The Exchanges

Q over much larger, um, sets of data. They may have access to SAP, SaaS apps and historical data and a variety of other things in that act. And so what, what do you think changes in terms of how you store access, interact with data in the context of like the agentic world? Or what, what else do you think needs to change? Do dashboards go away? Like what shifts?

A I actually think we'll see more dashboards because this will be the only way to kind of let us figure out what they have going on in the world, because first coding agent has started to write most of the call that running in the world. So that's indirectly, but also agents activating other agents would activate other agents and Trying to keep track of the non-human identity or that it becomes almost an impossible task. So many actors inside your organization, when it's so very hard for a human to understand the, the, the change of possibility. And this is a part of what you're seeing in, you know, proliferation of, of cybersecurity companies. How many cybersecurity companies you see in, in, uh, NHI, in non-human identity right now, an infinite amount. And there's a reason for that. It became a number one, number two problem right now. In addition to that, second thing is endpoint. You see endpoint security, which looked like it sold them so many great companies around it. And they were just, you know, a few, a few years ago when endpoint was a completely different problem with TDRs. And now everything that's happening, you see people are running agents. Today on, or on the laptops, and the agents sometimes connected to other networks, and it connected, and they are connected to, I think, and maybe on OpenClaw, and connected to, to your WhatsApp, but also to your internal networ…

AI assessment note: “I actually think we'll see more dashboards because this will be the only way”

Q How much of the, um, existing data infrastructure do you think survives all this? So, you know, there's all the ETL data engineering infrastructure. Uh, that, you know, people have been building and deploying over the last decade. Did that stick around? Does that shift? Does that change? Like how quickly does all this up end?

A So you see there's a strong compelling event to pretty much change everything because the plumbing today is very limited and everyone built a solution to their set of problems. So think about what happens now. Gonen goes downstairs after recording this podcast and he really wants coffee. So go to the store and buy his coffee and he puts on his credit card. Now there's a transaction and this is written in some database somewhere. Okay. Right. So someone needs to, today what they're doing, they extracting the data, putting somewhere and that's it. Someone else at some point takes this data and processing it in some other way and that's it. So there's no connection with all of those stuff. And every person is very different. They don't have the context of what happened before. And the reason it wasn't, and the reason is very simple. It wasn't so important before. To have all the context, all the data for an organization, because you could only do with the data things you really intended to do to begin with. So you have a single purpose in your mind when acting on the data. Today, it's very different. Today, you understand that you can collect, if you are able to smartly collect and clean all your data and make sure you start in an efficient manner, and if you can activate that efficiently, You can let a team go wide with all the data that they have, and the more data that they hav…

AI assessment note: “there's a strong compelling event to pretty much change everything because the plumbing today is very limited”

Q on the data set was Mercor. Right. In terms of the bankruptcy bid process. And so it's interesting. You had multiple different companies in the AI world bidding on a bankrupt airlines enterprise data set, which is fascinating. Yes. Do you think we'll be seeing a lot more of that in the future? Like, do you think we're going to basically be seeing these like out of bankruptcy data buys?

A So for, we've seen it for multiple use cases. That's what's really cool about it. And you see, of course, see other companies are continuously trying to already trying to buy Data. If you're a tech data CEO today, I can tell that you constantly get a, a questions. Are you willing to sell your data? I knew it all over, and it seems that's going to be a significant, a significant trend as you go. I'm hearing about, you know, labs going to Wall Street and trying to buy data from a hedge funds and try to understand how to map and analyze companies. So you, you see a data that was accrued throughout the years by companies. Which was usually like tapes. It was usually, you know, sitting on a shelf collecting dust. And all of a sudden this becomes very important. And you see companies now realize that first, what I have today that differentiates me than anyone else is my data. And this data is gold. And actually I can actually leverage that to get more value for my company and to Uh, continue, uh, building my business when AI is actually coming and, and, and, and, and, and, and, uh, in the playing grounds. It seems that everyone can start, even large and small companies basically have the same, uh, uh, the, the, the, the, the same playing field. And the only real advantage that the company have today is of course the people, But it's the data that they've approved, because everyone ha…

AI assessment note: “and it seems that's going to be a significant, a significant trend as you go.”

Q things for their own use cases. I guess in the case of something like Spirit Airline, is it customer support for building like a airline app? Like what, what do you think they're actually going to do with this information? Is it something else that's the internal Documents. Like, I'm just sort of curious, like what, what is the, the reinforcement learning, or is it like a customer support agent?

A Yeah. But I think if, if, if you're, uh, if you're trying to build agents today and trying to, you can't just build them a lab, you need to train them on, on, on, on new data, on, on some training data. And it's very hard to find very good data sets. You see that Harvey just released a, a, a, a legal data set a few days ago. And But you don't find too many good data sets that doesn't look like real synthetic data that can actually be used to really look like the real world. And I think that Spirit Airlines can be used both as an airline company, but also as a large enterprise, as a place where lots of people work, a lot of, you know, the hierarchy, middle management, top management, and workers working together. And You know, if you're looking at what other, uh, public data sets that you have out there, there, there aren't a lot of those. There's the, the, seriously, I'm speaking with companies asking what kind of data do you have? What do you train on? You will find stuff, for example, the annual data is out there in public, and people are actually using that as read data from a company because now a company work, uh, uh, uh, works like any reason is, it's so very hard to find data that will help you To work like in the real world. Anytime you see someone building an agent or building a new application, you know, most of them don't really work. You have to go to the world. You…

AI assessment note: “Spirit Airlines can be used both as an airline company, but also as a large enterprise”

Q And I think along the way, you kind of realize if you have all this data from a backup perspective and you have all their customer history over all time, you can start using that for interesting application areas. Um, what are, what are some of those directions where you're seeing customers take this, the, the, the sort of full history of data that, that you all have to represent?

A So, as you mentioned, ah, when we started, I said I had this crazy person starting a non-AI company in the world, and the AI tailwind became absolutely insane, and made sure that, ah, data becomes the most important thing that an organization have. When you can think about it, ah, models, ah, compute, everything is relatively ephemeral, ah, almost zero switching costs, and those are infrastructure, important parts of the infrastructure. For the industry. But if you're a company, whether you're a hotel chain, or you're a food chain, a technology company, it doesn't matter. The most valuable thing that you have is actually your data. And you see more and more companies finding this out. You know, just two days ago, you saw Google buy something from the bankrupt Spirit Airlines. They didn't buy airplanes. They bought the data. They bought the data for ten million dollars, because they think it's very important in that, in that perspective, they're using it to train models.

AI assessment note: “they're using it to train models.”

Q What, what sort of tooling are you all building at Eon to allow people to make use their data for AI applications? Like, how are you thinking about this problem yourselves or what, what sort of tools are your customers asking for?

A So let, let, let's go back from the, the, the problem statement and why there are so many tools for data and processing. Why, why, why do you need new tools? Isn't it sold already so many great companies built throughout the years and everyone understand data is important. So to put it this way, um, Back in the days, every data team could find their own data, decide what project do they have, and, you know, get data, do something with that. Very tactical. They were using, I don't know, some great companies, Fiverr, DBT, Monte Carlo, all the data tools that exist, you know, in order to fulfill their tasks. And for some of the data, they didn't even know exist. It was, it was locked. Why was it locked? Because there are, Multiple business unit owners across the same company. And let's say, uh, uh, you're a data team leader in, in some company and you are based in San Francisco, or we are now here, uh, in New York and both are also different business unit, uh, uh, uh, leaders. And now There's this thing called AI, and even the boss is playing with ShareGPT, so the CEO and the share, and the board, and the shareholders, they understand that AI is real. So they're coming to you, and they tell you a lot, um, we have a lot of data in the organization. We now realize data is new oil. We can actually activate it with the new tools that we have today. We couldn't before. Do something wit…

AI assessment note: “let's go back from the, the, the problem statement”