No Priors: Artificial Intelligence | Technology | Startups - Rethinking Legacy Data Infrastructure with Eon Co-Founders Ofir Ehrlich and Gonen Stein

Episode Date: August 27, 2026

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 [e-mail] 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

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. 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 handing sensitive data. It's a good thing and bad thing that everyone inside the organization can become builders. We live in very interesting times. Today, I know priors were joined by Wafira Erlich and Gonenstein, the co-founders of Eon. Eon is a cloud backup, disaster recovery, centric service is 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. Afeo Gonn, thank you so much for doing Manda Pires today. It's great to see you. Absolutely. Thanks for having us. Yeah. So one thing that you guys are doing at Eon is, or actually, why don't you get 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. 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 and 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, and apply their AI models and LLMs on top of that data that's ingested from a variety of sources. 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 some of those directions where you're seeing customers take this sort of full history of data that you all have or represent? So as you mentioned, when we started, I said that is crazy person starting a non-AI company in an AI world. And the AI tailwind became absolutely insane and made sure that data becomes the most important thing that an organization have. When you can think about it, models, compute, everything is relatively phenomenal, almost zero switching costs. And those are important part of the infrastructure. for the industry.

But if you're a company, whether you're hotel chain, your food chain 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 $10 million dollars because they think

It's very important in that in that perspective, they're using that to train models. I think the rumor, too, is that the other bidder 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 airline's enterprise data set, which is fascinating. Do you think we'll be seeing a lot more of that in the future? Do you think we're going to basically be seeing these out-of-bankruptcy databases? So we've seen it for multiple use cases. That's what's really cool about it. And you see Mercos,

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'm doing it all over. And it seems that's going to be a significant trend as you go.

I'm hearing about, you know, labs going through Wall Street and try to buy data from hedge funds and try to understand how to map and analyze companies. So you see 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 I can actually leverage that to get more value for my company and to continue building my business when AI is actually coming and fat on the playgrounds. It seems that everyone can start even large and small companies basically have the same the same playing field. And the only real advantage that the company have today is, of course,

there are people, but also the data that they're approved because everyone has access to all of those cool new tools. Yeah, it's become a moat. I guess in terms of that, 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 post-training and reinforcement learning. and there's companies like Applied Comput and others are just trying to provide these sources of services

where you can fine-tune models or open source models against specific datasets. 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? 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, what is the reinforcement learning or is it like a customer? support agent. Yeah, but think if you're, if you're trying to build agents, so then trying to, you can just build them a lab, you need to train them on, on new data, on some training data. And it's very hard to find very good data sets. You see that how we just released a legal data set a few days ago. But you don't find too many good data sets that doesn't look like

really synthetic data that can actually be used to really local. like the real world. And I think that spirit airlines can be used both as an airline company, but also as an large enterprise as a place where a lot of people work, a lot of, you know, the hierarchy in middle management, talk management 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 you're trying on.

Here, find stuff, for example, the annual 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. 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 that when you go to customers. You can buy data and train in-house.

So when you first release your products, every new product that you have, 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 massive, think about it contains sensitive information, like BII, financial information, so and so forth, and actually be able to build real world stuff on the book that. Yeah, and Google, obviously, they're in this travel space for a while, right?

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 this 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. What's the very good tooling, are you all building an Eon to a lot of people that make you see their data for AI applications? Like, how are you thinking about this problem yourselves or what sort of tools are your customers asking for? So let's go back from the problem statement. and why there's so many tools for data and processing,

why do you need new tools? Isn't it? So already so many great companies be throughout the years, and everyone understands data is important. So I'll 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, 5-front, DBT, Montacalo, 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 locked. Why was it locked? Because there are multiple business unit owners across the same company.

And let's say, you're a data team either in some company and you are based in San Francisco or we're now here in New York. And both are also different business unit. leaders. And now there's this thing called AI, and even the boss is playing with Chef GPT, so the CEO and the board and the shareholders, they understand that AI is real.

So they're coming to you, and they tell you a lot, we have a lot of data in the organization. We now realized data's new oil. We can actually activate it with the new tools that we have today. We couldn't before. Do something with a 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 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? 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 somehow get to Myanmar. a leader of a business unit, I have data probably. And somehow, you convince 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 were. They contain production data, because of sensitive information.

You know, there's always this server that no one knows what he's doing, that's connected to the power, what they're ritual physically, that everyone's afraid to turn off because we don't know what's in there. So we have all of all of that. And let's say that somehow I know what's in there.

Now I need to bring engineers and compromise maybe security and compliance and production, out time. And to extract the data, just to give it to you and storage in a very inefficient matter, it's very hard. We understood that there's a problem with how this works because we have different incentives.

You are 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 run away. I don't accidentally have the salary of the CEO inside my data and it's actually going to be used for training or post-training by you. 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 compliance security. Compliance, we're actually keeping audit. And because data is classified, I know that I'm not accidentally going to share with you sensitive information that you shouldn't have eventually in your data.

We can do it in a very cost-efficient and performance way. So you can actually do it from all over the place, bring it to you, and actually do it. That sounds like there's three or four things that you're solving for. One is you're aggregating lots of history. and current data for people. Number two is you're able to then mask personally identified information or other fields that they don't unnecessarily share it or set permissions on top of that.

And then third, it sounds like all this can then be exposed into AI models for sort of their uses or applications. And the key point allowed 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, converted into this new data foundation format that's stored much more efficiently and provide the mapping classification access control and connected into the AI workflows. 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 escaped sandboxes,

and do all sorts of things. And, you know, there may be broader things afoot in terms of why that's happening beyond just that agent of capabilities. Like, who knows how these things are set up or configured or, you know, sometimes a little bit uncertain whether, you know, 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 CESA should be thinking about all this? Yeah. So up until now, the concerns came from human threats, right? So this is 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 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 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. So being able to detect when that happens, look for irregular right 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. Something that I noted is that six months ago,

no one would even discuss with me, but a few months ago, pretty much every person I know, every either in a company tells me either they are afraid of that happening to them or it personally happened to 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 and decide what really running on my data I don't know no longer I understand I need to be prepared for both external threats because, you know, all the new murder's 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 bridge, whether it's malicious or not, and we need to be able to handle it and act accordingly. It's a very weird situation today. Yeah. How do you think about the broader enterprise stack and agents? So the current stack really evolved around people or humans asking their predefined analytical questions. So we have warehouses, we have dashboards, we have the ETL pipelines, we have BI, and agents 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 in that act.

And so 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 else do you think needs to change? Do dashboards go away? Like what shifts? 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 is 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, to activate other agents, and trying to keep track of the non-human identity or that it becomes

almost impossible tasks. So many actors inside your organization, when it's so very hard for a human to understand 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 an NHI, in non-human identity right now, an infinite amount. And there's a reason for that. It became 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 a few years ago, when endpoint was a complete different problem with DBS. Now, everything that's happening, you see, people are running agents today on the laptops. And the agents sometimes connected to other networks, and they are connected to, think, and maybe on OpenClaught connected to your WhatsApp, but also to your internal network and also to other applications.

And you see, it's very hard for the VPO 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, like a lovable or any other software that you have for themselves,

putting company data there. 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 an organization, not bound by the rules of the organization, and not necessarily running within the premises of the organization, but handing sensitive data, which is the property of the organization could be exposed to the world, it could be incorrectly used, and it becomes a big problem.

It's a good thing and bad thing that everyone inside the organization can become builders. whether you're a social media manager, whether you're a Phoenix person, whether you're in legal or finance. So it's amazing, but it's also we live in very interesting times in that perspective. How much of the existing data infrastructure do you think survives all this? So, you know, there's all the ETL data engineering infrastructure that, you know, people have been building and deploying over the last, you know, decade. Did that stick around? Does that shift? How quickly does all this up end?

So you see there's a strong compelling event to pretty much change everything because the climbing 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 buys 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 are doing the data, putting in somewhere and that's it. Someone else at some point takes this data and processing 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 of all the data, and a personal organization, because you could only do with the data things you really intend you to do to begin with.

So you add 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 store an efficient manner. And if you can activate that efficiently, you can let that thing 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, all 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 a list of all the people in New York who love burgers and another person in the organization who have, 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. New York love burgers on pizza because 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.

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've seen companies, first they're collecting tons more data than before. 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 general by those new agents.

So there's a lot of noise in the data. There's a lot of value and noise as well. 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's been created is actually usable. It doesn't apply with the old tools that were very, some of them were incredible. Phythron was an incredible company, DBT and so on and so forth, but very niche, very specific tools for that purpose. So this creates a very interesting, brave new world.

You've seen companies like Databricks, one of the most incredible companies on the planet, in my opinion. And look at it, I have more and more data coming in. I don't necessarily know where it is. I'll 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 myself 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 build our own databases. They want to take charge of how data is being used. Data has been created.

And it's completely different than how any other people use that, just three or five years ago. 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 in general, 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.

Makes sense. And you see 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 match. anymore, not trying to go to actually getting value for every token that we have, because it comes 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, paying millions and even more than that if you need to, but get the value that you can from actually doing so. So it's very expensive, very lucrative. Let's make it relatively as least expensive as you can have it. 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 at AWS, you really saw that migration from on-prem to the cloud at like a huge scale in terms of 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? What do you use sort of the cloud era versus AI or what are takeaways or lessons that you can apply across them? Yeah, I think, again, it's like that, but on steroids. And even before we sold our last company, Cloud and the Rito AWS, we supported similar large-scale enterprise migrations with the other hyperscalers, with Azure and with GCP, where our product was integrated, OEM'd 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. 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. 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 just someone else's computer, but who knows what it is, it's hard to explain to, and my grandmother about the cloud. AI, everyone understands AI.

Everyone lived from the chat GPPT moment when we all asked what AI could do. Oh my God, this is incredible. So they're getting pushed by C levels, by CEO, by the board, by the shareholders. Use AI for the business. Otherwise, otherwise were relevant. So you see people doing it,

both for the value that you get from AI, also 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. One example is what's happening with forward-deployed engineers. It used to be something local services, penalty 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. 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. There's some that it takes a year or two or more, but they need to have it now.

And the only way they can actually get deployed and become AI much faster is by, letting strong engineers who understand what they're doing and coming with the tool looks that they've created in top Silicon Valley startups and sometimes

larger companies to come and transform those organizations 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, buying, using product-led growth

by a really, really fast AI infrastructure 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 Dev Tools because the world is very fragmented, people don't want to move so fast, 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, and then through the FDM, Ocean, then going to banks and then will replace engineering that you don't want to do with our engineers, making you focus with the things that you do want to do. So leveraging on all fronts. So it became super, super, super interesting. The world has changed 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 transport up into an AI company, we can all win. We can create an average, make 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 understand that their existing processes are somewhat mundane, and they need to do something about it, but it's scary, but you have to do it. So it's a fascinating thing to see. 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 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 going to be better because of that.

Amazing. Well, thank you so much for joining you today. A fair and no one. Very interesting, wide-ranging conversation on data. And I really appreciate it. Thank you. Thank you. Thank you.

Thank you. Thank you. Thank you. Thank you. Find us on Twitter at No Pryor's Pod. Subscribe to our YouTube channel. If you want to see our faces, follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week.

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