 Hey, welcome back to theCUBE's coverage of CloudNative SecurityCon, the inaugural event in Seattle. I'm John Furrier, host of theCUBE here in the Palo Alto Studios. We're calling it theCUBE Center. It's kind of like our sports center for tech. It's kind of remote coverage. We've been doing this now for a few years. We're going to amp it up this year as more events are remote and happening all around the world. So we're going to continue the coverage with this segment focusing on the data stack, entrepreneurial opportunities around all things security. And as obviously data is involved and our next guest is a friend of theCUBE and CUBE alumni from 2013 entrepreneur himself turned now venture capitalist angel investor with his own firm, Ofer Kahane, managing director. Sonoma Ventures, formerly the founder of Origami Solta in two and a few years back, focusing now on having a lot of fun angel investing on boards, focusing on data driven applications and stacks around that and all the stuff going on and really in the wheelhouse for what's going on around security data. Ofer, great to see you. Thanks for coming on. My pleasure, great to be back, been a while. So you're kind of on easy street now, you. You did the entrepreneurial venture, you've worked hard. We were on together in 2013 when theCUBE just started Excel partners had an event in Stanford, Excel and they had all the features there. You know, we interviewed Satya Nutella who was just a manager at Microsoft at that time. He was there. He's now the CEO. Yeah, he was. Lots of change in nine years, but congratulations on your venture you sold and you got an exit there and now you're doing a lot of investments. I'd love to get your take because this is really the biggest change I've seen in the past 12 years around an inflection point around a lot of converging forces. Data, which big data 10 years ago was a big part of your career, but now it's accelerated with cloud scale. You're seeing people building scale on top of other clouds and becoming their own cloud. You're seeing data being a big part of it. Cyber security kind of is not really changed much but it's the most important thing everyone's talking about. So developers are involved, data is involved, a lot of entrepreneurial opportunities. So I'd love to get your take on how you see the current situation as it relates to what's gone on the past five years or so. What's the big story? So a lot of big story, but I think a lot of it has to do with a promise of making value from data, whether it's for cybersecurity, for FinTech, for DevOps, for RevTech startups and companies. There's a lot of challenges in actually driving and monetizing the value from data with velocity. Historically, the challenge has been more around how do I store data at massive scale? And then you had the big data infrastructure company like Cloudera, MapR and others deal with it from a scale perspective, from a storage perspective. Then you had a whole layer of companies that evolved to deal with how do I index massive scales of data for a quick querying and federated acts of et cetera. But now a lot of those underlying problems, if you will, have been sold to a certain extent. Although they're always being stretched given the scale of data, I mean, its utility is becoming more and more massive in particular with AI use cases being very prominent right now. The next level is how to actually make value from the data. How do I manage a full life cycle of data in complex environments with complex organizations, complex use cases? And having seen this from the inside of origami logic because we dealt a lot of large corporations and posted acquisition by Intuit and a lot of the startups I'm involved with, it's clear that we're now on to that next step. And you have fundamental new paradigms such as data mesh to attempt to address that complexity and responsibly scaling access and democratizing access and value monetization from data across large organizations. We have a slew of startups that are evolving to help the entire life cycle of data from the data engineering side of it to the data analytics side of it to the AI use cases side of it. And it feels like the early days are an extent of the revolution that we've seen in transition from traditional databases to data warehouses to cloud-based data processing and big data. It feels like we're the genesis of that next wave. And it's super, super exciting for me at least as someone who's sitting more on the coach seat rather than being on the pitch and building startups helping folks as they go through those motions. So that's awesome. I want to get into some of these data infrastructure dynamics you mentioned, but before that talk to the audience around what you're working on now. You've been a successful entrepreneur. You're focused on angel investing. So super seed stage. What kind of deals are you looking at? What's interesting to you? What is Sonoma Ventures looking for? And what are some of the entrepreneurial dynamics that you're seeing right now from a startup standpoint? So at Immaculable, just a little bit of background of my history because it shapes very heavily what I'm looking at. So I've been very fortunate with my entrepreneurial career. Founded three startups, all three of them are successful. Final two were sold, the first one merged and went public. And my third career has been about data, moving data, passing data, processing data, generating insights from it. And at this phase, I wanted to really evolve from just going and building start number four, going through the same motions again, then your adventure a little bit too old for that I guess. But the next best thing is to sit from a point whereby it can be more elevated than the one I'm dealing with and broader into if the variety of startups I'm focused on rather than just do your own thing and just go very, very deep into it. Now what specifically am I focused on at Sonoma Ventures? So basically looking at what I refer to as a data-driven application stack. Anything from the low level data infrastructure that helps any persona in the data universe maximize value for data from their particular point of view for their particular role, whether it's data analysts, data scientists, data engineers, cloud engineers, DevOps folks, et cetera. All the way up to the application layer in applications that are very data heavy. And what are very typical data heavy applications? Tintec, Cyber, Web 3, revenue technologies and product and DevOps. So these are areas we're focused on. I have almost 23, 24 startups in the portfolio that span all these different areas. And this is in terms of the aperture. Now, typically focus on pre-seed seed sometimes a little bit later stage but this is a primary focus and it's really about partnering with entrepreneurs and helping them make a few original mistakes before the mistakes I made and take it to the next level whatever the milestone they're driving with so I'm very, very hands on with many of those startups. Now, what is it that's happening right now in history why it's so exciting? So on one hand you have this scaling of data and its complexity yet lagging value creation from it across those different personas we've touched on. So that's one fundamental opportunity which is secular. The other one which is more a cyclic situation is the fact that we're going through a down cycle in tech as is very evident in the public markets and everything we're hearing about funding going slower and lower terms shifting more into the hands of typical VCs versus in pern or friendly market and so on and so forth and a very significant amount of playoffs. Now when you combine these two trends together you're observing a very interesting thing that a lot of folks really bright folks who have sold a startup to a company or have been in the guts of the large startup or a large corporation have hands on experience all those challenges spoken about earlier and to maximizing value from data irrespective of their role in a specific angle or vantage point they have on those challenges. So for many of them it's an opportunity to know let me now start a startup, I've been laid off maybe or my company stock isn't doing as much as well as it's used to as a large corporation. Now I have an opportunity to actually go and take my entrepreneurial passion and apply to a prominent experience as part of this larger company. And you see a lot of folks who are emerging with these great ideas. So it's a very, very exciting period of time to innovate. It's interesting, you know, a lot of people look at I mean I look at Snowflake as an example of a company that refactored data warehouses. They just basically took data warehouse and put it on the cloud and call it a data cloud. That to me was compelling. They didn't pay any cap X. They wrote Amazon's wave there. So similar thing going on with data you mentioned this and I see it as an enabling opportunity. So whether it's cybersecurity, fintech, whatever vertical, you have an enablement. Now you mentioned data infrastructure. It's a super exciting area as there's so many stacks emerging. We got an analytics stack, there's real-time stacks, there's data lakes, AI stack, foundational models. So you're seeing a explosion of stacks, different tools probably will emerge. So how do you look at that as a seasoned entrepreneur now investor? Is that a good thing? Is that just more of the market? Cause it just seems like more and more kind of decomposed stacks targeted at use pieces seems to be a trend. And how do you vet that? Is it, you know? So it's a great observation. And if you take a step back and look at the evolution of technology or last 30 years, if you longer, you always see these cycles of expansion, fragmentation, contraction, expansion, contraction, go decentralized, go centralized, go decentralized, go centralized as manifested in different types of technology, paradigm, from client server to storage, to microservices to et cetera, et cetera. So I think we're going through another big bang to certain extent. We're about to end up with more specialized data stacks or specific use cases as you need performance, the data models, the tooling to best adapt to the particular task at hand and particular personas at hand. As the needs of the data analysts are quite different from the needs of MNL engineer, which is quite different from the needs of the data engineer. And what happens is when you end up with these siloed stacks, you end up with new fragmentation and new gaps that need to be filled with a new layer of innovation. And I suspect that in parts of what we're seeing right now in terms of the next wave of data innovation, whether it's in a service of no FinTech use cases or cyber use cases or other is a set of tools that end up having to try and stitch together those elements and bridge between them. So I see that there's a fantastic gap to innovate around. I see also a fundamental need in creating a common data language and common data management processes and governance across those different personas because ultimately the same underlying data these folks need, I'll date in different mediums, different access models, different velocities, et cetera. The subject matter, if you will, the underlying raw data and some of the taxonomies that are right on top of it do need to be consistent. So once again, a great opportunity to innovate whether it's about semantic layers, whether it's about data mesh, whether it's about the ICD tools for data engineers and so on and so forth. I got to ask you, first of all, I see you have a friend and you brought into the interview you have a dog in the background with made up of cameo appearance, that's awesome. Sitting right next to you making sure everything's going well. On the AI thing, because I think that's the hot trend here. You're starting to see the chat GPT has got everyone excited because it's kind of that first time you see kind of next gen functionality, large language models, where you can bring data in and it integrates well. But to me, I think connecting the dots just kind of speaks to the beginning of what will be a trend of really blending of data stacks together or blending of models. And so as more data modeling emerges, you start to have this AI stack kind of situation where you have things out there that you can compose. It's almost very developer friendly, conceptually. This is kind of new, but kind of the same concepts been working on with Google and others. How do you see this emerging as an investor? What are some of the things that you're excited about around the chat GPT kind of things that's happening? Because it brings it mainstream. Again, a million downloads fastest application to get a million downloads, even among other the successes. So it's obviously hit an error. People are talking about it. What's your take on that? Yeah, so I think it's a great point and clearly it feels like an iPhone moment, right? To the industry in this case, AI and lots of applications. And I think there's a high level, probably three different layers of innovation. One is on top of those platforms. What use cases can one bring to the table that would drive it on top of the chat GPT-like service? Where by the startup, the company can bring some unique data sets to infuse and add value on top of it by custom focusing it and purpose building it for a particular use case or particular vertical, whether it's applying it to customer service in a particular vertical, applying it to, I don't know, marketing content creation and so on and so forth. That's one category. And I do know that as one of my startups is in Y Combinator this season, a winter 23, they're saying that a very large chunk of the YC companies in this cycle are about GPT use cases. So we'll see a flurry of that. The next layer, the one below that is those actually provide those platforms or the chat GPT, whatever will emerge from the partnership with Microsoft and any competitive players that emerge from other startups or from the big cloud providers, whether it's Facebook, if they ever get into this and Google, which will clearly well as they need to survive around search. The third layer is enabling layer. As you're going to have more and more of those different large language models and use case running on top of it, the underlying layers all the way down to cloud infrastructure, the data infrastructure and the entire set of tools and systems that take raw data and massage it into useful labeled contextualized features and data to feed the models, the AI models whether it's during training or during inference stages in production. Personally, my focus is more on the infrastructure than an application use cases. And I believe that there's going to be a massive amount of innovation opportunity around that to reach cost-effective quality there models that are deployed easily and maintained easily or at least with as little pain as possible at scale. So there are startups that are dealing with it in various areas. Some are about focusing on labeling automation. Some about fairness, about speaking about cyber protecting models from threats through data and other issues with it and so on and so forth. And I believe that this will be to a big driver for massive innovation infrastructure layer. Awesome. And I love how you mentioned the iPhone moment. I was, I called the browser moment because it felt that way for me personally but I think from a business model standpoint there is that iPhone shift. It's not the blackberry, it's a whole other thing. And I like that. But I do have to ask you because this is interesting to mention, iPhone, iPhone's mostly proprietary. So in these machine learning foundational models you're starting to see proprietary hardware bolt-on, acceleration, bundled together for faster, faster uptake. And now you got open source emerging as two things. It's almost iPhone and Android situation happening. So what's your view on that? Because, you know, there's pros and cons for either one. You're seeing a lot of these machine learning models that are very proprietary, but they work. And do you care, right? And then you got open source, which is like, okay, let's get some open source code and let people verify it and then build on with that. Is it a balance? Is it mutually exclusive? What's your view? I think it's going to be, market will drive the proportion of both. And I think for a certain use case it'll end up with more proprietary offerings. With certain use cases, I guess the fundamental infrastructure or chat to be like, let's say a large language models and all the use cases running on top of it, that's likely going to be more platform oriented and open source and will allow innovation. Think of it as the equivalent of iPhone apps or Android apps running on top of those platforms as an AI apps. So we'll have a lot of that. Now, when you start going a little bit more into the guts, the lower layers, then it's clear that's for performance reasons, in particular for certain use cases, will end up with more proprietary offerings, whether it's advanced silicon, such as some of the silicon that emerged from entrepreneurs who have left Google around TensorFlow and all the silicon that powers that. You'll see a lot of innovation that area as well, that hopefully intends to improve the cost efficiency of running what large AI or the workloads, both in France and in learning stages. You know, I got to ask you because this has come up a lot around Azure and Microsoft, Microsoft pretty good move getting into the chat GPV and the open AI because I was talking to someone who's a hardcore Amazon developer and they said they swore they would never use Azure, right? One of those types. And they're spinning up Azure servers to get access to the API. So the developers are flocking, as you mentioned, the YC class is all doing large data things because you're now programmed with data which is amazing, which is amazing. So what's your take on? I know you got to be kind of neutral because you're an investor, you got Amazon has to respond, Google essentially did all the work. So they have to have the solution. So I'm expecting Google to have something very compelling but Microsoft right now is going to just might run the table on developers, this new wave of data developers. What's your take on the cloud responses to this? What's Amazon? What do you think AWS is going to do? What should Google be doing? What's your take? So each of them is going for a slightly different angle, of course. I'll say Google I think has massive assets in the AI space and their underlying cloud platform, I think has been designed to support such complicated workloads, but they have yet to go as far as opening it up the same way a chat GPT is now in that Microsoft partnership on Azure. Good question regarding Amazon, AWS has had a significant investment in AI related infrastructure. Seeing it through my startups, through other lens as well, how will they respond to that higher layer above and beyond the low level? If you will, AI enabling apparatuses, how do they elevate to at least one or two layers above and get to the same chat GPT layer? Good question, is there an acquisition that will make sense for them to accelerate it? Maybe, is there in-house development that it can reapply from a different domain towards that possibly? But I just suspect we'll end up with acquisitions as the arms race around the next level of cloud wars emerges. And it's going to be no longer just about the basic tooling for basic cloud-based applications and the infrastructure and the cost management, but rather faster time to deliver AI and data heavy applications. Once again, each one of those cloud suppliers or vendors coming with different assets and different pros and cons, all of them will need to just elevate the level of the fight, if you will in this case, to the AI layer. It's going to be very interesting, the different stacks on the data infrastructure, like I mentioned, analytics, data lake, AI, all happening, it's going to be interesting to see how this turns into this AI cloud, like data clouds, data operating system. So, super fascinating area over thank you for coming on and sharing your expertise with us. Great to see you and congratulations on the work. I'll give you the final word here. Give a plug-in for what you're looking for for startup pre-seeds. What's the kind of profile that gets your attention from a pre-seed candidate or entrepreneur? Cool, first of all, it's my pleasure. Enjoy our chats as always. Hopefully the next one is not going to be in nine years. As to what I'm looking for, ideally smart data entrepreneurs who have come from a particular domain problem or problem domain, that they understand, they felt them in their own, you know, 10 fingers or you know, millions and billions of neurons in their brains and they figured out a way to solve it. Whether it's a data infrastructure play, cloud infrastructure play or a very, very smart application takes advantage of data at scale, these are the things I'm looking for. One final, final question I have to ask you because you're a seasoned entrepreneur as now coach. What's different about the current entrepreneurial environment right now vis-a-vis the past decade? What's new? Is it different, highly accelerated? What advice do you give entrepreneurs out there who are putting together their plan? Obviously, global resource pool now of engineering. You know, it might not be yesterday's formula for success to put an adventure together to get to that product market fit. What's new and different? What's your advice to the folks out there about what's different about the current environment for being an entrepreneur? Fantastic, so I think it's a great question. So I think there's a few axes of difference compared to let's say, five years ago, 10 years ago, 15 years ago. First and foremost, given the amount of infrastructure out there, the amount of open source technologies, the amount of developer toolkits and frameworks, tying to develop an application, at least the application layer is much faster than ever. So it's faster and cheaper, Tim. So most part, unless you're building very fundamental core deep tech where you still have a big technology challenge to deal with. And absent that, the challenge shifts more to how do you manage my resources to find part market fit? How are you integrating the GTM lens to go to market as early as possible in the product market fit cycle? Such that you reach from pre-C to C, from C to A, from A to B with an optimal amount of philosophy and a minimal amount of resources. One big difference specifically as of let's say, beginning of this year, late last year is that money is no longer free for entrepreneurs, which means that you need to upgrade and build startup in an environment with a lot more constraints. And in my mind, some of the best startups that have ever been built and some of the big market changing, generational changing, if you will, technology starts in their respective industry verticals have actually emerged from these times. And these tend to be the smartest best startups that emerge because they operate with a lot less money. Money is not as available for them, which means that they need to make tough decisions and make protocols every day, which you don't need to do. You can push this, kick the count down the road when you have plenty of money and it cushions for a lot of mistakes. You don't have that cushion. And hopefully you end up with companies with a more agile, more, if you will, resilience and better cultures in making those tough decisions that start to make every day, which is why I'm super, super excited to see the next batch of amazing unicorns, true unicorns, not just valuation, market rising with the water type unicorns that emerged from this particular era, which we're in the beginning of. And very much enjoy working within thepreneurs during this difficult time at times. The next 24 months, there's the next wave, like you said, best time to do a company. Remember Airbnb's pitch was, we'll rent cots in apartments and sell cereal. Boy, a lot of people pass on that deal in that last down market that are going to be in danger. So the crazy ideas might not be that bad. It's all about the entrepreneurs and a hard percent. This is a big wave and it's certainly happening. Oprah, thank you for sharing. Obviously data is going to change. All the markets, refactoring, security, FinTech, user experience, applications will be changed by data, data operating system. Thanks for coming on. Thanks for sharing, appreciate it. My pleasure. Have a good one. Okay, more coverage for the cloud and native security con inaugural event. Data will be the key for cybersecurity. The cubes coverage continues after this break.