if a standard is adopted a standard and if a standard is adopted look what happened with SQL one big will look what happened with SQL one big will look what happened with SQL one big will win there will be competition and it win there will be competition and it win there will be competition and it would be fun but but it never would be fun but but it never would be fun but but it never materialized so materialized so materialized so >> that's interesting you know when you >> that's interesting you know when you >> that's interesting you know when you think about it all the programming think about it all the programming think about it all the programming languages or whatever you want to call languages or whatever you want to call languages or whatever you want to call them in the world and new ones come them in the world and new ones come them in the world and new ones come along and old ones die but it does seem along and old ones die but it does seem along and old ones die but it does seem the one constant throughout time has the one constant throughout time has the one constant throughout time has been SQL. Um, you know, new things come been SQL. Um, you know, new things come been SQL. Um, you know, new things come and go, but we all still have to know and go, but we all still have to know and go, but we all still have to know SQL to talk to the databases. And when SQL to talk to the databases. And when SQL to talk to the databases. And when if you had to find a language that more if you had to find a language that more if you had to find a language that more people knew broadly, it might be SQL. people knew broadly, it might be SQL. people knew broadly, it might be SQL. Um, Um, Um, >> it probably is because Tut to actually >> it probably is because Tut to actually >> it probably is because Tut to actually lo point in his uh in his keynote. I lo point in his uh in his keynote. I lo point in his uh in his keynote. I mean, we probably have a good 20 I mean mean, we probably have a good 20 I mean mean, we probably have a good 20 I mean the NoSQL thing started to emerge with the NoSQL thing started to emerge with the NoSQL thing started to emerge with the Facebook. So I said like you know the Facebook. So I said like you know the Facebook. So I said like you know 2007 2010. So we probably have a good 30 2007 2010. So we probably have a good 30 2007 2010. So we probably have a good 30 years where all business data will end years where all business data will end years where all business data will end up in aeriational database. up in aeriational database. up in aeriational database. >> Yeah. Well, you know, it's funny you >> Yeah. Well, you know, it's funny you >> Yeah. Well, you know, it's funny you talk about Facebook actually like you talk about Facebook actually like you talk about Facebook actually like you know when Mark Zuckerberg first built know when Mark Zuckerberg first built know when Mark Zuckerberg first built it, he was like a lot of developers it, he was like a lot of developers it, he was like a lot of developers remember the whole lamp stack. Um and so remember the whole lamp stack. Um and so remember the whole lamp stack. Um and so you know he was he built it on PHP uh you know he was he built it on PHP uh you know he was he built it on PHP uh and and he used my SQL uh cuz it was you and and he used my SQL uh cuz it was you and and he used my SQL uh cuz it was you know you're a student in school and know you're a student in school and know you're a student in school and whatever. And then that's how Facebook whatever. And then that's how Facebook whatever. And then that's how Facebook started. I remember how uh and you're started. I remember how uh and you're started. I remember how uh and you're right cuz then all of a sudden at some right cuz then all of a sudden at some right cuz then all of a sudden at some point we started getting all these NoSQL point we started getting all these NoSQL point we started getting all these NoSQL databases and young kids said they're databases and young kids said they're databases and young kids said they're better even though I remind them that

better even though I remind them that better even though I remind them that you know almost all the data on the you know almost all the data on the you know almost all the data on the planet is in relational databases and planet is in relational databases and planet is in relational databases and mainframes and other things and doing mainframes and other things and doing mainframes and other things and doing billions of transactions per day. But billions of transactions per day. But billions of transactions per day. But Facebook actually did a lot of work to Facebook actually did a lot of work to Facebook actually did a lot of work to make my SQL better uh with their make my SQL better uh with their make my SQL better uh with their engineering and then they also uh did engineering and then they also uh did engineering and then they also uh did some stumping to uh PHP. There was I some stumping to uh PHP. There was I some stumping to uh PHP. There was I remember it was called Project Hip Hop remember it was called Project Hip Hop remember it was called Project Hip Hop and it was basically okay a lot of and it was basically okay a lot of and it was basically okay a lot of people knew PHP but it wasn't like some people knew PHP but it wasn't like some people knew PHP but it wasn't like some super performant language for web and super performant language for web and super performant language for web and they did a project hip hop that would they did a project hip hop that would they did a project hip hop that would dynamically compile dynamically compile dynamically compile uh that into C to C code uh to get uh that into C to C code uh to get uh that into C to C code uh to get better performance. um you know better performance. um you know better performance. um you know databases when I think of a non- databases when I think of a non- databases when I think of a non- relational database that I worked with relational database that I worked with relational database that I worked with back a long time ago back a long time ago back a long time ago I remember having to work with a AS400 I remember having to work with a AS400 I remember having to work with a AS400 and the notion of having to this is and the notion of having to this is and the notion of having to this is going to sound strange chain to files going to sound strange chain to files going to sound strange chain to files does that even make any sense it might does that even make any sense it might does that even make any sense it might have been a hierarchical database and it have been a hierarchical database and it have been a hierarchical database and it was just a bizarre way to get to data was just a bizarre way to get to data was just a bizarre way to get to data and someone was saying well I think it's and someone was saying well I think it's and someone was saying well I think it's technically a DB2400 technically a DB2400 technically a DB2400 But you chain the files and and I But you chain the files and and I But you chain the files and and I remember there were I I was like the remember there were I I was like the remember there were I I was like the only I'm on this project. It was uh some only I'm on this project. It was uh some only I'm on this project. It was uh some you know manufacturing company and there you know manufacturing company and there you know manufacturing company and there were a bunch of guys who were RPG were a bunch of guys who were RPG were a bunch of guys who were RPG programmers. Do you remember RPG? So programmers. Do you remember RPG? So programmers. Do you remember RPG? So they they used RPG 400 and I'm sitting they they used RPG 400 and I'm sitting they they used RPG 400 and I'm sitting there with Visual Basic 5 and O uh ODBC there with Visual Basic 5 and O uh ODBC there with Visual Basic 5 and O uh ODBC drivers to try to talk to this thing.

drivers to try to talk to this thing. drivers to try to talk to this thing. >> Yeah. Not not totally surprised >> Yeah. Not not totally surprised >> Yeah. Not not totally surprised actually. to two points to two things of actually. to two points to two things of actually. to two points to two things of what you said and I think you know even what you said and I think you know even what you said and I think you know even when Facebook actually switch at least when Facebook actually switch at least when Facebook actually switch at least for their main feed to a more NoSQL for their main feed to a more NoSQL for their main feed to a more NoSQL because of scalability issues because of scalability issues because of scalability issues >> they kept I don't know if it's still the >> they kept I don't know if it's still the >> they kept I don't know if it's still the case but they kept my SQL for analytics case but they kept my SQL for analytics case but they kept my SQL for analytics for a long long time for a long long time for a long long time >> which is actually the point the problem >> which is actually the point the problem >> which is actually the point the problem is you you you still have and I think is you you you still have and I think is you you you still have and I think still the reality and there was also you still the reality and there was also you still the reality and there was also you know lotus notes at that time where you know lotus notes at that time where you know lotus notes at that time where you had actually this this kind of battle had actually this this kind of battle had actually this this kind of battle between between a structured and between between a structured and between between a structured and unstructured data Yeah. Yeah. unstructured data Yeah. Yeah. unstructured data Yeah. Yeah. >> So structured data makes sense for >> So structured data makes sense for >> So structured data makes sense for everything that is transactional or and everything that is transactional or and everything that is transactional or and I would say your business transaction I would say your business transaction I would say your business transaction because it's an abstraction layers and because it's an abstraction layers and because it's an abstraction layers and make it easy by computers to process it. make it easy by computers to process it. make it easy by computers to process it. >> Yeah. >> Yeah. >> Yeah. >> So much but then you have the reality of >> So much but then you have the reality of >> So much but then you have the reality of as you said your text files and as you said your text files and as you said your text files and paragraphs and language and and post paragraphs and language and and post paragraphs and language and and post feeds which are which will not fit in feeds which are which will not fit in feeds which are which will not fit in well in a SQL. well in a SQL. well in a SQL. >> Right. And now we have the tech that is >> Right. And now we have the tech that is >> Right. And now we have the tech that is actually like AI and all this geni which actually like AI and all this geni which actually like AI and all this geni which is very tweak toler human text.

is very tweak toler human text. is very tweak toler human text. >> Yeah. >> Yeah. >> Yeah. >> But but it doesn't work. That's actually >> But but it doesn't work. That's actually >> But but it doesn't work. That's actually one of the big challenge with the Alison one of the big challenge with the Alison one of the big challenge with the Alison thing Ellison thing is is how how we can thing Ellison thing is is how how we can thing Ellison thing is is how how we can retransform that data into something retransform that data into something retransform that data into something that geni can work very well because that geni can work very well because that geni can work very well because it's only recently even though that and it's only recently even though that and it's only recently even though that and I haven't checked that but I've seen an I haven't checked that but I've seen an I haven't checked that but I've seen an announcement recently with entropic and announcement recently with entropic and announcement recently with entropic and code that that those genai can finally code that that those genai can finally code that that those genai can finally work well with spreadsheets which is a work well with spreadsheets which is a work well with spreadsheets which is a kind of a first level of relational I kind of a first level of relational I kind of a first level of relational I mean at least mean at least mean at least >> it is tables >> it is tables >> it is tables >> I tell people all the time I was like >> I tell people all the time I was like >> I tell people all the time I was like yeah you know think Think of your Excel yeah you know think Think of your Excel yeah you know think Think of your Excel spreadsheet and that workbook that's spreadsheet and that workbook that's spreadsheet and that workbook that's your table. You may have had multiple your table. You may have had multiple your table. You may have had multiple workbooks in a spreadsheet and then workbooks in a spreadsheet and then workbooks in a spreadsheet and then those are multiple tables. You may not those are multiple tables. You may not those are multiple tables. You may not had a relationship because you're right had a relationship because you're right had a relationship because you're right when you're taking a beginner who when you're taking a beginner who when you're taking a beginner who doesn't understand relational theory and doesn't understand relational theory and doesn't understand relational theory and all that. Now where I think where I all that. Now where I think where I all that. Now where I think where I think Oracle and and Edison especially think Oracle and and Edison especially think Oracle and and Edison especially in this speech which I only I only in this speech which I only I only in this speech which I only I only really watched the beginning the the really watched the beginning the the really watched the beginning the the kind of the first two third in details kind of the first two third in details kind of the first two third in details but we because solving the problem for but we because solving the problem for but we because solving the problem for and then this is what I wrote in my my and then this is what I wrote in my my and then this is what I wrote in my my article was mentioning because the the article was mentioning because the the article was mentioning because the the the first level which is taking one the first level which is taking one the first level which is taking one table and transforming you know this of table and transforming you know this of table and transforming you know this of transaction into text is not complicated transaction into text is not complicated transaction into text is not complicated that can be done that can be done that can be done >> right >> right >> right >> problem is the relational so if you have >> problem is the relational so if you have >> problem is the relational so if you have two in the spreadsheet two in the spreadsheet two in the spreadsheet >> and there is a foreign key, primary key >> and there is a foreign key, primary key >> and there is a foreign key, primary key between the two. This is where how do between the two. This is where how do between the two. This is where how do you transform that back into a text that you transform that back into a text that you transform that back into a text that can be used by this is where it gets can be used by this is where it gets can be used by this is where it gets very complicated. So then there is very complicated. So then there is very complicated. So then there is another set of technologies apparently another set of technologies apparently another set of technologies apparently calling you calling you calling you schema embedding and this was my main

schema embedding and this was my main schema embedding and this was my main point which is this is where I start point which is this is where I start point which is this is where I start laughing like hell because designing laughing like hell because designing laughing like hell because designing good schemas I try to sell worldwide. It good schemas I try to sell worldwide. It good schemas I try to sell worldwide. It did kind of work in France and in did kind of work in France and in did kind of work in France and in Germany, but in in the UK and in the US, Germany, but in in the UK and in the US, Germany, but in in the UK and in the US, I tried to sell design tools so that I tried to sell design tools so that I tried to sell design tools so that people could create and maintain the people could create and maintain the people could create and maintain the database schema effectively and database schema effectively and database schema effectively and correctly. correctly. correctly. >> Yeah. >> Yeah. >> Yeah. >> Never worked. >> Never worked. >> Never worked. >> But I I but I love those tools. Uh >> But I I but I love those tools. Uh >> But I I but I love those tools. Uh >> yeah, >> yeah, >> yeah, >> Irwin was one of all the interrer and and was and was our main and and was and was our main competitors. We never reach we reach competitors. We never reach we reach competitors. We never reach we reach position two in the US. We never reach position two in the US. We never reach position two in the US. We never reach position one. But you know this is I position one. But you know this is I position one. But you know this is I mean this is I believe still relevant mean this is I believe still relevant mean this is I believe still relevant even to IoT. But this is the hilarious even to IoT. But this is the hilarious even to IoT. But this is the hilarious part. I'll tell you the whole story and part. I'll tell you the whole story and part. I'll tell you the whole story and it's going to be just three minutes. So it's going to be just three minutes. So it's going to be just three minutes. So we our view because in France there was we our view because in France there was we our view because in France there was actually a methodology called Murray. actually a methodology called Murray. actually a methodology called Murray. You may have heard of it mur methodology You may have heard of it mur methodology You may have heard of it mur methodology where you actually start with a where you actually start with a where you actually start with a conceptual when you want to build a conceptual when you want to build a conceptual when you want to build a system that has data. You start with system that has data. You start with system that has data. You start with what's called a conceptual model. We what's called a conceptual model. We what's called a conceptual model. We say, "Oh, I have a customer. I have an say, "Oh, I have a customer. I have an say, "Oh, I have a customer. I have an order." A customer places an order.

order." A customer places an order. order." A customer places an order. These are all the attributes in the These are all the attributes in the These are all the attributes in the order. An order as a as a core, you order. An order as a as a core, you order. An order as a as a core, you know, elements with shipping and and know, elements with shipping and and know, elements with shipping and and ordering. Then there are all the lines. ordering. Then there are all the lines. ordering. Then there are all the lines. So, you design your concepts So, you design your concepts So, you design your concepts independently of the database. And then independently of the database. And then independently of the database. And then you push a button and say, "Oh, I want you push a button and say, "Oh, I want you push a button and say, "Oh, I want to use relational database oracle or I to use relational database oracle or I to use relational database oracle or I want to use this or that." And then it want to use this or that." And then it want to use this or that." And then it generate the physical implementation generate the physical implementation generate the physical implementation with the primary key, secondary key, with the primary key, secondary key, with the primary key, secondary key, references, joins, all this kind of references, joins, all this kind of references, joins, all this kind of stuff. And then you implement. So we stuff. And then you implement. So we stuff. And then you implement. So we started selling that we are very started selling that we are very started selling that we are very successful in France because we think successful in France because we think successful in France because we think that oh you think first then you blah that oh you think first then you blah that oh you think first then you blah blah and you implement. blah and you implement. blah and you implement. >> Yeah. >> Yeah. >> Yeah. >> Then we went to the US and Owen was >> Then we went to the US and Owen was >> Then we went to the US and Owen was already there and the US said uh you already there and the US said uh you already there and the US said uh you know what I already have an Oracle know what I already have an Oracle know what I already have an Oracle database that I designed in a hurry database that I designed in a hurry database that I designed in a hurry because I wanted to ship my product. Can because I wanted to ship my product. Can because I wanted to ship my product. Can you scan it and give me a schema your you scan it and give me a schema your you scan it and give me a schema your things because I don't really care about things because I don't really care about things because I don't really care about this conceptual model. I want to this conceptual model. I want to this conceptual model. I want to understand how it is right now. So that understand how it is right now. So that understand how it is right now. So that anecdote actually explained the whole anecdote actually explained the whole anecdote actually explained the whole process. So people were much more process. So people were much more process. So people were much more focused on creating tables and kind focused on creating tables and kind focused on creating tables and kind putting references and and doing it a putting references and and doing it a putting references and and doing it a bit the messy way and then they wanted a bit the messy way and then they wanted a bit the messy way and then they wanted a way to re-engineer that and make it way to re-engineer that and make it way to re-engineer that and make it better. The problem is most of the times better. The problem is most of the times better. The problem is most of the times they were not re-engineering and make it they were not re-engineering and make it they were not re-engineering and make it better. So my point is even if you look better. So my point is even if you look better. So my point is even if you look at the schema in the vast majority of at the schema in the vast majority of at the schema in the vast majority of database systems they have been created database systems they have been created database systems they have been created initially not very well they've been initially not very well they've been initially not very well they've been patched many many time over years. So patched many many time over years. So patched many many time over years. So you have redundancy you have violation you have redundancy you have violation you have redundancy you have violation of the code models you have circular of the code models you have circular of the code models you have circular references. So it's a gigantic mess. So references. So it's a gigantic mess. So references. So it's a gigantic mess. So good luck taking those schemas and good luck taking those schemas and good luck taking those schemas and putting them into some AI and expect putting them into some AI and expect putting them into some AI and expect some great result. Good luck.

some great result. Good luck. some great result. Good luck. >> Good luck. >> Good luck. >> Good luck. >> Remember when we used to be obsessed >> Remember when we used to be obsessed >> Remember when we used to be obsessed with things like second, third, fourth, with things like second, third, fourth, with things like second, third, fourth, normal form. normal form. normal form. >> Yeah. >> Yeah. >> Yeah. >> In databases. >> In databases. >> In databases. >> Okay. >> Okay. >> Okay. >> Yeah. So the approach to start from a >> Yeah. So the approach to start from a >> Yeah. So the approach to start from a and actually the interesting thing with and actually the interesting thing with and actually the interesting thing with this conceptual model and actually this this conceptual model and actually this this conceptual model and actually this is a this is the entity relationship uh is a this is the entity relationship uh is a this is the entity relationship uh methodology the equivalent murray and methodology the equivalent murray and methodology the equivalent murray and the antity relationship the the I don't the antity relationship the the I don't the antity relationship the the I don't remember who created that method remember who created that method remember who created that method methodology but the concept are the same methodology but the concept are the same methodology but the concept are the same and the beauty about that is that at the and the beauty about that is that at the and the beauty about that is that at the conceptual level it's independent from conceptual level it's independent from conceptual level it's independent from the implementation so you can use the implementation so you can use the implementation so you can use control file you can use an object control file you can use an object control file you can use an object database it's just when you go from this database it's just when you go from this database it's just when you go from this oh I have all the concept oh I have all the concept oh I have all the concept all the attributes all the the all the attributes all the the all the attributes all the the connections between the data. Now let's connections between the data. Now let's connections between the data. Now let's implement in a relational model in implement in a relational model in implement in a relational model in sequential file in object file you can sequential file in object file you can sequential file in object file you can you have actually different rules. you have actually different rules. you have actually different rules. >> Yeah. >> Yeah. >> Yeah. >> So it's very powerful tool but it >> So it's very powerful tool but it >> So it's very powerful tool but it requires discipline which is a violation requires discipline which is a violation requires discipline which is a violation of the MVP concept. of the MVP concept. of the MVP concept. >> Absolutely. Those crazy MVP people just >> Absolutely. Those crazy MVP people just >> Absolutely. Those crazy MVP people just throwing [ __ ] at the wall and hope it throwing [ __ ] at the wall and hope it throwing [ __ ] at the wall and hope it sticks and works. It's always a balance sticks and works. It's always a balance sticks and works. It's always a balance because we we actually are actually because we we actually are actually because we we actually are actually witness you know in France and to some witness you know in France and to some witness you know in France and to some extent in Germany in some European extent in Germany in some European extent in Germany in some European countries where people would spend like countries where people would spend like countries where people would spend like months in the conceptual model months in the conceptual model months in the conceptual model >> right >> right >> right >> so and that's why the tool was effective >> so and that's why the tool was effective >> so and that's why the tool was effective because the tool allow you to do a first because the tool allow you to do a first because the tool allow you to do a first conceptual model get your implementation conceptual model get your implementation conceptual model get your implementation and then iterate with the different and then iterate with the different and then iterate with the different >> Yeah I know you're right um >> Yeah I know you're right um >> Yeah I know you're right um >> hey what's up >> hey what's up >> hey what's up >> hey we're talking about relational >> hey we're talking about relational >> hey we're talking about relational databases and AI and you know we're kind databases and AI and you know we're kind databases and AI and you know we're kind of kind of going through the our kind of

of kind of going through the our kind of of kind of going through the our kind of our discussion of the Oracle event and our discussion of the Oracle event and our discussion of the Oracle event and Larry Ellison's talk and Demetri Demetri Larry Ellison's talk and Demetri Demetri Larry Ellison's talk and Demetri Demetri had a lot to say about it as you can had a lot to say about it as you can had a lot to say about it as you can imagine and imagine and imagine and >> really >> really >> really >> were you there unlike >> were you there unlike >> were you there unlike >> No I I saw Rob Rob post I said >> No I I saw Rob Rob post I said >> No I I saw Rob Rob post I said >> I was there but he I guess they had it >> I was there but he I guess they had it >> I was there but he I guess they had it online or on YouTube or something. online or on YouTube or something. online or on YouTube or something. >> Yeah. Yeah. So I I saw Rob post and I >> Yeah. Yeah. So I I saw Rob post and I >> Yeah. Yeah. So I I saw Rob post and I said hang on a second and just explain said hang on a second and just explain said hang on a second and just explain the whole idea that Leonard how the whole idea that Leonard how the whole idea that Leonard how fundamentally relational database were fundamentally relational database were fundamentally relational database were created because the technology at the created because the technology at the created because the technology at the time our our the computing industry time our our the computing industry time our our the computing industry wouldn't allow you to process you know wouldn't allow you to process you know wouldn't allow you to process you know human things like orders and customers. human things like orders and customers. human things like orders and customers. So we create this abstraction So we create this abstraction So we create this abstraction >> and now that computers understand human >> and now that computers understand human >> and now that computers understand human text with JI we're going to have to text with JI we're going to have to text with JI we're going to have to retransform back to the to be able to retransform back to the to be able to retransform back to the to be able to use it. So I immediately thought well use it. So I immediately thought well use it. So I immediately thought well relational databases suck for geni. So relational databases suck for geni. So relational databases suck for geni. So how do you solve that problem and I how do you solve that problem and I how do you solve that problem and I don't give a he actually didn't give any don't give a he actually didn't give any don't give a he actually didn't give any >> I I have something I have something >> I I have something I have something >> I I have something I have something it was invented a long time ago.

it was invented a long time ago. it was invented a long time ago. >> Good. Let's read that. It's the freaking >> Good. Let's read that. It's the freaking >> Good. Let's read that. It's the freaking coolest name. It's the coolest data type coolest name. It's the coolest data type coolest name. It's the coolest data type ever. It's called a freaking blob. ever. It's called a freaking blob. ever. It's called a freaking blob. >> Yeah, but the problem >> Yeah, but the problem >> Yeah, but the problem >> everything a blob. >> everything a blob. >> everything a blob. >> Yeah, but >> Yeah, but >> Yeah, but >> make everything a freaking blob. >> make everything a freaking blob. >> make everything a freaking blob. >> No, but yeah, >> No, but yeah, >> No, but yeah, >> but the problem is that creating a blob >> but the problem is that creating a blob >> but the problem is that creating a blob from a set of structural SQL data is from a set of structural SQL data is from a set of structural SQL data is complicated because it was complicated complicated because it was complicated complicated because it was complicated to deblo. to deblo. to deblo. >> But but the blob blob the purpose of the >> But but the blob blob the purpose of the >> But but the blob blob the purpose of the blob was not to it's just a data type. blob was not to it's just a data type. blob was not to it's just a data type. You know, it was interesting because You know, it was interesting because You know, it was interesting because even at that time, I mean, think about even at that time, I mean, think about even at that time, I mean, think about it. The relational database created the it. The relational database created the it. The relational database created the first richest man in the world coming first richest man in the world coming first richest man in the world coming out of Silicon Valley and that was Larry out of Silicon Valley and that was Larry out of Silicon Valley and that was Larry Ellison. People remember that. Ellison. People remember that. Ellison. People remember that. >> Yeah. >> Yeah. >> Yeah. >> He was the richest man in the world for >> He was the richest man in the world for >> He was the richest man in the world for a long time. a long time. a long time. >> Yeah. No, >> Yeah. No, >> Yeah. No, >> that's true. >> that's true. >> that's true. >> Yeah. In this case, he Yeah. You know, >> Yeah. In this case, he Yeah. You know, >> Yeah. In this case, he Yeah. You know, we're wondering why didn't IBM get the we're wondering why didn't IBM get the we're wondering why didn't IBM get the early lead because all the research came early lead because all the research came early lead because all the research came out of IBM. But when we think about it, out of IBM. But when we think about it, out of IBM. But when we think about it, we talk about entrepreneurs and startups we talk about entrepreneurs and startups we talk about entrepreneurs and startups and here on the show often and here on the show often and here on the show often >> and sometimes someone else may have the >> and sometimes someone else may have the >> and sometimes someone else may have the idea, but execution matters and Ellison idea, but execution matters and Ellison idea, but execution matters and Ellison executed in a way that IBM didn't at the executed in a way that IBM didn't at the executed in a way that IBM didn't at the time time time >> and he got early lead.

>> and he got early lead. >> and he got early lead. >> It's actually worse than that because >> It's actually worse than that because >> It's actually worse than that because DB2 came much much later. the pure DB2 came much much later. the pure DB2 came much much later. the pure research of the the the the SQL research of the the the the SQL research of the the the the SQL construct that we explained with with construct that we explained with with construct that we explained with with Rob a second ago with the data Rob a second ago with the data Rob a second ago with the data description language and the query description language and the query description language and the query language was pure research work IBM just language was pure research work IBM just language was pure research work IBM just didn't believe in it initially they didn't believe in it initially they didn't believe in it initially they didn't implement anything they started didn't implement anything they started didn't implement anything they started implementing much later when Oracle implementing much later when Oracle implementing much later when Oracle >> but you got to remember what Larry did >> but you got to remember what Larry did >> but you got to remember what Larry did he applied it and created the he applied it and created the he applied it and created the application layer application layer application layer >> this is this is a miss it was much later >> this is this is a miss it was much later >> this is this is a miss it was much later in the beginning it was No, it's much in the beginning it was No, it's much in the beginning it was No, it's much much later. It was in the mid to late much later. It was in the mid to late much later. It was in the mid to late 90s in the original. The original was 90s in the original. The original was 90s in the original. The original was just a basic technology. What Larry did just a basic technology. What Larry did just a basic technology. What Larry did which we discussed with Rob is he had an which we discussed with Rob is he had an which we discussed with Rob is he had an extremely aggressive selling strategy extremely aggressive selling strategy extremely aggressive selling strategy where he was really basically concurring where he was really basically concurring where he was really basically concurring going to an account selling going away going to an account selling going away going to an account selling going away don't providing any support selling don't providing any support selling don't providing any support selling expensive consultant to help people expensive consultant to help people expensive consultant to help people figure it out. But he was doing deals figure it out. But he was doing deals figure it out. But he was doing deals deals deals deals. So the go-to market deals deals deals. So the go-to market deals deals deals. So the go-to market was extremely strong. But to your point was extremely strong. But to your point was extremely strong. But to your point in the mid90s when people started to in the mid90s when people started to in the mid90s when people started to realize oh I pay all those million realize oh I pay all those million realize oh I pay all those million dollars in databases and I don't know dollars in databases and I don't know dollars in databases and I don't know what to use because it's a technology he what to use because it's a technology he what to use because it's a technology he realized that the switch to applications realized that the switch to applications realized that the switch to applications to pull or to maintain the platform was to pull or to maintain the platform was to pull or to maintain the platform was extremely important. So that was phase extremely important. So that was phase extremely important. So that was phase two. He he was the first one to two. He he was the first one to two. He he was the first one to understand the concept that the understand the concept that the understand the concept that the applications pull or in his case keep applications pull or in his case keep applications pull or in his case keep the platform.

the platform. the platform. >> Yeah. My first my first version of >> Yeah. My first my first version of >> Yeah. My first my first version of Oracle that I ever worked with Oracle that I ever worked with Oracle that I ever worked with >> I think was in 94 I think it was Oracle >> I think was in 94 I think it was Oracle >> I think was in 94 I think it was Oracle 7 was the first time I ever played with 7 was the first time I ever played with 7 was the first time I ever played with it. it. it. >> Yeah. I still was four so it was much >> Yeah. I still was four so it was much >> Yeah. I still was four so it was much earlier. earlier. earlier. >> Prior to that I was >> Prior to that I was >> Prior to that I was >> old. >> old. >> old. >> Prior to that Yeah. Yeah. Luckily I'm >> Prior to that Yeah. Yeah. Luckily I'm >> Prior to that Yeah. Yeah. Luckily I'm only 25. Can't you tell? only 25. Can't you tell? only 25. Can't you tell? >> Yeah. >> Yeah. >> Yeah. >> Well, if you look If you look If you >> Well, if you look If you look If you >> Well, if you look If you look If you look at Larson, sorry to make a point look at Larson, sorry to make a point look at Larson, sorry to make a point here during this keynote. He looks like here during this keynote. He looks like here during this keynote. He looks like a 120 years old guys are trying to look a 120 years old guys are trying to look a 120 years old guys are trying to look like 60. like 60. like 60. >> He's had some work done. >> He's had some work done. >> He's had some work done. >> Yeah. >> Yeah. >> Yeah. >> Multiple times. >> Multiple times. >> Multiple times. >> Some major work. >> Some major work. >> Some major work. >> Yeah. He Yeah, because he's I think 81 >> Yeah. He Yeah, because he's I think 81 >> Yeah. He Yeah, because he's I think 81 or 82 years old. or 82 years old. or 82 years old. >> So, the fact that he's still getting up >> So, the fact that he's still getting up >> So, the fact that he's still getting up there. Now, here's the thing. We were there. Now, here's the thing. We were there. Now, here's the thing. We were wondering what was going on because they wondering what was going on because they wondering what was going on because they delayed his keynote by an hour. delayed his keynote by an hour. delayed his keynote by an hour. Everybody's lined up to go in and then Everybody's lined up to go in and then Everybody's lined up to go in and then all of a sudden they're like, "Oh, he's all of a sudden they're like, "Oh, he's all of a sudden they're like, "Oh, he's not going to come on till 2:30 instead not going to come on till 2:30 instead not going to come on till 2:30 instead of 1:30." And then people started going, of 1:30." And then people started going, of 1:30." And then people started going, "What's going on? Did he have a heart "What's going on? Did he have a heart "What's going on? Did he have a heart attack or is, you know, attack or is, you know, attack or is, you know, >> waiting for the drug to kick in, >> waiting for the drug to kick in, >> waiting for the drug to kick in, >> wait for the drug to kick in so he can >> wait for the drug to kick in so he can >> wait for the drug to kick in so he can be Yeah. No.

be Yeah. No. be Yeah. No. >> And then and then there's the giant >> And then and then there's the giant >> And then and then there's the giant room, room, room, but then when you look at where he was but then when you look at where he was but then when you look at where he was sitting, it looked like they created sitting, it looked like they created sitting, it looked like they created another stage somewhere else and they another stage somewhere else and they another stage somewhere else and they were kind of projecting it. They It were kind of projecting it. They It were kind of projecting it. They It looked like he wasn't really on the looked like he wasn't really on the looked like he wasn't really on the stage in the big room. It looked like stage in the big room. It looked like stage in the big room. It looked like they recreated what looked like the they recreated what looked like the they recreated what looked like the stage and then filmed it onto it. I stage and then filmed it onto it. I stage and then filmed it onto it. I don't know. It looked kind of weird, don't know. It looked kind of weird, don't know. It looked kind of weird, like it was somewhere else and not in like it was somewhere else and not in like it was somewhere else and not in the actual room. Uh, but you're right. the actual room. Uh, but you're right. the actual room. Uh, but you're right. It might They may have had to wait for It might They may have had to wait for It might They may have had to wait for the drugs to kick in the drugs to kick in the drugs to kick in >> or maybe it's his digital twin and his I >> or maybe it's his digital twin and his I >> or maybe it's his digital twin and his I noticed like sometimes there was a noticed like sometimes there was a noticed like sometimes there was a flicker and it was like the Matrix or or flicker and it was like the Matrix or or flicker and it was like the Matrix or or or Tupac or Michael Jackson on stage as or Tupac or Michael Jackson on stage as or Tupac or Michael Jackson on stage as a Avatar. a Avatar. a Avatar. >> You see, you see Black. Yeah, exactly. >> You see, you see Black. Yeah, exactly. >> You see, you see Black. Yeah, exactly. Exactly. So, so for me first was dbase. Exactly. So, so for me first was dbase. Exactly. So, so for me first was dbase. Um, then Paradox. Do you remember Um, then Paradox. Do you remember Um, then Paradox. Do you remember Paradox? Paradox? Paradox? >> Yeah, Paradox was there. >> Yeah, Paradox was there. >> Yeah, Paradox was there. >> That was kind of the pre was kind of >> That was kind of the pre was kind of >> That was kind of the pre was kind of halfway to SQL or three.

halfway to SQL or three. halfway to SQL or three. >> Yeah, I did Paradox. I did Fox Pro. >> Yeah, I did Paradox. I did Fox Pro. >> Yeah, I did Paradox. I did Fox Pro. >> Fox Pro >> Fox Pro >> Fox Pro >> and then DB2. >> and then DB2. >> and then DB2. >> I did I didn't do DB2 a little later. I >> I did I didn't do DB2 a little later. I >> I did I didn't do DB2 a little later. I did do like I kind of mentioned the did do like I kind of mentioned the did do like I kind of mentioned the chain. I did DB2 400. Um I did access chain. I did DB2 400. Um I did access chain. I did DB2 400. Um I did access and then access was the gateway drug to and then access was the gateway drug to and then access was the gateway drug to SQL server. SQL server. SQL server. Um there you Um there you Um there you >> well there was nothing in common. SQL >> well there was nothing in common. SQL >> well there was nothing in common. SQL server is something that Microsoft stole server is something that Microsoft stole server is something that Microsoft stole from cybase. I remember I was at the from cybase. I remember I was at the from cybase. I remember I was at the time. time. time. >> That's right. >> That's right. >> That's right. >> Case was awesome. >> Case was awesome. >> Case was awesome. >> I remember doing cybase. Yeah. >> I remember doing cybase. Yeah. >> I remember doing cybase. Yeah. >> Transact transacts SQL. Right. >> Transact transacts SQL. Right. >> Transact transacts SQL. Right. >> Yeah. TSQL. That's right. >> Yeah. TSQL. That's right. >> Yeah. TSQL. That's right. >> And Oracle was PL/SQL. Right. >> And Oracle was PL/SQL. Right. >> And Oracle was PL/SQL. Right. >> Yeah. Much much much later. No, it it >> Yeah. Much much much later. No, it it >> Yeah. Much much much later. No, it it was uh PL/SQL was programming part of was uh PL/SQL was programming part of was uh PL/SQL was programming part of it. But it. But it. But >> but um yeah, >> but um yeah, >> but um yeah, >> SQL had programming capabilities from >> SQL had programming capabilities from >> SQL had programming capabilities from the very very the very very the very very >> but and you remember what was the big >> but and you remember what was the big >> but and you remember what was the big war at that time? The big technology war war at that time? The big technology war war at that time? The big technology war that time between Oracle and CBase.

that time between Oracle and CBase. that time between Oracle and CBase. >> Triggers. >> Triggers. >> Triggers. >> Oh no. Triggers was a piece of [ __ ] >> Oh no. Triggers was a piece of [ __ ] >> Oh no. Triggers was a piece of [ __ ] technology that put in secrets. technology that put in secrets. technology that put in secrets. Yeah, but Yeah, but Yeah, but >> it was like MCP of today, right? >> it was like MCP of today, right? >> it was like MCP of today, right? >> No, there was those kind of procedure >> No, there was those kind of procedure >> No, there was those kind of procedure call you had in their their user call you had in their their user call you had in their their user interface that was previsual basic. I I interface that was previsual basic. I I interface that was previsual basic. I I the company I work for, we were doing the company I work for, we were doing the company I work for, we were doing services on that. Every single customer services on that. Every single customer services on that. Every single customer we talked to hated the triggers in in in we talked to hated the triggers in in in we talked to hated the triggers in in in in B SQL. in B SQL. in B SQL. >> What was the war? We were always >> What was the war? We were always >> What was the war? We were always the war was page level locking versus the war was page level locking versus the war was page level locking versus role level locking. Oh role level locking. Oh role level locking. Oh >> yeah. Because because >> yeah. Because because >> yeah. Because because >> yeah because cybase was doing page level >> yeah because cybase was doing page level >> yeah because cybase was doing page level locking because it was much faster the locking because it was much faster the locking because it was much faster the time and Oracle was doing role level time and Oracle was doing role level time and Oracle was doing role level locking which mean that every sing was locking which mean that every sing was locking which mean that every sing was going through a transaction consistency going through a transaction consistency going through a transaction consistency issue and Oracle up to seven actually issue and Oracle up to seven actually issue and Oracle up to seven actually had serious scalability issue because of had serious scalability issue because of had serious scalability issue because of that. But obviously if you only use page that. But obviously if you only use page that. But obviously if you only use page level locking you have to be much more level locking you have to be much more level locking you have to be much more conscious at the transaction and how you conscious at the transaction and how you conscious at the transaction and how you make them work. make them work. make them work. >> Absolutely. Yep. doing role level >> Absolutely. Yep. doing role level >> Absolutely. Yep. doing role level locking was a considered a superior locking was a considered a superior locking was a considered a superior better way to do it but to your point it better way to do it but to your point it better way to do it but to your point it could hurt scalability.

could hurt scalability. could hurt scalability. >> It is at the time because technology was >> It is at the time because technology was >> It is at the time because technology was limited but it was actually the right limited but it was actually the right limited but it was actually the right direction. But the thing is you know the direction. But the thing is you know the direction. But the thing is you know the the the the main reason why I believe uh the the the main reason why I believe uh the the the main reason why I believe uh Oracle won the war against cybase is Oracle won the war against cybase is Oracle won the war against cybase is because cybase was very tied to a because cybase was very tied to a because cybase was very tied to a specific industry and a specific specific industry and a specific specific industry and a specific partnership. It was really financial partnership. It was really financial partnership. It was really financial instit in institution Wall Street instit in institution Wall Street instit in institution Wall Street transaction some cybase and unbeatable transaction some cybase and unbeatable transaction some cybase and unbeatable and cybase is still was still 10 years and cybase is still was still 10 years and cybase is still was still 10 years ago and it's probably still the case 70% ago and it's probably still the case 70% ago and it's probably still the case 70% of Wall Street was still running on of Wall Street was still running on of Wall Street was still running on cybase. Yeah, but it but that was about cybase. Yeah, but it but that was about cybase. Yeah, but it but that was about the time that ERP was really taking off, the time that ERP was really taking off, the time that ERP was really taking off, right? I remember right? I remember right? I remember >> that was way before. No, that >> that was way before. No, that >> that was way before. No, that >> when it was getting really ridiculous. >> when it was getting really ridiculous. >> when it was getting really ridiculous. >> That's true. The battle was already >> That's true. The battle was already >> That's true. The battle was already >> they were not part of that that they >> they were not part of that that they >> they were not part of that that they were not part of that party. I mean, were not part of that party. I mean, were not part of that party. I mean, when you think about what the, you know, when you think about what the, you know, when you think about what the, you know, the accounting firms were doing, you the accounting firms were doing, you the accounting firms were doing, you know, the prei know, the prei know, the prei guys were doing at the time, they were, guys were doing at the time, they were, guys were doing at the time, they were, I mean, um, cuz I lived it. I saw it, I mean, um, cuz I lived it. I saw it, I mean, um, cuz I lived it. I saw it, right? And I, and you know, I barely saw right? And I, and you know, I barely saw right? And I, and you know, I barely saw Caiase at the party.

Caiase at the party. Caiase at the party. >> Uh, at all. It was all Oracle and at the >> Uh, at all. It was all Oracle and at the >> Uh, at all. It was all Oracle and at the app level it was SAP. app level it was SAP. app level it was SAP. >> Well, we we talked about that before you >> Well, we we talked about that before you >> Well, we we talked about that before you joined. joined. joined. >> We talked about that before you join, >> We talked about that before you join, >> We talked about that before you join, you in a sense that I was saying that you in a sense that I was saying that you in a sense that I was saying that the two things that Larry Edison did the two things that Larry Edison did the two things that Larry Edison did extremely well. The first one is the extremely well. The first one is the extremely well. The first one is the vision that SQL would be the will become vision that SQL would be the will become vision that SQL would be the will become had the potential to become a standard had the potential to become a standard had the potential to become a standard and the second one you understood the and the second one you understood the and the second one you understood the concept of the application pulling the concept of the application pulling the concept of the application pulling the platform. platform. platform. >> Yeah. But you know what you know your >> Yeah. But you know what you know your >> Yeah. But you know what you know your earlier statement that you know um um earlier statement that you know um um earlier statement that you know um um our relational databases suck for Gen our relational databases suck for Gen our relational databases suck for Gen AI. I was like well who gives a [ __ ] AI. I was like well who gives a [ __ ] AI. I was like well who gives a [ __ ] because Gen AI is pretty shitty for a because Gen AI is pretty shitty for a because Gen AI is pretty shitty for a lot of other stuff. So, you know, um, lot of other stuff. So, you know, um, lot of other stuff. So, you know, um, here's the thing that I I, you know, I'm here's the thing that I I, you know, I'm here's the thing that I I, you know, I'm increasingly realizing as I study this increasingly realizing as I study this increasingly realizing as I study this whole idea of AI factory. So, I was at whole idea of AI factory. So, I was at whole idea of AI factory. So, I was at OCP, right? Um, and you know, this stuff OCP, right? Um, and you know, this stuff OCP, right? Um, and you know, this stuff is the old stuff is not going to go is the old stuff is not going to go is the old stuff is not going to go away. You know, the this brown field, away. You know, the this brown field, away. You know, the this brown field, this massive brown field, you you're this massive brown field, you you're this massive brown field, you you're going to have to deal with it. And going to have to deal with it. And going to have to deal with it. And you're going to also have to deal with you're going to also have to deal with you're going to also have to deal with the fact that some of the old way of the fact that some of the old way of the fact that some of the old way of doing stuff is way more efficient than doing stuff is way more efficient than doing stuff is way more efficient than dumping that crap into a large language dumping that crap into a large language dumping that crap into a large language model or and then you know here's the model or and then you know here's the model or and then you know here's the other funny thing I was listening in on other funny thing I was listening in on other funny thing I was listening in on the dream dream force the dream dream force the dream dream force >> thing. The most hilarious thing that >> thing. The most hilarious thing that >> thing. The most hilarious thing that they introduced into agent force is this they introduced into agent force is this they introduced into agent force is this thing called agent scripts. Did you see thing called agent scripts. Did you see thing called agent scripts. Did you see that? I posted about it. This is like that? I posted about it. This is like that? I posted about it. This is like literally working your way all the way literally working your way all the way literally working your way all the way backwards. And basically what is the the

backwards. And basically what is the the backwards. And basically what is the the idea is that you can embed code into idea is that you can embed code into idea is that you can embed code into your agent configuration. your agent configuration. your agent configuration. It's like well then what the hell is the It's like well then what the hell is the It's like well then what the hell is the use of the the agent itself right when use of the the agent itself right when use of the the agent itself right when you're you're you're going to be coding you're you're you're going to be coding you're you're you're going to be coding x percentage of what it's supposed to x percentage of what it's supposed to x percentage of what it's supposed to do. So, not only are you isolating the do. So, not only are you isolating the do. So, not only are you isolating the large language model in into sort of large language model in into sort of large language model in into sort of this agentic shell within the agent this agentic shell within the agent this agentic shell within the agent itself, you're making it programmatic. itself, you're making it programmatic. itself, you're making it programmatic. And then when you start to see all the And then when you start to see all the And then when you start to see all the the um configuration you have to do to the um configuration you have to do to the um configuration you have to do to make this thing do consistently and with make this thing do consistently and with make this thing do consistently and with quality what you want it to do, it's a quality what you want it to do, it's a quality what you want it to do, it's a [ __ ] ton of work. You know, I I'm like [ __ ] ton of work. You know, I I'm like [ __ ] ton of work. You know, I I'm like I'm sitting there looking at this thing I'm sitting there looking at this thing I'm sitting there looking at this thing going, "Okay, okay, if I was an going, "Okay, okay, if I was an going, "Okay, okay, if I was an administrator, how difficult would this administrator, how difficult would this administrator, how difficult would this be to manage, right? All these people be to manage, right? All these people be to manage, right? All these people going off creating all these rogue going off creating all these rogue going off creating all these rogue agents and then you as a a user, how agents and then you as a a user, how agents and then you as a a user, how much do you have to learn to actually much do you have to learn to actually much do you have to learn to actually make these things do what you want to do make these things do what you want to do make these things do what you want to do at scale?" Right? You know, we I I at scale?" Right? You know, we I I at scale?" Right? You know, we I I >> It's nothing It's nothing close to this >> It's nothing It's nothing close to this >> It's nothing It's nothing close to this fire and forget notion. This uh what do fire and forget notion. This uh what do fire and forget notion. This uh what do you call it? This no code idea that that you call it? This no code idea that that you call it? This no code idea that that was wrapped around aentic AI just 6 was wrapped around aentic AI just 6 was wrapped around aentic AI just 6 months ago.

months ago. months ago. >> But I think you >> But I think you >> But I think you >> Yeah, but I I haven't details of that. I >> Yeah, but I I haven't details of that. I >> Yeah, but I I haven't details of that. I will. But my my the what pops to mind is will. But my my the what pops to mind is will. But my my the what pops to mind is geni as we were saying earlier is very geni as we were saying earlier is very geni as we were saying earlier is very effective when you work on your human effective when you work on your human effective when you work on your human text and social text. Now when you start text and social text. Now when you start text and social text. Now when you start getting into the world of data whether getting into the world of data whether getting into the world of data whether it's databases Oracle and Salesforce it it's databases Oracle and Salesforce it it's databases Oracle and Salesforce it is in relational data model. So you have is in relational data model. So you have is in relational data model. So you have to transform back this data in something to transform back this data in something to transform back this data in something that the geni will understand and I that the geni will understand and I that the geni will understand and I suspect that this cryptic thing is suspect that this cryptic thing is suspect that this cryptic thing is exactly that he's saying go talk to the exactly that he's saying go talk to the exactly that he's saying go talk to the data bring it codes to get it into a data bring it codes to get it into a data bring it codes to get it into a package form that you can send it to the package form that you can send it to the package form that you can send it to the geni so you can understand it I suspect geni so you can understand it I suspect geni so you can understand it I suspect to solve that problem which is the to solve that problem which is the to solve that problem which is the hardest problem that you know Alison hardest problem that you know Alison hardest problem that you know Alison spoke for you know an hour and a half spoke for you know an hour and a half spoke for you know an hour and a half and he was like a good 45 minutes on and he was like a good 45 minutes on and he was like a good 45 minutes on this he didn't explain at all how this this he didn't explain at all how this this he didn't explain at all how this is going to work. So, but is going to work. So, but is going to work. So, but >> yeah. Yeah. Yeah. I mean, you know, um >> yeah. Yeah. Yeah. I mean, you know, um >> yeah. Yeah. Yeah. I mean, you know, um what was it 25 what was it 25 what was it 25 uh almost 30 years ago? Oh, Jesus uh almost 30 years ago? Oh, Jesus uh almost 30 years ago? Oh, Jesus Christ. Yeah. Um he was saying Christ. Yeah. Um he was saying Christ. Yeah. Um he was saying um we have a a multilanguage database.

um we have a a multilanguage database. um we have a a multilanguage database. And so if you create a somebody creates And so if you create a somebody creates And so if you create a somebody creates an invoice over here in Japan and you an invoice over here in Japan and you an invoice over here in Japan and you know somebody in the US wants to look it know somebody in the US wants to look it know somebody in the US wants to look it up, well guess what? We solved that up, well guess what? We solved that up, well guess what? We solved that problem. No, you didn't. problem. No, you didn't. problem. No, you didn't. >> Well, that was application. That was >> Well, that was application. That was >> Well, that was application. That was application. was 25 years ago. Yeah. So, application. was 25 years ago. Yeah. So, application. was 25 years ago. Yeah. So, you know, um you got to double click on you know, um you got to double click on you know, um you got to double click on all this stuff and some of all this all this stuff and some of all this all this stuff and some of all this stuff is nice thinking. stuff is nice thinking. stuff is nice thinking. >> Yeah. >> Yeah. >> Yeah. >> You know, you know, I just got off a a >> You know, you know, I just got off a a >> You know, you know, I just got off a a briefing call and you know, I was just briefing call and you know, I was just briefing call and you know, I was just pitched a bunch of, you know, marketing pitched a bunch of, you know, marketing pitched a bunch of, you know, marketing hoopla and you you double click on a hoopla and you you double click on a hoopla and you you double click on a couple things. There's like nothing couple things. There's like nothing couple things. There's like nothing there. I mean, you know, it's just you there. I mean, you know, it's just you there. I mean, you know, it's just you can't you can't you know what? Somebody can't you can't you know what? Somebody can't you can't you know what? Somebody told me at the conference, if you want told me at the conference, if you want told me at the conference, if you want to learn something, don't talk to to learn something, don't talk to to learn something, don't talk to management. management. management. >> Boom. >> Boom. >> Boom. >> If you want to know what's going on, >> If you want to know what's going on, >> If you want to know what's going on, don't talk to management. don't talk to management. don't talk to management. >> Except if you want to learn about how to >> Except if you want to learn about how to >> Except if you want to learn about how to make money. But uh anyway, by the way, make money. But uh anyway, by the way, make money. But uh anyway, by the way, >> maybe not even that. >> maybe not even that. >> maybe not even that. >> I think that the I think there was a I >> I think that the I think there was a I >> I think that the I think there was a I mean to maybe to reanchor back into the mean to maybe to reanchor back into the mean to maybe to reanchor back into the the theme of the of our series here, the theme of the of our series here, the theme of the of our series here, there was a second part which I watched there was a second part which I watched there was a second part which I watched much much more much more quickly. So you much much more much more quickly. So you much much more much more quickly. So you might have more insight than I Rob but might have more insight than I Rob but might have more insight than I Rob but there was a whole stuff about you know there was a whole stuff about you know there was a whole stuff about you know IoT visionary subject that Laris uncover IoT visionary subject that Laris uncover IoT visionary subject that Laris uncover which I actually found again it didn't which I actually found again it didn't which I actually found again it didn't give any give any give any practical exact example but there was a practical exact example but there was a practical exact example but there was a lot so the focus I I I presently lot so the focus I I I presently lot so the focus I I I presently surprised but you know everything surprised but you know everything surprised but you know everything relating to health everything related to relating to health everything related to relating to health everything related to agriculture and so there was a lot of agriculture and so there was a lot of agriculture and so there was a lot of IoT related getting IoT data and using

IoT related getting IoT data and using IoT related getting IoT data and using that that was actually more interesting that that was actually more interesting that that was actually more interesting that the whole oh we have the data and that the whole oh we have the data and that the whole oh we have the data and data database because it's a private data database because it's a private data database because it's a private data and with JI you're going to get data and with JI you're going to get data and with JI you're going to get rich and I'm going to get rich no all rich and I'm going to get rich no all rich and I'm going to get rich no all IoT so maybe you can comment more than I IoT so maybe you can comment more than I IoT so maybe you can comment more than I on that Rob but it was it was actually on that Rob but it was it was actually on that Rob but it was it was actually find quite interesting find quite interesting find quite interesting >> yeah I mean I think I think the point of >> yeah I mean I think I think the point of >> yeah I mean I think I think the point of once a year putting Larry out there and once a year putting Larry out there and once a year putting Larry out there and and we don't know how much longer he's and we don't know how much longer he's and we don't know how much longer he's going to be able to keep doing this going to be able to keep doing this going to be able to keep doing this because he like you know he's 82 81 because he like you know he's 82 81 because he like you know he's 82 81 >> 200 more years >> 200 more years >> 200 more years >> and he was but he was uh I think the >> and he was but he was uh I think the >> and he was but he was uh I think the point of it is hey here's our leader and point of it is hey here's our leader and point of it is hey here's our leader and he's going to pontificate on some highle he's going to pontificate on some highle he's going to pontificate on some highle concepts concepts concepts Um and and so he kind of talked at high Um and and so he kind of talked at high Um and and so he kind of talked at high level about AI. He told you know there's level about AI. He told you know there's level about AI. He told you know there's a lot of us who already know everything a lot of us who already know everything a lot of us who already know everything he's talking about. There's many many he's talking about. There's many many he's talking about. There's many many more people who have never heard that in more people who have never heard that in more people who have never heard that in their life and so it probably seemed their life and so it probably seemed their life and so it probably seemed insightful to them. Um insightful to them. Um insightful to them. Um >> and then he and then yeah you're right. >> and then he and then yeah you're right. >> and then he and then yeah you're right. He talked about you know because the He talked about you know because the He talked about you know because the only real announcement at the whole only real announcement at the whole only real announcement at the whole event if I had to really break it down event if I had to really break it down event if I had to really break it down was we got a new database. You know was we got a new database. You know was we got a new database. You know >> what which one was that? >> what which one was that? >> what which one was that? >> Number 26. uh you know database 26 AI um >> Number 26. uh you know database 26 AI um >> Number 26. uh you know database 26 AI um you know and he talked about how we're you know and he talked about how we're you know and he talked about how we're gonna gonna gonna he had he had he had >> autonomous is this like beyond how is it >> autonomous is this like beyond how is it >> autonomous is this like beyond how is it different from autonomous database?

different from autonomous database? different from autonomous database? Well, it it it's at the core of Well, it it it's at the core of Well, it it it's at the core of autonomous. Autonomous is kind of an autonomous. Autonomous is kind of an autonomous. Autonomous is kind of an higher level construct that makes sure higher level construct that makes sure higher level construct that makes sure it can do everything without people. But it can do everything without people. But it can do everything without people. But the core of the database itself is just, the core of the database itself is just, the core of the database itself is just, hey, it's 26. And what he was getting at hey, it's 26. And what he was getting at hey, it's 26. And what he was getting at was, hey, most of the world's data is in was, hey, most of the world's data is in was, hey, most of the world's data is in Oracle databases and all these Oracle databases and all these Oracle databases and all these businesses use it. They know SQL. businesses use it. They know SQL. businesses use it. They know SQL. They're not going to rip out all their They're not going to rip out all their They're not going to rip out all their tables, but they want to do this AI tables, but they want to do this AI tables, but they want to do this AI stuff. And he was kind they're just kind stuff. And he was kind they're just kind stuff. And he was kind they're just kind of talking about how you could do a of talking about how you could do a of talking about how you could do a combination, you know, how it could combination, you know, how it could combination, you know, how it could ingest data, vectorize it in ve ingest data, vectorize it in ve ingest data, vectorize it in ve integrated vector databases like we use integrated vector databases like we use integrated vector databases like we use to do AI uh cuz that's what all those to do AI uh cuz that's what all those to do AI uh cuz that's what all those things use under the hood. things use under the hood. things use under the hood. >> And it was it was kind of like almost >> And it was it was kind of like almost >> And it was it was kind of like almost like can you do joins across your like can you do joins across your like can you do joins across your relational tables of your business and relational tables of your business and relational tables of your business and the vectorzed stuff and and blend it and the vectorzed stuff and and blend it and the vectorzed stuff and and blend it and [ __ ] like that. [ __ ] like that. [ __ ] like that. when when the very first slide I saw when when the very first slide I saw when when the very first slide I saw you. So yeah, obviously we had the you. So yeah, obviously we had the you. So yeah, obviously we had the analyst day the very first day where we analyst day the very first day where we analyst day the very first day where we learned everything that they were learned everything that they were learned everything that they were ultimately going to talk about at the ultimately going to talk about at the ultimately going to talk about at the keynote and they had a simple slide that keynote and they had a simple slide that keynote and they had a simple slide that I understand and I kind of talk about a I understand and I kind of talk about a I understand and I kind of talk about a lot. It was like you've got your private lot. It was like you've got your private lot. It was like you've got your private data in this database and you've got data in this database and you've got data in this database and you've got here's a pre-trained LLM and we're going here's a pre-trained LLM and we're going here's a pre-trained LLM and we're going to put it together and you know you're to put it together and you know you're to put it together and you know you're going to get your private AI for your going to get your private AI for your going to get your private AI for your company. You know, I talk about that all company. You know, I talk about that all company. You know, I talk about that all the time, having the enterprise brain.

the time, having the enterprise brain. the time, having the enterprise brain. And so I was like, yeah, the simple And so I was like, yeah, the simple And so I was like, yeah, the simple concept makes a lot of sense. People concept makes a lot of sense. People concept makes a lot of sense. People want to know at a high level, how do I want to know at a high level, how do I want to know at a high level, how do I use my corporate data and train an LLM use my corporate data and train an LLM use my corporate data and train an LLM to get all that cool value that I'm to get all that cool value that I'm to get all that cool value that I'm seeing in chat, GPT, and Claude, but I seeing in chat, GPT, and Claude, but I seeing in chat, GPT, and Claude, but I want my own private thing inside my want my own private thing inside my want my own private thing inside my company with my private data that's not company with my private data that's not company with my private data that's not been exposed to OpenAI or something like been exposed to OpenAI or something like been exposed to OpenAI or something like that. And so they were just I think that. And so they were just I think that. And so they were just I think they're just trying to simplify that. I they're just trying to simplify that. I they're just trying to simplify that. I have thought in my mind how do I build have thought in my mind how do I build have thought in my mind how do I build that myself because I talk about it a that myself because I talk about it a that myself because I talk about it a lot and I conceptualize how I would do lot and I conceptualize how I would do lot and I conceptualize how I would do that for a customer if I was doing a that for a customer if I was doing a that for a customer if I was doing a startup or a consulting thing and I in startup or a consulting thing and I in startup or a consulting thing and I in my mind I'm like okay I'd probably just my mind I'm like okay I'd probably just my mind I'm like okay I'd probably just grab Postgress and Postgress has PG grab Postgress and Postgress has PG grab Postgress and Postgress has PG vector because Process has all those vector because Process has all those vector because Process has all those plugins. plugins. plugins. >> Yeah. Yeah. Yeah. I would cobble >> Yeah. Yeah. Yeah. I would cobble >> Yeah. Yeah. Yeah. I would cobble something together probably with that something together probably with that something together probably with that and then I'd use my my latest IoT and then I'd use my my latest IoT and then I'd use my my latest IoT Thunderruck HP3 telemetry ingestion Thunderruck HP3 telemetry ingestion Thunderruck HP3 telemetry ingestion engine to pour stuff in there and then engine to pour stuff in there and then engine to pour stuff in there and then you got to you want to train fine-tune you got to you want to train fine-tune you got to you want to train fine-tune and rag out your your model there and rag out your your model there and rag out your your model there >> corporate data something like that and >> corporate data something like that and >> corporate data something like that and so I feel like they're just trying to do so I feel like they're just trying to do so I feel like they're just trying to do the same thing uh that I would cobble the same thing uh that I would cobble the same thing uh that I would cobble together and just have it built into a together and just have it built into a together and just have it built into a database and so they can sell more database and so they can sell more database and so they can sell more databases. Um, the other thing he talked databases. Um, the other thing he talked databases. Um, the other thing he talked about, well, he did say one thing that's about, well, he did say one thing that's about, well, he did say one thing that's really obvious, a lot of companies and a really obvious, a lot of companies and a really obvious, a lot of companies and a lot of wealthy individuals are pouring lot of wealthy individuals are pouring lot of wealthy individuals are pouring all the money they have into the this all the money they have into the this all the money they have into the this infrastructure that's being built right infrastructure that's being built right infrastructure that's being built right now. As you can imagine, they showed now. As you can imagine, they showed now. As you can imagine, they showed lots of videos, you know, with Clay

lots of videos, you know, with Clay lots of videos, you know, with Clay walking around with a hard hat on in walking around with a hard hat on in walking around with a hard hat on in Abalene, Texas at Stargate. Abalene, Texas at Stargate. Abalene, Texas at Stargate. >> And it's remarkable how fast they've >> And it's remarkable how fast they've >> And it's remarkable how fast they've been building that thing. It's insane. been building that thing. It's insane. been building that thing. It's insane. >> And and you've seen how fast Elon has >> And and you've seen how fast Elon has >> And and you've seen how fast Elon has built what is it? Colossus or whatever built what is it? Colossus or whatever built what is it? Colossus or whatever he calls it. he calls it. he calls it. >> Yeah. >> Yeah. >> Yeah. >> And uh and so Microsoft grabbed the >> And uh and so Microsoft grabbed the >> And uh and so Microsoft grabbed the dormant Foxcon fab that was going to dormant Foxcon fab that was going to dormant Foxcon fab that was going to that in Wisconsin that didn't get live. that in Wisconsin that didn't get live. that in Wisconsin that didn't get live. And so Microsoft's going to use that. Um And so Microsoft's going to use that. Um And so Microsoft's going to use that. Um so people are racing and they're so people are racing and they're so people are racing and they're spending just billions. I think spending just billions. I think spending just billions. I think trillions are going to get spent on this trillions are going to get spent on this trillions are going to get spent on this stuff. uh in this race to AGI or or stuff. uh in this race to AGI or or stuff. uh in this race to AGI or or super intelligence or something and I super intelligence or something and I super intelligence or something and I don't know if you get a prize at the end don't know if you get a prize at the end don't know if you get a prize at the end or if someone's going to get a trophy. or if someone's going to get a trophy. or if someone's going to get a trophy. >> No, I mean you know the talk is that >> No, I mean you know the talk is that >> No, I mean you know the talk is that increasingly now is that it's kind of increasingly now is that it's kind of increasingly now is that it's kind of like a dead end. It's it's basically like a dead end. It's it's basically like a dead end. It's it's basically going in the wrong direction. Um here's going in the wrong direction. Um here's going in the wrong direction. Um here's what I observed at OCP.

what I observed at OCP. what I observed at OCP. Um whatever is being built out today is Um whatever is being built out today is Um whatever is being built out today is based on assumptions about AI. Okay, based on assumptions about AI. Okay, based on assumptions about AI. Okay, that AI thesis, that FOMO thesis two that AI thesis, that FOMO thesis two that AI thesis, that FOMO thesis two years ago. And when you look at the years ago. And when you look at the years ago. And when you look at the here's a scary thing. This is the thing here's a scary thing. This is the thing here's a scary thing. This is the thing that should make all these guys [ __ ] that should make all these guys [ __ ] that should make all these guys [ __ ] their pants. Um, not only is the are the their pants. Um, not only is the are the their pants. Um, not only is the are the systems the core systems like the MVL 72 systems the core systems like the MVL 72 systems the core systems like the MVL 72 Blackwell and then eventually the 144 Blackwell and then eventually the 144 Blackwell and then eventually the 144 with Ver Rubin. Are those with Ver Rubin. Are those with Ver Rubin. Are those architecturally changing? the cooling architecturally changing? the cooling architecturally changing? the cooling systems, the you know the do the systems, the you know the do the systems, the you know the do the distribution units, the power distribution units, the power distribution units, the power distribution architectures, all this distribution architectures, all this distribution architectures, all this stuff and then the data center itself stuff and then the data center itself stuff and then the data center itself all of it is changing. So what and a lot all of it is changing. So what and a lot all of it is changing. So what and a lot of it is not going to be backward of it is not going to be backward of it is not going to be backward compatible which is because everything compatible which is because everything compatible which is because everything is moving so fast everyone's making um a is moving so fast everyone's making um a is moving so fast everyone's making um a a um an architectural and investment bet a um an architectural and investment bet a um an architectural and investment bet at one point in time and they you know at one point in time and they you know at one point in time and they you know if they can't capture a a return with if they can't capture a a return with if they can't capture a a return with that investment the obsolescence that investment the obsolescence that investment the obsolescence curve on this thing is highly aggressive curve on this thing is highly aggressive curve on this thing is highly aggressive in 2 years, that investment, if they in 2 years, that investment, if they in 2 years, that investment, if they don't recoup, they're going to be in big don't recoup, they're going to be in big don't recoup, they're going to be in big trouble. And so, all these new systems trouble. And so, all these new systems trouble. And so, all these new systems are going to be so different. The are going to be so different. The are going to be so different. The cooling architecture, everything is cooling architecture, everything is cooling architecture, everything is going to be so different, you're going going to be so different, you're going going to be so different, you're going to have to go and retrofit and rebuild to have to go and retrofit and rebuild to have to go and retrofit and rebuild almost everything if you want to

almost everything if you want to almost everything if you want to upgrade, right? So, one of the things upgrade, right? So, one of the things upgrade, right? So, one of the things they're trying to do at OCP is they're trying to do at OCP is they're trying to do at OCP is standardize like all and the entire standardize like all and the entire standardize like all and the entire system so it's um interoperable and um system so it's um interoperable and um system so it's um interoperable and um you know sort of uh you can kind of uh you know sort of uh you can kind of uh you know sort of uh you can kind of uh do a plug-andplay switch out hot switch do a plug-andplay switch out hot switch do a plug-andplay switch out hot switch out you know swaps and all that stuff of out you know swaps and all that stuff of out you know swaps and all that stuff of all the components and that standard is all the components and that standard is all the components and that standard is not necessarily there yet right so if not necessarily there yet right so if not necessarily there yet right so if you go to hot chips you hear about all you go to hot chips you hear about all you go to hot chips you hear about all these different rack designs but and these different rack designs but and these different rack designs but and their nuances and how they're different their nuances and how they're different their nuances and how they're different for every one of the hyperscalers. And for every one of the hyperscalers. And for every one of the hyperscalers. And so each of the hyperscalers also is so each of the hyperscalers also is so each of the hyperscalers also is pursu pursuing their own architecture. pursu pursuing their own architecture. pursu pursuing their own architecture. So from cooling to like if you look at So from cooling to like if you look at So from cooling to like if you look at Google, their whole system looks Google, their whole system looks Google, their whole system looks different. Then now you have like uh AMD different. Then now you have like uh AMD different. Then now you have like uh AMD introducing Helios which is a double introducing Helios which is a double introducing Helios which is a double it's like a freaking beast dude. It's it's like a freaking beast dude. It's it's like a freaking beast dude. It's like two racks wide. Then you have like two racks wide. Then you have like two racks wide. Then you have Nvidia introducing, you know, CPX and so Nvidia introducing, you know, CPX and so Nvidia introducing, you know, CPX and so that's a dual rack configuration for that's a dual rack configuration for that's a dual rack configuration for inference stuff.

inference stuff. inference stuff. Dude, it's going to be a freaking mess. Dude, it's going to be a freaking mess. Dude, it's going to be a freaking mess. Do you know what I'm saying? And OCP is Do you know what I'm saying? And OCP is Do you know what I'm saying? And OCP is funny because they're only starting to funny because they're only starting to funny because they're only starting to get into that conversation about how do get into that conversation about how do get into that conversation about how do we unify things and these guys aren't we unify things and these guys aren't we unify things and these guys aren't even thinking about yet even thinking about yet even thinking about yet what is the impact of doing this mass what is the impact of doing this mass what is the impact of doing this mass investment upfront and then um being investment upfront and then um being investment upfront and then um being able to um modernize these able to um modernize these able to um modernize these infrastructures that um that become infrastructures that um that become infrastructures that um that become obsolete very quickly. obsolete very quickly. obsolete very quickly. >> Yeah. But the and I don't know and don't >> Yeah. But the and I don't know and don't >> Yeah. But the and I don't know and don't even look at the details on that. But even look at the details on that. But even look at the details on that. But but I mean if the software abstraction but I mean if the software abstraction but I mean if the software abstraction is done well enough you you can always is done well enough you you can always is done well enough you you can always keep on using the old stuff. It would be keep on using the old stuff. It would be keep on using the old stuff. It would be just slower. So we're just making faster just slower. So we're just making faster just slower. So we're just making faster stuff. How much is the tie in between stuff. How much is the tie in between stuff. How much is the tie in between the software stack and the and then the the software stack and the and then the the software stack and the and then the cooling is basically the question is cooling is basically the question is cooling is basically the question is that that problem. that that problem. that that problem. >> So I don't I don't know but >> So I don't I don't know but >> So I don't I don't know but >> I don't know. I mean, you know, that's >> I don't know. I mean, you know, that's >> I don't know. I mean, you know, that's what I hear everyone say and that's a what I hear everyone say and that's a what I hear everyone say and that's a convenient answer and it never turns out convenient answer and it never turns out convenient answer and it never turns out that way. that way. that way. >> I just want to host a WordPress website.

>> I just want to host a WordPress website. >> I just want to host a WordPress website. >> What? >> What? >> What? >> I just want to host a WordPress website >> I just want to host a WordPress website >> I just want to host a WordPress website that talks to my SQL. Can How do I Can I that talks to my SQL. Can How do I Can I that talks to my SQL. Can How do I Can I do that? Do I need a black? do that? Do I need a black? do that? Do I need a black? >> Well, WordPress is a gigantic mess. Just >> Well, WordPress is a gigantic mess. Just >> Well, WordPress is a gigantic mess. Just Just get an just get clue to write a Just get an just get clue to write a Just get an just get clue to write a flask application. It's much more easy flask application. It's much more easy flask application. It's much more easy to maintain. It's also more than 25% to maintain. It's also more than 25% to maintain. It's also more than 25% entire internet now. entire internet now. entire internet now. >> Yeah. Well, yeah, but that doesn't mean >> Yeah. Well, yeah, but that doesn't mean >> Yeah. Well, yeah, but that doesn't mean it's good, my friend. it's good, my friend. it's good, my friend. >> No, it doesn't mean it's good. But as we >> No, it doesn't mean it's good. But as we >> No, it doesn't mean it's good. But as we have all learned, as I always take, as I have all learned, as I always take, as I have all learned, as I always take, as I always remind you, the flashbacks that always remind you, the flashbacks that always remind you, the flashbacks that at as you know, as Sun Microsystems was at as you know, as Sun Microsystems was at as you know, as Sun Microsystems was collapsing into the sea and Larry and uh collapsing into the sea and Larry and uh collapsing into the sea and Larry and uh Scott McNeely is on stage and he what Scott McNeely is on stage and he what Scott McNeely is on stage and he what did he say about the dot thing? did he say about the dot thing? did he say about the dot thing? Sometimes good enough is good enough. Sometimes good enough is good enough. Sometimes good enough is good enough. >> Yeah. What he meant is after a while >> Yeah. What he meant is after a while >> Yeah. What he meant is after a while people figured out they didn't have to people figured out they didn't have to people figured out they didn't have to spend millions on E10K servers. They spend millions on E10K servers. They spend millions on E10K servers. They could just buy a cheap Linux box and it could just buy a cheap Linux box and it could just buy a cheap Linux box and it was good enough and PHP was good enough was good enough and PHP was good enough was good enough and PHP was good enough and my SQL was also good enough. No and my SQL was also good enough. No and my SQL was also good enough. No trade wasn't brilliant but it was good trade wasn't brilliant but it was good trade wasn't brilliant but it was good enough enough enough >> because the real reason it is not it's >> because the real reason it is not it's >> because the real reason it is not it's good enough. The real reason that it good enough. The real reason that it good enough. The real reason that it makes good enough money.

makes good enough money. makes good enough money. >> That's the only reason. >> That's the only reason. >> That's the only reason. >> You can't forget that it's all about >> You can't forget that it's all about >> You can't forget that it's all about money. It's always about money and money. It's always about money and money. It's always about money and margins, margins, margins, >> but you know, well, but you know that I >> but you know, well, but you know that I >> but you know, well, but you know that I think that this is actually this might think that this is actually this might think that this is actually this might become a problem because and a very become a problem because and a very become a problem because and a very interesting society problem because if interesting society problem because if interesting society problem because if the if the theory we're carrying it the if the theory we're carrying it the if the theory we're carrying it here, which is this whole AI thing is a here, which is this whole AI thing is a here, which is this whole AI thing is a gigantic bubble for businesses and is gigantic bubble for businesses and is gigantic bubble for businesses and is going to burst at some point. The going to burst at some point. The going to burst at some point. The problem is I believe the technology is problem is I believe the technology is problem is I believe the technology is getting more and more adopted by getting more and more adopted by getting more and more adopted by individuals. So the people who are used individuals. So the people who are used individuals. So the people who are used to use chat GPT in their everyday life to use chat GPT in their everyday life to use chat GPT in their everyday life for free because it's funded by for free because it's funded by for free because it's funded by investor. If at some point this whole investor. If at some point this whole investor. If at some point this whole things collapse and investors say no no things collapse and investors say no no things collapse and investors say no no no no it's off. no no it's off. no no it's off. Imagine that somebody would show up Imagine that somebody would show up Imagine that somebody would show up today said you know what the iPhones not today said you know what the iPhones not today said you know what the iPhones not good for the environment. We're not good for the environment. We're not good for the environment. We're not going to do them anymore. We're going to going to do them anymore. We're going to going to do them anymore. We're going to stop them. People going to revolt. No, stop them. People going to revolt. No, stop them. People going to revolt. No, but I I think people are going to um but I I think people are going to um but I I think people are going to um well, you know, they're going to revolt well, you know, they're going to revolt well, you know, they're going to revolt and they're going to sue the hell out of and they're going to sue the hell out of and they're going to sue the hell out of a couple of companies and that's going a couple of companies and that's going a couple of companies and that's going to be it. But I think I think people to be it. But I think I think people to be it. But I think I think people will I think some people will pay.

will I think some people will pay. will I think some people will pay. >> Um there's probably going to be a much >> Um there's probably going to be a much >> Um there's probably going to be a much more urgent modality of monetizing more urgent modality of monetizing more urgent modality of monetizing um which is going to be the um which is going to be the um which is going to be the subscription. Everyone keeps talking subscription. Everyone keeps talking subscription. Everyone keeps talking about well you know we're going to have about well you know we're going to have about well you know we're going to have you we're going to charge you by like you we're going to charge you by like you we're going to charge you by like you know some sort of mechanism similar you know some sort of mechanism similar you know some sort of mechanism similar to a contract contract rate if we do x to a contract contract rate if we do x to a contract contract rate if we do x number of jobs for you or complete x number of jobs for you or complete x number of jobs for you or complete x number of let's say uh CSR calls then number of let's say uh CSR calls then number of let's say uh CSR calls then you pay us you know like you pay us you know like you pay us you know like you know um a per task rate. you know um a per task rate. you know um a per task rate. >> This never works. We tried that in the >> This never works. We tried that in the >> This never works. We tried that in the past. past. past. >> Yeah. Yeah. Yeah. Yeah. So, they're >> Yeah. Yeah. Yeah. Yeah. So, they're >> Yeah. Yeah. Yeah. Yeah. So, they're going to they're going to desperately going to they're going to desperately going to they're going to desperately just try to sell subscriptions, right? just try to sell subscriptions, right? just try to sell subscriptions, right? >> We're going to see we're going to see >> We're going to see we're going to see >> We're going to see we're going to see ads ads ads >> and they're going to lose a [ __ ] ton of >> and they're going to lose a [ __ ] ton of >> and they're going to lose a [ __ ] ton of money. money. money. >> Yeah. And then they're going to try to >> Yeah. And then they're going to try to >> Yeah. And then they're going to try to figure out how to inject ads and then figure out how to inject ads and then figure out how to inject ads and then >> Yeah. This is why I say to people, enjoy >> Yeah. This is why I say to people, enjoy >> Yeah. This is why I say to people, enjoy geni nowadays because we it to me it's geni nowadays because we it to me it's geni nowadays because we it to me it's and I'm the not the only one to say and I'm the not the only one to say and I'm the not the only one to say that, but geni today is the same way the that, but geni today is the same way the that, but geni today is the same way the internet was in the mid '9s.

internet was in the mid '9s. internet was in the mid '9s. >> Yeah. >> Yeah. >> Yeah. >> Space where you could go anywhere. >> Space where you could go anywhere. >> Space where you could go anywhere. Actually, it's already going down Actually, it's already going down Actually, it's already going down because there's so much pre-prompting because there's so much pre-prompting because there's so much pre-prompting that censor the models. But it's already that censor the models. But it's already that censor the models. But it's already going down. But from a usage standpoint, going down. But from a usage standpoint, going down. But from a usage standpoint, you can go to chat GPT, ask something you can go to chat GPT, ask something you can go to chat GPT, ask something and you're not bombarded by monetization and you're not bombarded by monetization and you're not bombarded by monetization [ __ ] But yes, it is coming. If [ __ ] But yes, it is coming. If [ __ ] But yes, it is coming. If actually if it comes through actually if it comes through actually if it comes through subscription, then that's great because subscription, then that's great because subscription, then that's great because I can just pay a subscription and not be I can just pay a subscription and not be I can just pay a subscription and not be bothered by anything. But I'm not sure bothered by anything. But I'm not sure bothered by anything. But I'm not sure the mass is there given the size of the mass is there given the size of the mass is there given the size of investment. If it's subsidized by investment. If it's subsidized by investment. If it's subsidized by individual by individual subscription I individual by individual subscription I individual by individual subscription I mean hey we have 10 world we have what's mean hey we have 10 world we have what's mean hey we have 10 world we have what's five billion people using it on the five billion people using it on the five billion people using it on the planet 20 bucks each that's what 100 planet 20 bucks each that's what 100 planet 20 bucks each that's what 100 billion per month is that enough to fund billion per month is that enough to fund billion per month is that enough to fund all the trillions we're going to put in all the trillions we're going to put in all the trillions we're going to put in infrastructure maybe actually maybe I infrastructure maybe actually maybe I infrastructure maybe actually maybe I don't know don't know don't know >> maybe maybe somebody's going to find a >> maybe maybe somebody's going to find a >> maybe maybe somebody's going to find a way to make this more efficient instead way to make this more efficient instead way to make this more efficient instead of just throwing more hardware and more of just throwing more hardware and more of just throwing more hardware and more electricity at it electricity at it electricity at it >> by either way I think Oracle to back to >> by either way I think Oracle to back to >> by either way I think Oracle to back to Oracle I was thinking of that on the Oracle I was thinking of that on the Oracle I was thinking of that on the side with my friend at GPT Oracle missed side with my friend at GPT Oracle missed side with my friend at GPT Oracle missed an opportunity. They should have renamed an opportunity. They should have renamed an opportunity. They should have renamed the database Oracle. A U R C A >> Oracle.

>> Oracle. >> People from Oracle, if you're listening >> People from Oracle, if you're listening >> People from Oracle, if you're listening to me, just send me a check for the to me, just send me a check for the to me, just send me a check for the idea. idea. idea. >> But you know what? Because when when >> But you know what? Because when when >> But you know what? Because when when people think about who has the ultimate people think about who has the ultimate people think about who has the ultimate wisdom like AI or whatever, you're wisdom like AI or whatever, you're wisdom like AI or whatever, you're right. Or you could have a picture. right. Or you could have a picture. right. Or you could have a picture. Remember in the Matrix, Neo has to go Remember in the Matrix, Neo has to go Remember in the Matrix, Neo has to go talk to the Oracle. talk to the Oracle. talk to the Oracle. >> Exactly. >> Exactly. >> Exactly. >> I know. Isn't that That's so >> I know. Isn't that That's so >> I know. Isn't that That's so >> And what does Oracle say to Neo? She >> And what does Oracle say to Neo? She >> And what does Oracle say to Neo? She said, "You're not ready yet. You're said, "You're not ready yet. You're said, "You're not ready yet. You're waiting." waiting." waiting." >> But But Rob but Rob, to learn our >> But But Rob but Rob, to learn our >> But But Rob but Rob, to learn our points, he ended up with the architect. points, he ended up with the architect. points, he ended up with the architect. >> He did end up with the architect. >> He did end up with the architect. >> He did end up with the architect. >> Yeah, you're not Neo, dude. >> Yeah, you're not Neo, dude. >> Yeah, you're not Neo, dude. >> That's a great food for thought. >> That's a great food for thought. >> That's a great food for thought. >> You're the architect. And the first >> You're the architect. And the first >> You're the architect. And the first version of the matrix was perfect and version of the matrix was perfect and version of the matrix was perfect and sublime and utopia and humans couldn't sublime and utopia and humans couldn't sublime and utopia and humans couldn't accept the program because humans don't accept the program because humans don't accept the program because humans don't know about perfection. They need know about perfection. They need know about perfection. They need suffering. suffering. suffering. >> This I totally agree with. >> This I totally agree with. >> This I totally agree with. >> This is why we invented religions. >> This is why we invented religions. >> This is why we invented religions. Sorry. Sorry. Sorry. >> That is weird. Yeah, we do need >> That is weird. Yeah, we do need >> That is weird. Yeah, we do need suffering. It's um in order for all the suffering. It's um in order for all the suffering. It's um in order for all the good stuff to actually be meaningful.

good stuff to actually be meaningful. good stuff to actually be meaningful. That was such a great scene in the That was such a great scene in the That was such a great scene in the second Matrix movie when he has the second Matrix movie when he has the second Matrix movie when he has the discussion with the architect because discussion with the architect because discussion with the architect because you're right, the architect was kind of you're right, the architect was kind of you're right, the architect was kind of dropping some broader societal wisdom in dropping some broader societal wisdom in dropping some broader societal wisdom in his speech to Neo there. Um that humans his speech to Neo there. Um that humans his speech to Neo there. Um that humans it was unbelievable to them a perfect it was unbelievable to them a perfect it was unbelievable to them a perfect world that everything's great and happy world that everything's great and happy world that everything's great and happy and per people their brains were like no and per people their brains were like no and per people their brains were like no that's not compute. We we don't know how that's not compute. We we don't know how that's not compute. We we don't know how to operate in that world. to operate in that world. to operate in that world. >> Yeah. But that is weird. That is how AGI >> Yeah. But that is weird. That is how AGI >> Yeah. But that is weird. That is how AGI was sold. It's utopia. was sold. It's utopia. was sold. It's utopia. >> Yeah, it's exact. I mean, it's like art, >> Yeah, it's exact. I mean, it's like art, >> Yeah, it's exact. I mean, it's like art, you know, life imitates art. It's like you know, life imitates art. It's like you know, life imitates art. It's like really creepy. Um, but you're right. really creepy. Um, but you're right. really creepy. Um, but you're right. That's exactly how it's been sold. It's That's exactly how it's been sold. It's That's exactly how it's been sold. It's morphing now and now it's all about like morphing now and now it's all about like morphing now and now it's all about like what? Sex bots what? Sex bots what? Sex bots and uh freaking commerce. And they're and uh freaking commerce. And they're and uh freaking commerce. And they're going to, you know, it's funny watching going to, you know, it's funny watching going to, you know, it's funny watching CNBC. you know, you see these guys come CNBC. you know, you see these guys come CNBC. you know, you see these guys come on and trying to make it look like if on and trying to make it look like if on and trying to make it look like if you know, oh, this is revolutionary you know, oh, this is revolutionary you know, oh, this is revolutionary because now like Walmart's doing because now like Walmart's doing because now like Walmart's doing something with chat GPT is like and it's something with chat GPT is like and it's something with chat GPT is like and it's going to be so beneficial for Walmart going to be so beneficial for Walmart going to be so beneficial for Walmart because people are going to buy twice as because people are going to buy twice as because people are going to buy twice as much toilet paper because they need much toilet paper because they need much toilet paper because they need twice as much toilet paper to wipe their twice as much toilet paper to wipe their twice as much toilet paper to wipe their ass, right? It's like, what?

ass, right? It's like, what? ass, right? It's like, what? How does that make any freaking sense? How does that make any freaking sense? How does that make any freaking sense? What are you talking about? You know, so What are you talking about? You know, so What are you talking about? You know, so the same e-commerce BS that we had the same e-commerce BS that we had the same e-commerce BS that we had before is going to be regurgitated in before is going to be regurgitated in before is going to be regurgitated in and and reformed into this BS AI and and reformed into this BS AI and and reformed into this BS AI commerce narrative. commerce narrative. commerce narrative. >> You mean pets? You mean pets.com did not >> You mean pets? You mean pets.com did not >> You mean pets? You mean pets.com did not work? work? work? >> And you know, people forget Amazon uh >> And you know, people forget Amazon uh >> And you know, people forget Amazon uh was tax subsidized. It's a It was um at was tax subsidized. It's a It was um at was tax subsidized. It's a It was um at a state level subsidized a state level subsidized a state level subsidized to the detriment of all the retailers, to the detriment of all the retailers, to the detriment of all the retailers, right? For the for years, right? For the for years, right? For the for years, >> the longest time. You're right. >> the longest time. You're right. >> the longest time. You're right. E-commerce if it was another state, E-commerce if it was another state, E-commerce if it was another state, you'd have to pay tax. You're right. you'd have to pay tax. You're right. you'd have to pay tax. You're right. >> Yeah. >> Yeah. >> Yeah. >> And for how many years did he lost >> And for how many years did he lost >> And for how many years did he lost money? money? money? >> People forgot >> People forgot >> People forgot >> forever. Yeah. And then people gave him >> forever. Yeah. And then people gave him >> forever. Yeah. And then people gave him a pass. a pass. a pass. >> People gave Bezos a pass. And um he >> People gave Bezos a pass. And um he >> People gave Bezos a pass. And um he never he didn't they didn't provide a never he didn't they didn't provide a never he didn't they didn't provide a dividend. He um sold this really dividend. He um sold this really dividend. He um sold this really wonderful narrative about cash flow wonderful narrative about cash flow wonderful narrative about cash flow which he was generating a lot of but um which he was generating a lot of but um which he was generating a lot of but um a lot of that was attributed to he was a lot of that was attributed to he was a lot of that was attributed to he was not accountable for any profit right for not accountable for any profit right for not accountable for any profit right for years.

years. years. >> I mean Amazon was not profitable to like >> I mean Amazon was not profitable to like >> I mean Amazon was not profitable to like 2010 like five or six years ago. Yeah. 2010 like five or six years ago. Yeah. 2010 like five or six years ago. Yeah. >> It was it was never but but you're right >> It was it was never but but you're right >> It was it was never but but you're right they played a game. They were like I'm they played a game. They were like I'm they played a game. They were like I'm I'm reinvesting it in the company. I'm reinvesting it in the company. I'm reinvesting it in the company. >> And they won they won. They fooled Wall >> And they won they won. They fooled Wall >> And they won they won. They fooled Wall Street into giving them free money. Street into giving them free money. Street into giving them free money. >> Cash. >> Cash. >> Cash. >> It was beautiful. I mean, but don't >> It was beautiful. I mean, but don't >> It was beautiful. I mean, but don't don't give them credit that they don't don't give them credit that they don't don't give them credit that they don't deserve, right? Which is, oh, they were deserve, right? Which is, oh, they were deserve, right? Which is, oh, they were so innovative and blah. No, they so innovative and blah. No, they so innovative and blah. No, they >> they fooled Wall Street. >> they fooled Wall Street. >> they fooled Wall Street. >> Jim Kramer got up there was telling >> Jim Kramer got up there was telling >> Jim Kramer got up there was telling everyone every day Amazon's the death everyone every day Amazon's the death everyone every day Amazon's the death star of uh retail while Amazon is like star of uh retail while Amazon is like star of uh retail while Amazon is like what 2% of global I mean it was a small what 2% of global I mean it was a small what 2% of global I mean it was a small number. uh of global retail. They're number. uh of global retail. They're number. uh of global retail. They're still small and uh and yeah, they had still small and uh and yeah, they had still small and uh and yeah, they had got all kinds of like nonsense um um got all kinds of like nonsense um um got all kinds of like nonsense um um that was basically giving them tailwind that was basically giving them tailwind that was basically giving them tailwind and they were subsidized. So and they were subsidized. So and they were subsidized. So >> yeah, but in the end, you know, the >> yeah, but in the end, you know, the >> yeah, but in the end, you know, the people don't look at the big picture is people don't look at the big picture is people don't look at the big picture is always the same thing. Amazon just about always the same thing. Amazon just about always the same thing. Amazon just about efficiency from an efficiency efficiency from an efficiency efficiency from an efficiency standpoint. As a consumer, it is more standpoint. As a consumer, it is more standpoint. As a consumer, it is more efficient. So long term it wins for for efficient. So long term it wins for for efficient. So long term it wins for for certain. No. Then they they then they certain. No. Then they they then they certain. No. Then they they then they created something that they didn't created something that they didn't created something that they didn't expect which was AWS.

expect which was AWS. expect which was AWS. >> No, but be Yeah, but that's something >> No, but be Yeah, but that's something >> No, but be Yeah, but that's something else. That that's that's a smart way to else. That that's that's a smart way to else. That that's that's a smart way to >> That's me. That's me with the architect and I and and full disclosure, I am the and I and and full disclosure, I am the architect. architect. architect. >> Oh my god. >> Oh my god. >> Oh my god. >> You know, but luckily I only sucked all >> You know, but luckily I only sucked all >> You know, but luckily I only sucked all the energy from a small town for like the energy from a small town for like the energy from a small town for like about 10 minutes in creating this video. about 10 minutes in creating this video. about 10 minutes in creating this video. So So So Hopefully everybody was okay. You know, Hopefully everybody was okay. You know, Hopefully everybody was okay. You know, >> you didn't have a brown out. >> you didn't have a brown out. >> you didn't have a brown out. >> The version I designed. >> The version I designed. >> The version I designed. >> Oh Jesus. >> Oh Jesus. >> Oh Jesus. >> It was sublime. It failed miserably >> It was sublime. It failed miserably >> It was sublime. It failed miserably because the humans we plugged in because the humans we plugged in because the humans we plugged in wouldn't accept the program. Humans only wouldn't accept the program. Humans only wouldn't accept the program. Humans only know suffering. The architect. know suffering. The architect. know suffering. The architect. >> Oh my god. Are you paying for that or is >> Oh my god. Are you paying for that or is >> Oh my god. Are you paying for that or is that free? that free? that free? >> It's free. It's Sora too. >> It's free. It's Sora too. >> It's free. It's Sora too. >> Sora. >> Sora. >> Sora. >> Yeah. Yeah. I pay I pay 20 bucks a month >> Yeah. Yeah. I pay I pay 20 bucks a month >> Yeah. Yeah. I pay I pay 20 bucks a month for Claude because it it's the best at for Claude because it it's the best at for Claude because it it's the best at it's the best writing code. it's the best writing code. it's the best writing code. >> Yeah. Yeah. But going back and all the >> Yeah. Yeah. But going back and all the >> Yeah. Yeah. But going back and all the other cool code generators that people other cool code generators that people other cool code generators that people are using are all using are using are all using are using are all using >> Enthropic on the back end. >> Enthropic on the back end. >> Enthropic on the back end. >> Yeah. But you know going back to like >> Yeah. But you know going back to like >> Yeah. But you know going back to like the databases I mean we talked about the databases I mean we talked about the databases I mean we talked about this like years ago. Remember that this like years ago. Remember that this like years ago. Remember that episode we were talking about episode we were talking about episode we were talking about object-oriented databases, vector object-oriented databases, vector object-oriented databases, vector databases and stuff like that. the blob.

databases and stuff like that. the blob. databases and stuff like that. the blob. That's not the first time we brought up That's not the first time we brought up That's not the first time we brought up blob, but it is interesting um that blob, but it is interesting um that blob, but it is interesting um that people were thinking about that back people were thinking about that back people were thinking about that back then. And the whole idea with then. And the whole idea with then. And the whole idea with object-oriented is that you have object object-oriented is that you have object object-oriented is that you have object oriented programming. And so there was oriented programming. And so there was oriented programming. And so there was sort it would it would disintermediate, sort it would it would disintermediate, sort it would it would disintermediate, you know, the meaning of those two you know, the meaning of those two you know, the meaning of those two worlds would disintermediate the need worlds would disintermediate the need worlds would disintermediate the need for um um you know, the relational for um um you know, the relational for um um you know, the relational database, right? Um because you have database, right? Um because you have database, right? Um because you have objects talking to object and you know a objects talking to object and you know a objects talking to object and you know a programmatic object um uh layer and um programmatic object um uh layer and um programmatic object um uh layer and um yeah it is weird that you know 20 like yeah it is weird that you know 20 like yeah it is weird that you know 20 like 30 years later all that stuff is 30 years later all that stuff is 30 years later all that stuff is becoming relevant. your point about um becoming relevant. your point about um becoming relevant. your point about um how do you make relational databases how do you make relational databases how do you make relational databases more more more um you know um work better with LLMs um you know um work better with LLMs um you know um work better with LLMs and um deal with all this stuff that's and um deal with all this stuff that's and um deal with all this stuff that's already this huge um install base of uh already this huge um install base of uh already this huge um install base of uh relational databases out there. Yeah.

relational databases out there. Yeah. relational databases out there. Yeah. that maybe not thinking about an LLM as that maybe not thinking about an LLM as that maybe not thinking about an LLM as like the whole solution, but using it as like the whole solution, but using it as like the whole solution, but using it as sort of this um translation layer. Do sort of this um translation layer. Do sort of this um translation layer. Do you know what I'm saying? Like an you know what I'm saying? Like an you know what I'm saying? Like an interface between the blob and the large interface between the blob and the large interface between the blob and the large language model because it can code well, language model because it can code well, language model because it can code well, right? And so you can train it um to right? And so you can train it um to right? And so you can train it um to understand and translate intent into understand and translate intent into understand and translate intent into let's say SQL um hopefully more reliably let's say SQL um hopefully more reliably let's say SQL um hopefully more reliably in the near future. But you know there's in the near future. But you know there's in the near future. But you know there's probably something workable there, probably something workable there, probably something workable there, right? But it's not about putting the right? But it's not about putting the right? But it's not about putting the LLM first. is really looking at okay if LLM first. is really looking at okay if LLM first. is really looking at okay if we're to merge um you know the world of we're to merge um you know the world of we're to merge um you know the world of LLMs with all the relational and uh LLMs with all the relational and uh LLMs with all the relational and uh transactional systems out there and data transactional systems out there and data transactional systems out there and data uh what would be a good architecture and uh what would be a good architecture and uh what would be a good architecture and application of large language models to application of large language models to application of large language models to make that happen right cuz that's right make that happen right cuz that's right make that happen right cuz that's right now that is that is one of the hugest now that is that is one of the hugest now that is that is one of the hugest problems problems problems >> yeah that's the challenge and and and >> yeah that's the challenge and and and >> yeah that's the challenge and and and I'm not sure there's an easy solution to I'm not sure there's an easy solution to I'm not sure there's an easy solution to that because you're that because you're that because you're >> no there isn't >> no there isn't >> no there isn't >> you're dealing with a semantic aspect >> you're dealing with a semantic aspect >> you're dealing with a semantic aspect and with also the the fact that most of and with also the the fact that most of and with also the the fact that most of those schemas were created by human and those schemas were created by human and those schemas were created by human and not purely and not wellmaintained which not purely and not wellmaintained which not purely and not wellmaintained which is the second point I was making about is the second point I was making about is the second point I was making about this whole thing is uh it's because I this whole thing is uh it's because I this whole thing is uh it's because I Rob and I discussed before you joined I Rob and I discussed before you joined I Rob and I discussed before you joined I I lived in metadata for many many years I lived in metadata for many many years I lived in metadata for many many years and uh if you look at most application and uh if you look at most application and uh if you look at most application the the metadata layer is very poor from the the metadata layer is very poor from the the metadata layer is very poor from a standpoint

a standpoint a standpoint >> so data is poor >> so data is poor >> so data is poor most of most of most of >> but that's easy to spot this is easy to >> but that's easy to spot this is easy to >> but that's easy to spot this is easy to spot poor metadata spot poor metadata spot poor metadata >> yeah Yeah. Yeah. >> yeah Yeah. Yeah. >> yeah Yeah. Yeah. >> And it to resolve you can clean data. >> And it to resolve you can clean data. >> And it to resolve you can clean data. ELT have been cleaning data for decades ELT have been cleaning data for decades ELT have been cleaning data for decades with some level of effectiveness with some level of effectiveness with some level of effectiveness cleaning metadata. Good luck cleaning metadata. Good luck cleaning metadata. Good luck >> because it's con >> because it's con >> because it's con >> you know like Yeah. And then in rel in >> you know like Yeah. And then in rel in >> you know like Yeah. And then in rel in regards to like enterprise AI right so I regards to like enterprise AI right so I regards to like enterprise AI right so I was on a call about that just you know was on a call about that just you know was on a call about that just you know recently recently recently >> you know we're going to build it >> you know we're going to build it >> you know we're going to build it >> with C3 C3.AI has been doing that for >> with C3 C3.AI has been doing that for >> with C3 C3.AI has been doing that for years. We're gonna build years. We're gonna build years. We're gonna build >> very well. >> very well. >> very well. >> We're gonna build it the we're gonna >> We're gonna build it the we're gonna >> We're gonna build it the we're gonna build this enterprise AI thing. build this enterprise AI thing. build this enterprise AI thing. >> Yeah. Yeah. >> Yeah. Yeah. >> Yeah. Yeah. >> It's already done. Tom Se's done. It's >> It's already done. Tom Se's done. It's >> It's already done. Tom Se's done. It's not very well, but open It's gonna be not very well, but open It's gonna be not very well, but open It's gonna be open source. We're going to We're going open source. We're going to We're going open source. We're going to We're going to give it to the people. to give it to the people. to give it to the people. >> Yeah. Open. >> Yeah. Open. >> Yeah. Open. >> Giving it to the people. >> Exactly. >> Exactly. >> We're getting better at this. >> We're getting better at this. >> We're getting better at this. >> I can't believe I'm like Korean and I >> I can't believe I'm like Korean and I >> I can't believe I'm like Korean and I can't freaking can't freaking can't freaking >> open souls. The second >> open souls. The second >> open souls. The second >> the second biggest lure of the >> the second biggest lure of the >> the second biggest lure of the >> Yeah. You know, see, I'm Korean. This >> Yeah. You know, see, I'm Korean. This >> Yeah. You know, see, I'm Korean. This should just like happen naturally.

should just like happen naturally. should just like happen naturally. >> Yeah. >> Yeah. >> Yeah. >> It's ridiculous. >> It's ridiculous. >> It's ridiculous. >> I know. >> Oh my gosh. All right. >> Oh my gosh. All right. >> My wife's Korean. So, >> My wife's Korean. So, >> My wife's Korean. So, >> yeah. >> yeah. >> yeah. >> Oh, your wife's Korean. >> Oh, your wife's Korean. >> Oh, your wife's Korean. >> Oh, wow. I didn't know that. >> Oh, wow. I didn't know that. >> Oh, wow. I didn't know that. >> Originally, she grew up in France, but >> Originally, she grew up in France, but >> Originally, she grew up in France, but originally Yeah. And so, we have been to originally Yeah. And so, we have been to originally Yeah. And so, we have been to Korea many times. Yeah. Korea many times. Yeah. Korea many times. Yeah. >> Oh, yeah. Yeah. some cult Korean culture >> Oh, yeah. Yeah. some cult Korean culture >> Oh, yeah. Yeah. some cult Korean culture in our home. in our home. in our home. >> Yeah. You know, the funny thing about >> Yeah. You know, the funny thing about >> Yeah. You know, the funny thing about that K-pop demon hunter thing, the that K-pop demon hunter thing, the that K-pop demon hunter thing, the people behind the voices are becoming people behind the voices are becoming people behind the voices are becoming more popular than the movie. more popular than the movie. more popular than the movie. >> The singers, >> The singers, >> The singers, >> it's crazy. >> it's crazy. >> it's crazy. >> Well, actually, there's that's an AI >> Well, actually, there's that's an AI >> Well, actually, there's that's an AI opportunity because the whole K-pop opportunity because the whole K-pop opportunity because the whole K-pop thing is totally manufactured in Korea. thing is totally manufactured in Korea. thing is totally manufactured in Korea. You know that all those bands are You know that all those bands are You know that all those bands are totally manufactured from the very totally manufactured from the very totally manufactured from the very beginning. So, maybe there's an AIPA beginning. So, maybe there's an AIPA beginning. So, maybe there's an AIPA here we can do. No, no. Actually, I here we can do. No, no. Actually, I here we can do. No, no. Actually, I think it's completely the reverse think it's completely the reverse think it's completely the reverse because you know the singer that does because you know the singer that does because you know the singer that does the main voice um singing voice she the main voice um singing voice she the main voice um singing voice she Well, actually all three singers they uh Well, actually all three singers they uh Well, actually all three singers they uh appeared on what's what's his name?

appeared on what's what's his name? appeared on what's what's his name? Jimmy uh Jimmy Jimmy uh Jimmy Jimmy uh Jimmy >> Jimmy Fallon. >> Jimmy Fallon. >> Jimmy Fallon. >> Fallon. >> Fallon. >> Fallon. >> Jimmy Kimmel. Jimmy Fallon. >> Jimmy Kimmel. Jimmy Fallon. >> Jimmy Kimmel. Jimmy Fallon. >> Yeah. And everybody was like Yeah. The >> Yeah. And everybody was like Yeah. The >> Yeah. And everybody was like Yeah. The thing is everybody wanted them to prove thing is everybody wanted them to prove thing is everybody wanted them to prove that they weren't AI. that they weren't AI. that they weren't AI. >> Yeah, good point. Which is real weird, >> Yeah, good point. Which is real weird, >> Yeah, good point. Which is real weird, right? You can't sub you can't assign right? You can't sub you can't assign right? You can't sub you can't assign celebrity to an AI. celebrity to an AI. celebrity to an AI. >> That's the point. Think out of the box. >> That's the point. Think out of the box. >> That's the point. Think out of the box. Return the thing. What if we make being Return the thing. What if we make being Return the thing. What if we make being AI as a singer cool? AI as a singer cool? AI as a singer cool? >> That's the opportunity. No, you can't >> That's the opportunity. No, you can't >> That's the opportunity. No, you can't because nobody gives a crap about a an because nobody gives a crap about a an because nobody gives a crap about a an AI hitting a a A5 um note because it's, AI hitting a a A5 um note because it's, AI hitting a a A5 um note because it's, you know, it's like what we appreciate you know, it's like what we appreciate you know, it's like what we appreciate is a human achievement, not AI's is a human achievement, not AI's is a human achievement, not AI's simulation or synthesis of achievement. simulation or synthesis of achievement. simulation or synthesis of achievement. >> Well, we should we should mark those >> Well, we should we should mark those >> Well, we should we should mark those words and revisit them on a regular words and revisit them on a regular words and revisit them on a regular basis. basis. basis. >> Yeah. Leonard Leonard, the only A5 thing >> Yeah. Leonard Leonard, the only A5 thing >> Yeah. Leonard Leonard, the only A5 thing that I care about is A5 beef. that I care about is A5 beef. that I care about is A5 beef. >> Oh, okay. Yeah.

Oh my god. Oh my god. >> For some reason, I thought you were >> For some reason, I thought you were >> For some reason, I thought you were going to say steak sauce, but close going to say steak sauce, but close going to say steak sauce, but close enough. enough. enough. >> That was A1. >> That was A1. >> That was A1. >> A1. I know. >> A1. I know. >> A1. I know. >> Oh my god. That's the most horrifying >> Oh my god. That's the most horrifying >> Oh my god. That's the most horrifying thing when you're at a high-end thing when you're at a high-end thing when you're at a high-end steakhouse and you look across the room steakhouse and you look across the room steakhouse and you look across the room and someone asks for A1 steak sauce and and someone asks for A1 steak sauce and and someone asks for A1 steak sauce and the person brings it out and you're just the person brings it out and you're just the person brings it out and you're just like, "Oh my god, you should just go to like, "Oh my god, you should just go to like, "Oh my god, you should just go to the Sizzler instead." the Sizzler instead." the Sizzler instead." >> Yeah, exactly. >> Yeah, exactly. >> Yeah, exactly. >> What are you doing here? Get out of >> What are you doing here? Get out of >> What are you doing here? Get out of here. Oh. Oh, don't get me started. It's here. Oh. Oh, don't get me started. It's here. Oh. Oh, don't get me started. It's like when people started showing up to like when people started showing up to like when people started showing up to high-end steakous not wearing a suit. Oh high-end steakous not wearing a suit. Oh high-end steakous not wearing a suit. Oh my god. I see guys wearing tracked my god. I see guys wearing tracked my god. I see guys wearing tracked shorts and shorts and shorts and >> I I I I took a customer to a high-end >> I I I I took a customer to a high-end >> I I I I took a customer to a high-end restaurant once and he show up in restaurant once and he show up in restaurant once and he show up in sanders and shorts and sanders and shorts and sanders and shorts and >> and he ordered this very sophisticated >> and he ordered this very sophisticated >> and he ordered this very sophisticated shrimp dish and he asked for sauce. Oh, shrimp dish and he asked for sauce. Oh, shrimp dish and he asked for sauce. Oh, >> I mean, but you know what's crazy is >> I mean, but you know what's crazy is >> I mean, but you know what's crazy is that's the vision of Idiocracy. When you that's the vision of Idiocracy. When you that's the vision of Idiocracy. When you watch Idiocracy, watch Idiocracy, watch Idiocracy, everyone's like dressed like crap. You everyone's like dressed like crap. You everyone's like dressed like crap. You know what is it? President Kamacho has know what is it? President Kamacho has know what is it? President Kamacho has like a gigantic medallion.

like a gigantic medallion. like a gigantic medallion. >> I I need to watch it again. >> I I need to watch it again. >> I I need to watch it again. >> Oh god. So Oh my god. Yeah, >> Oh god. So Oh my god. Yeah, >> Oh god. So Oh my god. Yeah, >> thanks for giving me That's a good tip >> thanks for giving me That's a good tip >> thanks for giving me That's a good tip for the weekend. I need to watch this for the weekend. I need to watch this for the weekend. I need to watch this movie again. movie again. movie again. >> I love that movie. >> I love that movie. >> I love that movie. >> Oh my god. You have to watch it probably >> Oh my god. You have to watch it probably >> Oh my god. You have to watch it probably once a month just to stay in tune with once a month just to stay in tune with once a month just to stay in tune with what reality was reality. what reality was reality. what reality was reality. >> And and you have to read The Prince for >> And and you have to read The Prince for >> And and you have to read The Prince for Machave every year. Machave every year. Machave every year. >> Oh, never if you I mean if you guys have >> Oh, never if you I mean if you guys have >> Oh, never if you I mean if you guys have never read The Prince for Machave. never read The Prince for Machave. never read The Prince for Machave. >> No, >> No, >> No, >> do it this I mean it's a small book. >> do it this I mean it's a small book. >> do it this I mean it's a small book. It's it's super easy. It's it's super easy. It's it's super easy. >> I'm surprised I'm I'm surprised you >> I'm surprised I'm I'm surprised you >> I'm surprised I'm I'm surprised you haven't read it, man. Rob, you read haven't read it, man. Rob, you read haven't read it, man. Rob, you read everything. everything. everything. >> Okay, but I haven't read this one. So, >> Okay, but I haven't read this one. So, >> Okay, but I haven't read this one. So, let me go bring up the Kindle machine. let me go bring up the Kindle machine. let me go bring up the Kindle machine. Yeah, Yeah, Yeah, >> I mean we should do we should do a >> I mean we should do we should do a >> I mean we should do we should do a Prince episode someday. It's Prince episode someday. It's Prince episode someday. It's >> all right. So, it's a short one. I can >> all right. So, it's a short one. I can >> all right. So, it's a short one. I can read it pretty fast. All right. read it pretty fast. All right. read it pretty fast. All right. >> 600 years ago and it's still almost 600 >> 600 years ago and it's still almost 600 >> 600 years ago and it's still almost 600 years ago, I think. And it's almost the years ago, I think. And it's almost the years ago, I think. And it's almost the one of the best business books, by the one of the best business books, by the one of the best business books, by the way, in terms of conquering way, in terms of conquering way, in terms of conquering >> and sustain and sustaining power. >> and sustain and sustaining power. >> and sustain and sustaining power. >> Wow. >> Wow. >> Wow. >> Well, looking forward to hanging out >> Well, looking forward to hanging out >> Well, looking forward to hanging out with you next week, dude.

with you next week, dude. with you next week, dude. >> Oh, I know. We're going to be at Lenovo, >> Oh, I know. We're going to be at Lenovo, >> Oh, I know. We're going to be at Lenovo, man. I'm going to be getting all these man. I'm going to be getting all these man. I'm going to be getting all these think pads, you know. think pads, you know. think pads, you know. with OLED screens and stuff like that with OLED screens and stuff like that with OLED screens and stuff like that just like you do, you know. just like you do, you know. just like you do, you know. >> Would you sing the same sentence? >> Would you sing the same sentence? >> Would you sing the same sentence? >> So, like this. >> So, like this. >> So, like this. >> Look at >> Look at >> Look at >> I think I think I have one. >> I think I think I have one. >> I think I think I have one. >> Actually, I I really like this man. I >> Actually, I I really like this man. I >> Actually, I I really like this man. I think they did a really great job. think they did a really great job. think they did a really great job. >> Is that the Snapdragon one? >> Is that the Snapdragon one? >> Is that the Snapdragon one? >> No, no, this is the Aura edition. >> No, no, this is the Aura edition. >> No, no, this is the Aura edition. >> Aura? Okay. >> Aura? Okay. >> Aura? Okay. >> Yeah, it's >> Yeah, it's >> Yeah, it's >> You'll like it. So >> You'll like it. So >> You'll like it. So >> this is a vintage one. It's probably >> this is a vintage one. It's probably >> this is a vintage one. It's probably running Windows two. running Windows two. running Windows two. >> Yeah. I think like the new fashion is uh >> Yeah. I think like the new fashion is uh >> Yeah. I think like the new fashion is uh you know like for enterprise it's uh you know like for enterprise it's uh you know like for enterprise it's uh having a little bit of heft you know having a little bit of heft you know having a little bit of heft you know instead of going like you know I'm I'm instead of going like you know I'm I'm instead of going like you know I'm I'm talking to you now on a laptop that I talking to you now on a laptop that I talking to you now on a laptop that I feel like I'm now learning has way too feel like I'm now learning has way too feel like I'm now learning has way too much heft. It's a 16inch MacBook Pro M4 much heft. It's a 16inch MacBook Pro M4 much heft. It's a 16inch MacBook Pro M4 and it's super powerful but it also and it's super powerful but it also and it's super powerful but it also weighs 1,000 lbs. Yeah.

weighs 1,000 lbs. Yeah. weighs 1,000 lbs. Yeah. >> And it is too heavy. I I was like I >> And it is too heavy. I I was like I >> And it is too heavy. I I was like I don't know, don't know, don't know, >> dude. It's overkill. You know what I >> dude. It's overkill. You know what I >> dude. It's overkill. You know what I use? I just use a MacBook u MacBook Air use? I just use a MacBook u MacBook Air use? I just use a MacBook u MacBook Air 15 in and it kills, man. And it's 15 in and it kills, man. And it's 15 in and it kills, man. And it's >> a M uh it's it's an M2 >> a M uh it's it's an M2 >> a M uh it's it's an M2 >> and it still kills. >> and it still kills. >> and it still kills. >> I have an M1. It's still I mean it >> I have an M1. It's still I mean it >> I have an M1. It's still I mean it depends what you do with it obviously. depends what you do with it obviously. depends what you do with it obviously. >> Yeah. Yeah. Yeah. Yeah. Oh no. They're >> Yeah. Yeah. Yeah. Yeah. Oh no. They're >> Yeah. Yeah. Yeah. Yeah. Oh no. They're they're amazing. they're amazing. they're amazing. >> I never into the 16 in one. They are the >> I never into the 16 in one. They are the >> I never into the 16 in one. They are the too bulky. It's too bulky. It's too bulky. It's >> this thing. Yeah, it's it is too bulky. >> this thing. Yeah, it's it is too bulky. >> this thing. Yeah, it's it is too bulky. It's very thick. Very very heavy. It's very thick. Very very heavy. It's very thick. Very very heavy. >> 15 in Air. It, you know, it's just like >> 15 in Air. It, you know, it's just like >> 15 in Air. It, you know, it's just like the iPad. iPad. I didn't get the Pro. I the iPad. iPad. I didn't get the Pro. I the iPad. iPad. I didn't get the Pro. I just got the like you. I just got the just got the like you. I just got the just got the like you. I just got the Air. And you know what? It's way more Air. And you know what? It's way more Air. And you know what? It's way more than enough. than enough. than enough. >> I do video editing on it, too. So, >> I do video editing on it, too. So, >> I do video editing on it, too. So, >> so if if you don't do video editing, if >> so if if you don't do video editing, if >> so if if you don't do video editing, if you just do, you know, geeky fun Linux you just do, you know, geeky fun Linux you just do, you know, geeky fun Linux stuff, I have a bunch of old MacBook Air stuff, I have a bunch of old MacBook Air stuff, I have a bunch of old MacBook Air 11 in running Ubuntu. 11 in running Ubuntu. 11 in running Ubuntu. >> Oh, >> Oh, >> Oh, >> it's magic. Actually, I use those old >> it's magic. Actually, I use those old >> it's magic. Actually, I use those old one to run stuff in the house like one to run stuff in the house like one to run stuff in the house like oldition. They're great. I mean, and oldition. They're great. I mean, and oldition. They're great. I mean, and they still run, you know, they don't run they still run, you know, they don't run they still run, you know, they don't run the latest Mac OS with all the freaking the latest Mac OS with all the freaking the latest Mac OS with all the freaking multiffactor whatever [ __ ] But on multiffactor whatever [ __ ] But on multiffactor whatever [ __ ] But on Ubuntu, they are dream machines. As soon Ubuntu, they are dream machines. As soon Ubuntu, they are dream machines. As soon as you put Linux on an old machine, you as you put Linux on an old machine, you as you put Linux on an old machine, you realize that there's something wrong in realize that there's something wrong in realize that there's something wrong in the matrix because Windows and Mac OS the matrix because Windows and Mac OS the matrix because Windows and Mac OS were running like crap, but you put were running like crap, but you put were running like crap, but you put Linux and it runs like a bat out of hell

Linux and it runs like a bat out of hell Linux and it runs like a bat out of hell and you're like, and you're like, and you're like, >> you know what, you know, >> you know what, you know, >> you know what, you know, >> Mac is not bad, but >> Mac is not bad, but >> Mac is not bad, but >> we've been saying, you know, people, the >> we've been saying, you know, people, the >> we've been saying, you know, people, the Linux people have been saying, oh, we're Linux people have been saying, oh, we're Linux people have been saying, oh, we're going to get the whole world running going to get the whole world running going to get the whole world running Linux on the desktop. And, you know, Linux on the desktop. And, you know, Linux on the desktop. And, you know, it's never really happened. even though it's never really happened. even though it's never really happened. even though quietly it took over the mobile space quietly it took over the mobile space quietly it took over the mobile space and it may come up you know and with and it may come up you know and with and it may come up you know and with Chromebooks and and now they're going to Chromebooks and and now they're going to Chromebooks and and now they're going to bring Android to the desktop but along bring Android to the desktop but along bring Android to the desktop but along the way and so and it was always a the way and so and it was always a the way and so and it was always a struggle and it never really succeeded struggle and it never really succeeded struggle and it never really succeeded you know but there's enough people now you know but there's enough people now you know but there's enough people now and it's about something else there are and it's about something else there are and it's about something else there are Microsoft and I guess Apple to some Microsoft and I guess Apple to some Microsoft and I guess Apple to some extent they're starting to embed AI into extent they're starting to embed AI into extent they're starting to embed AI into the operating system and you know like the operating system and you know like the operating system and you know like co-pilot on the newest update to Windows co-pilot on the newest update to Windows co-pilot on the newest update to Windows Window 11, the the second half of 25 Window 11, the the second half of 25 Window 11, the the second half of 25 update, it's starting to do too much and update, it's starting to do too much and update, it's starting to do too much and they're starting to embed things into they're starting to embed things into they're starting to embed things into the OS that are just fly in the face of the OS that are just fly in the face of the OS that are just fly in the face of privacy. privacy. privacy. >> Yeah. >> Yeah. >> Yeah. >> You know, and and you know, >> You know, and and you know, >> You know, and and you know, >> we've been saying that on day one. If >> we've been saying that on day one. If >> we've been saying that on day one. If you remember when day one we start you remember when day one we start you remember when day one we start talking about AI in the US, I was the talking about AI in the US, I was the talking about AI in the US, I was the one saying no f way.

one saying no f way. one saying no f way. >> This is >> This is >> This is terribly awful. terribly awful. terribly awful. >> Yeah. But you know what? >> Yeah. But you know what? >> Yeah. But you know what? >> Linux might be the only way to break >> Linux might be the only way to break >> Linux might be the only way to break free of a surveillance laptop, free of a surveillance laptop, free of a surveillance laptop, you know. you know. you know. >> Yeah. I'm hoping Apple will lean into, >> Yeah. I'm hoping Apple will lean into, >> Yeah. I'm hoping Apple will lean into, you know, that um you know, protecting you know, that um you know, protecting you know, that um you know, protecting privacy instead of like going to the privacy instead of like going to the privacy instead of like going to the dark side. But um you know what? Hey dark side. But um you know what? Hey dark side. But um you know what? Hey dude, they you know they announced the dude, they you know they announced the dude, they you know they announced the new um what do you call it? Uh A5 or M5. new um what do you call it? Uh A5 or M5. new um what do you call it? Uh A5 or M5. >> Yes, M5. Oh my god, dude. That thing. >> Yes, M5. Oh my god, dude. That thing. >> Yes, M5. Oh my god, dude. That thing. So, they took um what was in the 19 the So, they took um what was in the 19 the So, they took um what was in the 19 the A19 Pro, you know, the neural A19 Pro, you know, the neural A19 Pro, you know, the neural accelerators? They ported over that. accelerators? They ported over that. accelerators? They ported over that. That thing I bet you million bucks is a That thing I bet you million bucks is a That thing I bet you million bucks is a an immense beast because what those an immense beast because what those an immense beast because what those things do, they actually things do, they actually things do, they actually uh convert or at least um they provide uh convert or at least um they provide uh convert or at least um they provide tensor cores for um you know matrix tensor cores for um you know matrix tensor cores for um you know matrix multiplication which GPUs don't multiplication which GPUs don't multiplication which GPUs don't intrinsically do. So, you know what I'd intrinsically do. So, you know what I'd intrinsically do. So, you know what I'd love to see is what Apple's doing on the love to see is what Apple's doing on the love to see is what Apple's doing on the server side, man. Because they, you server side, man. Because they, you server side, man. Because they, you know, people keep saying, "Oh, you know, know, people keep saying, "Oh, you know, know, people keep saying, "Oh, you know, they're behind. They don't do shit."

they're behind. They don't do shit." they're behind. They don't do shit." And, you know, AI, blah, blah, blah. As And, you know, AI, blah, blah, blah. As And, you know, AI, blah, blah, blah. As a I'm I'm thinking, how do you even a I'm I'm thinking, how do you even a I'm I'm thinking, how do you even know? The one thing you know is they know? The one thing you know is they know? The one thing you know is they they manufacture their servers they manufacture their servers they manufacture their servers right out of Texas. What part of server right out of Texas. What part of server right out of Texas. What part of server out of Texas did you not understand? you out of Texas did you not understand? you out of Texas did you not understand? you know, so these guys have to be doing know, so these guys have to be doing know, so these guys have to be doing something that, you know, obviously something that, you know, obviously something that, you know, obviously they're not going to sell the stuff, but they're not going to sell the stuff, but they're not going to sell the stuff, but um you know, they have this AMX um you know, they have this AMX um you know, they have this AMX extension that they incorporated into um extension that they incorporated into um extension that they incorporated into um the M series early on and they're I the M series early on and they're I the M series early on and they're I think they're just extending it and just think they're just extending it and just think they're just extending it and just embedding it into um the GPUs so that embedding it into um the GPUs so that embedding it into um the GPUs so that now the GPUs are more are more like what now the GPUs are more are more like what now the GPUs are more are more like what uh Nvidia's GPUs do. uh Nvidia's GPUs do. uh Nvidia's GPUs do. >> Yeah. So are you telling me that GPUs >> Yeah. So are you telling me that GPUs >> Yeah. So are you telling me that GPUs don't do matrix math by natively by don't do matrix math by natively by don't do matrix math by natively by >> not not not natively you the software so >> not not not natively you the software so >> not not not natively you the software so they software create at a kernel level they software create at a kernel level they software create at a kernel level um enables them to do molt math um enables them to do molt math um enables them to do molt math operations right it's the kernel so what operations right it's the kernel so what operations right it's the kernel so what the but these neural accelerators are the but these neural accelerators are the but these neural accelerators are geared toward that they're optimized for geared toward that they're optimized for geared toward that they're optimized for molt math and so you know you're going molt math and so you know you're going molt math and so you know you're going to have a separate kernel for that And to have a separate kernel for that And to have a separate kernel for that And it that's why I think you're seeing um it that's why I think you're seeing um it that's why I think you're seeing um dramatically improved um AI performance dramatically improved um AI performance dramatically improved um AI performance on the generation and and on the iPhone on the generation and and on the iPhone on the generation and and on the iPhone 17 Pro. It it does some crazy stuff. Um 17 Pro. It it does some crazy stuff. Um 17 Pro. It it does some crazy stuff. Um I'm pretty actually impressed with the

I'm pretty actually impressed with the I'm pretty actually impressed with the 17 Pro. 17 Pro. 17 Pro. >> Yeah, me too. It's it's pretty amazing. >> Yeah, me too. It's it's pretty amazing. >> Yeah, me too. It's it's pretty amazing. >> Yeah. Yeah. Really well. And >> Yeah. Yeah. Really well. And >> Yeah. Yeah. Really well. And >> the the photos are freaking ridiculous. >> the the photos are freaking ridiculous. >> the the photos are freaking ridiculous. >> Has your Has your orange phone started >> Has your Has your orange phone started >> Has your Has your orange phone started turning pink yet? No. Why? No. Why? >> I saw that. I saw that on the internet. >> I saw that. I saw that on the internet. >> I saw that. I saw that on the internet. People are showing pictures. So, they're People are showing pictures. So, they're People are showing pictures. So, they're It's starting to discolor and stuff like It's starting to discolor and stuff like It's starting to discolor and stuff like really really really >> And you don't think it's AI, Rob? And >> And you don't think it's AI, Rob? And >> And you don't think it's AI, Rob? And you don't think it's AI? you don't think it's AI? you don't think it's AI? >> Yeah. It's on the internet, so it must >> Yeah. It's on the internet, so it must >> Yeah. It's on the internet, so it must be true. be true. be true. >> It's on the internet. And so, the >> It's on the internet. And so, the >> It's on the internet. And so, the internet told me that the orange phone's internet told me that the orange phone's internet told me that the orange phone's going to start to turn pink. And they going to start to turn pink. And they going to start to turn pink. And they also told me, which may be true, that if also told me, which may be true, that if also told me, which may be true, that if you happen to like kind of ding it or you happen to like kind of ding it or you happen to like kind of ding it or hit it against something, you know, it's hit it against something, you know, it's hit it against something, you know, it's because now it's aluminum, you start you because now it's aluminum, you start you because now it's aluminum, you start you realize it's not really orange all the realize it's not really orange all the realize it's not really orange all the way through, you you see silver metal way through, you you see silver metal way through, you you see silver metal aluminum underneath like if you chip aluminum underneath like if you chip aluminum underneath like if you chip part of your phone, like you hit it, you part of your phone, like you hit it, you part of your phone, like you hit it, you know, know, know, >> you'll see it'll show silver exposed >> you'll see it'll show silver exposed >> you'll see it'll show silver exposed beneath the orange. So beneath the orange. So beneath the orange. So >> yeah, I I I do have a recommendation for >> yeah, I I I do have a recommendation for >> yeah, I I I do have a recommendation for those people. You could stop dropping those people. You could stop dropping those people. You could stop dropping your phones.

your phones. your phones. >> Are you saying, Rob, that people were >> Are you saying, Rob, that people were >> Are you saying, Rob, that people were thinking that there is this thing called thinking that there is this thing called thinking that there is this thing called orange aluminiums that we can mine? orange aluminiums that we can mine? orange aluminiums that we can mine? >> They thought that there was, but I saw >> They thought that there was, but I saw >> They thought that there was, but I saw these videos on the internet. Let me see these videos on the internet. Let me see these videos on the internet. Let me see what website was. I was on samsung.com what website was. I was on samsung.com what website was. I was on samsung.com and they showed me all the videos of bad and they showed me all the videos of bad and they showed me all the videos of bad things happening to the iPhone. things happening to the iPhone. things happening to the iPhone. >> So ridiculous. Yeah, I've seen some >> So ridiculous. Yeah, I've seen some >> So ridiculous. Yeah, I've seen some people drop it from incredible heights people drop it from incredible heights people drop it from incredible heights and it kind of surviving. I have a and it kind of surviving. I have a and it kind of surviving. I have a question, you know. Has anyone seen the question, you know. Has anyone seen the question, you know. Has anyone seen the iPhone Air in the wild yet? iPhone Air in the wild yet? iPhone Air in the wild yet? >> No. >> No. >> No. >> No. >> No. >> No. >> Well, I mean, we we really having first >> Well, I mean, we we really having first >> Well, I mean, we we really having first world problems. world problems. world problems. >> I want to see it. I want to see a >> I want to see it. I want to see a >> I want to see it. I want to see a shootout. The iPhone Air versus the shootout. The iPhone Air versus the shootout. The iPhone Air versus the Samsung Galaxy Edge. Remember, cuz last Samsung Galaxy Edge. Remember, cuz last Samsung Galaxy Edge. Remember, cuz last spring they came out with their spring they came out with their spring they came out with their >> Oh, yeah. >> Oh, yeah. >> Oh, yeah. >> And it was like, hey, we're first. >> And it was like, hey, we're first. >> And it was like, hey, we're first. Samsung's always beating Apple in Samsung's always beating Apple in Samsung's always beating Apple in everything, but it doesn't make any everything, but it doesn't make any everything, but it doesn't make any difference. difference. difference. >> Well, I mean, You know what I mean? Like >> Well, I mean, You know what I mean? Like >> Well, I mean, You know what I mean? Like Apple shows up like three years later Apple shows up like three years later Apple shows up like three years later with the technology and they go, "Oh, we with the technology and they go, "Oh, we with the technology and they go, "Oh, we invented it." It's like whatever.

invented it." It's like whatever. invented it." It's like whatever. >> Apple is a lifestyle. >> Apple is a lifestyle. >> Apple is a lifestyle. >> It's a lifestyle. You're right. It's a >> It's a lifestyle. You're right. It's a >> It's a lifestyle. You're right. It's a lifestyle thing. Absolutely. You know, lifestyle thing. Absolutely. You know, lifestyle thing. Absolutely. You know, it's so I can say I feel bad for Samsung it's so I can say I feel bad for Samsung it's so I can say I feel bad for Samsung or Android, but Samsung is still the or Android, but Samsung is still the or Android, but Samsung is still the number one smartphone manufacturer in number one smartphone manufacturer in number one smartphone manufacturer in the world. So, I guess the world. So, I guess the world. So, I guess >> and I would I would actually argue that >> and I would I would actually argue that >> and I would I would actually argue that Samsung is is probably the closest to Samsung is is probably the closest to Samsung is is probably the closest to Apple than the others. Yeah, I think Apple than the others. Yeah, I think Apple than the others. Yeah, I think they really understand the concept of uh they really understand the concept of uh they really understand the concept of uh of uh becoming of uh becoming of uh becoming >> I'll go do review. I'm gonna maybe I'll >> I'll go do review. I'm gonna maybe I'll >> I'll go do review. I'm gonna maybe I'll go to Best Buy. I have yet to see or go to Best Buy. I have yet to see or go to Best Buy. I have yet to see or play with the Galaxy Edge thin device. play with the Galaxy Edge thin device. play with the Galaxy Edge thin device. Maybe I'll go somewhere I can hold the Maybe I'll go somewhere I can hold the Maybe I'll go somewhere I can hold the Edge and the and the Air. Edge and the and the Air. Edge and the and the Air. >> Yeah. No, I think the Edge >> Yeah. No, I think the Edge >> Yeah. No, I think the Edge >> I think they did a better job with the >> I think they did a better job with the >> I think they did a better job with the Edge. It's slightly thicker. Edge. It's slightly thicker. Edge. It's slightly thicker. >> Yeah, slightly. But it's got the 200 me. >> Yeah, slightly. But it's got the 200 me. >> Yeah, slightly. But it's got the 200 me. It's got the 200 megapixel camera. It's got the 200 megapixel camera. It's got the 200 megapixel camera. >> Like they did a better job with the >> Like they did a better job with the >> Like they did a better job with the camera with the Edge. Yeah, camera with the Edge. Yeah, camera with the Edge. Yeah, >> cuz they gave you two cameras and actual >> cuz they gave you two cameras and actual >> cuz they gave you two cameras and actual optical and yeah, zoom. optical and yeah, zoom. optical and yeah, zoom. >> Yeah, but but the Fusion the Apple >> Yeah, but but the Fusion the Apple >> Yeah, but but the Fusion the Apple Fusion cameras are pretty damn good, Fusion cameras are pretty damn good, Fusion cameras are pretty damn good, man. Um man. Um man. Um but you have to have more than one.

but you have to have more than one. but you have to have more than one. >> Yeah, >> Yeah, >> Yeah, >> can't just have one. A lot of people the >> can't just have one. A lot of people the >> can't just have one. A lot of people the the what do you call it? The wide angle, the what do you call it? The wide angle, the what do you call it? The wide angle, they miss the wide angle or telephoto. I they miss the wide angle or telephoto. I they miss the wide angle or telephoto. I don't know which one. don't know which one. don't know which one. >> Photo right angle. You're right. That's >> Photo right angle. You're right. That's >> Photo right angle. You're right. That's a good one. a good one. a good one. >> You know what? I wonder if we should >> You know what? I wonder if we should >> You know what? I wonder if we should stop this show. stop this show. stop this show. >> Yeah, we should because this >> Yeah, we should because this >> Yeah, we should because this >> Oh, yeah. It's lead game >> Oh, yeah. It's lead game >> Oh, yeah. It's lead game >> way too freaking long. Oh my god. All >> way too freaking long. Oh my god. All >> way too freaking long. Oh my god. All right. Okay. right. Okay. right. Okay. >> But we're so entertaining and people are >> But we're so entertaining and people are >> But we're so entertaining and people are still with us. still with us. still with us. >> We're entertaining ourselves. That's >> We're entertaining ourselves. That's >> We're entertaining ourselves. That's why. why. why. >> Exactly. Exactly. >> Exactly. Exactly. >> Exactly. Exactly. >> Compment yourself. At least one person >> Compment yourself. At least one person >> Compment yourself. At least one person is happy. is happy. is happy. >> Exactly. I just want to thank everybody >> Exactly. I just want to thank everybody >> Exactly. I just want to thank everybody for joining us on this edition of for joining us on this edition of for joining us on this edition of relational database love. love. >> Yeah. Yeah. you know, um, where you got >> Yeah. Yeah. you know, um, where you got >> Yeah. Yeah. you know, um, where you got to hear the leaders, the the ancestors, to hear the leaders, the the ancestors, to hear the leaders, the the ancestors, the godfathers of relational databases the godfathers of relational databases the godfathers of relational databases were here. You know, we invented data were here. You know, we invented data were here. You know, we invented data types, columns, rows, rowle locking types, columns, rows, rowle locking types, columns, rows, rowle locking versus page level locking, foreign versus page level locking, foreign versus page level locking, foreign indexes, indexes, indexes, >> indexes, secondary indexes, triggers, >> indexes, secondary indexes, triggers, >> indexes, secondary indexes, triggers, stored procedures. We remember when stored procedures. We remember when stored procedures. We remember when Oracle put in Java stored procedures Oracle put in Java stored procedures Oracle put in Java stored procedures because they thought Java was a big because they thought Java was a big because they thought Java was a big deal. And then Microsoft put in C stored deal. And then Microsoft put in C stored deal. And then Microsoft put in C stored procedures.

procedures. procedures. >> Oh my god. uh because Oracle put in Java >> Oh my god. uh because Oracle put in Java >> Oh my god. uh because Oracle put in Java store procedures. Um store procedures. Um store procedures. Um >> crimes against humanity, >> crimes against humanity, >> crimes against humanity, >> crimes against you know I remember in >> crimes against you know I remember in >> crimes against you know I remember in the '9s most people when they were the '9s most people when they were the '9s most people when they were building their client server apps if building their client server apps if building their client server apps if they wanted the best performance they they wanted the best performance they they wanted the best performance they actually put all the business logic in actually put all the business logic in actually put all the business logic in store procedures instead of putting it store procedures instead of putting it store procedures instead of putting it in a middle tier or in a client app in a middle tier or in a client app in a middle tier or in a client app because you got better performance. Uh because you got better performance. Uh because you got better performance. Uh store you know the store you know the store you know the >> you're giving me nightmares >> you're giving me nightmares >> you're giving me nightmares >> it executed faster. So remember >> it executed faster. So remember >> it executed faster. So remember Oh, you know, I know Dimmitri is not a Oh, you know, I know Dimmitri is not a Oh, you know, I know Dimmitri is not a fan of store procedures or triggers, but fan of store procedures or triggers, but fan of store procedures or triggers, but hey, I used to be Mr. Merge Replication, hey, I used to be Mr. Merge Replication, hey, I used to be Mr. Merge Replication, okay, at Microsoft. They flew me to okay, at Microsoft. They flew me to okay, at Microsoft. They flew me to France to save the TJ because the France to save the TJ because the France to save the TJ because the triggers were eating up too much CPU and triggers were eating up too much CPU and triggers were eating up too much CPU and I had to show them how to tune it and I had to show them how to tune it and I had to show them how to tune it and everything like that because replication everything like that because replication everything like that because replication used triggers and stored procs to keep used triggers and stored procs to keep used triggers and stored procs to keep everything, you know. So, anyway, so everything, you know. So, anyway, so everything, you know. So, anyway, so much about that information you don't much about that information you don't much about that information you don't care about. IoT IoT care about. IoT IoT care about. IoT IoT Join us next week where we promise to Join us next week where we promise to Join us next week where we promise to talk about something really relevant and talk about something really relevant and talk about something really relevant and maybe a little bit younger than Larry maybe a little bit younger than Larry maybe a little bit younger than Larry Ellison. You never know.

Ellison. You never know. Ellison. You never know. And we've been And we've been And we've been [Music]