The information economy as a rod. The world is teeming with innovation as new business models, we invent every industry industry. Inside Analysis is your source of information and insight about how to make the most of this exciting new era. Learn more and inside analysis dot Comside Analysis dot com. And now here's your host, Eric Kavanaugh. All Right, ladies and gentleman, welcome back to
the Gilman Coast to Coast radio show all of doctor Information of Economy. It's time for Inside Analysis or should be Eric Kavanaugh with my European Chappel Gang or Eve Mulferents out there. And we were just chit chatting for our usual Monday catch up and we picked up on a couple of things and I want to really dive into this because there's some interesting ideas that are being hashed out right
now in our industry and it team varying format and tumultuous time. And I would have to say, and what sputned this all was our show last week which was great with Jamak de Gani from next Data and also Doug Kimball and Simmitpou Fromanto Text and Onto Text of course does knowledge graphs. Next Data, which is a Jamak's new company. She came from thought Works, where she came up with this whole concept of data mesh. At this company, now
she's building the data mesh. But it's a small company and they're from what I can tell, I think they're self funded, so they they're working I'm sure, around the clock to try to build this stuff out. And the analogy that she gave was that they're trying to do for data what Kubernets did
for processing. So, of course Kubernets containerization, container orchestration you sort of break down the monolithic approach to rating system but sort of a de facto operating system, this container orchestration into lots of little bitty pieces that then interact almost like legos, connecting as they need to and phemeral way to get something done. Well, wouldn't it be nice to have something like that for data?
And you know, I really started thinking to myself a number of different things, and Eve, I'll bring you into comment on this in just one second. But you know, when I did this show last week, I did a search for what is data mesh on Google? And oh my goodness, there are like thirteen, fourteen, fifteen different companies all sponsoring content on Google right now and the Google search engine for what is data mesh? So you got like fifteen sixteen and there may be twenty. I stopped counting at a
certain point. They're all over the map, and I'm thinking to myself, is this thing just off to the races now? Is it is? All control of data mesh been lost because you have all these folks doing their own things. So what is it? And data mesh is different than big data, Like big data took off like Gangbusters ten fifteen years ago because of all these giant social engines spinning out all sorts of data, because of all the
different ways they were able to capture data. Now that isn't just transactional. That is systems data for example, machine data basically, So that was a little bit different. Data mesh is a much more or it should be a
much more cogent and cohesive approach to working with data. But then you have all these foundational models coming out, and there's data modeling, and I remember as I was trying to understand data modeling, really it's all about performance and understanding the intersection of your data and whatever you want to do with it on the production side. So that kind of governs Is it a snowflake model? What is going to be you know, the actual data model that you put
into play. And that struck an idea with you, Eve about your thoughts way back when with data models. But what do you think first about this? Is it something specific or is it just amorphous? What do you think about that? And how does it relate to data models and structure data and all that stuff. Oh well, that's a that's a whole bunch of eric data as a concept. What for me is most strike is the concept of data products, which means you're trying to create some value out of your data
and focus on that small element. That's one thing. An important other part is the governance, that security of those data products which you provide so it doesn't travel to each and everyone and it's freely available, you have it in the controlled environment. I think those are very important principles of a data mesh.
And you stated it pretty well in how the history of the big data and everything came along in such a way we thought big data was hot together with data science, where we thought, okay, let's throw all the data into a data leak. It's cheap, we can capture all the data and very lightly with data science and algorithms and pattern mining, we can find some hidden gems into that data leak. But then we thought, well, we saw that it was too much. We didn't have any context about that.
So data scientists had to have the knowledge of the business to find those hidden gems. So a lot of communication with the business to understand what is meaningful, what is worthful in your data, and then pulling that out and giving the insights. But that still is a lot of work preparing the data and making it available. And I think we're now with the foundational models at the stage where you can indeed throw all that data at the foundational model and have
the foundational model give you the most interesting connections between your data elements. If we call foundational model, it's a large language model. These are words and how the words are connected. And what changpt as a language model does is it tries to predict the next possible word which is following in a sentence. So we analyzed all these data and says, hey, very likely this is
the next word, or this is in the most context. These are the words that tie together, and if you look into a business context, and
the data what you have. This could be meaningful that we can take these foundation models and try to navigate through all the data what we have and say, in the most of the cases, this customer is available in this country, for example, and then highlights all the valuable information what you have, so understand the understandment and understanding your data elements by applying foundational models on top of that, and then an extra layer of governing and securing that part.
I think that's interesting what you say, I know this little piece of information and this is valuable for me, and you're, for example, in the marketing department, and another person is in the sales department and has as well that valuable piece, but you have a hard time tying to together. And if the foundation model comes along and say hey, you're missing this valuable piece, connect it all together, then you're creating that datamash and you're governing that
all around. I think that's that's definitely a possible ability where we could go. And with all the technology that now is available, we are coming into that self driving data management. If you look at how cars operate, they give signals to each other and they're aware of their environment, and if you can put that in place on top of all your data. That would be for me, well, crazy, it's the technology and all the all the
algorithms that are doing the work for me. What are we doing? Typically, if you're doing data modeling, you're looking at the data and you say, how would we split it up? Like you said, for having the performance, you had vertical partitioning, yet horizontal partitioning depending on how you will
query your data. That's why dimensional models and snowflake models came to be as well, because they had a different behavior on top of the data compared to online transactional data, and you had to store them in a different way. And that was just the expertise that you made a good data model and a good data model I call it, which is the best model fit for purpose?
And yeah, now now with the knowledge graphs and all of that together for me, that's that's a valuable concept that we can get so much faster insights in our data. Then you could do as as a human being. Otherwise you had to go through all the data, you had to cleanse the data, you had to optimize it, and then you could start doing that. And this is in a certain way be done by by a lot of the foundational models and the algorithms, we still have the issue of data quality.
I mean, if you put in bad data into the algorithms, you get bad insights, and that's still a big concern. By using foundational models. Yeah, well yeah, so the hallucinations that these things come up with me and again they are predictive engines and they have a corpus of text on which they've been trained. Now the next phase, and I guess I want to see if we can go to this certain place here, the intersection of the nexus of large language models or foundational models and data mesh. That could
get very interesting. And you know, you think about how container orchestration works. You've got these containers. They have a little system process inside and they are self describing. So they land somewhere, they open up and they go, oh, here's what I've got, and it gets it gets dropped into the production line, basically into the processing line. It does its thing, and then it goes away, and then another pod opens up and it does its thing and then it goes away. Well, you know, if you
could do that with data, that would get interesting. So you'd have some sort of wrapper around it. That says, you know who I am, this is the system I came from, what kind of data I have? And you land and you do something and you kind of go on your way. I guess that's that's the vision. What I'm trying to figure out is, you know, what are some of the functions that are baked into these foundations models? Because you can just think about all the gray area in business
intelligence. So we spend all this time pulling data from transactional systems, putting it into this model, and then you query the model to understand what's going on. Well, that query side is at least half the battle, right knowing which question to ask. It's like the prompt when you're dealing with large language models. The prompt is really important. You have to be very specific in telling you what you wanted to give you. It's not like Google,
which just gives you a whole bunch of stuff to look up. Although you'll notice Google changed, I don't know, maybe six or twelve months ago, where a lot of times if you ask it for a definition of something, what it'll do is it'll go to a page that it's founded indexed, and it'll present that definition to you and then a link to that page. So it's they're trying to get to the place where they're really telling you what something
is, at least in terms of definition. But what's cool about a foundational model, of course, is you can see all kinds of different things. You can give me, you know, tell me twenty of the most innovative vendors, and data science and is going to give you its whole scoring, which it's doing on the fly. Right. It's not like it creative as index and it's waiting for you to ask for it. No, it's it's just waiting for you to prompt it to do something and then coming up with
whatever makes most sense. So like, if you think about that, these this entire industry of data management, of trying to understand what's happening in the world, where what decisions should I make? These foundational models themselves have greatly disrupted the whole process. And that's why every vendor and their grandmother are all talking about lms. It's like they're talking about two things, data mesh and
the LMS all day long. And like, you know, we're going to do a show on data science in August, but it's like you knows data science fading. Now, are we gonna stop talking less about data science and more about data mesh and foundational models. I don't know, what do you
think? Yeah, talking about data mesh because well, every every five to seven years we need to reinvent something hot word where we can get about and che GPTs all around the place, a large language models, foundational models, because we see there is a big potential in how you can put that into place. I think, like you said, you're listening, you're asking give me a top ten list of of data science companies, and it's all about
the contextualization of what you have in such a way. That's why you said as well, the prompts need to be very specific to get the output what you're expecting in such a way. And that was back in the days as well. Big challenge on building data models, on building data warehouses. For example, if you have Paris, it's available, well, it's in three places that I know of. There are cities called Paris all over the world.
All this contextualization, I think, with such a vast data set with a lot of value in there, you can make up your mind that at this parents in the States or spouse in France and having that capability into the models, into your data warehouse. That's that's really the future where we're going for me. Yeah, yeah, this is uh, it's it's fascinating. I mean from a business perspective, I think the bottom line is that the song remains the same and that we still need to be looking at these different
models and understanding. But you know, the other takeaway I had from a Terra Data conference recently. They were talking about data warehouse, data lakehouse, lakehouse architecture, things of this nature, and I thought to myself, you know, there's another whole set of applications that are coming down the pike. They're AI driven, and that are really going to change how the apps interact
with the data. So you know, if you look at Snowflake and what they're doing with their new transactional apps, as they're saying, oh, why don't you just build your transactional apps inside our environments and that way they can spin right out of your curated, trusted data. So that's clearly a trajectory for them. But you know, then, like I'm saying, there's this other side of the equation where I think that there's a lot of heavy lifting
we've been doing for a lot of time. Now that's probably not going to be as necessary anymore, just the amount of raw data that we move to get into place to be able to analyze. I think some of the use cases that were fed by that model are going to be fed other ways and we won't need to be doing all this heavy lifting on the data management side. But maybe I'm wrong. What do you think about that? Well, we still have to make very likely a distinction between structured and unstructured data.
And in the unstructured data, that's the part where we're talking about foundational models. It can help us as well in the structured part. But typically where you say, well, I think there is a lot of value in these data sets and I don't know how to connect it. Throw it in your lakehouse. And then on top of that you can get the contextualization a few years back again, and still the data catalog gets a lot of attention. It's indexing the data what you have, labeling it, understanding your data.
It's typically what what what in the video tex all the all the all the janitaries who are doing they were indexing the books so you could find it based upon author, based upon title, based upon the type of book. And this is what the foundational models are doing for us right now with all the data what we have around, and that's where I think it's going. It's merging more transactional data together with unstructured data and analytical data. And if you
see you mentioned just snowflake how they are doing that. Another technology what I saw, is capable of doing that a single store. They have the analytical and the operational engine into one database, so you can't move all your data all around all the right. Depending on which type of question you're asking, the technology is formach enough to give you the answer. And that's already a big step forward what we've seen in the new technologies on how they're developing.
So yeah, it's exciting times to see where all of this is going into the data management space, but finally having our digital footprint transferred into real business insights. Yeah. Well, and so you bring up data catalogs, and I'm almost wondering, you know, does the foundational model become your data catalog?
It seems to me that's what it's probably best at doing is understanding entities and the relationships between them, and that's really what a data catalog is supposed to be doing for you is explaining the entities and helping you understand where they
go and what they do. I mean, you know, I think what we're going to see here are these sort of single instance large language models who as I talking to someone just the other day who was talking about how that architecture works and that you know, you can just set up your own instance of an LM in Microsoft as your and you can do all kind of fun stuff with it, and then it's just like a database that you've set up, so it's already you know, to a point where it is instantiated just
for your company, right, and so you can control what goes into the rest of the model what doesn't etc to me. That's you know, that's probably the holy grail. But I'm sure there's a lot of loose ends to tie. You know. That's the problem, right, It's any any new solution can solve eighty percent of your problems, but the last twenty percent it's always going to be a pain. And that brings us to the end of
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fifty seven eighty three. Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh, right ful, pfect, your anci anouts talking to the legendary eat mookers at SEVENS data over from Belgium. And we're noticing that people are
waking up now. You know, I mentioned at the ter Data conference my input about different architectures, the data warehouse versus the data lake versus the lakehouse, and these different architectures, and that is important stuff less important in a number of different ways going forward, just because this new generation of apps is going to be able to get signal from almost anywhere. And I don't think you're going to need to do all the massaging of the data and all the
management and movement of the data. I think you're going to be able to get signal from just a couple threads. Think click stream analysis for example. Just to throw something out there, there's so much that we can now gather from web browsers as people are moving around where their mouse goes, what they click on, where they go, what they do to ascertain intents and then
serve up some interesting piece of contents. And you know, I have to think that Amazon and some of the other real big marketplaces are doing a lot of stuff in this space. You kind of see little bits and pieces of it of the changing of the guard, if you will, about how things are done. They're getting more clever about abandoned carts, for example. Many more companies are getting clever about that noticing, hey, you know, are
you still looking for a new mattress kind of thing? And there are engines that drive all these applications, right, and there's a lot of legacy technology out there doing it. But I'm positive that you're going to see at least the major players like Amazon and the other e commerce engines kind of jumping on this stuff and leveraging a new generation of analytical capability driven by AI, driven by algorithms, and again the nexus to maybe it's data mesh, maybe it's
just leveraging these foundational models. But the point is there are new ways of doing things. They're going to be leaner, they're going to be more cost efficient, and I think they're going to be more accurate. What was the one I saw a stat that kind of blew my mind to end. I'm trying to remember who it was, but they basically said that they're like ninety two percent accurate in terms of their predictions of what someone's going to do.
And this is a oh, I know it was. It was one I was talking to Mendix for the folks at Mendix, and what they're doing, of course, is digital transformation. They've come up. It's a really I love Mendix and they're they're a version ten, so they've been around for a long time. What she was saying is that their AI is now making suggestions to people as they're going through and designing these workflows, saying, hey, do you want to add this button? Do you want to do this?
Do you want to do that? And she said it's like ninety two percent of the time. I think, does the number the user says yes, I want to do that. So you had mentioned how the foundational models, the language models, they're trying to figure out what word are you going to use next right, what makes most sense or what paragraph, what subject area, et cetera. And it's a cascading you can tell sort of a cascading engine where it's like ri, you know what is the general theme? Okay,
I got it. You can actually tell when you you know, you prompt and you see weight and like m it's like thinking and then now how it comes right so and then of course it stops, you can go continue to generate, and that obviously is uh, you know, a sort of guard rail in the system to keep it from just going on and on and on. They don't want to go on and on and on. They want
it to be as cost efficient as possible. Right, But my point is if you, if you kind of metaphorically do the math here and realize, all right, these foundational models know a lot already. They have a whole lot of understanding about the relationship of concepts and objects and places and things and industries. And you you built that onto a traditional or you underpin a traditional business intelligence system or an analytics system, and things get really interesting, really
fast. So I think that this next generation of AI technology, it's going to be there's gonna be a lot of lightweight stuff that kind of sits on the top and just looks for a signal and then does something. And I think that's gonna be a huge change in how all this stuff gets done. But what do you think there's kind of personal assistance? What you get that if you say they listen to the signals. But yeah, it's it's it's
a rich context aware system what we now have. And with our human brains we have a kind of limitation of how much context or attention points we can get at the same time. And these models can have a few millions at the same time. So I think that's that's where you say where the power is in having the models and in the AI that can help us in really assisting in what we are doing. And it's amazing, like you say, nine predictability. So I was thinking when you were saying that, are we
human beings all in the same behavior? Are we acting in the same way. If you take about one hundred parameters and can classify any human being by the one hundred parameters, very likely any pursue percent of the people fit into that that segment. That's really really amazing to see. I was thinking, well, at the same time, at the small ends, if you see them moving across the path, yeah, yeah, yeah, right, So it's an ecosystem that works together, and I'm always thinking of the AI systems
with the neural networks below there on how they operates. And it's in a certain way that we are mimicking just our environment around us, the biology and the systems that are already in place, and we're finding a new way of
developing systems that will help us in augment what we are doing. And yeah, it's it's it's it's an exciting time whereas see a lot of U applications of AI will be and that's thanks to the rich environment what you have and the scale what you can do. Another example is in the healthcare diagnosis, where you have the system that analyzes all analysis of the doctor and gathers that together and then connected to the various diseases, what you have seeing, connected
to the various patients, and then can really assist you in taking a certain decision based upon all the science. And that's very likely where we are going. Still aside the privacy and everything what we have to take into account be careful with. But yeah, we see that developing in very specific AI applications. We will what will be built in the future to help you and having those little satellites, those little specific assistance that help you decide on soca.
Well, like so the gentleman lou Simon was his name from Optima he made. He's a super smart guy. Very impressed with these folks. I want to learn more about what they're doing. But he was saying, for example, like you just said, in the healthcare space, when you consider the corpus of data that we already have scientific data on liver disease, on lung
disease, on all these different on heart disease for example. You know, his point was, these foundational models that are trained on real world medical data, they are going to be so much more powerful and knowledgeable than any individual doctor. And as he pointed out, today is the dumbest it'll ever be. So it's going to be learning day after day after day after day. Now, you know, things do change, Environmental conditions change, I mean,
lots of things can change over time. But nonetheless, you know, you think about how that's going to just transform the healthcare industry and the time
we spend working on stuff. I mean healthcare from my perspective, I mean, there's tremendously advanced aspects of healthcare and science in big pharma and in other places but then there's this just monster of a bureaucracy and legacy mindsets and processes and workflows, and you know, bureaucracy I think is the is the appropriate term, and you know you're just kind of crashing up against that wall. You know. Just to give an example here, they just built a new
VA hospital in New Orleans a number of years ago. It just got to finished, and I wonder how much that cost because it's a huge building. I mean, it's just absolutely gargantuan and it's like, okay, so one billion dollar project broke ground in June twenty ten and features one point six million square feet in eight buildings spread it over thirty one acre campus, to which
I ask, why, like why do we need these huge buildings? I almost think the size of the buildings are meant to be a metaphor for how important it is to us. We don't need these huge structures anymore. They're incredibly expensive to air condition. I mean, can you imagine the AC built
for these places? And that's why it costs two thousand dollars for an MRI And you think about how many MRI machines you could have built for a billion dollars and it's a whole heck of a lot that you would just then have in small little office buildings around town, because now you're forcing people to drive in. It's almost like the Lakehouse, right, You're just forcing them to commando the Lakehouse. Et other data back out hydrate or ETL data EEL reverse
ETL. Right. Anyway, what I'm kind of speaking to is the this mountain of inefficiency that's baked into system today, including the actual structures like look at downtown San Francisco, look at Salesforce Tower. I mean, wow, what a you know, a symbol of the old ways of doing things and now they're all trying to do RTO. I saw, I'm getting a tangent here, but I'm getting excited. I saw an article came out and it was you may have seen this. It was like an artist transition of what
all these homeworkers are gonna look like in thirty years. You're gonna be hunched over, and you're gonna have a big belly, and you're gonna have bad blood pressure and all this stuff there. Which big tech company that's pushing RTO got that story placed because it's like I can do exercise at home. I could do whatever I want at home. I don't have to just sit in front of my desk. So my point is like, they are these there are these old constructs, and we see them in our business too, like
the data warehouses, the monolithic applications. SAP is still a modelith right, even they're like, oh, I guess we have to do something with this Kubernetes thing. I don't know. Man, the bigger they come, the harder they fall, is what I'm saying. What do you think. Yeah, SAP is moving in a different direction with still a monolith, but they're they're they're following up and like they once said that they're an oil tanker, and it takes a while before they get of course, but once they they're
up, they hid it and they moved forward. I was thinking about, like you said, with all the massive buildings of the hospital, it's it's it's moving in a different way of treatment. We do more preventive healthcare. If you can trace back in the yeah, by by the source of why you have a disease and can start treating that as of your ten years or five years or whatever, then you prevent of having the disease off the wall
in the end and then it's less less expensive. And with all the data that we are now tracing, we're very much capable of doing that preventive healthcare instead of doing the aftercare. And see what you've got the symptoms, and we treat the symptoms. We got something generic that's good for am, B and C, and we kind of treat them all in the same way. It's the problem with and tell my people are getting used to the antibiotics and
are right anymore. But if you see that that just the these institutions, they earned so much money on just this way of working, so it will be hard to cut down these processes because they're so established in just treating um the results and not doing to preventive healthcare. Right now, that's a good point. And the organizational structures, you know, that's where I'm I'm seeing there's going to be tremendous change here. We've just seen it in our industry.
I mean, you know, let's be honest, it's been a blood bath the last six or seven months or so. You know. My my cynical side thinks that certain companies waited until after the November elections here in the States to announce their layoffs because they're very politically motivated. They didn't want the world to think that there is a recession coming. And it's like, Okay, everything was fine until remember ninth and then all we've gotta lay off ten
percent of our workforce. We're gonna lay off thirty percent of or workforce. I mean, I know companies they let out thirty three percent of their workforce. You know, that's a lot of people to lay off. And then what do you do then, Like, how do you pick up the pieces and get back to work? You know, And the answer is going to be stuff like foundational models, using that to bring out some of your marketing copy. You know, they're they're just going to be different ways of doing
things. But I think it's gonna be disruptive. I think it's going to be disruptive for a while. You know, one of my longtime clients, she's been working on an acquisition. They just got acquired, and he referred to certain this environment today is a nuclear winter. It's like, oh no, that doesn't sound good at all. But necessity breeds innovation, right, because that we're gonna have to change. We're gonna have to change, and I think it is going to be the agile companies to figure out how to
carve out the niches and and make some money. What do you think, Yeah, they're still there. They will carve up the niches. But still the big companies have still big pockets, so they can do a lot of They have more power to do these things. Like you were just referring before, Tata Data investing a quarter rebellion to transform their legacy system into a cloud based system. And that's that's very hard as a smaller company to compete against
decoding. But what I see and talking by or to a lot of peers and experts as well as skills that we now very much need or data scientists, that's exactly critical thinking. It's questioning what we are so used to see, what we are used to do, and that critical thinking will be a very very important aspect, a soft skill that a lot of people will need in the future. It's as well with with the large language models, we
will learn in a different way than traditionally. Traditionally, or you are educated, you read a lot of books, you stored all the information in your head, and that's where you are smart. If you had all that knowledge stored. Now all the knowledge is available, but you have you need to have the capabilities of questioning what you get as the answer, a questioning of how do things work, how do they fit within what we already know?
And that's that's going to be transformational in like you say, the type of companies that we will see in the future, and when you will be capable of having that kind of culture, that environment of doing things in this type of way of being really critical about things and then moving forward, that's that's going to be transformational. Yeah, it is. And I think that basically it's every developer, for example, is going to become a ten X developer
or they talk about that the ten X developer. I mean, who was I I came at which vendor it was? It might have been get hub. I thought it might have been someone else, But on their website they said, you know now, and this is huge. Now, these engines
can tell you what your code means. Right. It's not just that they can write code for you, it's that you can feed them code and say, hey, what is this doing, and it'll come back and tell you, Oh, what it's doing is it's reaching into this system and pulling that score and then it's doing this, and it's doing that. You're just like, oh my goodness that see I was talking to the I think it was the guy from answer Rocket, and this was a sort of an epiphany for
me. Think about the coball developers and how much application has been built in coball from thirty and forty years ago, and there are no coball developers anymore. Well, yeah there are. It's called chat chypt or it's called co pilot or whatever. So it's now you can feed this stuff in there. Throw what the hell is it doing? Oh that's what it was doing. Oh good, Just changes change that. That's what it's going to be.
It's going to be kind of changing things. You're gonna use these engines to create the for the bulk, but the clay model, and then the human is going to go in and do the fine tuckey and chain in music shop things like that to get it just right. But both don't set that would be right back. And do you own an annuity either fixed rate, indexed or variable? Are you paying high fees and getting low returns? If so, Annuity General would like you to have this free book to learn the pitfalls
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eight fifty seven eighty three. Welcome back to Inside Analysis. Here's your host, Eric Kavanaugh. Fright, folks back here in front of our three four and all kind of taking here today. Folk, Like I said, I had it to epiphany with coball, coball, all these people are worried, and no coball there are barred. It's called copilot in particular, which is a forgithub. Right, And you just mentioned in the break there that now
you can create these snake applications. What's that all about? Yeah, you get the large language models that are trained to build applications for you called it's a plugin on a check GPT called to a GPT engineer. You install it on your local machine and you say, for example, I want to build that snake application. So it builds all the male modules, all the libraries, all the environments, whatever you need, and you get your base application.
I think in a few minutes that you have that application, then you can start weak again and optimizing. I was thinking of building a few applications in that way. Apparently the second version is well, what I saw on the video is already in say the topic of application, and that you can can build an even of such a high quality that you think, hey, guys, everybody can become a programmer. Simply said so yeah, next to which is true. I mean still, I think for a while now it's
going to take human involvements to put the finishing touches on these things. That last twenty percent. We just already talked about it, the preto principle, right, eighty percent is going to be done by these engines. The last
twenty percent it's going to be done checking things and fine tuning. But I mean even simple applications like I use constant contact for email, and I noticed, as of three or four months ago, when you would when you would use your own htmail may be older than this, but when you use your own htmail that a vendor would give us to promote their stuff, you just plug that into the window and it'll there's a button at the top it says click here to have the errors fixed for you. I'm like okay, click
boom, errors fixed. You're like that's fun. Why not like you know, click here to fix my financial future? Like okay, alreadio right it sometimes it works so well. But the point being like, oh my god, that is a huge or league game changer. It just being able to analyze the legacy code. So the stuff that's you know, running some of these really old systems that still runs. One of my friends, buddy of mind is a smart techies like Unix systems, because I know Unix systems have
been running for thirty years, never restarted, never had a problem. Like analytics was based on Unix, wasn't it. Yeah? Yeah, And if you see how that works with with the large language models, you have now so many plugins where you can say, okay, give me this overview, put it in a Mermaid graph, and you have you have your diagrams available. So imagine that you put that to your cobal codes and say, just
give me all the interactions with the systems. You don't have to go into the code, understand the code, then take it out, put it in an over diagram type of system. Uh. That already is a lot of work, but imagine just maintaining that the diagrams as such, if you change something in the goat all your diagrams and the visualization, they change it along
with what you are doing. So that's that's really powerful where you say you have to maintain it in one place and the rest will follow based upon understanding of what you all are doing. And I was thinking of another example of all these insane SQL queries that I saw from three four pages long, which takes you about five days to understand what they're really trying to do with EsCl.
Throw it in the large language models and have an understanding of how to simplify it, how to break it down and debug it and really finding the issues with all these queries. Yeah, I mean so many possibilities what we have with the models and how to have that content, which understanding of systems in place and not our only systems. When we're talking about if you think about how health desks and service desks can be optimized, now you have to
think about all the questions that your customers can ask. Now you guide it in a certain way and the model learns by having new questions or related questions and giving those insights to customers, or you just feed it the model base what you already have, and they will pop out the questions that very likely
can be asked by your customers if they're communicating with the chambob. So you can get a very personalized assistance if you're calling your energy company or your bank or whatever, and they will be very knowledgeable compared to what the limitations we have as a human being. Right, yeah, and wow, I mean it is going to change. I think you are going to see it's probably happening already. Prompt engineers experts in large language models to do to examine your
code base. I mean that that would be if I'm a CIO, That's probably one of the first things I'm going to do is figure out, all right, how can we instance you at our own large language model. You know. The guy I was talking to is like, just do it in the cloud and as your you set it up and it's off to the races. You can start playing around with it and just start examining your code base. Look at wherever you're having the most problems. Is a customer service?
Is it downtime for example? Is it just performance? Our system is running slow today. That's where I think it's going to get very interesting is when, as you just suggested, and maybe you have to get this plug in, this engineer or some other plug in, But basically I want this system. I want this large language model to examine all the log files for all my applications for you know, the last day, for example, and just
tell me what the hell is going on. That gets very interesting, very very quickly, because this machine to machine conversation, even though it is complex to a certain extent, it's not as complex as the English language or the French language. Rights it's pretty simple. What's happening. They're just sending calls back and forth and sending packets back and forth. So you know, I wonder, are we going to be able to re architect information landscapes? And
I think the answer is yes. What do you think, Yeah, well, re architecture. I think we can build sub optimal systems. That's the advantage. In a certain way. We can put up sub optimal systems and the technology or the algorithms will help us optimize that. So we can learn from the from from the algorithms, how it is optimizing your systems for future architecture. So learning from your mistakes thanks to the algorithms and the inside and
was thinking of an example. Keybo is one of those those players in the market. What they do they look They now specifically work with Snowflake, and the big problem was everybody was putting everything in the Snowflake databases in the cloud, but their bill went pretty much well ten poled up, so big surprise. So what they do They look at at the log files and your query behavior and the usage of your databases, and based upon that, they know
how to optimize the Snowflake system. They know how to scale from from the medium large and extra large type of systems. They know how to optimize the memory and so on and so forth. So based upon when you need which
type of resources, they scale up and scale down automatically. You even can, for example, say hey, by the end of the month, we definitely need so much more compute power because it's an end of must closure and I couldn't care about it, couldn't care about the costs in a certain way, but we do want to have this animal closure done into hours, for example, and coordinate the system and tell the system this is how I want to work, and they do. They have a lifting for you, just
bringing your build down by by sixty percent. So that's really insane. Why you say, okay, we can focus upon building the suboptimal systems, because before we need to understand all the ins and outs of the system to have it optimized to have the optimal architecture. It's going to take a long while because we don't have all the experts. And I think there there's going to
be a lot of these systems which are coming in. And I was thinking back in the days where yet the data virtualization systems that have the knowledge of how to upt to you that performance what you are looking for. So we were the data virtualization systems where a rule based system on how to optimize systems. But now with the algorithms, they really look at the behaviors and connect everything what is under the hood, and then based upon these sites, optimize
your system. So we're still at the beginning off of having insane systems optimizing each other. And where it's going. I do wonder, like one would last topic to dive into, there's obviously a cost for open AI. There's a cost for Microsoft when you're deploying that environment. Of course they're charging you now, but I wonder when we're gonna when when's that other shoe gonna drop?
Because I have to think it's probably gonna come, you know, probably the next six to nine months or something specifically from open Ai because they gotta make money. I mean, they're getting investments, right, They're gonna a lot of money to be able to spread this stuff out. But at a certain point they're going to you know, change and it's the freemium model, right, you know, all of a sudden, Okay, well I was free. Now it's gonna be X amount of money. I mean, I
wonder what's going to happen there. It's going to be a cost per query, you know, what's like, what's the story? That's my guess is that you know you're gonna be able to do and they're still probably figuring out how to charge for all that. But what do you think is that going to be three months? Six months? When is when is the other shoe gonna drop? Financially? Well, I think it's it's coming pretty pretty quickly.
If you see, you're already have a check GPT plus on top of open Ai of all the extensions which are comic, so we're going their direction. What we saw is that that based upon how you are charges based upon a token tokens one token is one hundred words something like that. And you see a lot of other language models, they are only a tenth of diffraction
of an opening isis. So there we see already that they're optimizing on how they can train their models to consume less resources, might be more comfortable. And that's going to be a big challenge and and and and and a focus for all the big models which are out there and become profitable. But yeah, it's a trade off. Maybe Microsoft says, yeah, we just keep on offering the large language models, and we take our profit based upon all
the services, what we offer on the infrastructure, on the compute. That can be a model that is working for them, and we can't anticipate what is their business model. But that's what I've heard that some companies just say it's how Amazon grew. In fact, they offered a lot of thanks for free, but they took the profit on top of the services and the infrastructure what they are offering, so they moved a bit where the profit came from.
Wow, that's what you do, folks in the real world. Well, you're talking every buddy Bobbers, you know, inside Analysis semi email income on Inside Analysis dot com. You've been listening to Inside Analysis, miss something today, yesterday, last week. Check out our podcasts at WWWKCAA radio dot com. We leave no listener behind. Happy Fourth of July from this radio station. It's time to celebrate again this year with the Native Sons of the
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