Latest / Architecting the AI Enterprise / Episode 5: Is SaaS Dead?

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Episode 5: Is SaaS Dead? · Architecting the AI Enterprise

• Marketing: Hi, I'm Gary Homerman, CEO founder Uncork. Hi, Dave Ferrucci, ⁓ Chief AI officer and CTO at Uncork. So, you know, this is going to be one time I am going to be so out of my league in the room here today. I've never felt less technical between Dave and our guest, Kafir Garage, who's CEO of Mindfulness, spelled like mindfulness with the extra A for AI, which is very creative. But Kafir and I go back all the way through about 2013, 2012, when, you know, I was a CIO in MetLife. He was head of technology at BlackRock. ⁓ he then went on to become the chief innovation officer at BlackRock. He has been behind the scenes with us since Uncork was launched, helping us from every aspect, from the technical side, ⁓ which has been amazing. But I have never met I've never been in the room with two more technical individuals and My intelligence level is down here. They're definitely up here. I did though get come armed with a brand new panel that our chief evangelist Rich Army with, which means I could play the role of the buttons and all these little gadgets here, which I'm gonna do, by the way. So ⁓ with Jeopardy there for Dave. Kafir, just give you have an amazing background. I remember all the way from the Europe, ⁓ to Israel to US, to back just give us as much as you can. What's What could you share that's not national secrets here or anything? first of all, thank you very much for inviting me and it's a pleasure to be here. ⁓ I've been ⁓ at Ancork indeed over the years. I think ⁓ my first ⁓ interaction with Ancorg that was not you was ⁓ a forum of your ⁓ developers in the firm where I did an open mic session. Yes ⁓ and ⁓ kind of an AMA ⁓ And I managed to ⁓ talk with people about software development, about ⁓ the future of ⁓ automation ⁓ in software and all kind of other things ⁓ and it was ⁓ fantastic. I think ⁓ it it was with Tony Kim here. ⁓ that was ⁓ when he did the investment and I came over to do the technical piece. So for those who don't know, Tony Kim led our series C, he participated in B. I have never met an investor like Tony. So he He will literally email me and say, Hey Gary, I'm coming from the West Coast to East Coast. When do you have time? I'll sit with you and talk strategy of what I'm seeing. That's s I can't tell you how few investors actually do that reach out. T Tony is amazing and ⁓ I managed to do with Tony some amazing things ⁓ in the AI world, believe it or not, in the last couple of years, ⁓ that they're quite ⁓ you know, pioneering if you want. ⁓ so ⁓ that's a very good connection into Angkor. That's and Tony was one of the f the ones that basically said years ago SAS is dead. What's your c give me your feeling? Let's jump right in and then we'll come back to your idea. SAS is dead. I I I'm not sure about it. ⁓ I I I think you know, majority of example of SAS is dead, and again, I I'm not you know, ⁓ you want to jump directly in, that's a jump of two trillion dollar loss for the market of software that happened over two days in February. Yes. Yeah, and it's not a joke. ⁓ it needs to be taken very seriously. ⁓ with that said, I think i it's kind of you know, it's an eighty twenty game, like most of the problems. If you got the piece of sans that was doing for you ⁓ all kind of things that you don't need and just a few things that you needed, I assume right now ⁓ when you're going to go into this new ⁓ directions of ⁓ ⁓ AI working with your prompt from a line. And ⁓ later on when we're going to go into ⁓ you know native AI ⁓ companies, ⁓ the this this this kind of software ⁓ will be dead. ⁓ but if you are ⁓ running a piece of software that is entrenched into process that is doing a lot of things that ⁓ ⁓ AI ⁓ will not be ⁓ getting there because ⁓ regulations, because ⁓ you are uncomfortable ⁓ sharing certain type of data sets because all kind of other reasons. So that's kind of the 20% of the map. ⁓ they have a great future, they have buy they buying themselves time and they will be able to reinvent themselves ⁓ either ⁓ with ⁓ what's coming up into the na you know ⁓ agents journey and we'll discuss about it I'm sure ⁓ whenever you want. But ⁓ otherwise ⁓ with this kind of ⁓ native AI approach, ⁓ they will be ⁓ they will be saved. And ⁓ you know I I I think that ⁓ you know people they need to to ask themselves also how this is happening. We had A lot of things when people say things are dead and they are still alive with us, very much alive. I remember when I built at HP a practice called data center transformation with a lot of friends around me. And ⁓ even prior to HP and people say that data centers are dead ⁓ because of cloud and data centre transformation ⁓ came to kind of make this transition into it. Right now, not only data centers are not dead, they are the core of the topic in the world nowadays. You call them AI data center, hyperscale, whatever you want to call them. But at the end of the day, by the way, you know, things they do not live up there in the cloud, they live in data centers. People still don't figure it out till nowadays. ⁓ you are charging these tokens for things that they are happening at the end of the day in data centers. And if you did not pay attention to that, Jensen Huang is Charging a lot of money for what he's putting into these data centers. So data center transformation was something. Then, till nowadays, all over the world, people are talking about the digital transformation. And the digital transformation might include, you know, moving to cloud, doing all kinds of things, touching AI. So that was also a program that a lot of companies they managed to do. I think the next thing maybe should be something like a like a SaaS transformation, you know, that will basically take SaaS. ⁓ for where it needs to be ⁓ you know ⁓ replaced, augmented, or or ⁓ reengineered ⁓ with ⁓ with AI. And that's also connected with ⁓ big problem that we have nowadays with AI, ⁓ that might be also related to ⁓ the SASPOCalypse, right? The story there is people look at these things nowadays in a very simplistic way. And they are asking, you know, How would you look at this? How would you would you invest in this? How would you ⁓ approach this from all kinds of ways? And what people forgot, the basic things of anything about you know technology at the end of the day, it's a system, it has block diagrams, it has a way to be ⁓ analyzed, and people they just lose it. Even Jensen Huang, when he's trying to do ⁓ you know, ⁓ to simplify it for for the public to understand that, in particular for investors, right? He's presenting this, you know, you know, layers of a cake and and and he's giving an explanation with that. And I think that's very important for people not to forget about it. So when you're talking about ⁓ you know SAS and what this before it's dead, it needs to be replaced by something. You know, spoiler alert, it's not going to be replaced by one prompt. And the examples of people that they replace it with one prompt, like the example of, ⁓ I've been able to replace a workflow into project management with ⁓ I don't know, a few ⁓ prompts and maybe some base forty four or I don't know what people did there and all kind of examples that came into the newspapers. It's very simple functions. These are not entrenched functions into an enterprise. ⁓ when you look at some of these ⁓ SAS products. You look into implementation times that they go between months to more than a year. And this is not happening because the companies that they are providing this kind of services are ⁓ incapable. This is happening because the processes are difficult, because there is governance, because there is things to go through data sets, because a lot of reasons. You know, for me, ⁓ I had the biggest frustration where basically I was thinking that I can do any kind of ⁓ corporate application ⁓ running with AI ⁓ three to four months. That was my target. So wait just to make sure vibe coding. This is you were gonna vibe code anything internal before even vibe coding. Before even vibe coding. Just you know j just do something with AI, ML AI, let's call it. Before even vibe vibe coding came later on. Right now when we say later it's months, right? This is crazy, right? But that's kind of how it is. So so basically, you know, I was totally disappointed. It took me, you know, sometimes, you know, six to eight months. And then I started looking around and talking with people that they were, you know, as advanced. Yeah. And so what was the problem? The problem is, you know, just to touch a data set to be kosherized, you know, by everybody, including teams that they were part of my organization. Okay, three to four months. In order to get access. In order to get access. And then you build the product. Building it's easy. Building it's easy. But right now the data is the cart in front of the horse that is the AI. That's the problem. So basically, you know you're having this situation and I was very frustrated but when you look at other bodies that they are even more ⁓ regulated, even more complicated, maybe ⁓ more ⁓ bureaucratic. I'm aware about, you know, corporations that it takes them, you know, twelve to eighteen months to put the data set ⁓ in front of people so they can touch. Well Nowadays, twelve to eighteen months, you know, y you're you're doomed if you're a small business. If you're a big business, if you're something that ⁓ you know it's running some mega or or scale kind of processes, maybe you're going to be delaying that, but not for a long time. So that's the complexity of ⁓ Suspocalypse ⁓ story. I think ⁓ i i it is it is requiring a rethinking. It's requiring ⁓ a a transformation and and you know people that they sit there and y if you're even looking into you know successes that you had with AI, you know that the successes they were very fast for things that they are like a process, like to repl replicate something that you can ⁓ easily explain. That's promptable. vibing him. Yeah. ⁓ vibing these things. But in the moment that things are getting complex, touching a lot of points. ⁓ we we spoke before here the before we started ⁓ the podcast about neurosymbolics. You know, imagine that you start running in all kind of place and all kind of neurosymbolic points that you cannot touch because of compliance, because of all kinds of reasons. ⁓ you're going to be ⁓ i i in in in some problems there. So ⁓ yeah. Dave, what do you think? About which part of it? That was that was amazing. I think I agree with many of the the the points are being raised. I I think we can underestimate You think about SAS, we can underestimate some of the complexity, the touch points, the the complex governance and workflows that these things do. It's you you pick one function and you could vibe code that function. But when you think about all the knowledge ⁓ that went into the construction of those systems with regard to how much they reflect about that business, you start really needing people who are experts at understanding that stuff. And the coding Is one part, the embodiment of all that business knowledge is another point. So I think it I sort of agree with Kafir that it it's not going to disappear. These things are not, they're gonna be around for a while. I think I think prices may be affected. I think that where the you know it's interesting you raised the point of where they're gonna go. I think they kind of get cannibalized from a bunch of different vectors, right? One of them is just people realizing they're probably overpaying for stuff they don't really use, and they can now they have the opportunity to build that themselves. ⁓ another place they might get cannibalized is startups that say, Hey, I know a lot about this. I can ground up, rebuild a lot of this from ⁓ from you know with AI. So it's kind of interesting because if I had a better idea, let's say, for a Salesforce or some big SaaS product, and I went to an investor and I said, if I spend two billion dollars, I think I can grab 5% of the market share because I have this differentiation or 10% of the market share, whatever it is. I'm making these numbers up. They might not work out, but you get the idea. And he says, you know what, given the risk and everything else and the upfront investment's not worth it. But now if I come and I say I can grab five or ten percent of the market share and it's only gonna cost me twenty million dollars, that's a different investment now. Yeah, totally different story. But you need to be careful with that. One thing, ⁓ I will make an observation that you know, AI it's used as a service, by the way. so so so you know. Maybe even the as a service model is not yet dead. Well, yeah, I I'm sorry. I just want to yeah, I you're making I just want to jump in there because that's why I don't like this question, Gary. Because SaaS is too broad of a term. ⁓ s everything is like SaaS. It's all running on the wild. Remember when SaaS was a bit the the big idea was you're not gonna send me a C D. I'm gonna get a web app. I mean that was the big idea. I think when we say SaaS, we're referring to these big companies like Salesforce. That not the technology of SAS. Yeah, but at the end of the day you cannot run away from the fact that you are, you know, it's the software and it's the model. So you need to approach it one way or the other. So the question is legitimate question, you know. I I I want to give you an example also to be careful with these things. ⁓ some t some people jump on these things and, you know, they started using ⁓ you know, ⁓ one European company I heard about, they Basically ⁓ they were using all kind of SAS products in order to run their call centers and they fired everybody in the call center and they stopped using all the SAS and they put AI agents to run everything. It took six months till they collapsed. After six months they hired everybody and paid more expensive for the SAS products than beforehand. Real slowly. So therefore y you know, i it's ⁓ you know People need to do these steps ⁓ you know, ⁓ fast and slow. ⁓ so I gotta ask, so you know, there's very few people I know could answer these questions. So in my mind, when we talk AI in today's world, there's the AI agents related to code generation. It's cogens. ⁓ how long have you been doing code generators and like seen code generators in your life? Like you've been doing ⁓ forever. Like that's so there's code generation we use. the Claude we use internally. Then there's the AI agents. And these are like the fairy tales. These are like that's the example of like we're gonna put in an agent and it's gonna take over all the back office functions and do all these but in a regulated business how risky is that in your mind? Well you just can't do it because first of all you will not have the level of explainability and compliance and atomicity that you need to have in such kind of a process when you're running this. So there is only compliance, good. Okay. And y you will need you will need to to basically in order to do that, y you know, it will be done. It will be done. I I did it in all kind of ways and all kind of applications, you know. For compliance done. D been there done that works. ⁓ it was Again eighty twenty. Eighty percent was easy, twenty percent eighty percent six months, twenty per s twenty per cent ⁓ more than a year. A you know, it it's not so easy, but you're getting there. Now, ⁓ y you know, when things are starting getting complicated and entrenched, the the the story will be their ⁓ you know, layers of agents that they will need to be ⁓ you know, running under. ⁓ you know a workflow or s some kind of ⁓ hyper automation, whatever you want to call it. Look, ⁓ i it's ⁓ the models of of developing and the models of charge will change because of that. So nowadays we're talking already about ⁓ you know workers, right? And the workers are basically ⁓ a bunch of ⁓ layer agents ⁓ that they are put together into a process automation ⁓ that it's visible, it's odd you know, audible, you o audible you can you can audit that, you can understand what is doing. And then things are getting complicated when they are getting too smart. So when people start right now, in some applications they can do it. Financial world or ⁓ you know even he healthcare, I don't think that you can do that. When these things are started learning and starting doing things autonomously, this is when the smart becomes a problem. You can do that. So Well I yeah, I was gonna say that, you know, you bring up an interesting point, sort of an escalation of the notion of hallucination, right? I mean hallucination is is there's two sides to the coin, right? On one hand, when you give an LM a problem to solve, it's typically underspecified. A lot of the hard work is specifying exactly what you want the solution to be. But the beauty of using an AI is it fills in all the gaps for you. So you could be underspecified. When it agrees with you, you're happy. When it doesn't agree with you, it gives you an unexpected outcome because you didn't completely specify it, you're upset and you call it hallucination. But everything it filled in was an elucination. When you have agents and you're amplifying that across a workflow and they're making a lot of decisions that you're you're you didn't necessarily sp specify that they should make or respect re responding to conditions that you didn't provide a deterministic program for, you're gonna be surprised. And that's and that surprise is going to amplify through that entire workflow. So this becomes extremely unpredictable. And it's extremely interesting because one of the things we thought were really beneficial about computers was that they were deterministic. They did exactly what you told them Over and over again, exactly the way you expected them to do it. It's what it's the advantage over using humans sometimes. Now we're kind of building these agents and we're saying we don't want the trouble of programming it because that's too hard. So we're gonna let the agent do all the work, make some of these decisions. Sometimes it's right, sometimes it's wrong. This now gets amplified. We don't understand the risks. They might be tolerable, they might be intolerable. What's your baseline? Is your baseline a deterministic process? Because if it is, it's gonna suck. Is your baseline a bunch of humans to make the same number of mistakes? Well, maybe that's a different baseline. But do they make the same type of mistakes? It's kind of a really uncharted territory, and it's we don't know the answer, and it's going to evolve and we're going to change things. But part of that whole ecosystem of the way it's happening right now presents this other really interesting phenomenon. Which is that you have people who are non-engineers and non-programmers saying, ⁓ I'm just gonna use an agent, so can tell the agent what to do and plug that into the workflow. Half the time those individuals, my claim, my hypothesis, my thesis, is half the time those individuals don't even understand the difference between needing a probabilistic process because you can't specify it, and a process that's completely specifiable. which you can create a deterministic, error free thing to do. So you're introducing unnecessary risk because of how easy it is for non-engineers to create those things who don't even understand the difference. You know, it I I you raise a couple of points there that make me smile. One is I heard, you know, ⁓ lately I I I I I read about ⁓ some people that they developers they got fired so they told some stories that suddenly right now companies where they got fired, ⁓ they basically let business people do vibe coding and the code is going directly into production, forget about four eyes, forget about everything. So speak about hallucination. What hallucination? You know, I I do not see good things coming out of it. And to the point that you mentioned related to specification, you know ⁓ I would I ⁓ you know, I I want people to pay attention ⁓ at the latest trend into prompting, ⁓ which ⁓ you know, spoiler alert, it's look a little bit like coding. If you want it's getting right. It's getting so specific you might as well write the code. And this is my point. For half the time. Looks exactly like code. Yeah, you you basically write the code, but right now, you know, you're more vertical because you're trying to see everything for for basically in order for for these prompts to be understood and to be able to be to run you know a a responsible AI, you need to write this different than you will write the prompt, because the prompt runs you know, the prompt runs doesn't matter how you write it, ⁓ you know, in your LLM it. you hit you know whatever you're hitting there. But now you know if you write that in the you know in the way that ⁓ you know ⁓ Dave is basically hinting, yeah you need to write it vertically such basically that all these things you can see them ⁓ real time how they are running audit. This is hilarious. And it's and it's it's hilarious, but it's not surprising, right? Because you can't get around the fact that if you want to control a formal system, you need a formal language. What's difference between a formal language and a natural language? Is a formal language is unambiguous. You can't hallucinate. It is what it is. And and so now what happens is we're realizing, gee, when we use natural language, the AI makes stuff up. No kidding. Because it's un it's it's ambiguous. You left things out. Who knows what you really mean? It made a thousand micro decisions when you weren't looking. And if you want that complete control. You're gonna have to b give a complete specification that's in an unambiguous meaning formal language. And guess what you've done? You've reinvented code. It's i you know through pseudocode. But I mean it's I mean it's it's funny, but it's it's ironic. But let's see what this is what what this is doing next, and it's bringing us back to the point of cost. Yes. Suddenly all these efforts they are making an AI agent slash future worker because we're going to, you know, f soon change the model of charge, be more expensive than, you know, the humans. Yeah. That's what we're seeing right now already. It's the question we ask is is your cost going up or down? Because we expect it to go down. You expected, but so what? You know, we expected all the technology, you know, world to to be ⁓ to do the same things that ⁓ we've seen going from ⁓ agricultural world to indust industrialized world. Which was quite significant, you jumped, I don't know, fifty percent to eighty percent, depending where you're looking at. We did not see these kind of things with technology. We hoped again that maybe right now we're going to see into this S curve exactly the former, you know, improvement with ⁓ AI. Currently, you know, you see when people are going and they're giving a hug to one of these ⁓ large AI companies. And ⁓ say I've seen a couple of ⁓ financial ⁓ companies around the world that they did that. And when they do that, you see the outcomes of this work. And you see we managed to save X amounts of tens of hours per month of productivity. That's why you came to to work with AI. You're in the business of making money. So you basically did not open your data sets of investments and all your investment stories to the to the AI, ⁓ you know, to the AI surface. You just let them look into how your people run operation and deal with productivity. So th th this is again, you know, ⁓ selective AI, ⁓ selectivity in the stories. And it's not about the AI companies, they are not amazing in what they are doing and very successful. It's just when they are tasked, they are not they are tasked with their hands behind. That's it's interesting because I've seen so many times of ⁓ and for those who don't know, when a company is about to IPO, the investment banks make a lot of money in during those I IPOs. And those investment banks Musk was the first person who came out openly in the past month or two and said, Hey, if you want a piece of our SpaceX IPO, you you're gonna license Grok. You're gonna but the reality was that's what's always happened. It always happened was if I'm gonna IPO a company, I'm gonna get the investment banks to be a client first. And The problem is a lot of the statements you're reading about are the productivity gains of these AI solutions are not real. You know, it's related to an IPO that's gonna come in the future and I wanna be my seat at the table, which is sucks. You know, it's ⁓ that's the reality. It's it's I do like by the way that enthropic that Claude is telling people to take a nap during the day. I really you know, I don't know if you read about that, but apparently it's been telling people it's time to take a go to sleep. Yeah. It's like you're working too hard, Goffia, you should go to sleep and take a little nap now. Can you imagine? You know, when they are telling you that you passed your ⁓ your amount of tokens for the session, why wouldn't it what why w you know, another way of telling you that is how why don't you go and take a nap? I I I I think the pr I think the prices are gonna go up and then and then I think it's gonna create demand for a much cheaper way to deploy inference. And it's gonna start, you're gonna see competition and it's gonna go back down. So I think we're gonna see, we've steadily been seeing a decrease for sure. I think that as the demand goes up and the value starts to be realized, and s at least in some fields, that its price is gonna go up. And I think that's gonna spark competition. We already know that you can run inference on small clusters of like M4, M5 chips and apples. ⁓ Apple zone five chips and you can do you could do it much more efficiently. We know that you can fine-tune smaller models ⁓ to be effective in many cases. You don't always have to be paying the giant, you know, ⁓ frontier models. but I think people have to be pushed a little bit. I think as the prices go up, they can go down. In other words, the technology is there for this to be done more cheaply. ⁓ the big cost is in training these massive models. I mean, the last tier, I think, you know, they evaluate these things. They have an ELO score. And the newest frontier models, you know, they're sometimes double or triple the price of the cheaper ones, are technically by the ELO score like five percent better. Yeah, but the reason for that is they've You know, you you you did Watson, you dealt with, you know, massive amounts of data. But you know, when when I build, you know, financial AI models, ⁓ you know, just like SaaS ⁓ that is basically SaaS was sold ⁓ you know, you need twenty percent of the feature but you're buying the whole SaaS. And this are the problem with these companies. The same thing happens with the foundational models right now, because when you build right now something that you need for your application to attack per se, suddenly you switch around four models and average ⁓ in order to get optimum for what you're trying to do because none of them are si you know, singularly killing what you need to do there. So that's an interesting ⁓ direction. Now when When when these kind of things are happening, I will tell you something. I I'm not ⁓ convinced that for example for financial market we need these large language models. I think there is a huge place for small and medium language models and even more need even more need for small, medium and large quantitative models, which currently people you know. So I can see in some of these ⁓ AI companies Either a bold direction or a bad decision on the management, that they are starting very much with the agenda to go and develop also small and medium models, that you will be able to take them into your enterprise, into your own environment, into your own private cloud, train them for what you need with your own data in a very easy way that it should be provided for you. And people are not doing not that, not giving you the pro the provision for that. They want to come and do for you solutions and ⁓ then it's expensive, it's prohibitive. consulting services. They built consulting companies to do. Everybody's building consulting services. The reason for that, that you need them. You know, you really need them, Gary. You know, people they don't have this kind of expertise inside. I I actually I I encourage people, all these big guys to to kind of pay attention to the enterprise. You know, take take take a learning from the book of Google, you know. Take take a look where, you know, TK started there in in in the cloud business and take a look where it is right now. ⁓ and just learn what was the what is the story there. You know, when they pay more attention to the client, and that's Larry's think biggest thing, right? Always, you know, be a student of the clients. That's what I learned from him, lesson number one. So I wanna ask main mainfulness, what is the business do? So what is tell me a little bit about what you created. It's just a platform for me to kind of continuing my ⁓ ideation ⁓ sessions to ⁓ wherever I want to go around the world and to talk with people about problems that they have. But this time, you know, I I'm allowed to sit on a board, I'm allowed to be ⁓ you know on advisory board. And if this is not so everyone knows the number of times I've asked Kirk to join us in that respect, and the answer was I can't I can't. Yeah. But ⁓ yeah you know, ⁓ the the story is ⁓ you know if I don't do this or that, ⁓ because of the spectrum of the topics that I'm touching, and maybe that's ⁓ related to what we did not discuss at the beginning of ⁓ what was my journey ⁓ in my career, I see a lot of things I see sometimes ⁓ deal flow almost equivalent to ⁓ to a V C and not because of my last piece of job at BlackRock, believe it or not. ⁓ so I can actually go and invest from our, you know, family office ⁓ and and I'm not doing funds, I'm not doing any kind of just ⁓ you know, our small ⁓ piece into that. So ⁓ I I do find myself sometimes ⁓ investing ⁓ small amount of money next to Nvidia or to other corporations because of ⁓ because I wa wha what I'm able to bring to the table or or ⁓ or or because maybe I don't I could see that. ⁓ This is amazing. let's ⁓ by the way, we could go for another five hours is kinda the way I feel here. The one question. So two businesses, one looking at the world from an agentic side, the other one looking at it as let's actually break down the problems deterministic, define the deterministic process run. Which is a better direction in your mind? Financial services, let's let's narrow it down or healthcare. You know. I I I don't think that it is a better direction there. ⁓ you you kind of need to ⁓ to to mix and match. I have no doubt about it. I completely agree. I completely agree. You you have to mix and match. Yeah. You will need to ⁓ basically ⁓ touch things that are, you know, low hanging fruits. So you're going to attack them, you know. If you have ⁓ you know, I had A a data factory of billions of data points. You put on that, you know, whatever you want. Six sigma on top of that, you're still in your hand with five hundred thousand mistakes. The data needs to go to a process, you know, you've worked in financial services. It needs to go through a certain risk procedure, ⁓ you know, some Monte Carlo simulation or other kind of things. Now ⁓ you cannot replace the process for replicating the data or or or ⁓ giving data to a client that is not going through this process because that's what is your fiduciary responsibility. But you can use, you know, a very straightforward process using, you know, probabilistic ⁓ literally, you know, advanced stats, aka machine learning. a and basically ⁓ create ⁓ a mega table for all these billions of data points to create imputation with a certain level of confidence that you have for that and can serve you for you know q a q c and then you know suddenly with a bunch of people ⁓ sitting in the middle you can close these things very very fast rather than tackling five hundred thousand mistakes early on. So the the story that remains right now between this kind of approaches is if I'm able to put you know smart comps in the middle, ⁓ you know, I can use some AI that is ⁓ less ⁓ you know understood you know has a little bit of more mistakes ⁓ but ⁓ but I know how to clear them up on the way. ⁓ you know, there are things that it's zero risk. So if it's zero risk, guess what? No AI and I'm familiar till nowadays with a lot of ⁓ financial services and banks around the world. That they say no agent is coming into my ⁓ perimeter. And I cannot blame them ⁓ as long as you know we even talk like you know Dave beforehand ⁓ he brought even the story of hallucination for companies like this. This is the end of the discussion, you know. What do you mean? I have zero risk, what hallucination are you talking about? Now, when you're talking about anything to do with operations and ⁓ you know, things that have to do with research. You know, I think an augmented approach of people, ⁓ you know, ⁓ deterministic, non deterministic type of applications, or what is coming next, these layers of agents, aka workers, with you know, doesn't matter how you there is a topology to that, right? So either it's, you know, plugins, skills, whatever you want to call these things that they are coming in. And even then, you know, like take the example of legal, you know. Why legal applications are still companies are very hot, you saw Harvey, Legora, all these why? In spite of the fact that you know you have Claude, you know, dropping the legal skills in amazing way and people because there are some people in the legal offices that they know how to use a little bit of, you know, prompt encoding, basic things, and they can interact with the Claude environments and they get some very good answers tailored to what they want. For majority of the lawyers they need a workflow, they need a structure. Is this the new SAS for you know, I don't know. But ⁓ you know, maybe it is, because if you think about the philosophy of it, it works in the same way, just in a modern in in the modern ⁓ you know ⁓ it i the modern topology if you want. And ⁓ very successful and ⁓ You know, and if you ask people in the law offices they will tell you, ⁓ this software is nothing. But also, you know, the cloud skill I don't know how to use. So what is the answer? ⁓ you know, I'm here, I need to be here. That's people that they just are, you know, they read the newspaper of a couple of years ago. So that's a problem. So the reality is that ⁓ this kind of a combination when you think about it. The introduction of the introduction of new workers treated, at real workers in the organization, that they do not do self-learning in the process of inference. And, you know, we talk a lot about these things, but ⁓ you know, Dave you brought this point of the inference side. You know, how much inference is right now into the market? Not a lot. Therefore, the experience for majority of the people is theoretical. It's ⁓ I believe that. ⁓ an in in general with AI ⁓ in all the layers ⁓ of the cake that ⁓ Jensen was putting there, we see nowadays a lot of ⁓ people that hallucinate and not AI that hallucinate that basically ⁓ you know people are going with ⁓ interdisciplinary ⁓ claims that they might not be correct one hundred percent. And we might do this kind of mistakes too from here and there. ⁓ so ⁓ you know we We just try to speak from experience, ⁓ that's the most important thing. But ⁓ I I ⁓ I I I I see ⁓ into the question that you ask the most important piece of it. We didn't touch it a lot today, and that was something that I dealt ⁓ you know, trying to run forward and put something that is h holding me off running. ⁓ what is governance. Governance. You know. Cultures of companies need to change. You need to allow people to touch AI wherever they meet AI, wherever they need to meet AI. You cannot stop this from happening. From one hand. From the other hand, you cannot find yourself like the story of majority of the companies, they got hit with ⁓ you know, ⁓ I have ⁓ million APIs, I don't know what they are doing, what's going you know, it it's a disaster. Imagine that with agents that actually do things more than an API, that have connections to more than one API. And they learn. And they and they ⁓ God forbid learn then you know or or or or or ⁓ you know or feedback i into the into the training models the the the the results might be catastrophic. So platforms so so you know you need to write the governance, you be to be there before ⁓ you know things are happening, otherwise you're running biography of ⁓ you know, you're running ⁓ you know, we had this incident and stuff. Go explain that to the bold. It's not ⁓ It's not a good story to tell. No. So I Yeah, all I want to say, th there is a big need for ⁓ you know, automatic governance that is going into all structure of an enterprise nowadays. ⁓ you need governance structures that ⁓ they are live, ⁓ very important. Live ⁓ and documents are okay. That's the way that we used to run investment committees, ⁓ you know, all the years. For AI governance, that's not good enough. That's not good enough. ⁓ i it it needs to live like, you know, in cybersecurity is the best example, you know. You you cannot protect ⁓ you know, from ⁓ agents attack with ⁓ regular ⁓ environments that you have. You need to to have, you know, the same level of sophistication. When we are building right now enterprise at the power of agents, at the power of AI workers, we need to have governance that runs at the same power. Dave, what do you think? What's the No, I mean I I couldn't agree more that I think it's not one or the other. We you know, because there are just we're addicted to solving a class of problem that requires there's sort of inference that LL make LLMs make that we're very hard to write deterministic pr ⁓ programs for. Very hard, if not sometimes impossible, because they just didn't process the data as efficiently as LLMs. So I think enterprise applications are going to be hybrid. And I think that line of saying, where should I I be using, where should I allow for or tolerate a probabilistic process where I couldn't solve it any other way? And where do I really rely on a deterministic process? And just how do you balance those two things? And you kind of have to know what your tolerance level is, you have to know what kind of investment you're making. But I think in the end it's gonna be hybrid. And I think with regard, I think there's just no question about that. And Kafir identified all the different kinds of strategies you have depending on what you're even trying to compute. I think ⁓ the other point on govern governance, I also couldn't agree more. I think there needs to be a governance layer. I think there really needs to be, I don't know if you said this, I didn't understand the words you said at the last minute, but even a dedicated process, runtime process that's constantly governing what's going on. ⁓ you want the benefit and the ease of deploying agents, great. But there's gotta be a traffic cop that knows exactly where the boundaries are. I you know, for me, again on the on the non AI side, I think about every time I had to update a Java virtual machine, a JBM, and what broke. Yeah. You know, like that's just you're you're not even changing code, you're changing the middleware layer and everything broke, you know, and y this was deprecated, this is no longer functioning, this did and I just can't picture The models being even if they weren't learning models, if they were closed and they knew what they were doing and they did it well, when that model needs to be updated for whatever reason, it's you're screwed. You know, you're gonna pray. You're just gonna be like it's comparing the J V ⁓ it's like that was easy. So speaking about exactly that topic, it's connected, you know, you hear more company with AI trying right now to to create self healing. startups, you know, in self healing world. But you know, with governance there is less talk about that. Less companies that they think this way. You know? So ⁓ I I think you know I I want to see how this is going to be ⁓ solved with self with with with self healing and amount of ⁓ y you know things that you need to to know how to connect. it's the same problem as the enterprise, you know. I I remember You know, at HP, even building that kind of call home function for a genate that I own. You know, not to speak about things that I do not own. Yeah, exactly. No, because you you know, for mission critical, you you want to be called before things are going down, in particular on you know, on on fault tolerant kind of topologies. So it it it's ⁓ AI might solve some of these things. On the governance, I think that people did not think about it. But again, you cannot run away with all this without having this solved one way or the other. Hundred percent. So I think we're gonna do only one interesting lightning round question each. I'm gonna let Dave pick first and I'll do second. Dave pick pick one. What's a fast one? ⁓ what's f I always like what did you last binge watch, if anything. ⁓ I'm you know, always binge watching Survivor. ⁓ I enjoy doing that with my daughter, a little daughter. We it's ⁓ it's ⁓ it's kind of ⁓ i with both of my daughters, ⁓ my son is le was less into that, but my daughters they were always ⁓ into it and it was kind of ⁓ housting. And lately I actually binge watched with my wife a series called Reset. There is a brilliant comedian by the name of Zodir Millel ⁓ and ⁓ it's called ⁓ i i it's ⁓ reset. Reset I yeah, it's it might be less available for ⁓ because of language barriers, but very good for people ⁓ that they are ⁓ I'm going to find the is there subtitles? I'm assuming there's captions. It's gotta be high. I I I don't know. But you might yeah. Did you ever my question, I'm gonna not use one of these, I'm gonna follow up on that. Yeah, whatever. Have you ever dreamed Survivor would be in the corporate world where you could vote people out in the corporate world? ⁓ I have. That's why I'm just curious. No, but what I did with Survivor, I created once ⁓ a management ⁓ you know exercise that ⁓ I used Survivor teams to solve all kind of problems in order to ⁓ identify skills ⁓ for technology and management and ⁓ and I deploy it ⁓ in the all the three regions. ⁓ and I run it myself. So I it's funny, I always wanted. There was one point when I was at MD at City where we were talking real estate rebuilding buildings, and I remember going to the person who ran real estate, sat down and go, I have an idea for you. I said every conference room should start out where the chairs are all the same height. And if you're speaking and it's valuable, you lift up with the sound of an angel. And if you're Speaking and it's not valuable, the chair goes down to the ground so you're standing. Like that's my only that's a survivor, like you should be able to vote people out eventually. I know a couple of companies that they will adopt that. I think one one day might have worked that before. I think I think I I I think that's exactly how Ray Dalio imagined it would happen. But it would depend on whether or not he liked what you were saying and then it would keep going up. Or or you were you were thinking the way he thought you should. It's great. I think so Kafir, ⁓ this is amazing. Thank you for joining us here. Thank you for all the years supporting on Coric and background and just advice. And so congrats on your your new business and ⁓ and your family office. And that's that's amazing. ⁓ thanks everyone for joining us. And ⁓ until next episode of you know, Architecting AI Enterprise. I hope everyone has a great time. Thank you. Thank you. Thank you to our listeners for tuning in. Make sure to like and subscribe. We hope to see you again in our next episode. Please send ideas for topics or guests that you'd like to hear from. We look forward to seeing you then. Thanks.