No PriorsRethinking Legacy Data Infrastructure with Eon Co-Founders Ofir Ehrlich and Gonen Stein
EVERY SPOKEN WORD
30 min read · 6,310 words- 0:00 – 0:59
Cold Open Trailer
- GSGonen Stein
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.
- OEOfir Ehrlich
Think of the non-technical people. They're not even aware for things like security or compliance or who's gonna 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 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.
- EGElad Gil
[upbeat music]
- 0:59 – 1:27
Ofir Ehrlich and Gonen Stein Introduction
- EGElad Gil
Today on No Priors, we're joined by Ofir Ehrlich and Gonen Stein, 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. Ofir, Gonen, thank you so much for joining me on No Priors today. It's great to see you.
- GSGonen Stein
Absolutely.
- OEOfir Ehrlich
Thanks for having us.
- 1:27 – 2:41
What Eon Does
- EGElad Gil
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 datasets. 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, to the enterprise data stack.
- GSGonen Stein
Yeah, sure. So, uh, what we do at, at a high level is we've created a, a new data foundation that runs, uh, in the cloud, and we provide multiple capabilities that allow customers to first map and classify their data across their, uh, environment, across multiple hyperscalers, and identify, uh, what they have, where they have it, uh, what's, uh, sensitive, not sensitive, and so on and so forth. Then we provide an ability to easily ingest that data from all these, uh, different sources, you know, structured, unstructured data, into this data foundation. And the data foundation then provides a very cost effec-effective way of both maintaining the data for, uh, 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, uh, ingested from a variety of sources.
- EGElad Gil
Yeah. And my sense is, I
- 2:41 – 6:43
Data as Moat
- EGElad Gil
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 realized 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, these, the, this sort of full history of data that they, that you all have or represent?
- OEOfir Ehrlich
So as you mentioned, uh, 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, uh, data becomes the most important thing that an organiza-organization have. When you can think about it, um, models, uh, uh, compute, everything is relatively ephemeral, uh, almost zero switching costs, and those are infrastruc- im-important part of the infrastructure for, uh, 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, uh, uh, buy, uh, uh, uh, something from the, uh, bankrupt, uh, uh, Spirit Airlines. They didn't buy airplanes. They bought the data. They bought the data for $10 million because they think it's very important. In that, in that perspective, they're using that to train models.
- EGElad Gil
I think the rumor too is that the other bidder on the dataset 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 dataset, which is fascinating.
- OEOfir Ehrlich
Yes.
- EGElad Gil
Do, do you think we'll be seeing a lot more of that in the future? Like, do you think we're gonna basically be seeing these, like, out of bankruptcy data buys?
- OEOfir Ehrlich
So for, we've seen it for multiple use cases. That's what's really cool about it, and you see Mercor, you see other companies are continuously trying to, already trying to buy data. If you are a tech data CEO today, I can tell you that you constantly get, uh, uh, uh, questions, "Are you willing to sell your data?" I hear it all over, and it seems that's gonna be a signifi- a, a significant trend as you go. I'm hearing about, you know, uh, uh, labs going to Wall Street and trying to buy data from, uh, hedge funds and, uh, try to understand how to map and analyze companies. So you, you see, uh, uh, data that, uh, was accrued throughout the years by companies, which was usually like tapes. It was usually, you know, uh, uh, uh, 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, flattening the playing grounds. It seems that everyone can sta- even large and small companies basically have the same, uh, uh, uh, the, the same, the, the, the same playing field. And the only real advantage that a company have today- Is, of course, their people, plus the data that they've approved, 'cause everyone has access to all of those cool new tools.
- GSGonen Stein
Yeah, it's become a moat.
- EGElad Gil
I guess in terms of the... I mean, people have been saying data's the new oil for a long time, and I was always a little bit skeptical of that statement. Um, but I feel like now what's happening is because of post-training and reinforcement learning and, you know, there's companies like Applied Compute and others who are starting to provide these sorts of services where you can fine-tune models or open source models against specific datasets. 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, a airline app?
- 6:43 – 9:39
Training Agents with Good Data
- EGElad Gil
Like, what, what do you think they're actually gonna do with this information? Is it something else? It's the internal documents? Like, I'm just sort of curious, like, what, uh, what is the, the reinforce- reinforcement learning, or is it, like, a customer support agent?
- OEOfir Ehrlich
Yeah. But, but, but think if, if, if you're, uh, if you're trying to build agents, so they're trying to... You can't just build them a lab, you need to train them on, on, on, on new data.
- EGElad Gil
Mm-hmm.
- OEOfir Ehrlich
On, on some training data, and it's very hard to find very good datasets. You see that Harvard just released a, a, a, a legal dataset just a few days ago, and... But you don't find too many good datasets that doesn't look like real synthetic data that-
- EGElad Gil
Mm-hmm
- OEOfir Ehrlich
... 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, a, a, a lot of, you know, the hierarchy, m-middle management, top management, uh, and workers working together. And y-you know, if you're looking at what other, uh, uh, public datasets do 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 are you training on?" You will find stuff, for example, the Amazon data is out there in public, and people are actually using that as real data from a company, doesn't how a company work, uh, uh, uh, works like. And the reason is, it's so very hard to find data that will help you to, uh, work like in the real world.
- EGElad Gil
Mm-hmm.
- OEOfir Ehrlich
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, they can, you know, buy data and train in-house, so when you first release your products, every new product that you have, it, it, it, you, you don't have to first interact with customers at your initial interaction. So I think that you're gonna 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 masked. When you think about it, it, there, 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.
- GSGonen Stein
Yeah. And Google obviously is, uh, it's not, it's not, uh, they, they're in this, uh, travel, uh, space for a while, right? They, they want this type of data. Uh, they're already monetizing it. This allows them to, uh, understand, train it, understand it, monetize it even further, and it's a unique situation, right? That, uh, uh, obviously, uh, people want to take a-advantage of, and I think we're gonna see more and more of that in such situations. And regardless of that, customers, uh, who have existing data want to be able to unlock that existing data as well.
- EGElad Gil
What, what sort
- 9:39 – 15:00
Data is the New Oil
- EGElad Gil
of tooling are you all building at Eon to allow people to make use of 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?
- OEOfir Ehrlich
So let, let's, let's go back from the, the, the, the problem statement. Uh, why... There are so many tools for data and processing, why, why, why do you need new tools? Isn't it solved 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, e-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, uh, some great companies, uh, Fiverr and DBT, Monte Carlo, all the data tools that e-exist, you know, in order to fulfill their tasks.
- EGElad Gil
Right.
- OEOfir Ehrlich
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, uh, are based in San Francisco, or we're now here, uh, in New York. And both of us has different business unit, uh, uh, uh, leaders. And now there's this thing called AI, and even the boss is playing with ChatGPT, so the CEO and the share- and the board and the shareholders, they all understand that AI is real.
- EGElad Gil
Mm-hmm.
- OEOfir Ehrlich
So they're coming to you, and they tell you, uh, uh, Elad, 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 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 are business unit leaders, if you even know us, maybe you don't. But let's say that you find somehow, uh, got to me, I'm a, a, a leader of a business unit. I, I have, uh, um, uh, 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, uh, uh, for the last 20 years. Some of them systems that no one really understands where That contains production data, that contains sensitive information. You know, there's always the server that no one knows what it's doing, but it's connected to the, uh, to the power, whe-whe-whether virtual or physically, that everyone's afraid to turn off because we don't know what's in there. So we had all, we had all, all of, all of that. And let's say that somehow I know what's in there. Now I need to bring engineers and tr- and, uh, compromise maybe security and compliance and, and, uh, production, uh, 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 with 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 tra-- to, to be, uh, 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 a very simple way, understand what it is, classify it, map it, understand context layer on top of that, as in build a semantic layer, and then be able to continuously bring all the data from me that is relevant without compromising production, without compromising 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 e-eventually in your data. We can do it in a very ca- uh, uh, cost-efficient and performant way, so we can actually do it from all over the place, bring it to you, and actually use it.
- EGElad Gil
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 shared 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-
- GSGonen Stein
Yeah, and, and the key, and the key point, Elad, is that, uh, 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, uh, different forms, but it's, uh, 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, it's stored much more efficiently and provide the mapping, classification, access control, um, and connect it into the AI workflows.
- 15:00 – 18:15
Autonomous Security Threats
- EGElad Gil
Mm-hmm. 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, uh, uh, 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 the agentic capabilities. Like who knows how these things are set up or configured or, you know. Um, sometimes a little bit uncertain whether, you know, there's, there's that much, uh, uh, how people are approaching these things. But, you know, fundamentally there's a lot of discussion of like AI security. Um, 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?
- GSGonen Stein
Yeah. So u-up until now, the concerns came from, uh, human threats, right? So this is, uh, uh, not you, where customers would come to us and say, "Hey, uh, we were, uh, exposed by this, uh, ransomware attack." So during our time at, uh, AWS, it was a very large customer that was impacted by ransomware. We, we thought that they were, uh, completely protected using our technology, the, uh, disaster recovery service that we managed there, and we learned, unfortunately, that the customer, uh, thought that they were protected. They weren't protected because they didn't, uh, map and classify and tag their resources properly, so it wasn't protected. And so 60% of the environment was, uh, was exposed by ransomware, and that's o-one of the reasons why we decided to launch Eon and solve that, uh, pain point around, uh, human threats such as ransomware. So being able to detect when that happens, look for, uh, irregular write patterns and entropy changes and things like that, protect against it, and then also allow customers to, uh, uh, 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, uh, actors, from, uh, AI agents that essentially have legitimate access to the environment with, uh, legitimate permissions into such and such databases, and all of a sudden, and this now happens very rapidly, uh, a table is all of a sudden dropped. Uh, fortunately for us, it's a very similar methodology in terms of detecting that and protecting against that and al-allowing to recover, but the velocity of that happening is extreme.
- OEOfir Ehrlich
Yeah. Something that I, I, I noted is that, like six months ago, no, no one would even discuss with me, but a few months ago, pretty much every person I meet, every, uh, uh, leader in a company tells me either they are afraid of that happening to them, or it personally happened to that per- to, to the, to the, the-- that person who is speaking with me, which is crazy. You see it all over the place. You're seeing real fear from, uh, uh, I no longer, uh, uh, decide what's really running on my data. I don't no longer understand. I need to be prepared for both external threats because, you know, all the new models make it much easier for attackers to, uh, uh, uh, come to me and, and, and attack me, but also from the inside with agents I actually approved running in my environment.
- EGElad Gil
Yeah.
- OEOfir Ehrlich
So it's a very, very tricky time. We need to assume breach, whether it's malicious or not, and we need to be able to handle it and, uh, act accordingly. It's a very weird situation today.
- EGElad Gil
Mm-hmm.
- 18:15 – 22:11
How Agents Change the Enterprise Stack
- EGElad Gil
Yeah. How do you think about the broader, um- Enterprise stack and agents. So, you know, the current stack really evolved around people or humans asking very pre-defined 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, um, sets of data. They may have access to SA- 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, 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?
- OEOfir Ehrlich
I actually think we'll see more dashboards because this will be the only way to kinda like figure out what the hell is going on in the world. Because first coding agent has started to write most of the code that's running in the world, so that's indirectly. But also agents activating other agents, which activate other agents, and trying to keep track of the non-human identity, uh, 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, the chain of responsibility. A- and this is a part of what you're seeing in a proliferation of, of cybersecurity companies. How many cybersecurity companies you see in, in, in, uh, NHI, in, in non-human identity right now? An infinite amount, and there's a reason for that.
- EGElad Gil
Yeah.
- OEOfir Ehrlich
It became a number one, number two problem right now. In addition to that, sec- second thing is, uh, endpoint. You see endpoint security, which looked like it solved them, so many great companies around it. And, uh, remember just, uh, uh, you know, a few, uh, a few years ago when endpoint was a complete different problems with EDRs. Uh, uh, now, uh, everything that's happening, you see people are running agents today on, on their laptops, and the agents sometimes connected to other networks, and it connect... And they are connected to, uh, think maybe on OpenClang, uh, uh, uh, connected to, uh, to, to your WhatsApp, but also to your internal network, and also to other applications. And you see it's very hard for the, the, for, for the VPO of ITs, for the CIOs to understand what should they do? On the one hand, they want to en- they are being pushed, pushed by the board, by the CEO, enable AI in my organization now. Don't block me.
- EGElad Gil
Mm-hmm.
- OEOfir Ehrlich
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, well, let's say kind of a, a, a, a lovable or, 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's gonna use this data, and they're all using all of, uh, 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 handling sensitive data-
- EGElad Gil
Mm-hmm
- OEOfir Ehrlich
... which is the property of the organization. Could be exposed to the world, it could be incorrect, could be incorrectly used, uh, and becomes a, a big problem. It's a good thing and bad thing that everyone inside the organization can become builders-
- EGElad Gil
Yes
- OEOfir Ehrlich
... whether you're a social media manager, whether you're a, a, a FinOps person, whether you're in, in, in, in, in legal or finance.
- EGElad Gil
Mm-hmm.
- OEOfir Ehrlich
So it's amazing, but it's also we're living in very interesting times, uh, in that perspective.
- 22:11 – 27:52
Re-imagining Data Infrastructure
- EGElad Gil
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, you know, decade. Do, do that stick around? Does that shift? Does that change? Like, how quickly does all this upend?
- OEOfir Ehrlich
So you see there's a strong competing element to pretty much change everything because-
- EGElad Gil
Yes
- OEOfir Ehrlich
... the, let's call it the plumbing today is very limited.
- EGElad Gil
Mm-hmm.
- OEOfir Ehrlich
And everyone built a solution to their set of problems. So think about what happens. Now Gonen goes downstairs after, uh, uh, recording this podcast, and he really wants coffee. So go to the store and buy, and, and buys coffee, and he puts on his credit card. Now, there's a transaction, and this is written in some database somewhere.
- EGElad Gil
Mm-hmm.
- OEOfir Ehrlich
Okay? Right. So someone needs to... Today, what they're doing, they're extracting the data, putting somewhere, and that's it. Someone else at some point takes this data and process 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 across an organization, because you could only do with the data things you really intended to do to begin with. So you had a single purpose in your mind when acting on the data.
- EGElad Gil
Mm-hmm. Mm-hmm.
- OEOfir Ehrlich
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 it in an efficient manner, and if you can activate that efficiently, 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-
- EGElad Gil
Mm-hmm
- OEOfir Ehrlich
... and the more context all that data that they have, the team handling that can create wonders. And, uh, 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 who have, who has a database of all the people in New York who love pizza. They don't know they can find a, a, a list of all the people in New York who love both burgers and pizza because none-- they didn't work together. Now, if you use it for posturing, use it for a, a... for the new capabilities, you can actually do wonders with that. You can actually start asking intelligent questions. You, you, you, you get the intelligent questions. You can start, uh, using that for your own purposes, and just something that you, you couldn't do before. So we've seen 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 data. You see data is growing out of proportions, so much of it, and so much of it is being generated by those new agents. So there's a lot of no- 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 from multiple locations, clean the noise, and make sure all of this data that's been, uh, created is actually usable.
- GSGonen Stein
Mm-hmm.
- OEOfir Ehrlich
And it doesn't apply with the old tools that were very... Some of them were incredible. Fivetran was an incredible company, DBT, and so on and so forth, but very niche, very specific tools for that purpose. Uh, so, so this creates a, a, a, a very interesting brave new world. You've seen companies like Databricks, you know, uh, one of the most incredible companies on the planet, in my opinion, uh, um, look at, "I have more and more data coming in."
- GSGonen Stein
Mm-hmm.
- OEOfir Ehrlich
"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.
- GSGonen Stein
Yeah.
- OEOfir Ehrlich
You already have the data, now you need to process that. Uh, but they are reinventing themselves all the time because they understand that more and more data is being generated by agents, and th-they thought, you know, 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 go-- We, we... Uh, and, and they wanna take charge of how data is being used, how, uh, data is being created.
- GSGonen Stein
Mm-hmm.
- OEOfir Ehrlich
And it's completely different than how any, uh, other people used that, you know, just three or five years ago.
- GSGonen Stein
Yeah. So the goal is really to e-enable, right? Enable this culture of, of builders and the c-culture of, uh, of agents with, uh, the ability to automatically help them understand what's there, automatically hel-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, the data that's created.
- OEOfir Ehrlich
Makes sense. And, and you, you're seeing 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 sort 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, uh, time of, uh, token, uh, token maxing anymore.
- GSGonen Stein
Mm-hmm.
- OEOfir Ehrlich
You know, trying to go to actually getting value for every token that we have, because it becomes more and more and more inven-- uh, uh, more and more and more, uh, expensive. So you wanna be very wise in... You don't want-- I don't wanna say not paying mil-millions. Pay 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.
- GSGonen Stein
Mm-hmm.
- 27:52 – 30:26
Cloud vs. AI Era Shift
- EGElad Gil
So I guess, um, 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 CloudEndure that was acquired by AWS. And at AWS, you really saw that migration, uh, 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, what do you view as sort of the cloud era versus AI era, and what are takeaways or lessons that you can apply across them?
- GSGonen Stein
Yeah. I think, uh, again, it's, uh, it's like that, but on, uh, but on steroids. Uh, and even before we, we sold our last company, CloudEndure, to AWS, we, uh, supported similar, uh, large scale enterprise migrations with the other hy-hyperscalers, with, uh, with Azure and with GCP, where our product was, uh, was integrated, OEM'd into the console. Uh, so very large enterprises that were moving, uh, thousands, tens of thousands or hundreds of thousands of servers, and then we saw those modernized further in, uh, in the cloud. Uh, and after we sold to AWS, we did that as part of the application migration service. Uh, 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, uh, from the, uh, human side. What we're seeing now in the, this crazy world of, uh, of AI and agents is that those transformations are hap-happening way faster, and, uh, and customers are losing control to a point where that's becoming k- uh, an inhibitor, right? Uh, not an enabler. They're stopping, they're pausing because they're afraid that things might break, that data might leak, that, uh, IP might, uh, break out, and, uh, and, and so they're looking desperately for this level of, uh, of understanding of what's happening and control.
- OEOfir Ehrlich
So it became so insane that, and, and, and so fast. And one of the reasons is that cloud, in my opinion, cloud is somewhat, somewhat abstract, 'cause 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, it's hard to explain to, uh, uh, my grandmother, uh, about a cloud. AI, everyone understands AI. Everyone under- E-everyone lived through the ChatGPT moment when we all asked what A-A-AI could do, and then, "Oh, my God, this is incredible." So they're getting pushed by, by C-levels, by CEO, by the board, by the shareholders. Use AI for the business, otherwise, otherwise we're irrelevant. So you see people doing it both for the value that you get from AI, but also for the, from the fear that you get from AI. And
- 30:26 – 34:31
How AI is Changing Companies
- OEOfir Ehrlich
you're seeing new trends of- For a first time in, in a lot of, in, in, in, in, in many years, you see how companies consume software in a brand-new way. Uh, uh, one example is, uh, what's happening with forward deployed engineers. Used to be something look like services, Palantir were doing that. No one really did understand what it means. Now everyone's doing that.
- EGElad Gil
Mm-hmm.
- OEOfir Ehrlich
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. They sometimes takes a year or, or, or two or more, but they need to have it now, and the only way they can actually getting deployed and, and, and become AI much faster is by letting a, a, a, a strong engineers who understand what they're doing and coming with the, uh, um, toolbox that they've created in top Silicon Valley comp- uh, uh, uh, startups and sometimes-
- EGElad Gil
Mm
- OEOfir Ehrlich
... 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, uh, buying using product-led growth by a, in really, really fast, uh, uh, uh, AI, uh, infrastructure with- which actually, uh, helped to build agents because everyone now want to build agent. Now, in the past, I was arguing that for the m- majority of things, PLG doesn't work, especially for dev tools, uh, because the world is very fragmented. People don't want to, uh, move so fast, so for- so forth. Now it became super hot looking companies like Kognition, for example, which is, you know, incredible company that were able to first go through a, a, a, a, a PLG. We use that, uh, that, that way in Eon. Uh, uh, uh, and then through the FD motion turning, going to banks and then will, uh, 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. Uh, 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 it's, they are very slow to adopt AI, but there are really great companies, for example, Long Lake, uh, uh, that say instead of you adopting AI, I know how to do that more efficiently. If I can buy the company and transform that into an AI company, we can all win. We can, uh, create arbitrage, make higher margin more efficiently, and this is a really radical new way for those companies to actually start using AI and 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 understand big data is important. They all understand that their existing processes are somewhat, uh, uh, uh, uh, mundane, and they need to do something about it, but it's scary, but you have to do it.
- EGElad Gil
Okay.
- OEOfir Ehrlich
So it's a, it's a fascinating thing to see.
- EGElad Gil
Mm-hmm.
- OEOfir Ehrlich
It's a fascinat- a fascinating evolution on what's-
- EGElad Gil
Yeah
- OEOfir Ehrlich
... going on right now in how co- how companies consume AI software, how consu- 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 gonna go, uh, the world, in my opinion, is gonna be better, uh, uh, because of that.
- EGElad Gil
Amazing. Well, thank you so much for joining me today, Ofir and Elad. Very, uh, interesting, wide-ranging conversation on data and AI. Really appreciate it.
- GSGonen Stein
Thank you. Our pleasure.
- OEOfir Ehrlich
Thank you so much, Elad. It was a pleasure.
- EGElad Gil
Okay.
- GSGonen Stein
All right.
- OEOfir Ehrlich
Thank you.
- GSGonen Stein
Thank you.
- 34:31 – 34:49
Conclusion
- SPSpeaker
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Episode duration: 34:50
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