Rethinking Legacy Data Infrastructure with Eon Co-Founders Ofir Ehrlich and Gonen Stein

Google’s purchase of Spirit Airlines’ data out of bankruptcy signaled a shift in how the tech world values real-world datasets. Although compute and models get much of the attention, in this landscape, it’s data that is a company’s protective moat. Eon CEO / Co-Founder Ofir Ehrlich and President / Co-Founder Gonen Stein join Elad Gil to talk about how Eon is redefining cloud backup into a secure data foundation designed to power and protect enterprise AI. Ofir and Gonen discuss why historical enterprise data is in demand by AI labs, and how Eon facilitates access to scattered and locked data across business units through providing the mapping, classification, and access controls needed to connect it into AI workflows. They also explore how traditional ransomware defenses must now protect against rogue AI agents with legitimate system permissions, concerns around the influx of autonomous agents and non-human identities, and the implications for the breakneck speed of AI adoption compared to the slowness of the cloud era. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Eon_io_ | @OfirEhrlich Chapters: 00:00 – Cold Open Trailer 00:59 – Ofir Ehrlich and Gonen Stein Introduction 01:27 – What Eon Does 02:41 – Data as Moat 06:43 – Training Agents with Good Data 09:39 – Data is the New Oil 15:00 – Autonomous Security Threats 18:15 – How Agents Change the Enterprise Stack 22:11 – Re-imagining Data Infrastructure 27:52 – Cloud vs. AI Era Shift 30:26 – How AI is Changing Companies 34:31 – Conclusion

• 0:00Speaker 00

  Up until now, the concerns came from human threats. What we're seeing now on steroids is that the same type of threat is coming from non-human actors, agents that essentially have legitimate access to the environment with legitimate permissions. Fortunately for us, it's a very similar methodology in terms of detecting that and protecting against that. But the velocity of that happening is extreme.

• 0:22Speaker 01

  Think of the non-technical people. They're not even aware for things like security or compliance or who is going to use this data. Maybe their agent that they are building are using other agents and they're not technical to even understand what it means. It creates a complete set of actors inside the organization, not bound by the rules of the organization and not necessarily running within the premises of the organization, but handling sensitive data. It's a good thing and bad thing that everyone inside the organization can become builders. We live in very interesting times.

• 0:59Speaker 02

  Today on the Priors, we're joined by Ophir Ehrlich and Gudmundstein, the co-founders of Eon. Eon is a cloud backup disaster recovery centric services designed for the AI era. In this discussion, we talk about data, AI, why Google bought out the data of Spirit Airlines out of bankruptcy, and what it means to really manage and use data infrastructure in the AI era. Ophir, Gudmund, thank you so much for joining me on the Priors today. It's great to see you.

• 1:25Speaker 00

  Absolutely. Thanks for having us.

• 1:26Speaker 02

  Yeah. So one thing that you guys are doing at Eon is, or actually, why don't you give a quick overview of Eon? And what it does really quickly, because I think that'll set the context for how we think about AI and data and models and fine tuning models. I think there's a whole stack that's built on top of different types of data sets. And so maybe we can start with what you all do. And then I think we'll kind of walk through like how the world is shifting relative to the enterprise data stack.

• 1:50Speaker 00

  Yeah, sure. So what we do at a high level is we've created a new data foundation that runs in the cloud. And we provide multiple capabilities that allow customers to first map and classify their data across their environment, across multiple hyperscalers and identify what they have, where they have it, what's sensitive, not sensitive, and so on and so forth. Then we provide an ability to easily ingest that data from all these different sources, structured, unstructured data into this data foundation. And the data foundation then provides a very cost effective way of both maintaining the data for protection and recovery, but also makes sense of the data. So it allows customers to very easily access it, query it, search through it,

• 2:33Speaker 00

  and apply their AI models and LLMs on top of that data that's ingested from a variety of sources.

• 2:40Speaker 02

  Yeah. And my sense is, I mean, your starting point was really as sort of backup and data recovery and protection service. 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. What are, what are some of those directions where you're seeing customers take this, the sort of full history of data that you all have to represent?

• 3:03Speaker 01

  So as you mentioned, when we started, I said, I'm this crazy person starting a non-AI company in an AI world. And the AI tailwind became absolutely insane and made sure that

• 3:18Speaker 01

  data becomes the most important thing that an organization have. When you can think about it, models, compute, everything is relatively ephemeral, almost zero switching cost. And those are 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, 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 their data, they bought their data for $10 million

• 4:01Speaker 01

  because they think it's very important in that perspective, they're using a two train models.

• 4:06Speaker 02

  I think the rumor too is that the other bidder on that data set was Merckor, 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.

• 4:23Speaker 01

  Yes.

• 4:24Speaker 02

  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?

• 4:30Speaker 01

  So we've seen it for multiple use cases. That's what's really cool about it. And you see Merckor, you 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 questions, are you willing to sell your data? I hear 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 hedge funds and try to understand how to map and analyze companies. So you see a data that was accrued throughout the years by companies,

• 5:13Speaker 01

  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 I can actually leverage it to get more value for my company and to continue building my business when AI is actually coming and, and, and, and, and facting the playgrounds.

• 5:49Speaker 01

  It seems that everyone, even large and small companies, basically have the same, the same, the same playing field. And the only real advantage that a company have today is, of course, their people, but it's the data that they have proved because everyone has access to all of those cool new tools.

• 6:10Speaker 00

  It's become a mode.

• 6:12Speaker 02

  I guess in terms of the, I mean, people have been saying data is a new oil for a long time. And I was always a little bit skeptical of that statement. But I feel like now what's happening is because of

• 6:22Speaker 02

  post-training and reinforcement learning and, you know, there's companies like Applied Computer and others who are starting to provide these sorts of services where you can fine tune models or open source models against specific data sets. Like, it seems like people are trying to optimize these things for their own use cases. I guess in the case of something like Spirit Airline, is it customer support for building like an airline app? Like, what do you think they're actually going to do with this information? Is it something else? It's the internal documents? Like, I'm just sort of curious, like, what is the reinforcement learning? Or is it like a customer support agent?

• 6:55Speaker 01

  Yeah, but I think if, if you're, 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 new data, on some training data. And it's very hard to find very good data sets. You see that Havi just released a legal data set just 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 a lot of people work, a lot of, you know, the hierarchy, middle management, top management,

• 7:40Speaker 01

  and workers working together. And, you know, if you're looking at what other public data sets do you have out there? There are a lot of those. There's the, seriously, I'm speaking with companies, asking what kind of data do you have? What are you training on? You're finding stuff, for example, the N1 data is out there in public. And people are actually using that as real data from a company, because how a company works, like, and the 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.

• 8:21Speaker 01

  You have to go to the world, you have to actually interact with real world companies in order to really build something significant. Now, you can do it when you go to customers, you can, you know, buy data and train in-house. So when you first release your products, every new product that you have,

• 8:41Speaker 01

  you don't have to first interact with customers at your initial interaction. So I think you're going to see more and more of that, both by creating new synthetic data, new synthetic data in new innovative ways, in addition to getting existing data, whether it's the real data, whether it's somehow mass, we'll think about it, it contains sensitive information like PII, financial information, so on and so forth. And actually be able to build real world stuff on top of that.

• 9:11Speaker 00

  Yeah. And Google obviously is, it's not, it's not, they're in this travel space for a while, right? They, they want this type of data, they're already monetizing it. This allows them to understand, train it, understand it, monetize it even further. And it's a unique situation, right, that obviously people want to take advantage of. And I think we're going to see more and more of that in such situations. And regardless of that, customers who have existing data want to be able to unlock that existing data as well.

• 9:38Speaker 02

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

• 9:48Speaker 01

  So let's go back from the problem statement. And why there are so many tools for data and processing, why, why do you need new tools? Isn't it solved already? So many great companies been throughout the years and everyone understands data is important. So to put it this way, 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 some great companies, FiveTran, DBT, Montecarlo, 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 existed, it was, this was locked.

• 10:31Speaker 01

  Why was it locked? Because there are multiple business unit owners across the same company. And let's say you're a data team leader in some company and you are based in San Francisco or we're now here in New York. And both are just different business unit leaders. And now there's this thing called AI. And even the boss is playing with chat GPT. So the CEO and the board and the shareholders, they all understand that AI is real. So they're coming to you and they tell you a lot, and we have a lot of data in the organization. We now realize data is new oil.

• 11:15Speaker 01

  We can actually activate it with the new tools that we have today. We couldn't before. Do something with the data, make it useful and use AI for that because it's valuable for us and because it's cool. What can you do? So you say, great, I've done this thing before, I just need to bring to. I I know all of those new cool things that coming out every day in Silicon Valley, I can just leverage them. The problem is, where's the data? And so you're coming to us and we have business unit leaders, if you even know us, maybe you don't. But let's say that you find somehow. It got to me, I'm a leader of a business unit, I have a data probably.

• 11:57Speaker 01

  And somehow you convinced me to give me access to my data. Now, I don't know what data do I have, I have a lot of people working for me. They have data in multiple systems for the last 20 years, some of them systems that no one really understands where. They contains production data, because it's sensitive information. You know, there's always this server that no one knows what it's doing, but it's connected to the to the power, what they have ritual physically that everyone's afraid to turn off. Because we don't know what's in there. So we have all we have all of all of that. And let's say that somehow I know what's in there.

• 12:34Speaker 01

  Now I need to bring engineers and and compromise maybe security and compliance and and a production, a uptime. And to extract the data, just to give it to you and store it in a very inefficient manner, it's very hard. We understood that there's a problem with how this works because we have different incentives. You were tasked with doing that, I'm tasked with making sure my systems work. And I'm tasked making sure that data is intact, no data is running away. I don't accidentally have the salary of the CEO inside my data. And it's actually going to be to be a use for training or post-training by you.

• 13:16Speaker 01

  So we at Eon solve it in a very different way. We can help you, not me, you, the data team leader. Find all the data that's in organization in a very simple way, understand what it is, classify it, map it. Understand context layer on top of that, build a semantic layer. And then be able to continuously bring all the data from me that is relevant without compromising production, without compress security. Compliance, we're actually keeping all it and because data is classified, I know that I'm not accidentally gonna share with you sensitive information that you shouldn't have. Eventually in your data.

• 13:59Speaker 01

  We could do it in a very cost efficient and performant way. So you can actually do it from all over the place, bring it to you and actually.

• 14:08Speaker 02

  So it sounds like there's three or four things that you're solving for. One is you're aggregating lots of historical and current data for people. Number two is you're able to then mask personally identified information or other fields that they don't want necessarily share it or set permissions on top of that. And then third is it sounds like all this can then be exposed into AI models for sort of their uses or applications and.

• 14:30Speaker 00

  Yeah, and the key and the key point a lot is that customers already have this data. That's kind of the ironic thing. Customers today already have this data, it's kept in their environment in different forms, but it's locked, it's not accessible. And usually it's very, very expensive, right? So we're able to take what customers already have, convert it into this new data foundation format that's much more stored much more efficiently. And provide the mapping, classification, access control and connect it into the AI workflows.

• 15:00Speaker 02

  How do you think about security? So there's been a lot of news recently about the labs where they'll have agents, like it's that escape sandboxes and all sorts of things. And, you know, there may be broader things afoot in terms of why that's happening beyond just that agent or capabilities, like who knows how these things are set up or configured or, you know.

• 15:18Speaker 02

  Sometimes it'll be uncertain whether, you know, there's there's that much. How people are pushing these things, but you know, fundamentally, there's a lot of discussion of like AI security. How do you think about that in the context of the enterprise stack, what people should do or not do, how CISOs should be thinking about all this?

• 15:34Speaker 00

  Yeah, so up until now, the concerns came from human threats, right? So this is a not you, where customers would come to us and say, hey, we were exposed by this ransomware attack. So during our time at AWS, it was a very large customer that was impacted by ransomware. We we thought that they were completely protected using our technology, the disaster recovery service that we managed there. And we learned unfortunately that the customer thought that they were protected, they weren't protected because they didn't map and classify and tag their resources properly. So it wasn't protected and so 60% of the environment was was exposed by ransomware. And that's one of the reasons why we decided to launch Eon and solve that pain point around human threats such as ransomware.

• 16:19Speaker 00

  So being able to detect when that happens, look for irregular write patterns and entropy changes and things like that, protect against it. And then also allow customers to recover in a granular fashion and very quickly. What we're seeing now on steroids is that the same type of threat is coming from non-human actors, from AI agents. That essentially have legitimate access to the environment with legitimate permissions into such and such databases. And all of a sudden, and this now happens very rapidly, a table is all of a sudden dropped. Fortunately for us, it's a very similar methodology in terms of detecting that and protecting against that and allowing to recover. But the velocity of that happening is extreme.

• 17:01Speaker 01

  Yeah, something I I noted is that like six months ago, no one would even discuss with me. But a few months ago, pretty much every person I meet, every leader in a company tells me either they are afraid of that happening to them. Or it personally happened to that to the that person who was speaking with me, which is crazy. You see it all over the place, you see real fear from a I no longer a decides what really running on my data. I don't know longer understand, I need to be prepared for both external threats.

• 17:44Speaker 01

  Because, you know, all the new models make it much easier for attackers to come to me and attack me. But also from the inside with agents, I actually approved running in my environment. So it's a very, very tricky time, we need to assume breach, whether it's malicious or not. And need to be able to handle it and act accordingly. It's a very weird situation today.

• 18:15Speaker 02

  Yeah. How do you think about the broader enterprise stack and agents? So, you know, the current stack really evolved around people or humans asking therapy to find analytical questions. So we have warehouses, we have dashboards, we have the ETL pipelines, we have BI. And agents maybe have may behave differently and more dynamically. They may be able to reason over much larger sets of data. They may have access to SaaS apps and historical data and a variety of other things and then act. And so what what do you think changes in terms of how you store access and interact with data in the context of like the agentic world? Or what what else do you think needs change? Do dashboards go away, like what shifts?

• 19:00Speaker 01

  I actually think we'll see more dashboards because this will be the only way to kind of let us figure out what the hell is going on in the world. Because first coding agent has suddenly tried most of the call that's 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 the organization. When it's so very hard for a human to understand the the chain of responsibility. And this is a part of what you're seeing in the proliferation of cybersecurity companies. How many cybersecurity companies you see in in an NHI in non-human identity right now, an infinite amount?

• 19:46Speaker 01

  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 problems with EDRs. And now everything that's happening, you see, people are running agents today on on the laptops. And the agents sometimes connected to other networks. And it connect they are connected to think maybe on open call and connected to to to your WhatsApp, but also to your internal network and also to other applications.

• 20:32Speaker 01

  And you see, it's very hard for the for the VP of ITs, for the CIOs to understand what should they do? On the one hand, they want to, they are being pushed, pushed by the board, by the CEO, enable AI in my organization now, don't block me. You can't block me. On the other hand, it's so scary. I mean, every person, don't even think of technical people, think of the non-technical people. Building something with, you know, let's say kind of a lovable or any other software that you have for themselves, putting company data there.

• 21:11Speaker 01

  They're not even aware for things like security or compliance or who is going to use this data? And they're all using all of those new cool things. So maybe their agent that they are building are using other agents and they're not technical to even understand what it means. So it creates a complete set of actors inside the organization, not bound by the rules of the organization. And not necessarily running within the premises of the organization, but handling sensitive data, which is the property of the organization. Could be exposed to the world, it could be could be incorrectly used and becomes a big problem.

• 21:52Speaker 01

  It's a good thing and bad thing that everyone inside the organization can become builders, whether you are a social media manager, whether you're a finance person, whether you're in in in legal or finance. So it's amazing, but it's also we live in very interesting times in that perspective.

• 22:11Speaker 02

  How much of the existing data infrastructure do you think survives all this? So, you know, there's all the ETL, data engineering infrastructure.

• 22:21Speaker 02

  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 upend?

• 22:30Speaker 01

  So you see, there's a strong competing element to pretty much change everything because it's called 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 it somewhere and that's it. Someone else at some point takes these data and processes in some other way and that's it. So there's no connection with all of those stuff.

• 23:14Speaker 01

  And every person is very different. They don't have the context of what happened before. And the reason wasn't, and the reason is very simple. It wasn't so important before to have all the context, all the data and personal organization. Because you could only do with the data, things you really intend you to do to begin with. So you had a single purpose in your mind when acting on the data.

• 23:38Speaker 01

  Today, it's very different. Today, you understand you can collect, if you're able to smartly collect and clean all your data. And make sure you store it in an efficient manner. And if you can activate that efficiently,

• 23:51Speaker 01

  you can let a team go wild with all the data that they have. And the more data that they have and the more high quality data that they have, and the more context on that data that they have, the team hunting that can create wonders and think of things which were unimaginable. Let's say that there's one person in organization who have all the list of all the people in New York who love burgers. And another person in the organization love, who has a database of all the people in New York who love pizza. They don't know they can find a list of all the people in New York who love burgers and pizza because not, they didn't work together. Now, if you use it for poster and use it for the new capabilities, you can actually do wonders with that.

• 24:34Speaker 01

  You can actually start asking intelligent questions, you data intelligent questions. You can start using it for your own purposes and just something that you couldn't do before. So you see in companies, first they're collecting tons more data than before. The amount of data being ingested is absolutely insane, especially comparing to earlier. We see trends continuously, both us and other companies that we're seeing in data. You see, data is growing out of proportions, so much of it, and so much of it being generated by those new agents. So there's a lot of noise in the data. There's a lot of value in noise as well.

• 25:15Speaker 01

  So you need tools that are able to both understand data for multiple occasions, clean the noise, and make sure all of these data that has been created is actually usable. It doesn't apply with the old tools that were very, some of them were incredible. Five Tron was an incredible company, DBT and so on and so forth, but burnish very specific tools for that purpose. So this creates a very interesting, brave new world. You see companies like Databricks, one of the most incredible companies on the planet, in my opinion.

• 25:54Speaker 01

  Look at, I have more and more data coming in. I don't necessarily know where it is and help you catalog the data and make use of that. But it's an after effect. You already have the data. Now you need to process that. But they are reinventing themselves all the time because they understand that more and more data has been generated by agents. And they thought, the way I see it is if you can't beat them, join them, we'll build our own agents, we'll build our own databases. We'll get, and they want to take charge of how data is being used, how data is being created. And it's completely different than how any other people use that just three or five years ago.

• 26:39Speaker 02

  So the goal is really to enable, right? Enable this culture of builders and the culture of agents with the ability to automatically help them understand what's there, automatically help them ingest the data without having to build manual pipelines for each and every application that is being built. And then also help them maintain control on top of the data that's created.

• 27:00Speaker 01

  Makes sense. And you see with every, with every data that you have, there's another problem right now. That lots of data is amazing, but it's scattered, which is a sound of problem. But then you need to access that. You need to pay for that for storage and of course tokens. And we're not in the time of a token maxing anymore. Trying to go to actually getting value for every token that we have because it comes more and more and more, more and more and more expensive. So you want to be very wise in, you don't want, I don't want to say not paying millions. Pay millions and even more than that if you need to, but get the value that you can from actually doing so.

• 27:45Speaker 01

  So it's very expensive, very lucrative. Let's make it relatively as least expensive as you can have it.

• 27:51Speaker 02

  So I guess, you know, the other thing that you guys have really lived through is the cloud transition. So prior to Eon, you started a company called Cloud Endor that was acquired by AWS. And AWS, you really saw that migration from on-prem to the cloud at like a huge scale in terms of that, that big sort of generational shift that had happened before this. How would you compare this infrastructure change to what's happening with AI right now? Like, what do you view sort of the cloud era versus AI era? And what are takeaways or lessons that you can apply across them?

• 28:27Speaker 00

  Yeah, I think again, it's like that, but on steroids. And even before we, we sold our last company, Cloud Endor to AWS, we supported similar large-scale enterprise migrations with the other hyperscalers, with Azure and with GCP, where our product was integrated OEMed into the console. So very large enterprises that were moving thousands, tens of thousands or hundreds of thousands of servers. And then we saw those modernized further in the cloud. And after we sold to AWS, we did that as part of the application migration service. But that's kind of where it ended and it required a lot of work, a lot of effort, both from a technology side as well as from the human side.

• 29:06Speaker 00

  What we're seeing now in this crazy world of AI and agents is that those transformations are happening way faster. And customers are losing control to a point where that's becoming an inhibitor, right? Not an enabler. They're stopping, they're pausing because they're afraid that things might break, that data might leak, that IP might break out. And so they're looking desperately for this level of understanding of what's happening and control.

• 29:33Speaker 01

  So it became so insane that, and so fast. And one of the reasons is that cloud, in my opinion, cloud is somewhat abstract. Because it's very hard to explain what it means. Cloud is basically just someone else's computer, but who knows what it is? It's hard to explain to my grandmother about the cloud. AI, everyone understands AI. Everyone lived through the Chachi PK moment. When we all asked what AI could do, and oh my God, this is incredible. So they're getting pushed by C-levels, by CEO, by the board, by the shareholders.

• 30:13Speaker 01

  Use AI for the business. Otherwise, otherwise we're relevant. So you see people doing it both for the value that you get from AI, also for the, from the fear that you get from AI. And you see new trends of, for a first time in a lot of, in many years, you see how companies consume software in a brand new way. And one example is what's happening with forward deployment engineers. It used to be something looked like services, Panatty were doing that. No one really didn't understand what it means. Now everyone's doing that. Now it seems that you come into a large legacy enterprise.

• 30:57Speaker 01

  They really want to adopt AI because they have to. The problem is they don't know how to do it. They understand that their processes are very long. They sometimes takes a year or two or more, but they need to have it now. And the only way they can actually get it deployed and become AI much faster is by letting a strong engineers who understand what they're doing and coming with the toolboxes that they've created in top secret startups and sometimes larger companies to come and transform those organizations.

• 31:38Speaker 01

  And you see them shrinking sales cycles and you see companies growing really fast because of that. You also see companies buying really fast, especially the new companies,

• 31:51Speaker 01

  buying, using product-led growth by really, really fast AI infrastructure with, which actually helped to build agents because everyone now wants to be an agent. Now, in the past, I was arguing that for the majority of things, PLG doesn't work, especially for DevTools. Because the world is very fragmented. People don't want to move so fast, so forth, so forth. Now it became super hot looking companies like Cognition, for example, which is incredible company that were able to first go through a PLG, we use that that way in Eon.

• 32:29Speaker 01

  And then through the FD motion telling, going to banks and then will replace engineering that you don't want to do with our engineers, making you focused with the things that you do want to do. So leveraging on all fronts. So it became super, super, super interesting. The world is changing so much. And one other really interesting way that companies are leveraging AI is they are very slow to adopt AI. But there are really great companies, for example, Long Lake, that say, instead of you adopting AI, I know how to do it more efficiently. If I can buy the company and transform that into an AI company, we can all win.

• 33:13Speaker 01

  We can create an advantage, make a higher margin more efficiently. And this is a really radical new way for those companies to actually start using AI, become more efficient. And we speak about this as a revolution, but I think we just started. Most companies still don't use AI. Most companies still at the beginning of this journey. They all understand that something is happening. They all understand the data is important. They all understand that their existing processes are somewhat mundane and they need to do something about it. But it's scary that you have to do it. So it's a fascinating thing to see.

• 33:51Speaker 01

  It's a fascinating evolution on what's going on right now in how companies consume AI software, how companies transform into being more modern, how much we're being pushed to do that. And I think that eventually, I know it's a very wild ride, but I think everyone is going to go, the world, in my opinion, is gonna be better because of that.

• 34:19Speaker 02

  Amazing. Well, thank you so much for joining me today. I feel like we had a very interesting, light-hearted conversation on data and AI. Really appreciate it.

• 34:25Speaker 00

  Thank you. Our pleasure.

• 34:27Speaker 01

  And it was a pleasure. Thank you.

• 34:29Speaker 00

  Thank you.

• 34:33Speaker 03

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