>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Spark became a top level Apache project in 2014, and then shortly thereafter, burst onto the big data scene. Spark, along with the cloud, transformed and in many ways, disrupted the big data market. Databricks optimized its tech stack for Spark and took advantage of the cloud to really cleverly deliver a managed service that has become a leading AI and data platform among data scientists and data engineers. However, emerging customer data requirements are shifting into a direction that will cause modern data platform players generally and Databricks, specifically, we think, to make some key directional decisions and perhaps even reinvent themselves. Hello and welcome to this week's wikibon theCUBE Insights, powered by ETR. In this Breaking Analysis, we're going to do a deep dive into Databricks. We'll explore its current impressive market momentum. We're going to use some ETR survey data to show that, and then we'll lay out how customer data requirements are changing and what the ideal data platform will look like in the midterm future. We'll then evaluate core elements of the Databricks portfolio against that vision, and then we'll close with some strategic decisions that we think the company faces. And to do so, we welcome in our good friend, George Gilbert, former equities analyst, market analyst, and current Principal at TechAlpha Partners. George, good to see you. Thanks for coming on. >> Good to see you, Dave. >> All right, let me set this up. We're going to start by taking a look at where Databricks sits in the market in terms of how customers perceive the company and what it's momentum looks like. And this chart that we're showing here is data from ETS, the emerging technology survey of private companies. The N is 1,421. What we did is we cut the data on three sectors, analytics, database-data warehouse, and AI/ML. The vertical axis is a measure of customer sentiment, which evaluates an IT decision maker's awareness of the firm and the likelihood of engaging and/or purchase intent. The horizontal axis shows mindshare in the dataset, and we've highlighted Databricks, which has been a consistent high performer in this survey over the last several quarters. And as we, by the way, just as aside as we previously reported, OpenAI, which burst onto the scene this past quarter, leads all names, but Databricks is still prominent. You can see that the ETR shows some open source tools for reference, but as far as firms go, Databricks is very impressively positioned. Now, let's see how they stack up to some mainstream cohorts in the data space, against some bigger companies and sometimes public companies. This chart shows net score on the vertical axis, which is a measure of spending momentum and pervasiveness in the data set is on the horizontal axis. You can see that chart insert in the upper right, that informs how the dots are plotted, and net score against shared N. And that red dotted line at 40% indicates a highly elevated net score, anything above that we think is really, really impressive. And here we're just comparing Databricks with Snowflake, Cloudera, and Oracle. And that squiggly line leading to Databricks shows their path since 2021 by quarter. And you can see it's performing extremely well, maintaining an elevated net score and net range. Now it's comparable in the vertical axis to Snowflake, and it consistently is moving to the right and gaining share. Now, why did we choose to show Cloudera and Oracle? The reason is that Cloudera got the whole big data era started and was disrupted by Spark. And of course the cloud, Spark and Databricks and Oracle in many ways, was the target of early big data players like Cloudera. Take a listen to Cloudera CEO at the time, Mike Olson. This is back in 2010, first year of theCUBE, play the clip. >> Look, back in the day, if you had a data problem, if you needed to run business analytics, you wrote the biggest check you could to Sun Microsystems, and you bought a great big, single box, central server, and any money that was left over, you handed to Oracle for a database licenses and you installed that database on that box, and that was where you went for data. That was your temple of information. >> Okay? So Mike Olson implied that monolithic model was too expensive and inflexible, and Cloudera set out to fix that. But the best laid plans, as they say, George, what do you make of the data that we just shared? >> So where Databricks has really come up out of sort of Cloudera's tailpipe was they took big data processing, made it coherent, made it a managed service so it could run in the cloud. So it relieved customers of the operational burden. Where they're really strong and where their traditional meat and potatoes or bread and butter is the predictive and prescriptive analytics that building and training and serving machine learning models. They've tried to move into traditional business intelligence, the more traditional descriptive and diagnostic analytics, but they're less mature there. So what that means is, the reason you see Databricks and Snowflake kind of side by side is there are many, many accounts that have both Snowflake for business intelligence, Databricks for AI machine learning, where Snowflake, I'm sorry, where Databricks also did really well was in core data engineering, refining the data, the old ETL process, which kind of turned into ELT, where you loaded into the analytic repository in raw form and refine it. And so people have really used both, and each is trying to get into the other. >> Yeah, absolutely. We've reported on this quite a bit. Snowflake, kind of moving into the domain of Databricks and vice versa. And the last bit of ETR evidence that we want to share in terms of the company's momentum comes from ETR's Round Tables. They're run by Erik Bradley, and now former Gartner analyst and George, your colleague back at Gartner, Daren Brabham. And what we're going to show here is some direct quotes of IT pros in those Round Tables. There's a data science head and a CIO as well. Just make a few call outs here, we won't spend too much time on it, but starting at the top, like all of us, we can't talk about Databricks without mentioning Snowflake. Those two get us excited. Second comment zeros in on the flexibility and the robustness of Databricks from a data warehouse perspective. And then the last point is, despite competition from cloud players, Databricks has reinvented itself a couple of times over the year. And George, we're going to lay out today a scenario that perhaps calls for Databricks to do that once again. >> Their big opportunity and their big challenge for every tech company, it's managing a technology transition. The transition that we're talking about is something that's been bubbling up, but it's really epical. First time in 60 years, we're moving from an application-centric view of the world to a data-centric view, because decisions are becoming more important than automating processes. So let me let you sort of develop. >> Yeah, so let's talk about that here. We going to put up some bullets on precisely that point and the changing sort of customer environment. So you got IT stacks are shifting is George just said, from application centric silos to data centric stacks where the priority is shifting from automating processes to automating decision. You know how look at RPA and there's still a lot of automation going on, but from the focus of that application centricity and the data locked into those apps, that's changing. Data has historically been on the outskirts in silos, but organizations, you think of Amazon, think Uber, Airbnb, they're putting data at the core, and logic is increasingly being embedded in the data instead of the reverse. In other words, today, the data's locked inside the app, which is why you need to extract that data is sticking it to a data warehouse. The point, George, is we're putting forth this new vision for how data is going to be used. And you've used this Uber example to underscore the future state. Please explain? >> Okay, so this is hopefully an example everyone can relate to. The idea is first, you're automating things that are happening in the real world and decisions that make those things happen autonomously without humans in the loop all the time. So to use the Uber example on your phone, you call a car, you call a driver. Automatically, the Uber app then looks at what drivers are in the vicinity, what drivers are free, matches one, calculates an ETA to you, calculates a price, calculates an ETA to your destination, and then directs the driver once they're there. The point of this is that that cannot happen in an application-centric world very easily because all these little apps, the drivers, the riders, the routes, the fares, those call on data locked up in many different apps, but they have to sit on a layer that makes it all coherent. >> But George, so if Uber's doing this, doesn't this tech already exist? Isn't there a tech platform that does this already? >> Yes, and the mission of the entire tech industry is to build services that make it possible to compose and operate similar platforms and tools, but with the skills of mainstream developers in mainstream corporations, not the rocket scientists at Uber and Amazon. >> Okay, so we're talking about horizontally scaling across the industry, and actually giving a lot more organizations access to this technology. So by way of review, let's summarize the trend that's going on today in terms of the modern data stack that is propelling the likes of Databricks and Snowflake, which we just showed you in the ETR data and is really is a tailwind form. So the trend is toward this common repository for analytic data, that could be multiple virtual data warehouses inside of Snowflake, but you're in that Snowflake environment or Lakehouses from Databricks or multiple data lakes. And we've talked about what JP Morgan Chase is doing with the data mesh and gluing data lakes together, you've got various public clouds playing in this game, and then the data is annotated to have a common meaning. In other words, there's a semantic layer that enables applications to talk to the data elements and know that they have common and coherent meaning. So George, the good news is this approach is more effective than the legacy monolithic models that Mike Olson was talking about, so what's the problem with this in your view? >> So today's data platforms added immense value 'cause they connected the data that was previously locked up in these monolithic apps or on all these different microservices, and that supported traditional BI and AI/ML use cases. But now if we want to build apps like Uber or Amazon.com, where they've got essentially an autonomously running supply chain and e-commerce app where humans only care and feed it. But the thing is figuring out what to buy, when to buy, where to deploy it, when to ship it. We needed a semantic layer on top of the data. So that, as you were saying, the data that's coming from all those apps, the different apps that's integrated, not just connected, but it means the same. And the issue is whenever you add a new layer to a stack to support new applications, there are implications for the already existing layers, like can they support the new layer and its use cases? So for instance, if you add a semantic layer that embeds app logic with the data rather than vice versa, which we been talking about and that's been the case for 60 years, then the new data layer faces challenges that the way you manage that data, the way you analyze that data, is not supported by today's tools. >> Okay, so actually Alex, bring me up that last slide if you would, I mean, you're basically saying at the bottom here, today's repositories don't really do joins at scale. The future is you're talking about hundreds or thousands or millions of data connections, and today's systems, we're talking about, I don't know, 6, 8, 10 joins and that is the fundamental problem you're saying, is a new data error coming and existing systems won't be able to handle it? >> Yeah, one way of thinking about it is that even though we call them relational databases, when we actually want to do lots of joins or when we want to analyze data from lots of different tables, we created a whole new industry for analytic databases where you sort of mung the data together into fewer tables. So you didn't have to do as many joins because the joins are difficult and slow. And when you're going to arbitrarily join thousands, hundreds of thousands or across millions of elements, you need a new type of database. We have them, they're called graph databases, but to query them, you go back to the prerelational era in terms of their usability. >> Okay, so we're going to come back to that and talk about how you get around that problem. But let's first lay out what the ideal data platform of the future we think looks like. And again, we're going to come back to use this Uber example. In this graphic that George put together, awesome. We got three layers. The application layer is where the data products reside. The example here is drivers, rides, maps, routes, ETA, et cetera. The digital version of what we were talking about in the previous slide, people, places and things. The next layer is the data layer, that breaks down the silos and connects the data elements through semantics and everything is coherent. And then the bottom layers, the legacy operational systems feed that data layer. George, explain what's different here, the graph database element, you talk about the relational query capabilities, and why can't I just throw memory at solving this problem? >> Some of the graph databases do throw memory at the problem and maybe without naming names, some of them live entirely in memory. And what you're dealing with is a prerelational in-memory database system where you navigate between elements, and the issue with that is we've had SQL for 50 years, so we don't have to navigate, we can say what we want without how to get it. That's the core of the problem. >> Okay. So if I may, I just want to drill into this a little bit. So you're talking about the expressiveness of a graph. Alex, if you'd bring that back out, the fourth bullet, expressiveness of a graph database with the relational ease of query. Can you explain what you mean by that? >> Yeah, so graphs are great because when you can describe anything with a graph, that's why they're becoming so popular. Expressive means you can represent anything easily. They're conducive to, you might say, in a world where we now want like the metaverse, like with a 3D world, and I don't mean the Facebook metaverse, I mean like the business metaverse when we want to capture data about everything, but we want it in context, we want to build a set of digital twins that represent everything going on in the world. And Uber is a tiny example of that. Uber built a graph to represent all the drivers and riders and maps and routes. But what you need out of a database isn't just a way to store stuff and update stuff. You need to be able to ask questions of it, you need to be able to query it. And if you go back to prerelational days, you had to know how to find your way to the data. It's sort of like when you give directions to someone and they didn't have a GPS system and a mapping system, you had to give them turn by turn directions. Whereas when you have a GPS and a mapping system, which is like the relational thing, you just say where you want to go, and it spits out the turn by turn directions, which let's say, the car might follow or whoever you're directing would follow. But the point is, it's much easier in a relational database to say, "I just want to get these results. You figure out how to get it." The graph database, they have not taken over the world because in some ways, it's taking a 50 year leap backwards. >> Alright, got it. Okay. Let's take a look at how the current Databricks offerings map to that ideal state that we just laid out. So to do that, we put together this chart that looks at the key elements of the Databricks portfolio, the core capability, the weakness, and the threat that may loom. Start with the Delta Lake, that's the storage layer, which is great for files and tables. It's got true separation of compute and storage, I want you to double click on that George, as independent elements, but it's weaker for the type of low latency ingest that we see coming in the future. And some of the threats highlighted here. AWS could add transactional tables to S3, Iceberg adoption is picking up and could accelerate, that could disrupt Databricks. George, add some color here please? >> Okay, so this is the sort of a classic competitive forces where you want to look at, so what are customers demanding? What's competitive pressure? What are substitutes? Even what your suppliers might be pushing. Here, Delta Lake is at its core, a set of transactional tables that sit on an object store. So think of it in a database system, this is the storage engine. So since S3 has been getting stronger for 15 years, you could see a scenario where they add transactional tables. We have an open source alternative in Iceberg, which Snowflake and others support. But at the same time, Databricks has built an ecosystem out of tools, their own and others, that read and write to Delta tables, that's what makes the Delta Lake and ecosystem. So they have a catalog, the whole machine learning tool chain talks directly to the data here. That was their great advantage because in the past with Snowflake, you had to pull all the data out of the database before the machine learning tools could work with it, that was a major shortcoming. They fixed that. But the point here is that even before we get to the semantic layer, the core foundation is under threat. >> Yep. Got it. Okay. We got a lot of ground to cover. So we're going to take a look at the Spark Execution Engine next. Think of that as the refinery that runs really efficient batch processing. That's kind of what disrupted the DOOp in a large way, but it's not Python friendly and that's an issue because the data science and the data engineering crowd are moving in that direction, and/or they're using DBT. George, we had Tristan Handy on at Supercloud, really interesting discussion that you and I did. Explain why this is an issue for Databricks? >> So once the data lake was in place, what people did was they refined their data batch, and Spark has always had streaming support and it's gotten better. The underlying storage as we've talked about is an issue. But basically they took raw data, then they refined it into tables that were like customers and products and partners. And then they refined that again into what was like gold artifacts, which might be business intelligence metrics or dashboards, which were collections of metrics. But they were running it on the Spark Execution Engine, which it's a Java-based engine or it's running on a Java-based virtual machine, which means all the data scientists and the data engineers who want to work with Python are really working in sort of oil and water. Like if you get an error in Python, you can't tell whether the problems in Python or where it's in Spark. There's just an impedance mismatch between the two. And then at the same time, the whole world is now gravitating towards DBT because it's a very nice and simple way to compose these data processing pipelines, and people are using either SQL in DBT or Python in DBT, and that kind of is a substitute for doing it all in Spark. So it's under threat even before we get to that semantic layer, it so happens that DBT itself is becoming the authoring environment for the semantic layer with business intelligent metrics. But that's again, this is the second element that's under direct substitution and competitive threat. >> Okay, let's now move down to the third element, which is the Photon. Photon is Databricks' BI Lakehouse, which has integration with the Databricks tooling, which is very rich, it's newer. And it's also not well suited for high concurrency and low latency use cases, which we think are going to increasingly become the norm over time. George, the call out threat here is customers want to connect everything to a semantic layer. Explain your thinking here and why this is a potential threat to Databricks? >> Okay, so two issues here. What you were touching on, which is the high concurrency, low latency, when people are running like thousands of dashboards and data is streaming in, that's a problem because SQL data warehouse, the query engine, something like that matures over five to 10 years. It's one of these things, the joke that Andy Jassy makes just in general, he's really talking about Azure, but there's no compression algorithm for experience. The Snowflake guy started more than five years earlier, and for a bunch of reasons, that lead is not something that Databricks can shrink. They'll always be behind. So that's why Snowflake has transactional tables now and we can get into that in another show. But the key point is, so near term, it's struggling to keep up with the use cases that are core to business intelligence, which is highly concurrent, lots of users doing interactive query. But then when you get to a semantic layer, that's when you need to be able to query data that might have thousands or tens of thousands or hundreds of thousands of joins. And that's a SQL query engine, traditional SQL query engine is just not built for that. That's the core problem of traditional relational databases. >> Now this is a quick aside. We always talk about Snowflake and Databricks in sort of the same context. We're not necessarily saying that Snowflake is in a position to tackle all these problems. We'll deal with that separately. So we don't mean to imply that, but we're just sort of laying out some of the things that Snowflake or rather Databricks customers we think, need to be thinking about and having conversations with Databricks about and we hope to have them as well. We'll come back to that in terms of sort of strategic options. But finally, when come back to the table, we have Databricks' AI/ML Tool Chain, which has been an awesome capability for the data science crowd. It's comprehensive, it's a one-stop shop solution, but the kicker here is that it's optimized for supervised model building. And the concern is that foundational models like GPT could cannibalize the current Databricks tooling, but George, can't Databricks, like other software companies, integrate foundation model capabilities into its platform? >> Okay, so the sound bite answer to that is sure, IBM 3270 terminals could call out to a graphical user interface when they're running on the XT terminal, but they're not exactly good citizens in that world. The core issue is Databricks has this wonderful end-to-end tool chain for training, deploying, monitoring, running inference on supervised models. But the paradigm there is the customer builds and trains and deploys each model for each feature or application. In a world of foundation models which are pre-trained and unsupervised, the entire tool chain is different. So it's not like Databricks can junk everything they've done and start over with all their engineers. They have to keep maintaining what they've done in the old world, but they have to build something new that's optimized for the new world. It's a classic technology transition and their mentality appears to be, "Oh, we'll support the new stuff from our old stuff." Which is suboptimal, and as we'll talk about, their biggest patron and the company that put them on the map, Microsoft, really stopped working on their old stuff three years ago so that they could build a new tool chain optimized for this new world. >> Yeah, and so let's sort of close with what we think the options are and decisions that Databricks has for its future architecture. They're smart people. I mean we've had Ali Ghodsi on many times, super impressive. I think they've got to be keenly aware of the limitations, what's going on with foundation models. But at any rate, here in this chart, we lay out sort of three scenarios. One is re-architect the platform by incrementally adopting new technologies. And example might be to layer a graph query engine on top of its stack. They could license key technologies like graph database, they could get aggressive on M&A and buy-in, relational knowledge graphs, semantic technologies, vector database technologies. George, as David Floyer always says, "A lot of ways to skin a cat." We've seen companies like, even think about EMC maintained its relevance through M&A for many, many years. George, give us your thought on each of these strategic options? >> Okay, I find this question the most challenging 'cause remember, I used to be an equity research analyst. I worked for Frank Quattrone, we were one of the top tech shops in the banking industry, although this is 20 years ago. But the M&A team was the top team in the industry and everyone wanted them on their side. And I remember going to meetings with these CEOs, where Frank and the bankers would say, "You want us for your M&A work because we can do better." And they really could do better. But in software, it's not like with EMC in hardware because with hardware, it's easier to connect different boxes. With software, the whole point of a software company is to integrate and architect the components so they fit together and reinforce each other, and that makes M&A harder. You can do it, but it takes a long time to fit the pieces together. Let me give you examples. If they put a graph query engine, let's say something like TinkerPop, on top of, I don't even know if it's possible, but let's say they put it on top of Delta Lake, then you have this graph query engine talking to their storage layer, Delta Lake. But if you want to do analysis, you got to put the data in Photon, which is not really ideal for highly connected data. If you license a graph database, then most of your data is in the Delta Lake and how do you sync it with the graph database? If you do sync it, you've got data in two places, which kind of defeats the purpose of having a unified repository. I find this semantic layer option in number three actually more promising, because that's something that you can layer on top of the storage layer that you have already. You just have to figure out then how to have your query engines talk to that. What I'm trying to highlight is, it's easy as an analyst to say, "You can buy this company or license that technology." But the really hard work is making it all work together and that is where the challenge is. >> Yeah, and well look, I thank you for laying that out. We've seen it, certainly Microsoft and Oracle. I guess you might argue that well, Microsoft had a monopoly in its desktop software and was able to throw off cash for a decade plus while it's stock was going sideways. Oracle had won the database wars and had amazing margins and cash flow to be able to do that. Databricks isn't even gone public yet, but I want to close with some of the players to watch. Alex, if you'd bring that back up, number four here. AWS, we talked about some of their options with S3 and it's not just AWS, it's blob storage, object storage. Microsoft, as you sort of alluded to, was an early go-to market channel for Databricks. We didn't address that really. So maybe in the closing comments we can. Google obviously, Snowflake of course, we're going to dissect their options in future Breaking Analysis. Dbt labs, where do they fit? Bob Muglia's company, Relational.ai, why are these players to watch George, in your opinion? >> So everyone is trying to assemble and integrate the pieces that would make building data applications, data products easy. And the critical part isn't just assembling a bunch of pieces, which is traditionally what AWS did. It's a Unix ethos, which is we give you the tools, you put 'em together, 'cause you then have the maximum choice and maximum power. So what the hyperscalers are doing is they're taking their key value stores, in the case of ASW it's DynamoDB, in the case of Azure it's Cosmos DB, and each are putting a graph query engine on top of those. So they have a unified storage and graph database engine, like all the data would be collected in the key value store. Then you have a graph database, that's how they're going to be presenting a foundation for building these data apps. Dbt labs is putting a semantic layer on top of data lakes and data warehouses and as we'll talk about, I'm sure in the future, that makes it easier to swap out the underlying data platform or swap in new ones for specialized use cases. Snowflake, what they're doing, they're so strong in data management and with their transactional tables, what they're trying to do is take in the operational data that used to be in the province of many state stores like MongoDB and say, "If you manage that data with us, it'll be connected to your analytic data without having to send it through a pipeline." And that's hugely valuable. Relational.ai is the wildcard, 'cause what they're trying to do, it's almost like a holy grail where you're trying to take the expressiveness of connecting all your data in a graph but making it as easy to query as you've always had it in a SQL database or I should say, in a relational database. And if they do that, it's sort of like, it'll be as easy to program these data apps as a spreadsheet was compared to procedural languages, like BASIC or Pascal. That's the implications of Relational.ai. >> Yeah, and again, we talked before, why can't you just throw this all in memory? We're talking in that example of really getting down to differences in how you lay the data out on disk in really, new database architecture, correct? >> Yes. And that's why it's not clear that you could take a data lake or even a Snowflake and why you can't put a relational knowledge graph on those. You could potentially put a graph database, but it'll be compromised because to really do what Relational.ai has done, which is the ease of Relational on top of the power of graph, you actually need to change how you're storing your data on disk or even in memory. So you can't, in other words, it's not like, oh we can add graph support to Snowflake, 'cause if you did that, you'd have to change, or in your data lake, you'd have to change how the data is physically laid out. And then that would break all the tools that talk to that currently. >> What in your estimation, is the timeframe where this becomes critical for a Databricks and potentially Snowflake and others? I mentioned earlier midterm, are we talking three to five years here? Are we talking end of decade? What's your radar say? >> I think something surprising is going on that's going to sort of come up the tailpipe and take everyone by storm. All the hype around business intelligence metrics, which is what we used to put in our dashboards where bookings, billings, revenue, customer, those things, those were the key artifacts that used to live in definitions in your BI tools, and DBT has basically created a standard for defining those so they live in your data pipeline or they're defined in their data pipeline and executed in the data warehouse or data lake in a shared way, so that all tools can use them. This sounds like a digression, it's not. All this stuff about data mesh, data fabric, all that's going on is we need a semantic layer and the business intelligence metrics are defining common semantics for your data. And I think we're going to find by the end of this year, that metrics are how we annotate all our analytic data to start adding common semantics to it. And we're going to find this semantic layer, it's not three to five years off, it's going to be staring us in the face by the end of this year. >> Interesting. And of course SVB today was shut down. We're seeing serious tech headwinds, and oftentimes in these sort of downturns or flat turns, which feels like this could be going on for a while, we emerge with a lot of new players and a lot of new technology. George, we got to leave it there. Thank you to George Gilbert for excellent insights and input for today's episode. I want to thank Alex Myerson who's on production and manages the podcast, of course Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Siliconangle.com, he does some great editing. Remember all these episodes, they're available as podcasts. Wherever you listen, all you got to do is search Breaking Analysis Podcast, we publish each week on wikibon.com and siliconangle.com, or you can email me at David.Vellante@siliconangle.com, or DM me @DVellante. Comment on our LinkedIn post, and please do check out ETR.ai, great survey data, enterprise tech focus, phenomenal. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis.

>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (bright upbeat music plays) >> Welcome back to Barcelona, everybody. You're watching theCUBE's coverage of MWC '23, my name is Dave Vellante. Just left a meeting with the CEO of Cisco, Chuck Robbins, to meet with Jeetu Patel, who's our Executive Vice President and General Manager of security and collaboration at Cisco. Good to see you. >> You never leave a meeting with Chuck Robbins to meet with Jeetu Patel. >> Well, I did. >> That's a bad idea. >> Walked right out. I said, hey, I got an interview to do, right? So, and I'm excited about this. Thanks so much for coming on. >> Thank you for having me. It's a pleasure. >> So, I mean you run such an important part of the business. I mean, obviously the collaboration business but also security. So many changes going on in the security market. Maybe we could start there. I mean, there hasn't been a ton of security talk here Jeetu, because I think it's almost assumed. It was 45 minutes into the keynote yesterday before anybody even mentioned security. >> Huh. >> Right? And so, but it's the most important topic in the enterprise IT world. And obviously is important here. So why is it you think that it's not the first topic that people mention. >> You know, it's a complicated subject area and it's intimidating. And actually that's one of the things that the industry screwed up on. Where we need to simplify security so it actually gets to be relatable for every person on the planet. But, if you think about what's happening in security, it's not just important for business it's critical infrastructure that if you had a breach, you know lives are cost now. Because hospitals could go down, your water supply could go down, your electricity could go down. And so it's one of these things that we have to take pretty seriously. And, it's 51% of all breaches happen because of negligence, not because of malicious intent. >> It's that low. Interesting. I always- >> Someone else told me the same thing, that they though it'd be higher, yeah. >> I always say bad user behavior is going to trump good security every time. >> Every single time. >> You can't beat it. But, you know, it's funny- >> Jeetu: Every single time. >> Back, the earlier part of last decade, you could see that security was becoming a board level issue. It became, it was on the agenda every quarter. And, I remember doing some research at the time, and I asked, I was interviewing Robert Gates, former Defense Secretary, and I asked him, yeah, but we're getting attacked but don't we have the best offense? Can't we have the best technology? He said, yeah but we have so much critical infrastructure the risks to United States are higher. So we have to be careful about how we use security as an offensive weapon, you know? And now you're seeing the future of war involves security and what's going on in Ukraine. It's a whole different ballgame. >> It is, and the scales always tip towards the adversary, not towards the defender, because you have to be right every single time. They have to be right once. >> Yeah. And, to the other point, about bad user behavior. It's going now beyond the board level, to it's everybody's responsibility. >> That's right. >> And everybody's sort of aware of it, everybody's been hacked. And, that's where it being such a complicated topic is problematic. >> It is, and it's actually, what got us this far will not get us to where we need to get to if we don't simplify security radically. You know? The experience has to be almost invisible. And what used to be the case was sophistication had to get to a certain level, for efficacy to go up. But now, that sophistication has turned to complexity. And there's an inverse relationship between complexity and efficacy. So the simpler you make security, the more effective it gets. And so I'll give you an example. We have this great kind of innovation we've done around passwordless, right? Everyone hates passwords. You shouldn't have passwords in 2023. But, when you get to passwordless security, not only do you reduce a whole lot of friction for the user, you actually make the system safer. And that's what you need to do, is you have to make it simpler while making it more effective. And, I think that's what the future is going to hold. >> Yeah, and CISOs tell me that they're, you know zero trust before the pandemic was like, yeah, yeah zero trust. And now it's like a mandate. >> Yeah. >> Every CISO you talk to says, yes we're implementing a zero trust architecture. And a big part of that is that, if they can confirm zero trust, they can get to market a lot faster with revenue generating or critical projects. And many projects as we know are being pushed back, >> Yeah. >> you know? 'Cause of the macro. But, projects that drive revenue and value they want to accelerate, and a zero trust confirmation allows people to rubber stamp it and go faster. >> And the whole concept of zero trust is least privileged access, right? But what we want to make sure that we get to is continuous assessment of least privileged access, not just a one time at login. >> Dave: 'Cause things change so frequently. >> So, for example, if you happen to be someone that's logged into the system and now you start doing some anomalous behavior that doesn't sound like Dave, we want to be able to intercept, not just do it at the time that you're authenticating Dave to come in. >> So you guys got a good business. I mentioned the macro before. >> Yeah. >> The big theme is consolidating redundant vendors. So a company with a portfolio like Cisco's obviously has an advantage there. You know, you guys had great earnings. Palo Alto is another company that can consolidate. Tom Gillis, great pickup. Guy's amazing, you know? >> Love Tom. >> Great respect. Just had a little webinar session with him, where he was geeking out with the analyst and so- >> Yeah, yeah. >> Learned a lot there. Now you guys have some news, at the event event with Mercedes? >> We do. >> Take us through that, and I want to get your take on hybrid work and what's happening there. But what's going on with Mercedes? >> Yeah so look, it all actually stems from the hybrid work story, which is the future is going to be hybrid, people are going to work in mixed mode. Sometimes you'll be in the office, sometimes at home, sometimes somewhere in the middle. One of the places that people are working more and more from is their cars. And connected cars are getting to be a reality. And in fact, cars sometimes become an extension of your home office. And many a times I have found myself in a parking lot, because I didn't have enough time to get home and I was in a parking lot taking a conference call. And so we've made that section easier, because we have now partnered with Mercedes. And they aren't the first partner, but they're a very important partner where we are going to have Webex available, through the connected car, natively in Mercedes. >> Ah, okay. So I could take a call, I can do it all the time. I find good service, pull over, got to take the meeting. >> Yeah. >> I don't want to be driving. I got to concentrate. >> That's right. >> You know, or sometimes, I'll have the picture on and it's not good. >> That's right. >> Okay, so it'll be through the console, and all through the internet? >> It'll be through the console. And many people ask me like, how's safety going to work over that? Because you don't want to do video calls while you're driving. Exactly right. So when you're driving, the video automatically turns off. And you'll have audio going on, just like a conference call. But the moment you stop and put it in park, you can have video turned on. >> Now, of course the whole hybrid work trend, we, seems like a long time ago but it doesn't, you know? And it's really changed the security dynamic as well, didn't it? >> It has, it has. >> I mean, immediately you had to go protect new endpoints. And those changes, I felt at the time, were permanent. And I think it's still the case, but there's an equilibrium now happening. People as they come back to the office, you see a number of companies are mandating back to work. Maybe the central offices, or the headquarters, were underfunded. So what's going on out there in terms of that balance? >> Well firstly, there's no unanimous consensus on the way that the future is going to be, except that it's going to be hybrid. And the reason I say that is some companies mandate two days a week, some companies mandate five days a week, some companies don't mandate at all. Some companies are completely remote. But whatever way you go, you want to make sure that regardless of where you're working from, people can have an inclusive experience. You know? And, when they have that experience, you want to be able to work from a managed device or an unmanaged device, from a corporate network or from a Starbucks, from on the road or stationary. And whenever you do any of those things, we want to make sure that security is always handled, and you don't have to worry about that. And so the way that we say it is the company that created the VPN, which is Cisco, is the one that's going to kill it. Because what we'll do is we'll make it simple enough so that you don't, you as a user, never have to worry about what connection you're going to use to dial in to what app. You will have one, seamless way to dial into any application, public application, private application, or directly to the internet. >> Yeah, I got a love, hate with my VPN. I mean, it's protecting me, but it's in the way a lot. >> It's going to be simple as ever. >> Do you have kids? >> I do, I have a 12 year old daughter. >> Okay, so not quite high school age yet. She will be shortly. >> No, but she's already, I'm not looking forward to high school days, because she has a very, very strong sense of debate and she wins 90% of the arguments. >> So when my kids were that age, I've got four kids, but the local high school banned Wikipedia, they can't use Wikipedia for research. Many colleges, I presume high schools as well, they're banning Chat GPT, can't use it. Now at the same time, I saw recently on Medium a Wharton school professor said he's mandating Chat GPT to teach his students how to prompt in progressively more sophisticated prompts, because the future is interacting with machines. You know, they say in five years we're all going to be interacting in some way, shape, or form with AI. Maybe we already are. What's the intersection between AI and security? >> So a couple very, very consequential things. So firstly on Chat GPT, the next generation skill is going to be to learn how to go out and have the right questions to ask, which is the prompt revolution that we see going on right now. But if you think about what's happening in security, and there's a few areas which are, firstly 3,500 hundred vendors in this space. On average, most companies have 50 to 70 vendors in security. Not a single vendor owns more than 10% of the market. You take out a couple vendors, no one owns more than 5%. Highly fractured market. That's a problem. Because it's untenable for companies to go out and manage 70 policy engines. And going out and making sure that there's no contention. So as you move forward, one of the things that Chat GPT will be really good for is it's fundamentally going to change user experiences, for how software gets built. Because rather than it being point and click, it's going to be I'm going to provide an instruction and it's going to tell me what to do in natural language. Imagine Dave, when you joined a company if someone said, hey give Dave all the permissions that he needs as a direct report to Chuck. And instantly you would get all of the permissions. And it would actually show up in a screen that says, do you approve? And if you hit approve, you're done. The interfaces of the future will get more natural language kind of dominated. The other area that you'll see is the sophistication of attacks and the surface area of attacks is increasing quite exponentially. And we no longer can handle this with human scale. You have to handle it in machine scale. So detecting breaches, making sure that you can effectively and quickly respond in real time to the breaches, and remediate those breaches, is all going to happen through AI and machine learning. >> So, I agree. I mean, just like Amazon turned the data center into an API, I think we're now going to be interfacing with technology through human language. >> That's right. >> I mean I think it's a really interesting point you're making. Now, from a security standpoint as well, I mean, the state of the art today in my email is be careful, this person's outside your organization. I'm like, yeah I know. So it's a good warning sign, but it's really not automated in any way. So two part question. One is, can AI help? You know, with the phishing, obviously it can, but the bad guys have AI too. >> Yeah. >> And they're probably going to be smarter than I am about using it. >> Yeah, and by the way, Talos is our kind of threat detection and response >> Yes. >> kind of engine. And, they had a great kind of piece that came out recently where they talked about this, where Chat GPT, there is going to be more sophistication of the folks that are the bad actors, the adversaries in using Chat GPT to have more sophisticated phishing attacks. But today it's not something that is fundamentally something that we can't handle just yet. But you still need to do the basic hygiene. That's more important. Over time, what you will see is attacks will get more bespoke. And in order, they'll get more sophisticated. And, you will need to have better mechanisms to know that this was actually not a human being writing that to you, but it was actually a machine pretending to be a human being writing something to you. And that you'll have to be more clever about it. >> Oh interesting. >> And so, you will see attacks get more bespoke and we'll have to get smarter and smarter about it. >> The other thing I wanted to ask you before we close is you're right on. I mean you take the top security vendors and they got a single digit market share. And it's like it's untenable for organizations, just far too many tools. We have a partner at ETR, they do quarterly survey research and one of the things they do is survey emerging technology companies. And when we look at in the security sector just the number of emerging technology companies that are focused on cybersecurity is as many as there are out there already. And so, there's got to be consolidation. Maybe that's through M & A. I mean, what do you think happens? Are company's going to go out of business? There's going to be a lot of M & A? You've seen a lot of companies go private. You know, the big PE companies are sucking up all these security companies and may be ready to spit 'em out and go back public. How do you see the landscape? You guys are obviously an inquisitive company. What are your thoughts on that? >> I think there will be a little bit of everything. But the biggest change that you'll see is a shift that's going to happen with an integrated platform, rather than point solution vendors. So what's going to happen is the market's going to consolidate towards very few, less than a half a dozen, integrated platforms. We believe Cisco is going to be one. Microsoft will be one. There'll be others over there. But these, this platform will essentially be able to provide a unified kind of policy engine across a multitude of different services to protect multiple different entities within the organization. And, what we found is that platform will also be something that'll provide, through APIs, the ability for third parties to be able to get their technology incorporated in, and their telemetry ingested. So we certainly intend to do that. We don't believe, we are not arrogant enough to think that every single new innovation will be built by us. When there's someone else who has built that, we want to make sure that we can ingest that telemetry as well, because the real enemy is not the competitor. The real enemy is the adversary. And we all have to get together, so that we can keep humanity safe. >> Do you think there's been enough collaboration in the industry? I mean- >> Jeetu: Not nearly enough. >> We've seen companies, security companies try to monetize private data before, instead of maybe sharing it with competitors. And so I think the industry can do better there. >> Well I think the industry can do better. And we have this concept called the security poverty line. And the security poverty line is the companies that fall below the security poverty line don't have either the influence or the resources or the know how to keep themselves safe. And when they go unsafe, everyone else that communicates with them also gets that exposure. So it is in our collective interest for all of us to make sure that we come together. And, even if Palo Alto might be a competitor of ours, we want to make sure that we invite them to say, let's make sure that we can actually exchange telemetry between our companies. And we'll continue to do that with as many companies that are out there, because actually that's better for the market, that's better for the world. >> The enemy of the enemy is my friend, kind of thing. >> That's right. >> Now, as it relates to, because you're right. I mean I, I see companies coming up, oh, we do IOT security. I'm like, okay, but what about cloud security? Do you that too? Oh no, that's somebody else. But, so that's another stove pipe. >> That's a huge, huge advantage of coming with someone like Cisco. Because we actually have the entire spectrum, and the broadest portfolio in the industry of anyone else. From the user, to the device, to the network, to the applications, we provide the entire end-to-end story for security, which then has the least amount of cracks that you can actually go out and penetrate through. The biggest challenges that happen in security is you've got way too many policy engines with way too much contention between the policies from these different systems. And eventually there's a collision course. Whereas with us, you've actually got a broad portfolio that operates as one platform. >> We were talking about the cloud guys earlier. You mentioned Microsoft. They're obviously a big competitor in the security space. >> Jeetu: But also a great partner. >> So that's right. To my opinion, the cloud has been awesome as a first line of defense if you will. But the shared responsibility model it's different for each cloud, right? So, do you feel that those guys are working together or will work together to actually improve? 'Cause I don't see that yet. >> Yeah so if you think about, this is where we feel like we have a structural advantage in this, because what does a company like Cisco become in the future? I think as the world goes multicloud and hybrid cloud, what'll end up happening is there needs to be a way, today all the CSPs provide everything from storage to computer network, to security, in their own stack. If we can abstract networking and security above them, so that we can acquire and steer any and all traffic with our service providers and steer it to any of those CSPs, and make sure that the security policy transcends those clouds, you would actually be able to have the public cloud economics without the public cloud lock-in. >> That's what we call super cloud Jeetu. It's securing the super cloud. >> Yeah. >> Hey, thanks so much for coming to theCUBE. >> Thank you for having me. >> Really appreciate you coming on our editorial program. >> Such a pleasure. >> All right, great to see you again. >> Cheers. >> All right, keep it right there. Dave Vellante with David Nicholson and Lisa Martin. We'll be back, right after this short break from MWC '23 live, in the Fira, in Barcelona. (bright music resumes) (music fades out)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> While never really meant to be a consumer tech event, the rapid ascendancy of smartphones sucked much of the air out of Mobile World Congress over the years, now MWC. And while the device manufacturers continue to have a major presence at the show, the maturity of intelligent devices, longer life cycles, and the disaggregation of the network stack, have put enterprise technologies front and center in the telco business. Semiconductor manufacturers, network equipment players, infrastructure companies, cloud vendors, software providers, and a spate of startups are eyeing the trillion dollar plus communications industry as one of the next big things to watch this decade. Hello, and welcome to this week's Wikibon CUBE Insights, powered by ETR. In this Breaking Analysis, we bring you part two of our ongoing coverage of MWC '23, with some new data on enterprise players specifically in large telco environments, a brief glimpse at some of the pre-announcement news and corresponding themes ahead of MWC, and some of the key announcement areas we'll be watching at the show on theCUBE. Now, last week we shared some ETR data that showed how traditional enterprise tech players were performing, specifically within the telecoms vertical. Here's a new look at that data from ETR, which isolates the same companies, but cuts the data for what ETR calls large telco. The N in this cut is 196, down from 288 last week when we included all company sizes in the dataset. Now remember the two dimensions here, on the y-axis is net score, or spending momentum, and on the x-axis is pervasiveness in the data set. The table insert in the upper left informs how the dots and companies are plotted, and that red dotted line, the horizontal line at 40%, that indicates a highly elevated net score. Now while the data are not dramatically different in terms of relative positioning, there are a couple of changes at the margin. So just going down the list and focusing on net score. Azure is comparable, but slightly lower in this sector in the large telco than it was overall. Google Cloud comes in at number two, and basically swapped places with AWS, which drops slightly in the large telco relative to overall telco. Snowflake is also slightly down by one percentage point, but maintains its position. Remember Snowflake, overall, its net score is much, much higher when measuring across all verticals. Snowflake comes down in telco, and relative to overall, a little bit down in large telco, but it's making some moves to attack this market that we'll talk about in a moment. Next are Red Hat OpenStack and Databricks. About the same in large tech telco as they were an overall telco. Then there's Dell next that has a big presence at MWC and is getting serious about driving 16G adoption, and new servers, and edge servers, and other partnerships. Cisco and Red Hat OpenShift basically swapped spots when moving from all telco to large telco, as Cisco drops and Red Hat bumps up a bit. And VMware dropped about four percentage points in large telco. Accenture moved up dramatically, about nine percentage points in big telco, large telco relative to all telco. HPE dropped a couple of percentage points. Oracle stayed about the same. And IBM surprisingly dropped by about five points. So look, I understand not a ton of change in terms of spending momentum in the large sector versus telco overall, but some deltas. The bottom line for enterprise players is one, they're just getting started in this new disruption journey that they're on as the stack disaggregates. Two, all these players have experience in delivering horizontal solutions, but now working with partners and identifying big problems to be solved, and three, many of these companies are generally not the fastest moving firms relative to smaller disruptive disruptors. Now, cloud has been an exception in fairness. But the good news for the legacy infrastructure and IT companies is that the telco transformation and the 5G buildout is going to take years. So it's moving at a pace that is very favorable to many of these companies. Okay, so looking at just some of the pre-announcement highlights that have hit the wire this week, I want to give you a glimpse of the diversity of innovation that is occurring in the telecommunication space. You got semiconductor manufacturers, device makers, network equipment players, carriers, cloud vendors, enterprise tech companies, software companies, startups. Now we've included, you'll see in this list, we've included OpeRAN, that logo, because there's so much buzz around the topic and we're going to come back to that. But suffice it to say, there's no way we can cover all the announcements from the 2000 plus exhibitors at the show. So we're going to cherry pick here and make a few call outs. Hewlett Packard Enterprise announced an acquisition of an Italian private cellular network company called AthoNet. Zeus Kerravala wrote about it on SiliconANGLE if you want more details. Now interestingly, HPE has a partnership with Solana, which also does private 5G. But according to Zeus, Solona is more of an out-of-the-box solution, whereas AthoNet is designed for the core and requires more integration. And as you'll see in a moment, there's going to be a lot of talk at the show about private network. There's going to be a lot of news there from other competitors, and we're going to be watching that closely. And while many are concerned about the P5G, private 5G, encroaching on wifi, Kerravala doesn't see it that way. Rather, he feels that these private networks are really designed for more industrial, and you know mission critical environments, like factories, and warehouses that are run by robots, et cetera. 'Cause these can justify the increased expense of private networks. Whereas wifi remains a very low cost and flexible option for, you know, whatever offices and homes. Now, over to Dell. Dell announced its intent to go hard after opening up the telco network with the announcement that in the second half of this year it's going to begin shipping its infrastructure blocks for Red Hat. Remember it's like kind of the converged infrastructure for telco with a more open ecosystem and sort of more flexible, you know, more mature engineered system. Dell has also announced a range of PowerEdge servers for a variety of use cases. A big wide line bringing forth its 16G portfolio and aiming squarely at the telco space. Dell also announced, here we go, a private wireless offering with airspan, and Expedo, and a solution with AthoNet, the company HPE announced it was purchasing. So I guess Dell and HPE are now partnering up in the private wireless space, and yes, hell is freezing over folks. We'll see where that relationship goes in the mid- to long-term. Dell also announced new lab and certification capabilities, which we said last week was going to be critical for the further adoption of open ecosystem technology. So props to Dell for, you know, putting real emphasis and investment in that. AWS also made a number of announcements in this space including private wireless solutions and associated managed services. AWS named Deutsche Telekom, Orange, T-Mobile, Telefonica, and some others as partners. And AWS announced the stepped up partnership, specifically with T-Mobile, to bring AWS services to T-Mobile's network portfolio. Snowflake, back to Snowflake, announced its telecom data cloud. Remember we showed the data earlier, it's Snowflake not as strong in the telco sector, but they're continuing to move toward this go-to market alignment within key industries, realigning their go-to market by vertical. It also announced that AT&T, and a number of other partners, are collaborating to break down data silos specifically in telco. Look, essentially, this is Snowflake taking its core value prop to the telco vertical and forming key partnerships that resonate in the space. So think simplification, breaking down silos, data sharing, eventually data monetization. Samsung previewed its future capability to allow smartphones to access satellite services, something Apple has previously done. AMD, Intel, Marvell, Qualcomm, are all in the act, all the semiconductor players. Qualcomm for example, announced along with Telefonica, and Erickson, a 5G millimeter network that will be showcased in Spain at the event this coming week using Qualcomm Snapdragon chipset platform, based on none other than Arm technology. Of course, Arm we said is going to dominate the edge, and is is clearly doing so. It's got the volume advantage over, you know, traditional Intel, you know, X86 architectures. And it's no surprise that Microsoft is touting its open AI relationship. You're going to hear a lot of AI talk at this conference as is AI is now, you know, is the now topic. All right, we could go on and on and on. There's just so much going on at Mobile World Congress or MWC, that we just wanted to give you a glimpse of some of the highlights that we've been watching. Which brings us to the key topics and issues that we'll be exploring at MWC next week. We touched on some of this last week. A big topic of conversation will of course be, you know, 5G. Is it ever going to become real? Is it, is anybody ever going to make money at 5G? There's so much excitement around and anticipation around 5G. It has not lived up to the hype, but that's because the rollout, as we've previous reported, is going to take years. And part of that rollout is going to rely on the disaggregation of the hardened telco stack, as we reported last week and in previous Breaking Analysis episodes. OpenRAN is a big component of that evolution. You know, as our RAN intelligent controllers, RICs, which essentially the brain of OpenRAN, if you will. Now as we build out 5G networks at massive scale and accommodate unprecedented volumes of data and apply compute-hungry AI to all this data, the issue of energy efficiency is going to be front and center. It has to be. Not only is it a, you know, hot political issue, the reality is that improving power efficiency is compulsory or the whole vision of telco's future is going to come crashing down. So chip manufacturers, equipment makers, cloud providers, everybody is going to be doubling down and clicking on this topic. Let's talk about AI. AI as we said, it is the hot topic right now, but it is happening not only in consumer, with things like ChatGPT. And think about the theme of this Breaking Analysis in the enterprise, AI in the enterprise cannot be ChatGPT. It cannot be error prone the way ChatGPT is. It has to be clean, reliable, governed, accurate. It's got to be ethical. It's got to be trusted. Okay, we're going to have Zeus Kerravala on the show next week and definitely want to get his take on private networks and how they're going to impact wifi. You know, will private networks cannibalize wifi? If not, why not? He wrote about this again on SiliconANGLE if you want more details, and we're going to unpack that on theCUBE this week. And finally, as always we'll be following the data flows to understand where and how telcos, cloud players, startups, software companies, disruptors, legacy companies, end customers, how are they going to make money from new data opportunities? 'Cause we often say in theCUBE, don't ever bet against data. All right, that's a wrap for today. Remember theCUBE is going to be on location at MWC 2023 next week. We got a great set. We're in the walkway in between halls four and five, right in Congress Square, stand CS-60. Look for us, we got a full schedule. If you got a great story or you have news, stop by. We're going to try to get you on the program. I'll be there with Lisa Martin, co-hosting, David Nicholson as well, and the entire CUBE crew, so don't forget to come by and see us. I want to thank Alex Myerson, who's on production and manages the podcast, and Ken Schiffman, as well, in our Boston studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor-in-chief over at SiliconANGLE.com. He does some great editing. Thank you. All right, remember all these episodes they are available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcasts. I publish each week on Wikibon.com and SiliconANGLE.com. All the video content is available on demand at theCUBE.net, or you can email me directly if you want to get in touch David.Vellante@SiliconANGLE.com or DM me @DVellante, or comment on our LinkedIn posts. And please do check out ETR.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. We'll see you next week at Mobile World Congress '23, MWC '23, or next time on Breaking Analysis. (bright music)

>> From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from The Cube and ETR. This is "Breaking Analysis" with Dave Vellante. >> The world's leading telcos are trying to shed the stigma of being monopolies lacking innovation. Telcos have been great at operational efficiency and connectivity and living off of transmission, and the costs and expenses or revenue associated with that transmission. But in a world beyond telephone poles and basic wireless and mobile services, how will telcos modernize and become more agile and monetize new opportunities brought about by 5G and private wireless and a spate of new innovations and infrastructure, cloud data and apps? Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this breaking analysis and ahead of Mobile World Congress or now, MWC23, we explore the evolution of the telco business and how the industry is in many ways, mimicking transformations that took place decades ago in enterprise IT. We'll model some of the traditional enterprise vendors using ETR data and investigate how they're faring in the telecommunications sector, and we'll pose some of the key issues facing the industry this decade. First, let's take a look at what the GSMA has in store for MWC23. GSMA is the host of what used to be called Mobile World Congress. They've set the theme for this year's event as "Velocity" and they've rebranded MWC to reflect the fact that mobile technology is only one part of the story. MWC has become one of the world's premier events highlighting innovations not only in Telco, mobile and 5G, but the collision between cloud, infrastructure, apps, private networks, smart industries, machine intelligence, and AI, and more. MWC comprises an enormous ecosystem of service providers, technology companies, and firms from virtually every industry including sports and entertainment. And as well, GSMA, along with its venue partner at the Fira Barcelona, have placed a major emphasis on sustainability and public and private partnerships. Virtually every industry will be represented at the event because every industry is impacted by the trends and opportunities in this space. GSMA has said it expects 80,000 attendees at MWC this year, not quite back to 2019 levels, but trending in that direction. Of course, attendance from Chinese participants has historically been very high at the show, and obviously the continued travel issues from that region are affecting the overall attendance, but still very strong. And despite these concerns, Huawei, the giant Chinese technology company. has the largest physical presence of any exhibitor at the show. And finally, GSMA estimates that more than $300 million in economic benefit will result from the event which takes place at the end of February and early March. And The Cube will be back at MWC this year with a major presence thanks to our anchor sponsor, Dell Technologies and other supporters of our content program, including Enterprise Web, ArcaOS, VMware, Snowflake, Cisco, AWS, and others. And one of the areas we're interested in exploring is the evolution of the telco stack. It's a topic that's often talked about and one that we've observed taking place in the 1990s when the vertically integrated IBM mainframe monopoly gave way to a disintegrated and horizontal industry structure. And in many ways, the same thing is happening today in telecommunications, which is shown on the left-hand side of this diagram. Historically, telcos have relied on a hardened, integrated, and incredibly reliable, and secure set of hardware and software services that have been fully vetted and tested, and certified, and relied upon for decades. And at the top of that stack on the left are the crown jewels of the telco stack, the operational support systems and the business support systems. For the OSS, we're talking about things like network management, network operations, service delivery, quality of service, fulfillment assurance, and things like that. For the BSS systems, these refer to customer-facing elements of the stack, like revenue, order management, what products they sell, billing, and customer service. And what we're seeing is telcos have been really good at operational efficiency and making money off of transport and connectivity, but they've lacked the innovation in services and applications. They own the pipes and that works well, but others, be the over-the-top content companies, or private network providers and increasingly, cloud providers have been able to bypass the telcos, reach around them, if you will, and drive innovation. And so, the right-most diagram speaks to the need to disaggregate pieces of the stack. And while the similarities to the 1990s in enterprise IT are greater than the differences, there are things that are different. For example, the granularity of hardware infrastructure will not likely be as high where competition occurred back in the 90s at every layer of the value chain with very little infrastructure integration. That of course changed in the 2010s with converged infrastructure and hyper-converged and also software defined. So, that's one difference. And the advent of cloud, containers, microservices, and AI, none of that was really a major factor in the disintegration of legacy IT. And that probably means that disruptors can move even faster than did the likes of Intel and Microsoft, Oracle, Cisco, and the Seagates of the 1990s. As well, while many of the products and services will come from traditional enterprise IT names like Dell, HPE, Cisco, Red Hat, VMware, AWS, Microsoft, Google, et cetera, many of the names are going to be different and come from traditional network equipment providers. These are names like Ericsson and Huawei, and Nokia, and other names, like Wind River, and Rakuten, and Dish Networks. And there are enormous opportunities in data to help telecom companies and their competitors go beyond telemetry data into more advanced analytics and data monetization. There's also going to be an entirely new set of apps based on the workloads and use cases ranging from hospitals, sports arenas, race tracks, shipping ports, you name it. Virtually every vertical will participate in this transformation as the industry evolves its focus toward innovation, agility, and open ecosystems. Now remember, this is not a binary state. There are going to be greenfield companies disrupting the apple cart, but the incumbent telcos are going to have to continue to ensure newer systems work with their legacy infrastructure, in their OSS and BSS existing systems. And as we know, this is not going to be an overnight task. Integration is a difficult thing, transformations, migrations. So that's what makes this all so interesting because others can come in with Greenfield and potentially disrupt. There'll be interesting partnerships and ecosystems will form and coalitions will also form. Now, we mentioned that several traditional enterprise companies are or will be playing in this space. Now, ETR doesn't have a ton of data on specific telecom equipment and software providers, but it does have some interesting data that we cut for this breaking analysis. What we're showing here in this graphic is some of the names that we've followed over the years and how they're faring. Specifically, we did the cut within the telco sector. So the Y-axis here shows net score or spending velocity. And the horizontal axis, that shows the presence or pervasiveness in the data set. And that table insert in the upper left, that informs as to how the dots are plotted. You know, the two columns there, net score and the ends. And that red-dotted line, that horizontal line at 40%, that is an indicator of a highly elevated level. Anything above that, we consider quite outstanding. And what we'll do now is we'll comment on some of the cohorts and share with you how they're doing in telecommunications, and that sector, that vertical relative to their position overall in the data set. Let's start with the public cloud players. They're prominent in every industry. Telcos, telecommunications is no exception and it's quite an interesting cohort here. On the one hand, they can help telecommunication firms modernize and become more agile by eliminating the heavy lifting and you know, all the cloud, you know, value prop, data center costs, and the cloud benefits. At the same time, public cloud players are bringing their services to the edge, building out their own global networks and are a disruptive force to traditional telcos. All right, let's talk about Azure first. Their net score is basically identical to telco relative to its overall average. AWS's net score is higher in telco by just a few percentage points. Google Cloud platform is eight percentage points higher in telco with a 53% net score. So all three hyperscalers have an equal or stronger presence in telco than their average overall. Okay, let's look at the traditional enterprise hardware and software infrastructure cohort. Dell, Cisco, HPE, Red Hat, VMware, and Oracle. We've highlighted in this chart just as sort of indicators or proxies. Dell's net score's 10 percentage points higher in telco than its overall average. Interesting. Cisco's is a bit higher. HPE's is actually lower by about nine percentage points in the ETR survey, and VMware's is lower by about four percentage points. Now, Red Hat is really interesting. OpenStack, as we've previously reported is popular with telcos who want to build out their own private cloud. And the data shows that Red Hat OpenStack's net score is 15 percentage points higher in the telco sector than its overall average. OpenShift, on the other hand, has a net score that's four percentage points lower in telco than its overall average. So this to us talks to the pace of adoption of microservices and containers. You know, it's going to happen, but it's going to happen more slowly. Finally, Oracle's spending momentum is somewhat lower in the sector than its average, despite the firm having a decent telco business. IBM and Accenture, heavy services companies are both lower in this sector than their average. And real quickly, snowflake's net score is much lower by about 12 percentage points relative to its very high average net score of 62%. But we look for them to be a player in this space as telcos need to modernize their analytics stack and share data in a governed manner. Databricks' net score is also much lower than its average by about 13 points. And same, I would expect them to be a player as open architectures and cloud gains steam in telco. All right, let's close out now on what we're going to be talking about at MWC23 and some of the key issues that we'll be unpacking. We've talked about stack disaggregation in this breaking analysis, but the key here will be the pace at which it will reach the operational efficiency and reliability of closed stacks. Telcos, you know, in a large part, they're engineering heavy firms and much of their work takes place, kind of in the basement, in the dark. It's not really a big public hype machine, and they tend to move slowly and cautiously. While they understand the importance of agility, they're going to be careful because, you know, it's in their DNA. And so at the same time, if they don't move fast enough, they're going to get hurt and disrupted by competitors. So that's going to be a topic of conversation, and we'll be looking for proof points. And the other comment I'll make is around integration. Telcos because of their conservatism will benefit from better testing and those firms that can innovate on the testing front and have labs and certifications and innovate at that level, with an ecosystem are going to be in a better position. Because open sometimes means wild west. So the more players like Dell, HPE, Cisco, Red Hat, et cetera, that do that and align with their ecosystems and provide those resources, the faster adoption is going to go. So we'll be looking for, you know, who's actually doing that, Open RAN or Radio Access Networks. That fits in this discussion because O-RAN is an emerging network architecture. It essentially enables the use of open technologies from an ecosystem and over time, look at O-RAN is going to be open, but the questions, you know, a lot of questions remain as to when it will be able to deliver the operational efficiency of traditional RAN. Got some interesting dynamics going on. Rakuten is a company that's working hard on this problem, really focusing on operational efficiency. Then you got Dish Networks. They're also embracing O-RAN. They're coming at it more from service innovation. So that's something that we'll be monitoring and unpacking. We're going to look at cloud as a disruptor. On the one hand, cloud can help drive agility, as we said earlier and optionality, and innovation for incumbent telcos. But the flip side is going to also do the same for startups trying to disrupt and cloud attracts startups. While some of the telcos are actually embracing the cloud, many are being cautious. So that's going to be an interesting topic of discussion. And there's private wireless networks and 5G, and hyperlocal private networks, they're being deployed, you know, at the edge. This idea of open edge is also a really hot topic and this trend is going to accelerate. You know, the importance here is that the use cases are going to be widely varied. The needs of a hospital are going to be different than those of a sports venue are different from a remote drilling location, and energy or a concert venue. Things like real-time AI inference and data flows are going to bring new services and monetization opportunities. And many firms are going to be bypassing traditional telecommunications networks to build these out. Satellites as well, we're going to see, you know, in this decade, you're going to have, you're going to look down at Google Earth and you're going to see real-time. You know, today you see snapshots and so, lots of innovations going in that space. So how is this going to disrupt industries and traditional industry structures? Now, as always, we'll be looking at data angles, right? 'Cause it's in The Cube's DNA to follow the data and what opportunities and risks data brings. The Cube is going to be on location at MWC23 at the end of the month. We got a great set. We're in the walkway between halls four and five, right in Congress Square, it's booths CS60. So we'll have a full, they're called Stan CS60. We have a full schedule. I'm going to be there with Lisa Martin, Dave Nicholson and the entire Cube crew, so don't forget to stop by. All right, that's a wrap. I want to thank Alex Myerson, who's on production and manages the podcast, Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor-in-chief over at Silicon Angle, does some great stuff for us. Thank you all. Remember, all these episodes are available as podcasts. Wherever you listen, just search "Breaking Analysis" podcasts I publish each week on wikibon.com and silicon angle.com. And all the video content is available on demand at thecube.net. You can email me directly at david.vellante@silicon angle.com. You can DM me at dvellante or comment on my LinkedIn post. Please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for The Cube Insights powered by ETR. Thanks for watching and we'll see you at Mobile World Congress, and/or at next time on "Breaking Analysis." (bright music) (bright music fades)

(gentle synth music) (music ends) >> Welcome back to Supercloud 2, an open industry collaboration between technologists, consultants, analysts, and of course practitioners to help shape the future of cloud. At this event, one of the key areas we're exploring is the intersection of cloud and data. And how building value on top of hyperscale clouds and across clouds is evolving, a concept of course we call "Supercloud". And we're pleased to welcome our friends from Enterprise Technology research, Erik Bradley and Darren Brabham. Guys, thanks for joining us, great to see you. we love to bring the data into these conversations. >> Thank you for having us, Dave, I appreciate it. >> Yeah, thanks. >> You bet. And so, let me do the setup on what is Supercloud. It's a concept that we've floated, Before re:Invent 2021, based on the idea that cloud infrastructure is becoming ubiquitous, incredibly powerful, but there's a lack of standards across the big three clouds. That creates friction. So we defined over the period of time, you know, better part of a year, a set of essential elements, deployment models for so-called supercloud, which create this common experience for specific cloud services that, of course, again, span multiple clouds and even on-premise data. So Erik, with that as background, I wonder if you could add your general thoughts on the term supercloud, maybe play proxy for the CIO community, 'cause you do these round tables, you talk to these guys all the time, you gather a lot of amazing information from senior IT DMs that compliment your survey. So what are your thoughts on the term and the concept? >> Yeah, sure. I'll even go back to last year when you and I did our predictions panel, right? And we threw it out there. And to your point, you know, there's some haters. Anytime you throw out a new term, "Is it marketing buzz? Is it worth it? Why are you even doing it?" But you know, from my own perspective, and then also speaking to the IT DMs that we interview on a regular basis, this is just a natural evolution. It's something that's inevitable in enterprise tech, right? The internet was not built for what it has become. It was never intended to be the underlying infrastructure of our daily lives and work. The cloud also was not built to be what it's become. But where we're at now is, we have to figure out what the cloud is and what it needs to be to be scalable, resilient, secure, and have the governance wrapped around it. And to me that's what supercloud is. It's a way to define operantly, what the next generation, the continued iteration and evolution of the cloud and what its needs to be. And that's what the supercloud means to me. And what depends, if you want to call it metacloud, supercloud, it doesn't matter. The point is that we're trying to define the next layer, the next future of work, which is inevitable in enterprise tech. Now, from the IT DM perspective, I have two interesting call outs. One is from basically a senior developer IT architecture and DevSecOps who says he uses the term all the time. And the reason he uses the term, is that because multi-cloud has a stigma attached to it, when he is talking to his business executives. (David chuckles) the stigma is because it's complex and it's expensive. So he switched to supercloud to better explain to his business executives and his CFO and his CIO what he's trying to do. And we can get into more later about what it means to him. But the inverse of that, of course, is a good CSO friend of mine for a very large enterprise says the concern with Supercloud is the reduction of complexity. And I'll explain, he believes anything that takes the requirement of specific expertise out of the equation, even a little bit, as a CSO worries him. So as you said, David, always two sides to the coin, but I do believe supercloud is a relevant term, and it is necessary because the cloud is continuing to be defined. >> You know, that's really interesting too, 'cause you know, Darren, we use Snowflake a lot as an example, sort of early supercloud, and you think from a security standpoint, we've always pushed Amazon and, "Are you ever going to kind of abstract the complexity away from all these primitives?" and their position has always been, "Look, if we produce these primitives, and offer these primitives, we we can move as the market moves. When you abstract, then it becomes harder to peel the layers." But Darren, from a data standpoint, like I say, we use Snowflake a lot. I think of like Tim Burners-Lee when Web 2.0 came out, he said, "Well this is what the internet was always supposed to be." So in a way, you know, supercloud is maybe what multi-cloud was supposed to be. But I mean, you think about data sharing, Darren, across clouds, it's always been a challenge. Snowflake always, you know, obviously trying to solve that problem, as are others. But what are your thoughts on the concept? >> Yeah, I think the concept fits, right? It is reflective of, it's a paradigm shift, right? Things, as a pendulum have swung back and forth between needing to piece together a bunch of different tools that have specific unique use cases and they're best in breed in what they do. And then focusing on the duct tape that holds 'em all together and all the engineering complexity and skill, it shifted from that end of the pendulum all the way back to, "Let's streamline this, let's simplify it. Maybe we have budget crunches and we need to consolidate tools or eliminate tools." And so then you kind of see this back and forth over time. And with data and analytics for instance, a lot of organizations were trying to bring the data closer to the business. That's where we saw self-service analytics coming in. And tools like Snowflake, what they did was they helped point to different databases, they helped unify data, and organize it in a single place that was, you know, in a sense neutral, away from a single cloud vendor or a single database, and allowed the business to kind of be more flexible in how it brought stuff together and provided it out to the business units. So Snowflake was an example of one of those times where we pulled back from the granular, multiple points of the spear, back to a simple way to do things. And I think Snowflake has continued to kind of keep that mantle to a degree, and we see other tools trying to do that, but that's all it is. It's a paradigm shift back to this kind of meta abstraction layer that kind of simplifies what is the reality, that you need a complex multi-use case, multi-region way of doing business. And it sort of reflects the reality of that. >> And you know, to me it's a spectrum. As part of Supercloud 2, we're talking to a number of of practitioners, Ionis Pharmaceuticals, US West, we got Walmart. And it's a spectrum, right? In some cases the practitioner's saying, "You know, the way I solve multi-cloud complexity is mono-cloud, I just do one cloud." (laughs) Others like Walmart are saying, "Hey, you know, we actually are building an abstraction layer ourselves, take advantage of it." So my general question to both of you is, is this a concept, is the lack of standards across clouds, you know, really a problem, you know, or is supercloud a solution looking for a problem? Or do you hear from practitioners that "No, this is really an issue, we have to bring together a set of standards to sort of unify our cloud estates." >> Allow me to answer that at a higher level, and then we're going to hand it over to Dr. Brabham because he is a little bit more detailed on the realtime streaming analytics use cases, which I think is where we're going to get to. But to answer that question, it really depends on the size and the complexity of your business. At the very large enterprise, Dave, Yes, a hundred percent. This needs to happen. There is complexity, there is not only complexity in the compute and actually deploying the applications, but the governance and the security around them. But for lower end or, you know, business use cases, and for smaller businesses, it's a little less necessary. You certainly don't need to have all of these. Some of the things that come into mind from the interviews that Darren and I have done are, you know, financial services, if you're doing real-time trading, anything that has real-time data metrics involved in your transactions, is going to be necessary. And another use case that we hear about is in online travel agencies. So I think it is very relevant, the complexity does need to be solved, and I'll allow Darren to explain a little bit more about how that's used from an analytics perspective. >> Yeah, go for it. >> Yeah, exactly. I mean, I think any modern, you know, multinational company that's going to have a footprint in the US and Europe, in China, or works in different areas like manufacturing, where you're probably going to have on-prem instances that will stay on-prem forever, for various performance reasons. You have these complicated governance and security and regulatory issues. So inherently, I think, large multinational companies and or companies that are in certain areas like finance or in, you know, online e-commerce, or things that need real-time data, they inherently are going to have a very complex environment that's going to need to be managed in some kind of cleaner way. You know, they're looking for one door to open, one pane of glass to look at, one thing to do to manage these multi points. And, streaming's a good example of that. I mean, not every organization has a real-time streaming use case, and may not ever, but a lot of organizations do, a lot of industries do. And so there's this need to use, you know, they want to use open-source tools, they want to use Apache Kafka for instance. They want to use different megacloud vendors offerings, like Google Pub/Sub or you know, Amazon Kinesis Firehose. They have all these different pieces they want to use for different use cases at different stages of maturity or proof of concept, you name it. They're going to have to have this complexity. And I think that's why we're seeing this need, to have sort of this supercloud concept, to juggle all this, to wrangle all of it. 'Cause the reality is, it's complex and you have to simplify it somehow. >> Great, thanks you guys. All right, let's bring up the graphic, and take a look. Anybody who follows the breaking analysis, which is co-branded with ETR Cube Insights powered by ETR, knows we like to bring data to the table. ETR does amazing survey work every quarter, 1200 plus 1500 practitioners that that answer a number of questions. The vertical axis here is net score, which is ETR's proprietary methodology, which is a measure of spending momentum, spending velocity. And the horizontal axis here is overlap, but it's the presence pervasiveness, and the dataset, the ends, that table insert on the bottom right shows you how the dots are plotted, the net score and then the ends in the survey. And what we've done is we've plotted a bunch of the so-called supercloud suspects, let's start in the upper right, the cloud platforms. Without these hyperscale clouds, you can't have a supercloud. And as always, Azure and AWS, up and to the right, it's amazing we're talking about, you know, 80 plus billion dollar company in AWS. Azure's business is, if you just look at the IaaS is in the 50 billion range, I mean it's just amazing to me the net scores here. Anything above 40% we consider highly elevated. And you got Azure and you got Snowflake, Databricks, HashiCorp, we'll get to them. And you got AWS, you know, right up there at that size, it's quite amazing. With really big ends as well, you know, 700 plus ends in the survey. So, you know, kind of half the survey actually has these platforms. So my question to you guys is, what are you seeing in terms of cloud adoption within the big three cloud players? I wonder if you could could comment, maybe Erik, you could start. >> Yeah, sure. Now we're talking data, now I'm happy. So yeah, we'll get into some of it. Right now, the January, 2023 TSIS is approaching 1500 survey respondents. One caveat, it's not closed yet, it will close on Friday, but with an end that big we are over statistically significant. We also recently did a cloud survey, and there's a couple of key points on that I want to get into before we get into individual vendors. What we're seeing here, is that annual spend on cloud infrastructure is expected to grow at almost a 70% CAGR over the next three years. The percentage of those workloads for cloud infrastructure are expected to grow over 70% as three years as well. And as you mentioned, Azure and AWS are still dominant. However, we're seeing some share shift spreading around a little bit. Now to get into the individual vendors you mentioned about, yes, Azure is still number one, AWS is number two. What we're seeing, which is incredibly interesting, CloudFlare is number three. It's actually beating GCP. That's the first time we've seen it. What I do want to state, is this is on net score only, which is our measure of spending intentions. When you talk about actual pervasion in the enterprise, it's not even close. But from a spending velocity intention point of view, CloudFlare is now number three above GCP, and even Salesforce is creeping up to be at GCPs level. So what we're seeing here, is a continued domination by Azure and AWS, but some of these other players that maybe might fit into your moniker. And I definitely want to talk about CloudFlare more in a bit, but I'm going to stop there. But what we're seeing is some of these other players that fit into your Supercloud moniker, are starting to creep up, Dave. >> Yeah, I just want to clarify. So as you also know, we track IaaS and PaaS revenue and we try to extract, so AWS reports in its quarterly earnings, you know, they're just IaaS and PaaS, they don't have a SaaS play, a little bit maybe, whereas Microsoft and Google include their applications and so we extract those out and if you do that, AWS is bigger, but in the surveys, you know, customers, they see cloud, SaaS to them as cloud. So that's one of the reasons why you see, you know, Microsoft as larger in pervasion. If you bring up that survey again, Alex, the survey results, you see them further to the right and they have higher spending momentum, which is consistent with what you see in the earnings calls. Now, interesting about CloudFlare because the CEO of CloudFlare actually, and CloudFlare itself uses the term supercloud basically saying, "Hey, we're building a new type of internet." So what are your thoughts? Do you have additional information on CloudFlare, Erik that you want to share? I mean, you've seen them pop up. I mean this is a really interesting company that is pretty forward thinking and vocal about how it's disrupting the industry. >> Sure, we've been tracking 'em for a long time, and even from the disruption of just a traditional CDN where they took down Akamai and what they're doing. But for me, the definition of a true supercloud provider can't just be one instance. You have to have multiple. So it's not just the cloud, it's networking aspect on top of it, it's also security. And to me, CloudFlare is the only one that has all of it. That they actually have the ability to offer all of those things. Whereas you look at some of the other names, they're still piggybacking on the infrastructure or platform as a service of the hyperscalers. CloudFlare does not need to, they actually have the cloud, the networking, and the security all themselves. So to me that lends credibility to their own internal usage of that moniker Supercloud. And also, again, just what we're seeing right here that their net score is now creeping above AGCP really does state it. And then just one real last thing, one of the other things we do in our surveys is we track adoption and replacement reasoning. And when you look at Cloudflare's adoption rate, which is extremely high, it's based on technical capabilities, the breadth of their feature set, it's also based on what we call the ability to avoid stack alignment. So those are again, really supporting reasons that makes CloudFlare a top candidate for your moniker of supercloud. >> And they've also announced an object store (chuckles) and a database. So, you know, that's going to be, it takes a while as you well know, to get database adoption going, but you know, they're ambitious and going for it. All right, let's bring the chart back up, and I want to focus Darren in on the ecosystem now, and really, we've identified Snowflake and Databricks, it's always fun to talk about those guys, and there are a number of other, you know, data platforms out there, but we use those too as really proxies for leaders. We got a bunch of the backup guys, the data protection folks, Rubric, Cohesity, and Veeam. They're sort of in a cluster, although Rubric, you know, ahead of those guys in terms of spending momentum. And then VMware, Tanzu and Red Hat as sort of the cross cloud platform. But I want to focus, Darren, on the data piece of it. We're seeing a lot of activity around data sharing, governed data sharing. Databricks is using Delta Sharing as their sort of place, Snowflakes is sort of this walled garden like the app store. What are your thoughts on, you know, in the context of Supercloud, cross cloud capabilities for the data platforms? >> Yeah, good question. You know, I think Databricks is an interesting player because they sort of have made some interesting moves, with their Data Lakehouse technology. So they're trying to kind of complicate, or not complicate, they're trying to take away the complications of, you know, the downsides of data warehousing and data lakes, and trying to find that middle ground, where you have the benefits of a managed, governed, you know, data warehouse environment, but you have sort of the lower cost, you know, capability of a data lake. And so, you know, Databricks has become really attractive, especially by data scientists, right? We've been tracking them in the AI machine learning sector for quite some time here at ETR, attractive for a data scientist because it looks and acts like a lake, but can have some managed capabilities like a warehouse. So it's kind of the best of both worlds. So in some ways I think you've seen sort of a data science driver for the adoption of Databricks that has now become a little bit more mainstream across the business. Snowflake, maybe the other direction, you know, it's a cloud data warehouse that you know, is starting to expand its capabilities and add on new things like Streamlit is a good example in the analytics space, with apps. So you see these tools starting to branch and creep out a bit, but they offer that sort of neutrality, right? We heard one IT decision maker we recently interviewed that referred to Snowflake and Databricks as the quote unquote Switzerland of what they do. And so there's this desirability from an organization to find these tools that can solve the complex multi-headed use-case of data and analytics, which every business unit needs in different ways. And figure out a way to do that, an elegant way that's governed and centrally managed, that federated kind of best of both worlds that you get by bringing the data close to the business while having a central governed instance. So these tools are incredibly powerful and I think there's only going to be room for growth, for those two especially. I think they're going to expand and do different things and maybe, you know, join forces with others and a lot of the power of what they do well is trying to define these connections and find these partnerships with other vendors, and try to be seen as the nice add-on to your existing environment that plays nicely with everyone. So I think that's where those two tools are going, but they certainly fit this sort of label of, you know, trying to be that supercloud neutral, you know, layer that unites everything. >> Yeah, and if you bring the graphic back up, please, there's obviously big data plays in each of the cloud platforms, you know, Microsoft, big database player, AWS is, you know, 11, 12, 15, data stores. And of course, you know, BigQuery and other, you know, data platforms within Google. But you know, I'm not sure the big cloud guys are going to go hard after so-called supercloud, cross-cloud services. Although, we see Oracle getting in bed with Microsoft and Azure, with a database service that is cross-cloud, certainly Google with Anthos and you know, you never say never with with AWS. I guess what I would say guys, and I'll I'll leave you with this is that, you know, just like all players today are cloud players, I feel like anybody in the business or most companies are going to be so-called supercloud players. In other words, they're going to have a cross-cloud strategy, they're going to try to build connections if they're coming from on-prem like a Dell or an HPE, you know, or Pure or you know, many of these other companies, Cohesity is another one. They're going to try to connect to their on-premise states, of course, and create a consistent experience. It's natural that they're going to have sort of some consistency across clouds. You know, the big question is, what's that spectrum look like? I think on the one hand you're going to have some, you know, maybe some rudimentary, you know, instances of supercloud or maybe they just run on the individual clouds versus where Snowflake and others and even beyond that are trying to go with a single global instance, basically building out what I would think of as their own cloud, and importantly their own ecosystem. I'll give you guys the last thought. Maybe you could each give us, you know, closing thoughts. Maybe Darren, you could start and Erik, you could bring us home on just this entire topic, the future of cloud and data. >> Yeah, I mean I think, you know, two points to make on that is, this question of these, I guess what we'll call legacy on-prem players. These, mega vendors that have been around a long time, have big on-prem footprints and a lot of people have them for that reason. I think it's foolish to assume that a company, especially a large, mature, multinational company that's been around a long time, it's foolish to think that they can just uproot and leave on-premises entirely full scale. There will almost always be an on-prem footprint from any company that was not, you know, natively born in the cloud after 2010, right? I just don't think that's reasonable anytime soon. I think there's some industries that need on-prem, things like, you know, industrial manufacturing and so on. So I don't think on-prem is going away, and I think vendors that are going to, you know, go very cloud forward, very big on the cloud, if they neglect having at least decent connectors to on-prem legacy vendors, they're going to miss out. So I think that's something that these players need to keep in mind is that they continue to reach back to some of these players that have big footprints on-prem, and make sure that those integrations are seamless and work well, or else their customers will always have a multi-cloud or hybrid experience. And then I think a second point here about the future is, you know, we talk about the three big, you know, cloud providers, the Google, Microsoft, AWS as sort of the opposite of, or different from this new supercloud paradigm that's emerging. But I want to kind of point out that, they will always try to make a play to become that and I think, you know, we'll certainly see someone like Microsoft trying to expand their licensing and expand how they play in order to become that super cloud provider for folks. So also don't want to downplay them. I think you're going to see those three big players continue to move, and take over what players like CloudFlare are doing and try to, you know, cut them off before they get too big. So, keep an eye on them as well. >> Great points, I mean, I think you're right, the first point, if you're Dell, HPE, Cisco, IBM, your strategy should be to make your on-premise state as cloud-like as possible and you know, make those differences as minimal as possible. And you know, if you're a customer, then the business case is going to be low for you to move off of that. And I think you're right. I think the cloud guys, if this is a real problem, the cloud guys are going to play in there, and they're going to make some money at it. Erik, bring us home please. >> Yeah, I'm going to revert back to our data and this on the macro side. So to kind of support this concept of a supercloud right now, you know Dave, you and I know, we check overall spending and what we're seeing right now is total year spent is expected to only be 4.6%. We ended 2022 at 5% even though it began at almost eight and a half. So this is clearly declining and in that environment, we're seeing the top two strategies to reduce spend are actually vendor consolidation with 36% of our respondents saying they're actively seeking a way to reduce their number of vendors, and consolidate into one. That's obviously supporting a supercloud type of play. Number two is reducing excess cloud resources. So when I look at both of those combined, with a drop in the overall spending reduction, I think you're on the right thread here, Dave. You know, the overall macro view that we're seeing in the data supports this happening. And if I can real quick, couple of names we did not touch on that I do think deserve to be in this conversation, one is HashiCorp. HashiCorp is the number one player in our infrastructure sector, with a 56% net score. It does multiple things within infrastructure and it is completely agnostic to your environment. And if we're also speaking about something that's just a singular feature, we would look at Rubric for data, backup, storage, recovery. They're not going to offer you your full cloud or your networking of course, but if you are looking for your backup, recovery, and storage Rubric, also number one in that sector with a 53% net score. Two other names that deserve to be in this conversation as we watch it move and evolve. >> Great, thank you for bringing that up. Yeah, we had both of those guys in the chart and I failed to focus in on HashiCorp. And clearly a Supercloud enabler. All right guys, we got to go. Thank you so much for joining us, appreciate it. Let's keep this conversation going. >> Always enjoy talking to you Dave, thanks. >> Yeah, thanks for having us. >> All right, keep it right there for more content from Supercloud 2. This is Dave Valente for John Ferg and the entire Cube team. We'll be right back. (gentle synth music) (music fades)

(upbeat music) >> Welcome back to the program, and we're going to dig into the number one topic on the minds of every technology organization, that's cybersecurity. You know, survey data from ETR, our data partner, shows that among CIOs and IT decision makers, cybersecurity continues to rank as the number one technology priority to be addressed in the coming year. That's ahead of even cloud migration and analytics. And with me to discuss this critical topic area, are Jim Shook, who's the Global Director of Cybersecurity and Compliance Practice at Dell Technologies, and he's joined by Andrew Gonzalez, who focuses on cloud and infrastructure consulting at DXC Technology. Gents, welcome, good to have you. >> Thanks Dave, great to be here. >> Thank you. >> Jim, let's start with you. What are you seeing from the front lines in terms of the attack surface and how are customers responding these days? >> It's always up and down and back and forth. The bad actors are smart, they adapt to everything that we do. So we're seeing more and more, kind of living off the land. They're not necessarily deploying malware, makes it harder to find what they're doing. And I think though, Dave, we've adapted and this whole notion of cyber resilience really helps our customers figure this out. And the idea there goes beyond cybersecurity, it's, let's protect as much as possible so we keep the bad actors out as much as we can, but then let's have the ability to adapt to, and recover to the extent that the bad actors are successful. So we're recognizing that we can't be perfect a hundred percent of the time against a hundred percent of the bad actors. Let's keep out what we can, but then recognize and have that ability to recover when necessary. >> Yeah, thank you. So Andrew, you know, I like what Jim was saying, about living off the land, of course, meaning using your own tooling against you, kind of hiding in plain sight, if you will. But, and as Jim was saying, you can't be perfect. But, so given that, what's your perspective on what good cybersecurity hygiene looks like? >> Yeah, so you have to understand what your crown jewel data looks like, what a good copy of a recoverable asset looks like when you look at an attack if it were to occur, right? How you get that copy of data back into production, and not only that but what that golden image actually entails. So, whether it's networking, storage, some copy of a source code, intellectual property, maybe seem to be data or an active directory or DNS dump, right? Understanding what your data actually entails that you can protect it, and that you can build out your recovery plan for it. >> So, and, where's that live? Where's that gold copy? You put in a yellow sticky? No, it's got to be, you got to be somewhere safe, right? So you have to think about that chain as well, right? >> Absolutely, yeah. So, a lot of folks have not gone through the exercise of identifying what that golden copy looks like. Everyone has a DR scenario, everyone has a DR strategy but actually identifying what that golden crown jewel data, let's call it, actually entails as one aspect of it and then where to put it, how to protect it, how to make it immutable and isolated? That's the other portion of it. >> You know, if I go back to sort of earlier part of last decade, you know, cybersecurity was kind of a checkoff item. And then as you got toward the middle part of the decade and I'd say clearly by 2016 it, security became a boardroom issue. It was on the agenda, you know, every quarter at the board meetings. So, compliance is no longer the driver, is my point. The driver is business risk, real loss of reputation or data, you know, or money, et cetera. What are the business implications of not having your cyber house in order today? >> They're extreme, Dave. I mean the, you know, bad actors are good at what they do. These losses by organizations, tens, hundreds of millions into the billions sometimes, plus the reputational damage that's difficult to really measure. There haven't been a lot of organizations that have actually been put out of business by an attack, at least not directly, if they're larger organizations, but that's also on the table too. So you can't just rely on, oh, we need to do, you know, A, B, and C because our regulators require it. You need to look at what the actual risk is to the business and then come up with the strategy from there. >> You know, Jim, staying with you, one of the most common targets we hear of attackers is to go after the backup corpus. So how should customers think about protecting themselves from that tactic? >> Well, Dave, you hit on it before, right? Everybody's had the backup and DR strategies for a long time going back to requirements that we had in place for physical disaster or human error. And that's a great starting point for a resilience capability, but that's all it is, is a starting point. Because the bad actors will, they also understand that you have those capabilities and they've adapted to that. In every sophisticated attack that we see the backup is a target, the bad actors want to take it out or corrupt it or do something else to that backup so that it's not available to you. That's not to say they're always successful and it's still a good control to have in place because maybe it will survive. But you have to plan beyond that. So, the capabilities that we talk about with resilience, let's harden that backup infrastructure. You've already got it in place, let's use the capabilities that are there like immutability and other controls to make it more difficult for the bad actors to get to. But then, as Andrew said, that gold copy, that critical systems, you need to protect that in something that's more secure which commonly we might say a cyber vault, although there's a lot of different capabilities for cyber vaulting, some far better than others, and that's some of the things that we focus on. >> You know, it's interesting, but I've talked to a lot of CIOs about this is, prior to the pandemic, they, you know, had their, as you're pointing out, Jim, they had their DR strategy in place but they felt like they weren't business resilient and they realized that when we had the forced march to digital. So, Andrew, are there solutions out there to help with this problem? Do you guys have an answer to this? >> Yeah, absolutely. So, I'm glad you brought up resiliency. We take a position that to be cyber resilient it includes operational resiliency, it includes understanding at the C-level what the implication of an attack means, as we stated, and then how to recover back into production. When you look at protecting that data, not only do you want to put it into what we call a vault, which is a Dell technology that is an offline immutable copy of your crown jewel data but also how to recover it in real-time. So DXC offers a, I don't want to call it a turnkey solution, since we architect these specific each client needs, right? When we look at what client data entails, their recovery point objectives, recovery time objectives, what we call quality of the restoration. But when we architect these out we look at not only how to protect the data but how to alert and monitor for attacks in real-time. How to understand what we should do when a breaches in progress. Putting together with our security operations centers a forensic and recovery plan and a runbook for the client. And then being able to cleanse and remediate so that we can get that data back into production. These are all services that DXC offers in conjunction with the Dell solution to protect and recover, and keep bad actors out. And if we can't keep 'em out, to ensure that we are back into production in short order. >> You know, this discussion we've been having about DR kind of versus resilience, and you were just talking about RPO and RTO, I mean, it used to be that a lot of firms wouldn't even test their recovery 'cause it was too risky or, you know, maybe they tested it on, you know, July 4th or something like that. But I'm inferring that's changed. I wonder if we could, you know, double click on recovery, how hard is it to test that recovery and how quickly are you seeing organizations recover from attacks? >> So it depends, right, on the industry vertical, what kind of data, again. Financial services client compared to a manufacturing client are going to be two separate conversations. We've seen it as quickly as being able to recover in six hours, in 12 hours. In some instances we have the grace period of a day to a couple days. We do offer the ability to run scenarios once a quarter where we can stand up in our systems the production data that we are protecting to ensure that we have a good recoverable copy, but it depends on the client. >> I really like the emphasis here, Dave, that you're raising and that Andrew's talking about. It's not on the technology of how the data gets protected it's focused on the recovery, that's all that we want to do. And so the solution with DXC really focuses on generating that recovery for customers. I think where people get a little bit twisted up on their testing capability is you have to think about different scenarios. So, there are scenarios where the attack might be small, it might be limited to a database or an application. It might be really broadly-based, like the NotPetya attacks from a few years ago. The regulatory environment, we call those attacks severe but plausible. So you can't necessarily test everything with the infrastructure but you can test some things with the infrastructure. Others, you might sit around on a tabletop exercise or walk through what that looks like to really get that recovery kind of muscle memory so that people know what to do when those things occur. But the key to it, as Andrew said before, have to focus down what are those critical applications? What do we need? What's most important? What has to come back first? And that really will go a long way towards having the right recovery points and recovery times from a cyber disaster. >> Yeah, makes sense, understanding the value of that data is going to inform you how to respond and how to prioritize. Andrew, one of the things that we hear a lot on theCUBE, especially lately is around, you know, IOT, IIOT, Industry 4.0, the whole OT security piece of it. And the problem being that, you know, traditionally operations technologies have been air-gapped often by design. But as businesses, increasingly they're driving initiatives like Industry 4.0 and they're connecting these OT systems to IT systems. They're, you know, driving efficiency, preventative maintenance, et cetera. So, a lot of data flowing through the pipes, if you will. What are you seeing in terms of the threats to critical infrastructure and how should customers think about addressing these issues? >> Yeah, so bad actors can come in many forms. We've seen instances of social engineering, we've seen, USB stick dropped in a warehouse. That data that is flowing through the IOT devices is as sensitive now as your core mainframe infrastructure data. So, when you look at it from a protection standpoint, conceptually it's not dissimilar from what we've been talking about, where you want to understand, again, what the most critical data is. Looking at IOT data and applications is no different than your core systems now, right? Depending on what your business is, right? So when we're looking at protecting these, yes, we want firewalls, yes, we want air gap solutions, yes, we want front-end protection but we're looking at it from a resiliency perspective. Putting that data, understanding what data entails to put in the vault from an IOT perspective is just as critical as it is for your core systems. >> Jim, anything you can add to this topic? >> Yeah, I think you hit on the key points there, everything is interconnected. So even in the days where maybe people thought the OT systems weren't online, oftentimes the IT systems are talking to them or controlling theM, SCADA systems, or perhaps supporting them. Think back to the pipeline attack of last year. All the public testimony was that the OT systems didn't get attacked directly but there was uncertainty around that and the IT systems hadn't been secured so that caused the OT systems to have to shut down. It certainly is a different recovery when you're shutting them down on your own versus being attacked but the outcome was the same that the business couldn't operate. So, you really have to take all of those into account. And I think that does go back to exactly what Andrew's saying, understanding your critical business services and then the applications and data, and other components that support those and drive those and making sure those are protected, you understand them, you have the ability to recover them if necessary. >> So guys, I mean you made the point, I mean, you're right, the adversary is highly capable, they're motivated 'cause the ROI is so, it's so lucrative. It's like this never ending battle that cybersecurity pros, you know, go through. It really is kind of frontline, sort of technical heroes, if you will. And so, but sometimes it just feels daunting. Why are you optimistic about the future of cyber from the good guys' perspective? >> I think we're coming at the problem the right way, Dave, so that focus. I'm so pleased with the idea that we are planning that the systems aren't going to be a hundred percent capable every single time and let's figure that out, right? That's real world stuff. So, just as the bad actors continue to adapt and expand, so do we. And I think the differences there, the common criminals, it's getting harder and harder for them. The more sophisticated ones, they're tough to beat all the time. And of course, you've raised the question of some nation states and other activities but there's a lot more information sharing, there's a lot more focus from the business side of the house and not just the IT side of the house that we need to figure these things out. >> Yeah, to add to that, I think furthering education for the client base is important. You brought up a point earlier, it used to be a boardroom conversation due to compliance reasons. Now as we have been in the market for a while we continue to mature the offerings, it's further education for not only the business itself but for the IT systems and how they interconnect, and working together so that these systems can be protected and continue to be evolved and continue to be protected through multiple frameworks as opposed to seeing it as another check the box item that the board has to adhere to. >> All right guys, we got to go. Thank you so much. Great conversation on a really important topic. Keep up the good work, appreciate it. >> Thanks Dan. >> Thank you. >> All right, thank you for watching. Stay tuned for more excellent discussions around the partnership between Dell Technologies and DXC Technology. We're talking about solving real world problems, how this partnership has evolved over time. Really meeting the changing enterprise landscape challenges. Keep it right there. (upbeat music)

>> Now we're recording. >> All right. >> Appreciate that, Hannah. >> Yeah, so I mean, I think in general we continue to do very, very well as a company. I think like everybody, there's economic headwinds today that are unavoidable, but I think we have a couple things going for us. One, we're in the cyberspace, which I think is, for the most part, recession proof as an industry. I think the impact of a recession will impact some vendors and some categories, but in general, I think the industry is pretty resilient. It's like the power industry, no? Recession or not, you still need electricity to your house. Cybersecurity is almost becoming a utility like that as far as the needs of companies go. I think for us, we also have the ability to do the security, the security operations, for a lot of companies, and if you look at the value proposition, the ROI for the cost of less than one to maybe two or three, depending on how big you are as a customer, what you'd have to pay for half to three security operations people, we can give you a full security operations. And so the ROI is is almost kind of brain dead simple, and so that keeps us going pretty well. And I think the other areas, we remove all that complexity for people. So in a world where you got other problems to worry about, handling all the security complexity is something that adds to that ROI. So for us, I think what we're seeing is mostly is some of the larger deals are taking a little bit longer than they have, some of the large enterprise deals, 'cause I think they are being a little more cautious about how they spend it, but in general, business is still kind of cranking along. >> Anything you can share with me that you guys have talked about publicly in terms of any metrics, or what can you tell me other than cranking? >> Yeah, I mean, I would just say we're still very, very high growth, so I think our financial profile would kind of still put us clearly in the cyber unicorn position, but I think other than that, we don't really share business metrics as a private- >> Okay, so how about headcount? >> Still growing. So we're not growing as fast as we've been growing, but I don't think we were anyway. I think we kind of, we're getting to the point of critical mass. We'll start to grow in a more kind of normal course and speed. I don't think we overhired like a lot of companies did in the past, even though we added, almost doubled the size of the company in the last 18 months. So we're still hiring, but very kind of targeted to certain roles going forward 'cause I do think we're kind of at critical mass in some of the other functions. >> You disclose headcount or no? >> We do not. >> You don't, okay. And never have? >> Not that I'm aware of, no. >> Okay, on the macro, I don't know if security's recession proof, but it's less susceptible, let's say. I've had Nikesh Arora on recently, we're at Palo Alto's Ignite, and he was saying, "Look," it's just like you were saying, "Larger deal's a little harder." A lot of times customers, he was saying customers are breaking larger deals into smaller deals, more POCs, more approvals, more people to get through the approval, not whole, blah, blah, blah. Now they're a different animal, I understand, but are you seeing similar trends, and how are you dealing with that? >> Yeah, I think the exact same trends, and I think it's just in a world where spending a dollar matters, I think a lot more oversight comes into play, a lot more reviewers, and can you shave it down here? Can you reduce the scope of the project to save money there? And I think it just caused a lot of those things. I think, in the large enterprise, I think most of those deals for companies like us and Palo and CrowdStrike and kind of the upper tier companies, they'll still go through. I think they'll just going to take a lot longer, and, yeah, maybe they're 80% of what they would've been otherwise, but there's still a lot of business to be had out there. >> So how are you dealing with that? I mean, you're talking about you double the size of the company. Is it kind of more focused on go-to-market, more sort of, maybe not overlay, but sort of SE types that are going to be doing more handholding. How have you dealt with that? Or have you just sort of said, "Hey, it is what it is, and we're not going to, we're not going to tactically respond to. We got long-term direction"? >> Yeah, I think it's more the latter. I think for us, it's we've gone through all these things before. It just takes longer now. So a lot of the steps we're taking are the same steps. We're still involved in a lot of POCs, we're involved in a lot of demos, and I don't think that changed. It's just the time between your POC and when someone sends you the PO, there's five more people now got to review things and go through a budget committee and all sorts of stuff like that. I think where we're probably focused more now is adding more and more capabilities just so we continue to be on the front foot of innovation and being relevant to the market, and trying to create more differentiators for us and the competitors. That's something that's just built into our culture, and we don't want to slow that down. And so even though the business is still doing extremely, extremely well, we want to keep investing in kind of technology. >> So the deal size, is it fair to say the initial deal size for new accounts, while it may be smaller, you're adding more capabilities, and so over time, your average contract values will go up? Are you seeing that trend? Or am I- >> Well, I would say I don't even necessarily see our average deal size has gotten smaller. I think in total, it's probably gotten a little bigger. I think what happens is when something like this happens, the old cream rises to the top thing, I think, comes into play, and you'll see some organizations instead of doing a deal with three or four vendors, they may want to pick one or two and really kind of put a lot of energy behind that. For them, they're maybe spending a little less money, but for those vendors who are amongst those getting chosen, I think they're doing pretty good. So our average deal size is pretty stable. For us, it's just a temporal thing. It's just the larger deals take a little bit longer. I don't think we're seeing much of a deal velocity difference in our mid-market commercial spaces, but in the large enterprise it's a little bit slower. But for us, we have ambitious plans in our strategy or on how we want to execute and what we want to build, and so I think we want to just continue to make sure we go down that path technically. >> So I have some questions on sort of the target markets and the cohorts you're going after, and I have some product questions. I know we're somewhat limited on time, but the historical focus has been on SMB, and I know you guys have gone in into enterprise. I'm curious as to how that's going. Any guidance you can give me on mix? Or when I talk to the big guys, right, you know who they are, the big managed service providers, MSSPs, and they're like, "Poo poo on Arctic Wolf," like, "Oh, they're (groans)." I said, "Yeah, that's what they used to say about the PC. It's just a toy. Or Microsoft SQL Server." But so I kind of love that narrative for you guys, but I'm curious from your words as to, what is that enterprise? How's the historical business doing, and how's the entrance into the enterprise going? What kind of hurdles are you having, blockers are you having to remove? Any color you can give me there would be super helpful. >> Yeah, so I think our commercial S&B business continues to do really good. Our mid-market is a very strong market for us. And I think while a lot of companies like to focus purely on large enterprise, there's a lot more mid-market companies, and a much larger piece of the IT puzzle collectively is in mid-market than it is large enterprise. That being said, we started to get pulled into the large enterprise not because we're a toy but because we're quite a comprehensive service. And so I think what we're trying to do from a roadmap perspective is catch up with some of the kind of capabilities that a large enterprise would want from us that a potential mid-market customer wouldn't. In some case, it's not doing more. It's just doing it different. Like, so we have a very kind of hands-on engagement with some of our smaller customers, something we call our concierge. Some of the large enterprises want more of a hybrid where they do some stuff and you do some stuff. And so kind of building that capability into the platform is something that's really important for us. Just how we engage with them as far as giving 'em access to their data, the certain APIs they want, things of that nature, what we're building out for large enterprise, but the demand by large enterprise on our business is enormous. And so it's really just us kind of catching up with some of the kind of the features that they want that we lack today, but many of 'em are still signing up with us, obviously, and in lieu of that, knowing that it's coming soon. And so I think if you look at the growth of our large enterprise, it's one of our fastest growing segments, and I think it shows anything but we're a toy. I would be shocked, frankly, if there's an MSSP, and, of course, we don't see ourself as an MSSP, but I'd be shocked if any of them operate a platform at the scale that ours operates. >> Okay, so wow. A lot I want to unpack there. So just to follow up on that last question, you don't see yourself as an MSSP because why, you see yourselves as a technology platform? >> Yes, I mean, the vast, vast, vast majority of what we deliver is our own technology. So we integrate with third-party solutions mostly to bring in that telemetry. So we've built our own platform from the ground up. We have our own threat intelligence, our own detection logic. We do have our own agents and network sensors. MSSP is typically cobbling together other tools, third party off-the-shelf tools to run their SOC. Ours is all homegrown technology. So I have a whole group called Arctic Wolf Labs, is building, just cranking out ML-based detections, building out infrastructure to take feeds in from a variety of different sources. We have a full integration kind of effort where we integrate into other third parties. So when we go into a customer, we can leverage whatever they have, but at the same time, we produce some tech that if they're lacking in a certain area, we can provide that tech, particularly around things like endpoint agents and network sensors and the like. >> What about like identity, doing your own identity? >> So we don't do our own identity, but we take feeds in from things like Okta and Active Directory and the like, and we have detection logic built on top of that. So part of our value add is we were XDR before XDR was the cool thing to talk about, meaning we can look across multiple attack surfaces and come to a security conclusion where most EDR vendors started with looking just at the endpoint, right? And then they called themselves XDR because now they took in a network feed, but they still looked at it as a separate network detection. We actually look at the things across multiple attack surfaces and stitch 'em together to look at that from a security perspective. In some cases we have automatic detections that will fire. In other cases, we can surface some to a security professional who can go start pulling on that thread. >> So you don't need to purchase CrowdStrike software and integrate it. You have your own equivalent essentially. >> Well, we'll take a feed from the CrowdStrike endpoint into our platform. We don't have to rely on their detections and their alerts, and things of that nature. Now obviously anything they discover we pull in as well, it's just additional context, but we have all our own tech behind it. So we operate kind of at an MSSP scale. We have a similar value proposition in the sense that we'll use whatever the customer has, but once that data kind of comes into our pipeline, it's all our own homegrown tech from there. >> But I mean, what I like about the MSSP piece of your business is it's very high touch. It's very intimate. What I like about what you're saying is that it's software-like economics, so software, software-like part of it. >> That's what makes us the unicorn, right? Is we do have, our concierges is very hands-on. We continue to drive automation that makes our concierge security professionals more efficient, but we always want that customer to have that concierge person as, is almost an extension to their security team, or in some cases, for companies that don't even have a security team, as their security team. As we go down the path, as I mentioned, one of the things we want to be able to do is start to have a more flexible model where we can have that high touch if you want it. We can have the high touch on certain occasions, and you can do stuff. We can have low touch, like we can span the spectrum, but we never want to lose our kind of unique value proposition around the concierge, but we also want to make sure that we're providing an interface that any customer would want to use. >> So given that sort of software-like economics, I mean, services companies need this too, but especially in software, things like net revenue retention and churn are super important. How are those metrics looking? What can you share with me there? >> Yeah, I mean, again, we don't share those metrics publicly, but all's I can continue to repeat is, if you looked at all of our financial metrics, I think you would clearly put us in the unicorn category. I think very few companies are going to have the level of growth that we have on the amount of ARR that we have with the net revenue retention and the churn and upsell. All those aspects continue to be very, very strong for us. >> I want to go back to the sort of enterprise conversation. So large enterprises would engage with you as a complement to their existing SOC, correct? Is that a fair statement or not necessarily? >> It's in some cases. In some cases, they're looking to not have a SOC. So we run into a lot of cases where they want to replace their SIEM, and they want a solution like Arctic Wolf to do that. And so there's a poll, I can't remember, I think it was Forrester, IDC, one of them did it a couple years ago, and they found out that 70% of large enterprises do not want to build the SOC, and it's not 'cause they don't need one, it's 'cause they can't afford it, they can't staff it, they don't have the expertise. And you think about if you're a tech company or a bank, or something like that, of course you can do it, but if you're an international plumbing distributor, you're not going to (chuckles), someone's not going to graduate from Stanford with a cybersecurity degree and go, "Cool, I want to go work for a plumbing distributor in their SOC," right? So they're going to have trouble kind of bringing in the right talent, and as a result, it's difficult to go make a multimillion-dollar investment into a SOC if you're not going to get the quality people to operate it, so they turn to companies like us. >> Got it, so, okay, so you're talking earlier about capabilities that large enterprises require that there might be some gaps, you might lack some features. A couple questions there. One is, when you do some of those, I inferred some of that is integrations. Are those integrations sort of one-off snowflakes or are you finding that you're able to scale those across the large enterprises? That's my first question. >> Yeah, so most of the integrations are pretty straightforward. I think where we run into things that are kind of enterprise-centric, they definitely want open APIs, they want access to our platform, which we don't do today, which we are going to be doing, but we don't do that yet today. They want to do more of a SIEM replacement. So we're really kind of what we call an open XDR platform, so there's things that we would need to build to kind of do raw log ingestion. I mean, we do this today. We have raw log ingestion, we have log storage, we have log searching, but there's like some of the compliance scenarios that they need out of their SIEM. We don't do those today. And so that's kind of holding them back from getting off their SIEM and going fully onto a solution like ours. Then the other one is kind of the level of customization, so the ability to create a whole bunch of custom rules, and that ties back to, "I want to get off my SIEM. I've built all these custom rules in my SIEM, and it's great that you guys do all this automatic AI stuff in the background, but I need these very specific things to be executed on." And so trying to build an interface for them to be able to do that and then also simulate it, again, because, no matter how big they are running their SIEM and their SOC... Like, we talked to one of the largest financial institutions in the world. As far as we were told, they have the largest individual company SOC in the world, and we operate almost 15 times their size. So we always have to be careful because this is a cloud-based native platform, but someone creates some rule that then just craters the performance of the whole platform, so we have to build kind of those guardrails around it. So those are the things primarily that the large enterprises are asking for. Most of those issues are not holding them back from coming. They want to know they're coming, and we're working on all of those. >> Cool, and see, just aside, I was talking to CISO the other day, said, "If it weren't for my compliance and audit group, I would chuck my SIEM." I mean, everybody wants to get rid of their SIEM. >> I've never met anyone who likes their SIEM. >> Do you feel like you've achieved product market fit in the larger enterprise or is that still something that you're sorting out? >> So I think we know, like, we're on a path to do that. We're on a provable path to do that, so I don't think there's any surprises left. I think everything that we know we need to do for that is someone's writing code for it today. It's just a matter of getting it through the system and getting into production. So I feel pretty good about it. I think that's why we are seeing such a high growth rate in our large enterprise business, 'cause we share that feedback with some of those key customers. We have a Customer Advisory Board that we share a lot of this information with. So yeah, I mean, I feel pretty good about what we need to do. We're certainly operate at large enterprise scales, so taking in the amount of the volume of data they're going to have and the types of integrations they need. We're comfortable with that. It's just more or less the interfaces that a large enterprise would want that some of the smaller companies don't ask for. >> Do you have enough tenure in the market to get a sense as to stickiness or even indicators that will lead toward retention? Have you been at it long enough in the enterprise or you still, again, figuring that out? >> Yeah, no, I think we've been at it long enough, and our retention rates are extremely high. If anything, kind of our net retention rates, well over 100% 'cause we have opportunities to upsell into new modules and expanding the coverage of what they have today. I think the areas that if you cornered enterprise that use us and things they would complain about are things I just told you about, right? There's still some things I want to do in my Splunk, and I need an API to pull my data out and put it in my Splunk and stuff like that, and those are the things we want to enable. >> Yeah, so I can't wait till you guys go public because you got Snowflake up here, and you got Veritas down here, and I'm very curious as to where you guys go. When's the IPO? You want to tell me that? (chuckling) >> Unfortunately, it's not up to us right now. You got to get the markets- >> Yeah, I hear you. Right, if the market were better. Well, if the market were better, you think you'd be out? >> Yeah, I mean, we'd certainly be a viable candidate to go. >> Yeah, there you go. I have a question for you because I don't have a SOC. I run a small business with my co-CEO. We're like 30, 40 people W-2s, we got another 50 or so contractors, and I'm always like have one eye, sleep with one eye open 'cause of security. What is your ideal SMB customer? Think S. >> Yeah. >> Would I fit? >> Yeah, I mean you're you're right in the sweet spot. I think where the company started and where we still have a lot of value proposition, which is companies like, like you said it, you sleep with one eye open, but you don't have necessarily the technical acumen to be able to do that security for yourself, and that's where we fit in. We bring kind of this whole security, we call it Security Operations Cloud, to bear, and we have some of the best professionals in the world who can basically be your SOC for less than it would cost you to hire somebody right out of college to do IT stuff. And so the value proposition's there. You're going to get the best of the best, providing you a kind of a security service that you couldn't possibly build on your own, and that way you can go to bed at night and close both eyes. >> So (chuckling) I'm sure something else would keep me up. But so in thinking about that, our Amazon bill keeps growing and growing and growing. What would it, and I presume I can engage with you on a monthly basis, right? As a consumption model, or how's the pricing work? >> Yeah, so there's two models that we have. So typically the kind of the monthly billing type of models would be through one of our MSP partners, where they have monthly billing capabilities. Usually direct with us is more of a longer term deal, could be one, two, or three, or it's up to the customer. And so we have both of those engagement models. Were doing more and more and more through MSPs today because of that model you just described, and they do kind of target the very S in the SMB as well. >> I mean, rough numbers, even ranges. If I wanted to go with the MSP monthly, I mean, what would a small company like mine be looking at a month? >> Honestly, I do not even know the answer to that. >> We're not talking hundreds of thousands of dollars a month? >> No. God, no. God, no. No, no, no. >> I mean, order of magnitude, we're talking thousands, tens of thousands? >> Thousands, on a monthly basis. Yeah. >> Yeah, yeah. Thousands per month. So if I were to budget between 20 and $50,000 a year, I'm definitely within the envelope. Is that fair? I mean, I'm giving a wide range >> That's fair. just to try to make- >> No, that's fair. >> And if I wanted to go direct with you, I would be signing up for a longer term agreement, correct, like I do with Salesforce? >> Yeah, yeah, a year. A year would, I think, be the minimum for that, and, yeah, I think the budget you set aside is kind of right in the sweet spot there. >> Yeah, I'm interested, I'm going to... Have a sales guy call me (chuckles) somehow. >> All right, will do. >> No, I'm serious. I want to start >> I will. >> investigating these things because we sell to very large organizations. I mean, name a tech company. That's our client base, except for Arctic Wolf. We should talk about that. And increasingly they're paranoid about data protection agreements, how you're protecting your data, our data. We write a lot of software and deliver it as part of our services, so it's something that's increasingly important. It's certainly a board level discussion and beyond, and most large organizations and small companies oftentimes don't think about it or try not to. They just put their head in the sand and, "We don't want to be doing that," so. >> Yeah, I will definitely have someone get in touch with you. >> Cool. Let's see. Anything else you can tell me on the product side? Are there things that you're doing that we talked about, the gaps at the high end that you're, some of the features that you're building in, which was super helpful. Anything in the SMB space that you want to share? >> Yeah, I think the biggest thing that we're doing technically now is really trying to drive more and more automation and efficiency through our operations, and that comes through really kind of a generous use of AI. So building models around more efficient detections based upon signal, but also automating the actions of our operators so we can start to learn through the interface. When they do A and B, they always do C. Well, let's just do C for them, stuff like that. Then also building more automation as far as the response back to third-party solutions as well so we can remediate more directly on third-party products without having to get into the consoles or having our customers do it. So that's really just trying to drive efficiency in the system, and that helps provide better security outcomes but also has a big impact on our margins as well. >> I know you got to go, but I want to show you something real quick. I have data. I do a weekly program called "Breaking Analysis," and I have a partner called ETR, Enterprise Technology Research, and they have a platform. I don't know if you can see this. They have a survey platform, and each quarter, they do a survey of about 1,500 IT decision makers. They also have a survey on, they call ETS, Emerging Technology Survey. So it's private companies. And I don't want to go into it too much, but this is a sentiment graph. This is net sentiment. >> Just so you know, all I see is a white- >> Yeah, just a white bar. >> Oh, that's weird. Oh, whiteboard. Oh, here we go. How about that? >> There you go. >> Yeah, so this is a sentiment graph. So this is net sentiment and this is mindshare. And if I go to Arctic Wolf... So it's typical security, right? The 8,000 companies. And when I go here, what impresses me about this is you got a decent mindshare, that's this axis, but you've also got an N in the survey. It's about 1,500 in the survey, It's 479 Arctic Wolf customers responded to this. 57% don't know you. Oh, sorry, they're aware of you, but no plan to evaluate; 19% plan to evaluate, 7% are evaluating; 11%, no plan to utilize even though they've evaluated you; and 1% say they've evaluated you and plan to utilize. It's a small percentage, but actually it's not bad in the random sample of the world about that. And so obviously you want to get that number up, but this is a really impressive position right here that I wanted to just share with you. I do a lot of analysis weekly, and this is a really, it's completely independent survey, and you're sort of separating from the pack, as you can see. So kind of- >> Well, it's good to see that. And I think that just is a further indicator of what I was telling you. We continue to have a strong financial performance. >> Yeah, in a good market. Okay, well, thanks you guys. And hey, if I can get this recording, Hannah, I may even figure out how to write it up. (chuckles) That would be super helpful. >> Yes. We'll get that up. >> And David or Hannah, if you can send me David's contact info so I can get a salesperson in touch with him. (Hannah chuckling) >> Yeah, great. >> Yeah, we'll work on that as well. Thanks so much for both your time. >> Thanks a lot. It was great talking with you. >> Thanks, you guys. Great to meet you. >> Thank you. >> Bye. >> Bye.

>> If you really want to make an impact to your business, it takes more than just moving your workloads into the cloud. So-called lift and shift is fine to reduce data center footprints and associated costs, but to really drive change, you don't want to simply "pave the cow path," as the saying goes. Rather, you need to think about the operating model, and that requires more comprehensive systems thinking. In other words, how will changes in technology affect business productivity? Or, you know what? Even flip that. What changes in my business process could lower cost, cut elapse times, and accelerate time to market, increase user productivity, and lower operational risks? And what role can technology play in supporting these mandates through modernization, automation, machine intelligence, and business resilience? And that's what we're here to discuss today. Welcome to Driving Business Results with Cloud Transformation, made Possible by Dell and DXC. My name is Dave Vellante, and today we're going to zoom out and explore many aspects of cloud transformation that leading organizations are acting on today. Yeah, sure, we're going to look at optimizing infrastructure, but we'll also dig deeper into cloud considerations, governance, compliance, and security angles, as well as the impact of emerging opportunities around edge and Industry 4.0. Our focus will be on how to remove barriers and help you achieve business outcomes. And to do this, our program features the long-term partnership between Dell and DXC. And we bring to this program six experts in three separate sessions, who are working directly with top organizations in virtually every industry to achieve high impact results. We're going to start with a conversation about cloud, the cloud operating model, and transforming key aspects of your infrastructure. And then we'll look into governance, security, and business resilience. And in our third session, we'll discuss exciting transformations that are occurring in smart manufacturing and facilities innovations. So let's get right into it with our first session. Enjoy the program. (bright music) Hello, and welcome to what is sure to be an insightful conversation about getting business results with cloud transformation. My name is Dave Vellante, and I'm here with James Miller, Chief Technologist for Cloud and Infrastructure Services, and Jay Dowling, Americas Sales Lead for Cloud and Infrastructure Services, both with DXC Technology. Gentlemen, thanks for your time today. Welcome to theCube. >> Great. Thanks for having us. >> Thank you Dave. Appreciate it. >> So let's get right into it. You know, I've talked to a lot of practitioners who've said, "Look, if you really want to drop zeros, like a lot of zeros to the bottom line, you can't just lift and shift." You really got to think about modernizing, the application portfolio. You got to think about your business model, and really think about transforming your business, particularly the operating model. So my first question, Jim, is, What role does the cloud play in modernization? >> Well, there are really three aspects that the, the cloud plays in modernization. You mentioned multiple zeros. One is cost optimization, and that can be achieved through business operations, through environmental, social, and governance. Also being more efficient with your IT investments. But that's not the only aspect. There's also agility and innovation. And that can be achieved through automation and productivity, speed to market for new features and functions, improvements in the customer experience, and the capability to metabolize a great deal more data in your environment, which the end result is an improvement in releasing of new things to the field. And finally, there's resilience. And I'm not really talking about IT resilience, but more of business resilience, to be able, to be able to handle operational risk, improve your securities and controls, deal with some of the talent gap that's in the industry, and also protect your brand reputation. So modernization is really about balancing these three aspects, cost optimization, agility and innovation, and resilience. >> So, so thank you for that. So Jay, I got to ask you, in the current climate, everybody's, you know, concerned, and there's not great visibility on the macro. So, Jim mentioned cost optimization. That seems to be one of the top areas that customers are focused on. The two I hear a lot are consolidating redundant vendors and optimizing cloud costs. So that's, you know, top of mind today. I think everybody really, you know, understands the innovation and, and, and agility piece, at least at a high level, maybe realizing it is different. And then the business resilience piece is really interesting because, you know, prior to the pandemic people, you know, they had a DR strategy, but they realized, "Wow, my business might not be that resilient." So Jay, my question to you is, What are you hearing when you talk to customers? What's the priority today? >> Yeah, the priority is an often overused term of digital transformation. You know, people want to get ready for next generation environments, customer experience, making sure they're improving, you know, how they engage with their clients and what their branding is. And what we find is a lot of clients don't have the underlying infrastructure in place today to get to where they want to get to. So cloud becomes an important element of that. But, you know, with DXC's philosophy, not everything goes to, not everything necessarily needs to go to cloud to be cost optimized, for instance. In many cases, you can run applications, you know, in your own data center, or on-prem, or in other environments, in a hybrid environment, or multi-cloud environment, and, and still be very optimized from a cost spend standpoint and also put yourself in position for modernization and for be able to do the, bring the things to the business that the clients are, you know, that their clients are looking for, like the CMO and the CFO, et cetera. Trying to use IT as a lever to drive business and to drive, you know, business acceleration and drive profitability, frankly. So there's a lot of dependency on infrastructure, but there's a lot of elements to it. And, and we advocate for, you know, there's not a single answer to that. We like to evaluate clients' environments and work with them to get them to an optimal target operating model, you know, so that they can really deliver on what the promises are for their departments. >> So if, let's talk about some of the, the barriers to realizing value in, in a context of modernization. We talked about cost optimization, agility, and, and, and resilience. But there's a business angle, and there's a technical angle here. 'Cause we always talk about people, process, and technology. Technology, oftentimes, CIOs will tell us, "Well, that's the easy part. We'll figured that out," whether it's true or not. But I agree, people and process is sometimes the tough one. So Jay, why don't you start. What do you see as the barriers, particularly from a business standpoint? >> I think people need to let their guard down and be open to the ideas that are, that are out there in the market from, you know, the, the standards that are being built by, you know, best in class models. And, and there's many people that have gone on, you know, cloud journeys and been very successful with it. There's others that have set high expectations with their business leaders that haven't necessarily met the goals that they need to meet or maybe haven't met them as quickly as they promised. So there's a, you know, there's a change management aspect that you'd need to look at with the, you know, with the environments. There's a, you know, there's a skillset set environment that they need to be prepared for. Do they have the people, you know, to deliver with the, you know, with the tools and the skills and the, and the models that that they're putting themselves in place for in the future versus where they are now? There's just a lot of, you know, there's a lot of different elements. It's not just a, "This price is better," or, "This can operate better than one environment over the other." I think we like to try to look at things holistically and make sure that, you know, we're being, you know, as much of a consultative advocate for the client, for where they want to go, what their destiny is, and based on what we've learned with other clients. You know, and we can bring those best practices forward because we've worked, you know, across such a broad spectra of clients versus them being somewhat contained and sometimes can't see outside of their own, you know, their own challenges, if you would. So they need, they need advocacy to help, you know, bring them to the next level. And we like to translate that through, you know, technology advances, which, you know, Jim's really good at doing for us. >> Yeah, Jim, is, is it, is it a, is the big barrier a skills issue, you know, bench strength? Are there other considerations from your perspective? >> Well, we, we've identified a number of factors that inhibit success of, of customers. One is thinking it's only a technology change in moving to cloud when it's much broader than that. There are changes in governance, changes in process that need to take place. The other is evaluating the cloud providers on their current pricing structure and performance. And, and we see pricing and structure changing dramatically every few months between the various cloud providers. And you have to be flexible enough to, to determine which providers you want. And it may not be feasible to just have a single cloud provider in this world. The other thing is a big bang approach to transformation, "I want to move everything, and I want to move it all at once." That's not necessarily the best approach. A well thought out cloud journey and strategy and timing your investments are really important to get at maximizing your business return on the journey to the cloud. And finally, not engaging stakeholders early and continuously. You have to manage expectations in moving to cloud on what business factors will get affected, how you will achieve your cost savings, and, and how you will achieve the business impact over the journey and reporting out on that with very strict metrics to all of the stakeholders. >> You know, mentioned multi-cloud just then. We had, in January 17th, we had our Supercloud 2 event. And Supercloud is basically, it's really multi, what multi-cloud should have been, I, I like to say. So it's this creating a common experience across clouds. And you guys were talking about, you know, there's different governance, there's different security, there's different pricing. So, and, and one of the takeaways from this event in talking to customers and practitioners and technologists is, you can't go it alone. So I wonder if you could talk about your partnership strategy, what do partners bring to the table, and what is, what is DXC's, you know, unique value? >> I'd be happy to lead with that if you'd like. >> Great. >> I, you know, we've got a vast partner ecosystem at DXC, given the size and, and the history of the company. I could use several examples. One of the larger partners in my particular space is Dell Technology, right? They're a great, you know, partner for us across many different areas of the business. It's not just a storage and compute play anymore. They're, they're on the edge. They're, you know, they're, they've got intelligence in their networking devices now. And they've really brought, you know, a lot of value to us as a partner. And, you know, there, there's somebody, you could look at Dell technology as somebody that might, you know, have a victim, you know, effect because of all the hyperscale activity and all the cloud activity. But they've really taken an outstanding attitude with this and say, "Listen, not all things are destined for cloud, or not all things would operate better in a cloud environment." And they like to be part of those discussions to see how they can, you know, how we can bring a multi-cloud environment, you know, both private and public, you know, to clients. And let's look at the applications and the infrastructure and, and what's, you know, what's the best optimal running environment, you know, for us to be able to bring, you know, the greatest value to the business with speed, with security, with, you know. And, you know, the things that they want to keep closest to the business are often things that you want to kind of, you know, keep on your premise or keep in your own data center. So they're, they're an ideal model of somebody that's resourced us well, partners with us well in the market. And, and we continue to grow that relationship day in and day out with those guys. And we really appreciate, you know, their support of our strategy, and, and we like to also compliment their strategy and work, you know, work together hand in hand in front of our clients. >> Yeah, you know, Jim, Matt Baker, who's the head of strategic planning at Dell talks about, "It's not a zero sum game." And I think, you know, you're right, Jay. I think initially people felt like, "Oh wow, it's, it is a zero sum game." But it's clearly not, and this idea of of, whether you call it supercloud or ubercloud or multicloud, clearly Dell is headed in in that direction. And I, you know, look at some of their future projects. There's their narrative. I'm curious from a technology standpoint, Jim, what your role is. Is it to make it all work? Is it to, you know, end to end? I wonder if you could help, you know, us understand that. >> Help us figure this out, Jim, here. (group laughing) >> Glad to expand on that. One of my key roles is developing our product roadmap for DXC offerings. And we do that roadmap in conjunction with our partners where we can leverage the innovation that our partners bring to the table. And we often utilize engineering resources from our partners to help us jointly build those offerings that adapt to changes in the market and also adapt to many of our customers changing needs over time. So my primary role is to look at the market, talk to our customers, and work with our partners to develop a product roadmap for delivering DXC products and services to our clients so that they can get the return on investment on their technology journeys. >> You know, we've been working with these two firms for a while now. Even predates, you know, the, the name DXC and that, that transformation. I'm curious as to what's, how you would respond to, "What's unique?" You know, you hear a lot about partnerships. You guys got a lot of competition. Dell has a lot of competition. What's specifically unique about this combination? >> I think, go ahead, Jim. >> I would say our unique approach, we call it cloud right. And that, that approach is making the right investments, at the right time, and on the right platforms. And our partners play a, play a key role in that. So we, we encourage our customers to not necessarily have a cloud first approach, but a cloud right approach where they place the workloads in the environment that is best suited from a technology perspective, a business perspective, and even a security and governance perspective. And, and the right approach might include mainframe. It might include an on-premises infrastructure. It could include private cloud, public cloud, and SaaS components all integrated together to deliver that value. >> Yeah, Jay, please. >> If you were... >> That is a complicated situation for a lot of customers. Chime in here. (Jay chuckles) >> And now, if you were speaking specifically to Dell here, like they, they also walk the talk, right? They invest in DXC as a partnership. They put people on the ground that their only purpose in life is to help DXC succeed with Dell in, you know, arm in arm in front of clients. And it's not, you know, it's not a winner take all thing at all. It's really a true partnership. They, they, they've brought solution resources. We have an account CTO. We've got executive sponsorship. We do regular QBR meetings. We have regular executive touchpoint meetings. It's really important that you keep a high level of intimacy with the client, with the partners, you know, and, and the, and the GSI community. And I, I've been with several GSIs, and, and this is an exceptional example of true partnership and commitment to success with Dell technology. I'm really extremely impressed on, on the engagement level that we've had there and, you know, continue to show a lot of support, you know, both for them. You know, there's other OEM partners, of course, in the market. There's always going to be other technology solutions for certain clients, but this has been a particularly strong element for us in our partnership and in our go-to-market strategy. >> Well, I think too, just my observation, is a lot of it's about trust. You guys have both earned the trust, the kind of, over the, over the years taking your arrows, you know, of over decades. And, and you know, that just doesn't happen overnight. So guys, I appreciate it. Thanks for your time. It's all about getting cloud right, isn't it? >> That's right. (chuckles) (Dave chuckles) >> Thank you Dave. Appreciate it very much. >> Dave, thank you. >> Jay, Jim, great to have you on. Keep it right there for more action on theCube. Be right back. (upbeat guitar music) (keyboard clicks) Welcome back to the program. My name is Dave Vellante, and in this session we're going to explore one of the more interesting topics of the day. IoT for smart factories and with me are Todd Edmunds, the Global CTO of Smart Manufacturing Edge and Digital Twins at Dell Technologies. That is such a cool title. (Todd chuckles) I want to be you. And Dr. Aditi Banerjee who's the Vice President, General Manager for Aerospace Defense and Manufacturing at DXC Technology. Another really cool title. Folks, welcome to the program. Thanks for coming on. >> Thank you. >> Thanks, Dave. Great to be here. >> Nice to be here. So, Todd, let's start with you. We hear a lot about Industry 4.0, smart factories, IIoT. Can you briefly explain like what is Industry 4.0 all about, and why is it important for the manufacturing industry? >> Yeah, sure, Dave. You know, it's been around for quite a while. And it's got, it's gone by multiple different names, as you said, Industry 4.0, smart manufacturing, industrial IoT, smart factory, but it all really means the same thing. Its really applying technology to get more out of the factories and the facilities that you have to do your manufacturing. So being much more efficient, implementing really good sustainability initiatives. And so we really look at that by saying, "Okay, what are we going to do with technology to really accelerate what we've been doing for a long, long time?" So it's really not, it's not new. It's been around for a long time. What's new is that manufacturers are looking at this not as a one-off, two-off, individual use case point of view. But instead they're saying, "We really need to look at this holistically, thinking about a strategic investment in how we do this, not to just enable one or two use cases, but enable many, many use cases across the spectrum." I mean, there's tons of them out there. There's predictive maintenance, and there's OEE, overall equipment effectiveness, and there's computer vision. And all of these things are starting to percolate down to the factory floor. But it needs to be done in a little bit different way. And, and, and really, to really get those outcomes that they're looking for in smart factory, or Industry 4.0, or however you want to call it, and truly transform. Not just throw an Industry 4.0 use case out there, but to do the digital transformation that's really necessary and to be able to stay relevant for the future. You know, I heard it once said that you have three options. Either you digitally transform and stay relevant for the future, or you don't and fade into history like 52% of the companies that used to be on the Fortune 500 since 2000, right? And so really that's a key thing, and we're seeing that really, really being adopted by manufacturers all across the globe. >> Yeah so, Aditi, that's like digital transformation is almost synonymous with business transformation. So is there anything you'd add to what Todd just said? >> Absolutely. Though, I would really add that what really drives Industry 4.0 is the business transformation, what we are able to deliver in terms of improving the manufacturing KPIs and the KPIs for customer satisfaction, right? For example, improving the downtime, you know, or decreasing the maintenance cycle of the equipments, or improving the quality of products, right? So I think these are a lot of business outcomes that our customers are looking at while using Industry 4.0 and the technologies of Industry 4.0 to deliver these outcomes. >> So Aditi, I wonder if I could stay with you. And maybe this is a bit esoteric. But when I first started researching IoT and, and, and Industrial IoT 4.0, et cetera, I felt, you know, while there could be some disruptions in the ecosystem, I kind of came to the conclusion that large manufacturing firms, aerospace defense companies, the firms building out critical infrastructure, actually had kind of an incumbent advantage in a great opportunity. Of course, then I saw on TV, somebody now they're building homes with 3D printers. Its like, blows your mind. So that's pretty disruptive, but, so, but they got to continue. The incumbents have to continue to invest in the future. They're well capitalized. They're pretty good businesses, very good businesses. But there's a lot of complexities involved in kind of connecting the old house to the new addition that's being built, if you will, or this transformation that we're talking about. So my question is, How are your customers preparing for this new era? What are the key challenges that they're facing and the, the blockers, if you will? >> Yeah, I mean the customers are looking at Industry 4.0 for greenfield factories, right? That is where the investments are going directly into building the factories with the new technologies, with the new connectivities, right, for the machines. For example, industrial IoT, having the right type of data platforms to drive computational analytics and outcomes, as well as looking at edge versus cloud type of technologies, right? Those are all getting built in the greenfield factories. However, for the install-based factories, right, that is where our customers are looking at, "How do I modernize these factories? How do I connect the existing machine?" And that is where some of the challenges come in on, you know, the legacy system connectivity that they need to think about. Also, they need to start thinking about cybersecurity and operation technology security, right, because now you are connecting the factories to each other, right? So cybersecurity becomes top of mind, right? So there is definitely investment that is involved. Clients are creating roadmaps for digitizing and modernizing these factories and investments in a very strategic way, right? So perhaps they start with the innovation program, and then they look at the business case, and they scale it up, right? >> Todd, I'm glad Aditi brought up security. Because if you think about the operations technology, you know, folks, historically, they air gapped, you know, the systems. That's how they created security. That's changed. The business came in and said, "Hey, we got to, we got to connect. We got to make it intelligent." So that's, that's got to be a big challenge as well. >> It, it, it absolutely is Dave. And, and you know, you can no longer just segment that because really, to get all of those efficiencies that we talk about, that IoT and Industrial IoT and Industry 4.0 promise, you have to get data out of the factory. But then you got to put data back in the factory. So no longer is it just firewalling everything is really the answer. So you really have to have a comprehensive approach to security, but you also have to have a comprehensive approach to the cloud and what that means. And does it mean a continuum of cloud all the way down to the edge, right down to the factory? It absolutely does because no one approach has the answer to everything. The more you go to the cloud, the broader the attack surface is. So what we're seeing is a lot of our customers approaching this from a, kind of that, that hybrid, you know, "write once, run anywhere" on the factory floor down to the edge. And one of the things we're seeing, too, is to help distinguish between what is the edge, and that, and, and bridge that gap between, like Dave, you talked about IT and OT. And also help that, what Aditi talked about, is the greenfield plants versus the brownfield plants that they call it, that are the legacy ones and modernizing those. Is, it's great to kind of start to delineate. What does that mean? Where's the edge? Where's the IT and the OT? We see that from a couple of different ways. We start to think about really two edges in a manufacturing floor. We talk about an industrial edge that sits, or some people call it a far edge or a thin edge, sits way down on that plan. It consists of industrial hardened devices that do that connectivity. The hard stuff about, "How do I connect to this obsolete legacy protocol and what do I do with it?" And create that next generation of data that has context. And then we see another edge evolving above that, which is much more of a data and analytics and enterprise grade application layer that sits down in the factory itself that helps figure out where we're going to run this. Does it connect to the cloud? Do we run applications on-prem? Because a lot of times that on-prem application is, is, needs to be done because that's the only way that its going to, it's going to work because of security requirements, because of latency requirements, performance, and a lot of times cost. It's really helpful to build that multiple edge strategy because then you kind of, you consolidate all of those resources, applications, infrastructure, hardware, into a centralized location. Makes it much, much easier to really deploy and manage that security. But it also makes it easier to deploy new applications, new use cases, and become the foundation for DXC's expertise and applications that they deliver to our customers as well. >> Todd, how complex are these projects? I mean, I feel like it's kind of the, the digital equivalent of building the Hoover Dam. I mean, it, it, it's, (chuckles) it, it, so. Yeah, how long does a typical project take? I know it varies, but what, you know, what are the critical success factors in terms of delivering business value quickly? >> Yeah, that's a great question in that, in that we're, you know, like I said at the beginning, we, this is not new. Smart factory and Industry 4.0 is not new. It's been, it's, people have been trying to implement the holy grail of smart factory for a long time. And what we're seeing is a switch, a little bit of a switch, or quite a bit of a switch, to where the enterprise and the IT folks are having a much bigger say and have a lot to offer to be able to help that complexity. So instead of deploying a computer here, and a gateway there, and a server there, I mean, you go walk into any manufacturing plant and you can see servers sitting underneath someone's desk or a, or a PC in a closet somewhere running a critical production application. So we're seeing the enterprise have a much bigger say at the table, much louder voice at the table to say, "We've been doing this at enterprise all the time. We, we know how to really consolidate, bring hyper-converged applications, hyper-converged infrastructure, to really accelerate these kind of applications, really accelerate the outcomes that are needed to really drive that smart factory, and start to bring that same capabilities down into the, on the factory floor." That way, if you do it once to make it easier to implement, you can repeat that. You can scale that. You can manage it much easily. And you can then bring that all together because you have the security in one centralized location. So we're seeing manufacturers, yeah, that first use case may be fairly difficult to implement and we got to go down in and see exactly what their problems are. But when the infrastructure is done the correct way, when that, think about how you're going to run that and how are you going to optimize the engineering. Well, let's take that, what you've done in that one factory, and then set. Let's that, make that across all the factories, including the factory that we're in, but across the globe. That makes it much, much easier. You really do the hard work once and then repeat, almost like a cookie cutter. >> Got it. Thank you. Aditi, what about the skillsets available to apply these, to these projects? You got to have knowledge of digital, AI, data, integration. Is there a talent shortage to get all this stuff done? >> Yeah, I mean definitely, a lot. Different types of skillsets are needed from a traditional manufacturing skillset, right? Of course, the basic knowledge of manufacturing is, is important. But the, the digital skillset sets like, you know, IoT, having a skillset in different protocols for connecting the machines, right, that experience that comes with it, data and analytics, security, augmented virtual reality programming. You know, again, looking at robotics and the digital twin. So you know, it's a lot more connectivity software, data driven skillsets that are needed to smart factory to life at scale. And, you know, lots of firms are, you know, recruiting these types of skill, resources with these skillsets to, you know, accelerate their smart factory implementation, as well as consulting firms like DXC Technology and others. We, we, we recruit. We, we train our talent to, to provide these services. >> Got it. Aditi, I wonder if we could stay on you. Let's talk about the partnership between DXC and Dell. What are you doing specifically to simplify the move to Industry 4.0 for customers? What solutions are you offering? How are you working together, Dell and DXC, to, to bring these to market? >> Yeah, Dell and DXC have a very strong partnership. You know, and we work very closely together to, to create solutions, to create strategies, and how we, we are going to jointly help our clients, right? So areas that we have worked closely together is edge compute, right, how that impacts the smart factory. So we have worked pretty closely in that area. We're also looked at vision technologies, you know. How do we use that at the edge to improve the quality of products, right? So we have several areas that we collaborate in. And our approach is that we, we want to bring solutions to our client, and as well as help them scale those solutions with the right infrastructure, the right talent, and the right level of security. So we bring a comprehensive solution to our clients. >> So, Todd, last question, kind of similar but different. You know, why Dell DXC? Pitch me. What's different about this partnership? You know, where do you, are you confident that, you know, you're going to be, deliver the best value to, to customers? >> Absolutely. Great question. You know, there's no shortage of bespoke solutions that are out there. There's hundreds of people that can come in and do individual use cases and do these things. And just, and, and, and that's, that's where it ends. What Dell and DXC Technology together bring to the table is, we do the optimization, the optimization of the engineering of those previously bespoke solutions upfront, together, right? The power of our scalables, enterprise-grade, structured, you know, industry standard infrastructure, as well as our expertise in delivering package solutions that really accelerate with DXC's expertise and reputation as a global, trusted, trusted advisor. Be able to really scale and repeat those solutions that DXC is so really, really good at. And, and Dell's infrastructure, and our, what, 30,000 people across the globe that are really, really good at that, at that scalable infrastructure, to be able to repeat. And then it really lessens the risk that our customers have and really accelerates those solutions. So it's again, not just one individual solutions, it's all of the solutions that not just drive use cases, but drive outcomes with those solutions. >> Yeah, the, you're right, the partnership has gone, I mean, I first encountered it back in, I think it was 2010, May of 2010, we had you, you guys both on theCube. I think you were talking about converged infrastructure. And I had a customer on, and it was, actually a manufacturing customer, was quite interesting. And back then it was, "How do we kind of replicate what's coming in the cloud?" And, and you guys have obviously taken it into the digital world. Really want to thank you for your time today. Great conversation, and love to have you back. >> Thank you so much. >> Absolutely. >> It was a pleasure speaking with you. >> I agree. >> All right, keep it right there for more discussions that educate and inspire on theCube. (bright music) Welcome back to the program and we're going to dig into the number one topic on the minds of every technology organization. That's cybersecurity. You know, survey data from ETR, our data partner, shows that among CIOs and IT decision makers, cybersecurity continues to rank as the number one technology priority to be addressed in the coming year. That's ahead of even cloud migration and analytics. And with me to discuss this critical topic area are Jim Shook, who's the Global Director of Cybersecurity and Compliance Practice at Dell Technologies, and he's joined by Andrew Gonzalez, who focuses on Cloud and Infrastructure consulting at DXC Technology. Gents, welcome. Good to have you. >> Thanks Dave. Great to be here. >> Thank you. >> Jim, let's start with you. What are you seeing from the front lines in terms of the attack surface, and, and how are customers responding these days? >> It's always up and down and back and forth. The bad actors are smart. They adapt to everything that we do. So we're seeing more and more kind of living off the land. They're not necessarily deploying malware. Makes it harder to find what they're doing. And I think though, Dave, we've, we've adapted, and this whole notion of cyber resilience really helps our customers figure this out. And the idea there goes beyond cybersecurity, it's, "Let's protect as much as possible, so we keep the bad actors out as much as we can. But then, let's have the ability to adapt to and recover to the extent that the bad actors are successful." So we're recognizing that we can't be perfect a hundred percent of the time against a hundred percent of the bad actors. Let's keep out what we can, but then recognize and have that ability to recover when necessary. >> Yeah, thank you. So Andrew, you know, I like what Jim was saying about living off the land, of course, meaning using your own tooling against you, kind of hiding in plain sight, if you will. But, and, and as Jim is saying, you, you can't be perfect. But, so given that, what's your perspective on what good cybersecurity hygiene looks like? >> Yeah, so you have to understand what your crown jewel data looks like, what a good copy of a recoverable asset looks like. When you look at an attack, if it were to occur, right, how you get that copy of data back into production. And not only that, but what that golden image actually entails. So, whether it's networking, storage, some copy of a source code, intellectual property, maybe CMBD data, or an active directory, or DNS dump, right? Understanding what your data actually entails so that you can protect it and that you can build out your recovery plan for it. >> So, and where's that live? Where's that gold copy? You put on a yellow sticky? No, it's got to be, (chuckles) you got to be somewhere safe, right? So you have to think about that chain as well, right? >> Absolutely. Yeah. You, so, a lot of folks have not gone through the exercise of identifying what that golden copy looks like. Everyone has a DR scenario, everyone has a DR strategy, but actually identifying what that golden crown jewel data, let's call it, actually entails is one aspect of it. And then where to put it, how to protect it, how to make it immutable and isolated, that's the other portion of it. >> You know, if I go back to sort of earlier part of last decade, you know, cybersecurity was kind of a checkoff item. And as you got toward the middle part of the decade, and I'd say clearly by 2016, it, security became a boardroom issue. It was on the agenda, you know, every quarter at the board meetings. So compliance is no longer the driver, is, is my point. The driver is business risk, real loss of reputation or data, you know, it's, or money, et cetera. What are the business implications of not having your cyber house in order today? >> They're extreme, Dave. I mean the, you know, the bad actors are good at what they do. These losses by organizations, tens, hundreds of millions into the billions sometimes, plus the reputational damage that's difficult to, to really measure. There haven't been a lot of organizations that have actually been put out of business by an attack, at least not directly on, if they're larger organizations. But that's also on the table, too. So you can't just rely on, "Oh we need to do, you know, A, B and C because our regulators require it." You need to look at what the actual risk is to the business, and then come up with a strategy from there. >> You know, Jim, staying with you, one of the most common targets we hear of attackers is to go after the backup corpus. So how should customers think about protecting themselves from that tactic? >> Well, Dave, you hit on it before, right? Everybody's had the backup and DR strategies for a long time going back to requirements that we had in place for physical disaster or human error. And that's a great starting point for resilience capability. But that's all it is, is a starting point. Because the bad actors will, they also understand that you have those capabilities, and, and they've adapted to that. In every sophisticated attack that we see, the backup is a target. The bad actors want to take it out, or corrupt it, or do something else to that backup so that it's not available to you. That's not to say they're always successful, and it's still a good control to have in place because maybe it will survive. But you have to plan beyond that. So the capabilities that we talk about with resilience, let's harden that backup infrastructure. You've already got it in place. Let's use the capabilities that are there like immutability and other controls to make it more difficult for the bad actors to get to. But then as Andrew said, that gold copy, that critical systems, you need to protect that in something that's more secure, which commonly we, we might say a cyber vault. Although, there's a lot of different capabilities for cyber vaulting, some far better than others, and that's some of the things that we focus on. >> You know, it's interesting, but I've talked to a lot of CIOs about this, is prior to the pandemic, they, you know, had their, as you're pointing out, Jim, they had their DR strategy in place, but they felt like they weren't business resilient. And they realized that when we had the forced march to digital. So Andrew, are there solutions out there to help with this problem? Do you guys have an answer to this? >> Yeah, absolutely. So I'm glad you brought up resiliency. We, we take a position that to be cyber resilient, it includes operational resiliency. It includes understanding at the C level what the implication of an attack means, as we stated, and then, how to recover back into production. When you look at protecting that data, not only do you want to put it into what we call a vault, which is a Dell technology that is an offline immutable copy of your crown jewel data, but also how to recover it in real time. So DXC offers a, I don't want to call it a turnkey solution since we architect these specific to each client needs, right, when we look at what client data entails, their recovery point, objectives, recovery time objectives, what we call quality of the restoration. But when we architect these out, we look at not only how to protect the data, but how to alert and monitor for attacks in real time, how to understand what we should do when a breach is in progress, putting together with our security operations centers, a forensic and recovery plan and a runbook for the client, and then being able to cleanse and remediate so that we can get that data back into production. These are all services that DXC offers in conjunction with the Dell solution to protect, and recover, and keep bad actors out. And if we can't keep them out to ensure that we are back into production in short order. >> You know, this, this discussion we've been having about DR kind of versus resilience, and, and you were just talking about RPO and RTO. I mean, it used to be that a lot of firms wouldn't even test their recovery 'cause it was too risky. Or, you know, maybe they tested it on, you know, July 4th or something like that. But, but it, I'm inferring that's changed. I wonder if we could, you know, double click on recovery? How hard is it to, to, to test that recovery, and, and how quickly are you seeing organizations recover from attacks? >> So it depends, right, on the industry vertical, what kind of data. Again, a financial services client compared to a manufacturing client are going to be two separate conversations. We've seen it as quickly as being able to recover in six hours, in 12 hours. In some instances we have the grace period of a day to a couple of days. We do offer the ability to run scenarios once a quarter where we can stand up in our systems the production data that we are protecting to ensure that we have a good recoverable copy. But it depends on the client. >> I really like the emphasis here, Dave, that you're raising and that Andrew's talking about. It's not on the technology of how the data gets protected. It's focused on the recovery. That's all that we want to do. And so the solution with DXC really focuses on generating that recovery for customers. I think where people get a little bit twisted up on their testing capability is, you have to think about different scenarios. So there are scenarios where the attack might be small. It might be limited to a database or an application. It might be really broadly based like the NotPetya attacks from a few years ago. The regulatory environment, we call those attacks severe but plausible. So you can't necessarily test everything with the infrastructure, but you can test some things with the infrastructure. Others, you might sit around on a tabletop exercise or walk through what that looks like to really get that, that recovery kind of muscle, muscle memory so that people know what to do when those things occur. But the key to it, as Andrew said before, have to focus down, "What are those critical applications? What do we need, what's most important? What has to come back first?" And that really will go a long way towards having the right recovery points and recovery times from a cyber disaster. >> Yeah, makes sense. Understanding the value of that data is going to inform you how to, how to respond and how to prioritize. Andrew, one of the things that we hear a lot on theCube, especially lately, is around, you know, IOT, IIOT, Industry 4.0, the whole OT security piece of it. And the problem being that, you know, traditionally, operations technologies have been air gapped, often by design. But as businesses, increasingly they're driving initiatives like Industry 4.0, and they're connecting these OT systems to IT systems. They're, you know, driving efficiency, preventative maintenance, et cetera. So a lot of data flowing through the pipes, if you will. What are you seeing in terms of the threats to critical infrastructure and how should customers think about addressing these issues? >> Yeah, so bad actors, you know, can come in many forms. We've seen instances of social engineering. We've seen, you know, a USB stick dropped in a warehouse. That data that is flowing through the IoT device is as sensitive now as your core mainframe infrastructure data. So when you look at it from a protection standpoint, conceptually, it's not dissimilar from what we've been been talking about where you want to understand, again, what the most critical data is. Looking at IoT data and applications is no different than your core systems now, right? Depending on what your, your business is, right? So when, when we're looking at protecting these, yes, we want firewalls, yes, we want air gap solutions, yes, we want front end protection, but we're looking at it from a resiliency perspective. Putting that data, understanding what what data entails to put in the vault from an IoT perspective is just as critical as as it is for your core systems. >> Jim, anything you can add to this topic? >> Yeah, I think you hit on the, the key points there. Everything is interconnected. So even in the days where maybe people thought the OT systems weren't online, oftentimes the IT systems are talking to them, or controlling them, SCADA systems, or perhaps supporting them. Think back to the pipeline attack of last year. All the public testimony was that the OT systems didn't get attacked directly. But there was uncertainty around that, and the IT systems hadn't been secured. So that caused the OT systems to have to shut down. It certainly is a different recovery when you're shutting them down on your own versus being attacked, but the outcome was the same that the business couldn't operate. So you really have to take all of those into account. And I think that does go back to exactly what Andrew's saying, understanding your critical business services, and then the applications and data and other components that support those and drive those, and making sure those are protected. You understand them, you have the ability to recover them if necessary. >> So guys, I mean, you made the point. I mean, you're right. The adversary is highly capable. They're motivated 'cause the ROI is so, it's so lucrative. It's like this never ending battle that cybersecurity pros, you know, go through. It really is kind of frontline sort of technical heroes, if you will. And so, but sometimes it just feels daunting. Why are you optimistic about the future of, of cyber from the good guy's perspective? >> I think we're coming at the problem the right way, Dave. So that, that focus, I'm so pleased with the idea that we are planning that the systems aren't going to be hundred percent capable every single time, and let's figure that out, right? That's, that's real world stuff. So just as the bad actors continue to adapt and expand, so do we. And I think the differences there, the common criminals, it's getting harder and harder for them. The more sophisticated ones, they're tough to beat all the time. And of course, you've raised the question of some nation states and other activities. But there's a lot more information sharing. There's a lot more focus from the business side of the house and not just the IT side of the house that we need to figure these things out. >> Yeah, to, to add to that, I think furthering education for the client base is important. You, you brought up a point earlier. It used to be a boardroom conversation due to compliance reasons. Now, as we have been in the market for a while, we continue to mature the offerings. It's further education for not only the business itself, but for the IT systems and how they interconnect, and working together so that these systems can be protected and continue to be evolved and continue to be protected through multiple frameworks as opposed to seeing it as another check the box item that the board has to adhere to. >> All right, guys, we got to go. Thank you so much. Great conversation on a, on a really important topic. Keep up the good work. Appreciate it. >> Thanks Dan. >> Thank you. >> All right, and thank you for watching. Stay tuned for more excellent discussions around the partnership between Dell Technologies and DXC Technology. We're talking about solving real world problems, how this partnership has evolved over time, really meeting the changing enterprise landscape challenges. Keep it right there. (bright music) Okay, we hope you enjoyed the program and learned some things about cloud transformation and modernizing your business that will inspire you to action. Now if you want to learn more, go to the Dell DXC partner page shown here, or click on the URL in the description. Thanks for watching everybody and on behalf of our supporters, Dell and DXC, good luck. And as always, get in touch if we can be of any assistance. (bright music)

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Confidential computing is a technology that aims to enhance data privacy and security by providing encrypted computation on sensitive data and isolating data from apps in a fenced off enclave during processing. The concept of confidential computing is gaining popularity, especially in the cloud computing space where sensitive data is often stored and of course processed. However, there are some who view confidential computing as an unnecessary technology in a marketing ploy by cloud providers aimed at calming customers who are cloud phobic. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we revisit the notion of confidential computing, and to do so, we'll invite two Google experts to the show, but before we get there, let's summarize briefly. There's not a ton of ETR data on the topic of confidential computing. I mean, it's a technology that's deeply embedded into silicon and computing architectures. But at the highest level, security remains the number one priority being addressed by IT decision makers in the coming year as shown here. And this data is pretty much across the board by industry, by region, by size of company. I mean we dug into it and the only slight deviation from the mean is in financial services. The second and third most cited priorities, cloud migration and analytics, are noticeably closer to cybersecurity in financial services than in other sectors, likely because financial services has always been hyper security conscious, but security is still a clear number one priority in that sector. The idea behind confidential computing is to better address threat models for data in execution. Protecting data at rest and data and transit have long been a focus of security approaches, but more recently, silicon manufacturers have introduced architectures that separate data and applications from the host system. Arm, Intel, AMD, Nvidia and other suppliers are all on board, as are the big cloud players. Now the argument against confidential computing is that it narrowly focuses on memory encryption and it doesn't solve the biggest problems in security. Multiple system images updates different services and the entire code flow aren't directly addressed by memory encryption, rather to truly attack these problems, many believe that OSs need to be re-engineered with the attacker and hacker in mind. There are so many variables and at the end of the day, critics say the emphasis on confidential computing made by cloud providers is overstated and largely hype. This tweet from security researcher Rodrigo Branco sums up the sentiment of many skeptics. He says, "Confidential computing is mostly a marketing campaign for memory encryption. It's not driving the industry towards the hard open problems. It is selling an illusion." Okay. Nonetheless, encrypting data in use and fencing off key components of the system isn't a bad thing, especially if it comes with the package essentially for free. There has been a lack of standardization and interoperability between different confidential computing approaches. But the confidential computing consortium was established in 2019 ostensibly to accelerate the market and influence standards. Notably, AWS is not part of the consortium, likely because the politics of the consortium were probably a conundrum for AWS because the base technology defined by the the consortium is seen as limiting by AWS. This is my guess, not AWS's words, and but I think joining the consortium would validate a definition which AWS isn't aligned with. And two, it's got a lead with this Annapurna acquisition. This was way ahead with Arm integration and so it probably doesn't feel the need to validate its competitors. Anyway, one of the premier members of the confidential computing consortium is Google, along with many high profile names including Arm, Intel, Meta, Red Hat, Microsoft, and others. And we're pleased to welcome two experts on confidential computing from Google to unpack the topic, Nelly Porter is head of product for GCP confidential computing and encryption, and Dr. Patricia Florissi is the technical director for the office of the CTO at Google Cloud. Welcome Nelly and Patricia, great to have you. >> Great to be here. >> Thank you so much for having us. >> You're very welcome. Nelly, why don't you start and then Patricia, you can weigh in. Just tell the audience a little bit about each of your roles at Google Cloud. >> So I'll start, I'm owning a lot of interesting activities in Google and again security or infrastructure securities that I usually own. And we are talking about encryption and when encryption and confidential computing is a part of portfolio in additional areas that I contribute together with my team to Google and our customers is secure software supply chain. Because you need to trust your software. Is it operate in your confidential environment to have end-to-end story about if you believe that your software and your environment doing what you expect, it's my role. >> Got it. Okay. Patricia? >> Well, I am a technical director in the office of the CTO, OCTO for short, in Google Cloud. And we are a global team. We include former CTOs like myself and senior technologists from large corporations, institutions and a lot of success, we're startups as well. And we have two main goals. First, we walk side by side with some of our largest, more strategic or most strategical customers and we help them solve complex engineering technical problems. And second, we are devise Google and Google Cloud engineering and product management and tech on there, on emerging trends and technologies to guide the trajectory of our business. We are unique group, I think, because we have created this collaborative culture with our customers. And within OCTO, I spend a lot of time collaborating with customers and the industry at large on technologies that can address privacy, security, and sovereignty of data in general. >> Excellent. Thank you for that both of you. Let's get into it. So Nelly, what is confidential computing? From Google's perspective, how do you define it? >> Confidential computing is a tool and it's still one of the tools in our toolbox. And confidential computing is a way how we would help our customers to complete this very interesting end-to-end lifecycle of the data. And when customers bring in the data to cloud and want to protect it as they ingest it to the cloud, they protect it at rest when they store data in the cloud. But what was missing for many, many years is ability for us to continue protecting data and workloads of our customers when they running them. And again, because data is not brought to cloud to have huge graveyard, we need to ensure that this data is actually indexed. Again, there is some insights driven and drawn from this data. You have to process this data and confidential computing here to help. Now we have end to end protection of our customer's data when they bring the workloads and data to cloud, thanks to confidential computing. >> Thank you for that. Okay, we're going to get into the architecture a bit, but before we do, Patricia, why do you think this topic of confidential computing is such an important technology? Can you explain, do you think it's transformative for customers and if so, why? >> Yeah, I would maybe like to use one thought, one way, one intuition behind why confidential commuting matters, because at the end of the day, it reduces more and more the customer's thresh boundaries and the attack surface. That's about reducing that periphery, the boundary in which the customer needs to mind about trust and safety. And in a way, is a natural progression that you're using encryption to secure and protect the data. In the same way that we are encrypting data in transit and at rest, now we are also encrypting data while in use. And among other beneficials, I would say one of the most transformative ones is that organizations will be able to collaborate with each other and retain the confidentiality of the data. And that is across industry, even though it's highly focused on, I wouldn't say highly focused, but very beneficial for highly regulated industries. It applies to all of industries. And if you look at financing for example, where bankers are trying to detect fraud, and specifically double finance where you are, a customer is actually trying to get a finance on an asset, let's say a boat or a house, and then it goes to another bank and gets another finance on that asset. Now bankers would be able to collaborate and detect fraud while preserving confidentiality and privacy of the data. >> Interesting. And I want to understand that a little bit more but I'm going to push you a little bit on this, Nelly, if I can because there's a narrative out there that says confidential computing is a marketing ploy, I talked about this upfront, by cloud providers that are just trying to placate people that are scared of the cloud. And I'm presuming you don't agree with that, but I'd like you to weigh in here. The argument is confidential computing is just memory encryption and it doesn't address many other problems. It is over hyped by cloud providers. What do you say to that line of thinking? >> I absolutely disagree, as you can imagine, with this statement, but the most importantly is we mixing multiple concepts, I guess. And exactly as Patricia said, we need to look at the end-to-end story, not again the mechanism how confidential computing trying to again, execute and protect a customer's data and why it's so critically important because what confidential computing was able to do, it's in addition to isolate our tenants in multi-tenant environments the cloud covering to offer additional stronger isolation. They called it cryptographic isolation. It's why customers will have more trust to customers and to other customers, the tenant that's running on the same host but also us because they don't need to worry about against threats and more malicious attempts to penetrate the environment. So what confidential computing is helping us to offer our customers, stronger isolation between tenants in this multi-tenant environment, but also incredibly important, stronger isolation of our customers, so tenants from us. We also writing code, we also software providers will also make mistakes or have some zero days. Sometimes again us introduced, sometimes introduced by our adversaries. But what I'm trying to say by creating this cryptographic layer of isolation between us and our tenants and amongst those tenants, we're really providing meaningful security to our customers and eliminate some of the worries that they have running on multi-tenant spaces or even collaborating to gather this very sensitive data knowing that this particular protection is available to them. >> Okay, thank you. Appreciate that. And I think malicious code is often a threat model missed in these narratives. Operator access, yeah, maybe I trust my clouds provider, but if I can fence off your access even better, I'll sleep better at night. Separating a code from the data, everybody's, Arm, Intel, AMD, Nvidia, others, they're all doing it. I wonder if, Nelly, if we could stay with you and bring up the slide on the architecture. What's architecturally different with confidential computing versus how operating systems and VMs have worked traditionally. We're showing a slide here with some VMs, maybe you could take us through that. >> Absolutely. And Dave, the whole idea for Google and now industry way of dealing with confidential computing is to ensure that three main property is actually preserved. Customers don't need to change the code. They can operate on those VMs exactly as they would with normal non-confidential VMs, but to give them this opportunity of lift and shift or no changing their apps and performing and having very, very, very low latency and scale as any cloud can, something that Google actually pioneer in confidential computing. I think we need to open and explain how this magic was actually done. And as I said, it's again the whole entire system have to change to be able to provide this magic. And I would start with we have this concept of root of trust and root of trust where we will ensure that this machine, when the whole entire post has integrity guarantee, means nobody changing my code on the most low level of system. And we introduce this in 2017 called Titan. It was our specific ASIC, specific, again, inch by inch system on every single motherboard that we have that ensures that your low level former, your actually system code, your kernel, the most powerful system is actually proper configured and not changed, not tampered. We do it for everybody, confidential computing included. But for confidential computing, what we have to change, we bring in AMD, or again, future silicon vendors and we have to trust their former, their way to deal with our confidential environments. And that's why we have obligation to validate integrity, not only our software and our former but also former and software of our vendors, silicon vendors. So we actually, when we booting this machine, as you can see, we validate that integrity of all of the system is in place. It means nobody touching, nobody changing, nobody modifying it. But then we have this concept of AMD secure processor, it's special ASICs, best specific things that generate a key for every single VM that our customers will run or every single node in Kubernetes or every single worker thread in our Hadoop or Spark capability. We offer all of that. And those keys are not available to us. It's the best keys ever in encryption space because when we are talking about encryption, the first question that I'm receiving all the time, where's the key, who will have access to the key? Because if you have access to the key then it doesn't matter if you encrypted or not. So, but the case in confidential computing provides so revolutionary technology, us cloud providers, who don't have access to the keys. They sitting in the hardware and they head to memory controller. And it means when hypervisors that also know about these wonderful things saying I need to get access to the memories that this particular VM trying to get access to, they do not decrypt the data, they don't have access to the key because those keys are random, ephemeral and per VM, but the most importantly, in hardware not exportable. And it means now you would be able to have this very interesting role that customers or cloud providers will not be able to get access to your memory. And what we do, again, as you can see our customers don't need to change their applications, their VMs are running exactly as it should run and what you're running in VM, you actually see your memory in clear, it's not encrypted, but God forbid is trying somebody to do it outside of my confidential box. No, no, no, no, no, they would not be able to do it. Now you'll see cyber and it's exactly what combination of these multiple hardware pieces and software pieces have to do. So OS is also modified. And OS is modified such way to provide integrity. It means even OS that you're running in your VM box is not modifiable and you, as customer, can verify. But the most interesting thing, I guess, how to ensure the super performance of this environment because you can imagine, Dave, that encrypting and it's additional performance, additional time, additional latency. So we were able to mitigate all of that by providing incredibly interesting capability in the OS itself. So our customers will get no changes needed, fantastic performance and scales as they would expect from cloud providers like Google. >> Okay, thank you. Excellent. Appreciate that explanation. So, again, the narrative on this as well, you've already given me guarantees as a cloud provider that you don't have access to my data, but this gives another level of assurance, key management as they say is key. Now humans aren't managing the keys, the machines are managing them. So Patricia, my question to you is, in addition to, let's go pre confidential computing days, what are the sort of new guarantees that these hardware-based technologies are going to provide to customers? >> So if I am a customer, I am saying I now have full guarantee of confidentiality and integrity of the data and of the code. So if you look at code and data confidentiality, the customer cares and they want to know whether their systems are protected from outside or unauthorized access, and that recovered with Nelly, that it is. Confidential computing actually ensures that the applications and data internals remain secret, right? The code is actually looking at the data, the only the memory is decrypting the data with a key that is ephemeral and per VM and generated on demand. Then you have the second point where you have code and data integrity, and now customers want to know whether their data was corrupted, tampered with or impacted by outside actors. And what confidential computing ensures is that application internals are not tampered with. So the application, the workload as we call it, that is processing the data, it's also, it has not been tampered and preserves integrity. I would also say that this is all verifiable. So you have attestation and these attestation actually generates a log trail and the log trail guarantees that, provides a proof that it was preserved. And I think that the offer's also a guarantee of what we call ceiling, this idea that the secrets have been preserved and not tampered with, confidentiality and integrity of code and data. >> Got it. Okay, thank you. Nelly, you mentioned, I think I heard you say that the applications, it's transparent, you don't have to change the application, it just comes for free essentially. And we showed some various parts of the stack before. I'm curious as to what's affected, but really more importantly, what is specifically Google's value add? How do partners participate in this, the ecosystem, or maybe said another way, how does Google ensure the compatibility of confidential computing with existing systems and applications? >> And a fantastic question by the way. And it's very difficult and definitely complicated world because to be able to provide these guarantees, actually a lot of work was done by community. Google is very much operate in open, so again, our operating system, we working with operating system repository OSs, OS vendors to ensure that all capabilities that we need is part of the kernels, are part of the releases and it's available for customers to understand and even explore if they have fun to explore a lot of code. We have also modified together with our silicon vendors a kernel, host kernel to support this capability and it means working this community to ensure that all of those patches are there. We also worked with every single silicon vendor as you've seen, and that's what I probably feel that Google contributed quite a bit in this whole, we moved our industry, our community, our vendors to understand the value of easy to use confidential computing or removing barriers. And now I don't know if you noticed, Intel is pulling the lead and also announcing their trusted domain extension, very similar architecture. And no surprise, it's, again, a lot of work done with our partners to, again, convince, work with them and make this capability available. The same with Arm this year, actually last year, Arm announced their future design for confidential computing. It's called Confidential Computing Architecture. And it's also influenced very heavily with similar ideas by Google and industry overall. So it's a lot of work in confidential computing consortiums that we are doing, for example, simply to mention, to ensure interop, as you mentioned, between different confidential environments of cloud providers. They want to ensure that they can attest to each other because when you're communicating with different environments, you need to trust them. And if it's running on different cloud providers, you need to ensure that you can trust your receiver when you are sharing your sensitive data workloads or secret with them. So we coming as a community and we have this attestation sig, the, again, the community based systems that we want to build and influence and work with Arm and every other cloud providers to ensure that we can interrupt and it means it doesn't matter where confidential workloads will be hosted, but they can exchange the data in secure, verifiable and controlled by customers way. And to do it, we need to continue what we are doing, working open, again, and contribute with our ideas and ideas of our partners to this role to become what we see confidential computing has to become, it has to become utility. It doesn't need to be so special, but it's what we want it to become. >> Let's talk about, thank you for that explanation. Let's talk about data sovereignty because when you think about data sharing, you think about data sharing across the ecosystem and different regions and then of course data sovereignty comes up. Typically public policy lags, the technology industry and sometimes is problematic. I know there's a lot of discussions about exceptions, but Patricia, we have a graphic on data sovereignty. I'm interested in how confidential computing ensures that data sovereignty and privacy edicts are adhered to, even if they're out of alignment maybe with the pace of technology. One of the frequent examples is when you delete data, can you actually prove that data is deleted with a hundred percent certainty? You got to prove that and a lot of other issues. So looking at this slide, maybe you could take us through your thinking on data sovereignty. >> Perfect. So for us, data sovereignty is only one of the three pillars of digital sovereignty. And I don't want to give the impression that confidential computing addresses it all. That's why we want to step back and say, hey, digital sovereignty includes data sovereignty where we are giving you full control and ownership of the location, encryption and access to your data. Operational sovereignty where the goal is to give our Google Cloud customers full visibility and control over the provider operations, right? So if there are any updates on hardware, software stack, any operations, there is full transparency, full visibility. And then the third pillar is around software sovereignty where the customer wants to ensure that they can run their workloads without dependency on the provider's software. So they have sometimes is often referred as survivability, that you can actually survive if you are untethered to the cloud and that you can use open source. Now let's take a deep dive on data sovereignty, which by the way is one of my favorite topics. And we typically focus on saying, hey, we need to care about data residency. We care where the data resides because where the data is at rest or in processing, it typically abides to the jurisdiction, the regulations of the jurisdiction where the data resides. And others say, hey, let's focus on data protection. We want to ensure the confidentiality and integrity and availability of the data, which confidential computing is at the heart of that data protection. But it is yet another element that people typically don't talk about when talking about data sovereignty, which is the element of user control. And here, Dave, is about what happens to the data when I give you access to my data. And this reminds me of security two decades ago, even a decade ago, where we started the security movement by putting firewall protections and login accesses. But once you were in, you were able to do everything you wanted with the data. An insider had access to all the infrastructure, the data and the code. And that's similar because with data sovereignty we care about whether it resides, where, who is operating on the data. But the moment that the data is being processed, I need to trust that the processing of the data will abide by user control, by the policies that I put in place of how my data is going to be used. And if you look at a lot of the regulation today and a lot of the initiatives around the International Data Space Association, IDSA, and Gaia-X, there is a movement of saying the two parties, the provider of the data and the receiver of the data are going to agree on a contract that describes what my data can be used for. The challenge is to ensure that once the data crosses boundaries, that the data will be used for the purposes that it was intended and specified in the contract. And if you actually bring together, and this is the exciting part, confidential computing together with policy enforcement, now the policy enforcement can guarantee that the data is only processed within the confines of a confidential computing environment, that the workload is cryptographically verified that there is the workload that was meant to process the data and that the data will be only used when abiding to the confidentiality and integrity safety of the confidential computing environment. And that's why we believe confidential computing is one necessary and essential technology that will allow us to ensure data sovereignty, especially when it comes to user control. >> Thank you for that. I mean it was a deep dive, I mean brief, but really detailed. So I appreciate that, especially the verification of the enforcement. Last question, I met you two because as part of my year end prediction post, you guys sent in some predictions and I wasn't able to get to them in the predictions post. So I'm thrilled that you were able to make the time to come on the program. How widespread do you think the adoption of confidential computing will be in 23 and what's the maturity curve look like, this decade in your opinion? Maybe each of you could give us a brief answer. >> So my prediction in five, seven years, as I started, it'll become utility. It'll become TLS as of, again, 10 years ago we couldn't believe that websites will have certificates and we will support encrypted traffic. Now we do and it's become ubiquity. It's exactly where confidential computing is getting and heading, I don't know we deserve yet. It'll take a few years of maturity for us, but we will be there. >> Thank you. And Patricia, what's your prediction? >> I will double that and say, hey, in the future, in the very near future, you will not be able to afford not having it. I believe as digital sovereignty becomes evermore top of mind with sovereign states and also for multi national organizations and for organizations that want to collaborate with each other, confidential computing will become the norm. It'll become the default, if I say, mode of operation. I like to compare that today is inconceivable. If we talk to the young technologists, it's inconceivable to think that at some point in history, and I happen to be alive that we had data at rest that was not encrypted, data in transit that was not encrypted, and I think that will be inconceivable at some point in the near future that to have unencrypted data while in use. >> And plus I think the beauty of the this industry is because there's so much competition, this essentially comes for free. I want to thank you both for spending some time on Breaking Analysis. There's so much more we could cover. I hope you'll come back to share the progress that you're making in this area and we can double click on some of these topics. Really appreciate your time. >> Anytime. >> Thank you so much. >> In summary, while confidential computing is being touted by the cloud players as a promising technology for enhancing data privacy and security, there are also those, as we said, who remain skeptical. The truth probably lies somewhere in between and it will depend on the specific implementation and the use case as to how effective confidential computing will be. Look, as with any new tech, it's important to carefully evaluate the potential benefits, the drawbacks, and make informed decisions based on the specific requirements in the situation and the constraints of each individual customer. But the bottom line is silicon manufacturers are working with cloud providers and other system companies to include confidential computing into their architectures. Competition, in our view, will moderate price hikes. And at the end of the day, this is under the covers technology that essentially will come for free. So we'll take it. I want to thank our guests today, Nelly and Patricia from Google, and thanks to Alex Myerson who's on production and manages the podcast. Ken Schiffman as well out of our Boston studio, Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor-in-chief over at siliconangle.com. Does some great editing for us, thank you all. Remember all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com where you can get all the news. If you want to get in touch, you can email me at david.vellante@siliconangle.com or dm me @DVellante. And you can also comment on my LinkedIn post. Definitely you want to check out etr.ai for the best survey data in the enterprise tech business. I know we didn't hit on a lot today, but there's some amazing data and it's always being updated, so check that out. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Confidential computing is a technology that aims to enhance data privacy and security, by providing encrypted computation on sensitive data and isolating data, and apps that are fenced off enclave during processing. The concept of, I got to start over. I fucked that up, I'm sorry. That's not right, what I said was not right. On Dave in five, four, three. Confidential computing is a technology that aims to enhance data privacy and security by providing encrypted computation on sensitive data, isolating data from apps and a fenced off enclave during processing. The concept of confidential computing is gaining popularity, especially in the cloud computing space, where sensitive data is often stored and of course processed. However, there are some who view confidential computing as an unnecessary technology in a marketing ploy by cloud providers aimed at calming customers who are cloud phobic. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, we revisit the notion of confidential computing, and to do so, we'll invite two Google experts to the show. But before we get there, let's summarize briefly. There's not a ton of ETR data on the topic of confidential computing, I mean, it's a technology that's deeply embedded into silicon and computing architectures. But at the highest level, security remains the number one priority being addressed by IT decision makers in the coming year as shown here. And this data is pretty much across the board by industry, by region, by size of company. I mean we dug into it and the only slight deviation from the mean is in financial services. The second and third most cited priorities, cloud migration and analytics are noticeably closer to cybersecurity in financial services than in other sectors, likely because financial services has always been hyper security conscious, but security is still a clear number one priority in that sector. The idea behind confidential computing is to better address threat models for data in execution. Protecting data at rest and data in transit have long been a focus of security approaches, but more recently, silicon manufacturers have introduced architectures that separate data and applications from the host system, ARM, Intel, AMD, Nvidia and other suppliers are all on board, as are the big cloud players. Now, the argument against confidential computing is that it narrowly focuses on memory encryption and it doesn't solve the biggest problems in security. Multiple system images, updates, different services and the entire code flow aren't directly addressed by memory encryption. Rather to truly attack these problems, many believe that OSs need to be re-engineered with the attacker and hacker in mind. There are so many variables and at the end of the day, critics say the emphasis on confidential computing made by cloud providers is overstated and largely hype. This tweet from security researcher Rodrigo Bronco, sums up the sentiment of many skeptics. He says, "Confidential computing is mostly a marketing campaign from memory encryption. It's not driving the industry towards the hard open problems. It is selling an illusion." Okay. Nonetheless, encrypting data in use and fencing off key components of the system isn't a bad thing, especially if it comes with the package essentially for free. There has been a lack of standardization and interoperability between different confidential computing approaches. But the confidential computing consortium was established in 2019 ostensibly to accelerate the market and influence standards. Notably, AWS is not part of the consortium, likely because the politics of the consortium were probably a conundrum for AWS because the base technology defined by the consortium is seen as limiting by AWS. This is my guess, not AWS' words. But I think joining the consortium would validate a definition which AWS isn't aligned with. And two, it's got to lead with this Annapurna acquisition. It was way ahead with ARM integration, and so it's probably doesn't feel the need to validate its competitors. Anyway, one of the premier members of the confidential computing consortium is Google, along with many high profile names, including Aem, Intel, Meta, Red Hat, Microsoft, and others. And we're pleased to welcome two experts on confidential computing from Google to unpack the topic. Nelly Porter is Head of Product for GCP Confidential Computing and Encryption and Dr. Patricia Florissi is the Technical Director for the Office of the CTO at Google Cloud. Welcome Nelly and Patricia, great to have you. >> Great to be here. >> Thank you so much for having us. >> You're very welcome. Nelly, why don't you start and then Patricia, you can weigh in. Just tell the audience a little bit about each of your roles at Google Cloud. >> So I'll start, I'm owning a lot of interesting activities in Google and again, security or infrastructure securities that I usually own. And we are talking about encryption, end-to-end encryption, and confidential computing is a part of portfolio. Additional areas that I contribute to get with my team to Google and our customers is secure software supply chain because you need to trust your software. Is it operate in your confidential environment to have end-to-end security, about if you believe that your software and your environment doing what you expect, it's my role. >> Got it. Okay, Patricia? >> Well, I am a Technical Director in the Office of the CTO, OCTO for short in Google Cloud. And we are a global team, we include former CTOs like myself and senior technologies from large corporations, institutions and a lot of success for startups as well. And we have two main goals, first, we walk side by side with some of our largest, more strategic or most strategical customers and we help them solve complex engineering technical problems. And second, we advice Google and Google Cloud Engineering, product management on emerging trends and technologies to guide the trajectory of our business. We are unique group, I think, because we have created this collaborative culture with our customers. And within OCTO I spend a lot of time collaborating with customers in the industry at large on technologies that can address privacy, security, and sovereignty of data in general. >> Excellent. Thank you for that both of you. Let's get into it. So Nelly, what is confidential computing from Google's perspective? How do you define it? >> Confidential computing is a tool and one of the tools in our toolbox. And confidential computing is a way how we would help our customers to complete this very interesting end-to-end lifecycle of the data. And when customers bring in the data to cloud and want to protect it as they ingest it to the cloud, they protect it at rest when they store data in the cloud. But what was missing for many, many years is ability for us to continue protecting data and workloads of our customers when they run them. And again, because data is not brought to cloud to have huge graveyard, we need to ensure that this data is actually indexed. Again, there is some insights driven and drawn from this data. You have to process this data and confidential computing here to help. Now we have end-to-end protection of our customer's data when they bring the workloads and data to cloud thanks to confidential computing. >> Thank you for that. Okay, we're going to get into the architecture a bit, but before we do Patricia, why do you think this topic of confidential computing is such an important technology? Can you explain? Do you think it's transformative for customers and if so, why? >> Yeah, I would maybe like to use one thought, one way, one intuition behind why confidential computing matters because at the end of the day, it reduces more and more the customer's thrush boundaries and the attack surface. That's about reducing that periphery, the boundary in which the customer needs to mind about trust and safety. And in a way is a natural progression that you're using encryption to secure and protect data in the same way that we are encrypting data in transit and at rest. Now, we are also encrypting data while in the use. And among other beneficials, I would say one of the most transformative ones is that organizations will be able to collaborate with each other and retain the confidentiality of the data. And that is across industry, even though it's highly focused on, I wouldn't say highly focused but very beneficial for highly regulated industries, it applies to all of industries. And if you look at financing for example, where bankers are trying to detect fraud and specifically double finance where a customer is actually trying to get a finance on an asset, let's say a boat or a house, and then it goes to another bank and gets another finance on that asset. Now bankers would be able to collaborate and detect fraud while preserving confidentiality and privacy of the data. >> Interesting and I want to understand that a little bit more but I got to push you a little bit on this, Nellie if I can, because there's a narrative out there that says confidential computing is a marketing ploy I talked about this up front, by cloud providers that are just trying to placate people that are scared of the cloud. And I'm presuming you don't agree with that, but I'd like you to weigh in here. The argument is confidential computing is just memory encryption, it doesn't address many other problems. It is over hyped by cloud providers. What do you say to that line of thinking? >> I absolutely disagree as you can imagine Dave, with this statement. But the most importantly is we mixing a multiple concepts I guess, and exactly as Patricia said, we need to look at the end-to-end story, not again, is a mechanism. How confidential computing trying to execute and protect customer's data and why it's so critically important. Because what confidential computing was able to do, it's in addition to isolate our tenants in multi-tenant environments the cloud offering to offer additional stronger isolation, they called it cryptographic isolation. It's why customers will have more trust to customers and to other customers, the tenants running on the same host but also us because they don't need to worry about against rats and more malicious attempts to penetrate the environment. So what confidential computing is helping us to offer our customers stronger isolation between tenants in this multi-tenant environment, but also incredibly important, stronger isolation of our customers to tenants from us. We also writing code, we also software providers, we also make mistakes or have some zero days. Sometimes again us introduce, sometimes introduced by our adversaries. But what I'm trying to say by creating this cryptographic layer of isolation between us and our tenants and among those tenants, we really providing meaningful security to our customers and eliminate some of the worries that they have running on multi-tenant spaces or even collaborating together with very sensitive data knowing that this particular protection is available to them. >> Okay, thank you. Appreciate that. And I think malicious code is often a threat model missed in these narratives. You know, operator access. Yeah, maybe I trust my cloud's provider, but if I can fence off your access even better, I'll sleep better at night separating a code from the data. Everybody's ARM, Intel, AMD, Nvidia and others, they're all doing it. I wonder if Nell, if we could stay with you and bring up the slide on the architecture. What's architecturally different with confidential computing versus how operating systems and VMs have worked traditionally? We're showing a slide here with some VMs, maybe you could take us through that. >> Absolutely, and Dave, the whole idea for Google and now industry way of dealing with confidential computing is to ensure that three main property is actually preserved. Customers don't need to change the code. They can operate in those VMs exactly as they would with normal non-confidential VMs. But to give them this opportunity of lift and shift though, no changing the apps and performing and having very, very, very low latency and scale as any cloud can, some things that Google actually pioneer in confidential computing. I think we need to open and explain how this magic was actually done, and as I said, it's again the whole entire system have to change to be able to provide this magic. And I would start with we have this concept of root of trust and root of trust where we will ensure that this machine within the whole entire host has integrity guarantee, means nobody changing my code on the most low level of system, and we introduce this in 2017 called Titan. So our specific ASIC, specific inch by inch system on every single motherboard that we have that ensures that your low level former, your actually system code, your kernel, the most powerful system is actually proper configured and not changed, not tempered. We do it for everybody, confidential computing included, but for confidential computing is what we have to change, we bring in AMD or future silicon vendors and we have to trust their former, their way to deal with our confidential environments. And that's why we have obligation to validate intelligent not only our software and our former but also former and software of our vendors, silicon vendors. So we actually, when we booting this machine as you can see, we validate that integrity of all of this system is in place. It means nobody touching, nobody changing, nobody modifying it. But then we have this concept of AMD Secure Processor, it's special ASIC best specific things that generate a key for every single VM that our customers will run or every single node in Kubernetes or every single worker thread in our Hadoop spark capability. We offer all of that and those keys are not available to us. It's the best case ever in encryption space because when we are talking about encryption, the first question that I'm receiving all the time, "Where's the key? Who will have access to the key?" because if you have access to the key then it doesn't matter if you encrypted or not. So, but the case in confidential computing why it's so revolutionary technology, us cloud providers who don't have access to the keys, they're sitting in the hardware and they fed to memory controller. And it means when hypervisors that also know about this wonderful things saying I need to get access to the memories, that this particular VM I'm trying to get access to. They do not decrypt the data, they don't have access to the key because those keys are random, ephemeral and per VM, but most importantly in hardware not exportable. And it means now you will be able to have this very interesting world that customers or cloud providers will not be able to get access to your memory. And what we do, again as you can see, our customers don't need to change their applications. Their VMs are running exactly as it should run. And what you've running in VM, you actually see your memory clear, it's not encrypted. But God forbid is trying somebody to do it outside of my confidential box, no, no, no, no, no, you will now be able to do it. Now, you'll see cyber test and it's exactly what combination of these multiple hardware pieces and software pieces have to do. So OS is also modified and OS is modified such way to provide integrity. It means even OS that you're running in your VM box is not modifiable and you as customer can verify. But the most interesting thing I guess how to ensure the super performance of this environment because you can imagine Dave, that's increasing and it's additional performance, additional time, additional latency. So we're able to mitigate all of that by providing incredibly interesting capability in the OS itself. So our customers will get no changes needed, fantastic performance and scales as they would expect from cloud providers like Google. >> Okay, thank you. Excellent, appreciate that explanation. So you know again, the narrative on this is, well, you've already given me guarantees as a cloud provider that you don't have access to my data, but this gives another level of assurance, key management as they say is key. Now humans aren't managing the keys, the machines are managing them. So Patricia, my question to you is in addition to, let's go pre-confidential computing days, what are the sort of new guarantees that these hardware based technologies are going to provide to customers? >> So if I am a customer, I am saying I now have full guarantee of confidentiality and integrity of the data and of the code. So if you look at code and data confidentiality, the customer cares and they want to know whether their systems are protected from outside or unauthorized access, and that we covered with Nelly that it is. Confidential computing actually ensures that the applications and data antennas remain secret. The code is actually looking at the data, only the memory is decrypting the data with a key that is ephemeral, and per VM, and generated on demand. Then you have the second point where you have code and data integrity and now customers want to know whether their data was corrupted, tempered with or impacted by outside actors. And what confidential computing ensures is that application internals are not tempered with. So the application, the workload as we call it, that is processing the data is also has not been tempered and preserves integrity. I would also say that this is all verifiable, so you have attestation and this attestation actually generates a log trail and the log trail guarantees that provides a proof that it was preserved. And I think that the offers also a guarantee of what we call sealing, this idea that the secrets have been preserved and not tempered with, confidentiality and integrity of code and data. >> Got it. Okay, thank you. Nelly, you mentioned, I think I heard you say that the applications is transparent, you don't have to change the application, it just comes for free essentially. And we showed some various parts of the stack before, I'm curious as to what's affected, but really more importantly, what is specifically Google's value add? How do partners participate in this, the ecosystem or maybe said another way, how does Google ensure the compatibility of confidential computing with existing systems and applications? >> And a fantastic question by the way, and it's very difficult and definitely complicated world because to be able to provide these guarantees, actually a lot of work was done by community. Google is very much operate and open. So again our operating system, we working this operating system repository OS is OS vendors to ensure that all capabilities that we need is part of the kernels are part of the releases and it's available for customers to understand and even explore if they have fun to explore a lot of code. We have also modified together with our silicon vendors kernel, host kernel to support this capability and it means working this community to ensure that all of those pages are there. We also worked with every single silicon vendor as you've seen, and it's what I probably feel that Google contributed quite a bit in this world. We moved our industry, our community, our vendors to understand the value of easy to use confidential computing or removing barriers. And now I don't know if you noticed Intel is following the lead and also announcing a trusted domain extension, very similar architecture and no surprise, it's a lot of work done with our partners to convince work with them and make this capability available. The same with ARM this year, actually last year, ARM announced future design for confidential computing, it's called confidential computing architecture. And it's also influenced very heavily with similar ideas by Google and industry overall. So it's a lot of work in confidential computing consortiums that we are doing, for example, simply to mention, to ensure interop as you mentioned, between different confidential environments of cloud providers. They want to ensure that they can attest to each other because when you're communicating with different environments, you need to trust them. And if it's running on different cloud providers, you need to ensure that you can trust your receiver when you sharing your sensitive data workloads or secret with them. So we coming as a community and we have this at Station Sig, the community-based systems that we want to build, and influence, and work with ARM and every other cloud providers to ensure that they can interop. And it means it doesn't matter where confidential workloads will be hosted, but they can exchange the data in secure, verifiable and controlled by customers really. And to do it, we need to continue what we are doing, working open and contribute with our ideas and ideas of our partners to this role to become what we see confidential computing has to become, it has to become utility. It doesn't need to be so special, but it's what what we've wanted to become. >> Let's talk about, thank you for that explanation. Let's talk about data sovereignty because when you think about data sharing, you think about data sharing across the ecosystem in different regions and then of course data sovereignty comes up, typically public policy, lags, the technology industry and sometimes it's problematic. I know there's a lot of discussions about exceptions but Patricia, we have a graphic on data sovereignty. I'm interested in how confidential computing ensures that data sovereignty and privacy edicts are adhered to, even if they're out of alignment maybe with the pace of technology. One of the frequent examples is when you delete data, can you actually prove the data is deleted with a hundred percent certainty, you got to prove that and a lot of other issues. So looking at this slide, maybe you could take us through your thinking on data sovereignty. >> Perfect. So for us, data sovereignty is only one of the three pillars of digital sovereignty. And I don't want to give the impression that confidential computing addresses it at all, that's why we want to step back and say, hey, digital sovereignty includes data sovereignty where we are giving you full control and ownership of the location, encryption and access to your data. Operational sovereignty where the goal is to give our Google Cloud customers full visibility and control over the provider operations, right? So if there are any updates on hardware, software stack, any operations, there is full transparency, full visibility. And then the third pillar is around software sovereignty, where the customer wants to ensure that they can run their workloads without dependency on the provider's software. So they have sometimes is often referred as survivability that you can actually survive if you are untethered to the cloud and that you can use open source. Now, let's take a deep dive on data sovereignty, which by the way is one of my favorite topics. And we typically focus on saying, hey, we need to care about data residency. We care where the data resides because where the data is at rest or in processing need to typically abides to the jurisdiction, the regulations of the jurisdiction where the data resides. And others say, hey, let's focus on data protection, we want to ensure the confidentiality, and integrity, and availability of the data, which confidential computing is at the heart of that data protection. But it is yet another element that people typically don't talk about when talking about data sovereignty, which is the element of user control. And here Dave, is about what happens to the data when I give you access to my data, and this reminds me of security two decades ago, even a decade ago, where we started the security movement by putting firewall protections and logging accesses. But once you were in, you were able to do everything you wanted with the data. An insider had access to all the infrastructure, the data, and the code. And that's similar because with data sovereignty, we care about whether it resides, who is operating on the data, but the moment that the data is being processed, I need to trust that the processing of the data we abide by user's control, by the policies that I put in place of how my data is going to be used. And if you look at a lot of the regulation today and a lot of the initiatives around the International Data Space Association, IDSA and Gaia-X, there is a movement of saying the two parties, the provider of the data and the receiver of the data going to agree on a contract that describes what my data can be used for. The challenge is to ensure that once the data crosses boundaries, that the data will be used for the purposes that it was intended and specified in the contract. And if you actually bring together, and this is the exciting part, confidential computing together with policy enforcement. Now, the policy enforcement can guarantee that the data is only processed within the confines of a confidential computing environment, that the workload is in cryptographically verified that there is the workload that was meant to process the data and that the data will be only used when abiding to the confidentiality and integrity safety of the confidential computing environment. And that's why we believe confidential computing is one necessary and essential technology that will allow us to ensure data sovereignty, especially when it comes to user's control. >> Thank you for that. I mean it was a deep dive, I mean brief, but really detailed. So I appreciate that, especially the verification of the enforcement. Last question, I met you two because as part of my year-end prediction post, you guys sent in some predictions and I wasn't able to get to them in the predictions post, so I'm thrilled that you were able to make the time to come on the program. How widespread do you think the adoption of confidential computing will be in '23 and what's the maturity curve look like this decade in your opinion? Maybe each of you could give us a brief answer. >> So my prediction in five, seven years as I started, it will become utility, it will become TLS. As of freakin' 10 years ago, we couldn't believe that websites will have certificates and we will support encrypted traffic. Now we do, and it's become ubiquity. It's exactly where our confidential computing is heeding and heading, I don't know we deserve yet. It'll take a few years of maturity for us, but we'll do that. >> Thank you. And Patricia, what's your prediction? >> I would double that and say, hey, in the very near future, you will not be able to afford not having it. I believe as digital sovereignty becomes ever more top of mind with sovereign states and also for multinational organizations, and for organizations that want to collaborate with each other, confidential computing will become the norm, it will become the default, if I say mode of operation. I like to compare that today is inconceivable if we talk to the young technologists, it's inconceivable to think that at some point in history and I happen to be alive, that we had data at rest that was non-encrypted, data in transit that was not encrypted. And I think that we'll be inconceivable at some point in the near future that to have unencrypted data while we use. >> You know, and plus I think the beauty of the this industry is because there's so much competition, this essentially comes for free. I want to thank you both for spending some time on Breaking Analysis, there's so much more we could cover. I hope you'll come back to share the progress that you're making in this area and we can double click on some of these topics. Really appreciate your time. >> Anytime. >> Thank you so much, yeah. >> In summary, while confidential computing is being touted by the cloud players as a promising technology for enhancing data privacy and security, there are also those as we said, who remain skeptical. The truth probably lies somewhere in between and it will depend on the specific implementation and the use case as to how effective confidential computing will be. Look as with any new tech, it's important to carefully evaluate the potential benefits, the drawbacks, and make informed decisions based on the specific requirements in the situation and the constraints of each individual customer. But the bottom line is silicon manufacturers are working with cloud providers and other system companies to include confidential computing into their architectures. Competition in our view will moderate price hikes and at the end of the day, this is under-the-covers technology that essentially will come for free, so we'll take it. I want to thank our guests today, Nelly and Patricia from Google. And thanks to Alex Myerson who's on production and manages the podcast. Ken Schiffman as well out of our Boston studio. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters, and Rob Hoof is our editor-in-chief over at siliconangle.com, does some great editing for us. Thank you all. Remember all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com where you can get all the news. If you want to get in touch, you can email me at david.vellante@siliconangle.com or DM me at D Vellante, and you can also comment on my LinkedIn post. Definitely you want to check out etr.ai for the best survey data in the enterprise tech business. I know we didn't hit on a lot today, but there's some amazing data and it's always being updated, so check that out. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (subtle music)

>> Welcome back to the program, and we're going to dig into the number one topic on the minds of every technology organization. That's cybersecurity. Survey data from ETR, our data partner, shows that among CIOs and IT decision makers, cybersecurity continues to rank as the number one technology priority to be addressed in the coming year. That's ahead of even cloud migration and analytics. And with me, to discuss this critical topic area, are Jim Shook, who's the global director of cybersecurity and compliance practice at Dell Technologies, and he's joined by Andrew Gonzalez, who focuses on cloud and infrastructure consulting at DXC Technology. Gents, welcome, good to have you. >> Thanks, Dave. Great to be here. >> Thank you. >> Jim, let's start with you. What are you seeing from the front lines in terms of the attack surface and how are customers responding these days? >> It's always up and down and back and forth. The bad actors are smart, they adapt to everything that we do, so, we're seeing more and more kind of living off the land, they're not necessarily deploying malware, makes it harder to find what they're doing. And I think, though, Dave, we've adapted and this whole notion of cyber resilience really helps our customers figure this out. And the idea there goes beyond cybersecurity, it's let's protect as much as possible, so we keep the bad actors out as much as we can, but then let's have the ability to adapt to and recover to the extent that the bad actors are successful. So, we're recognizing that we can't be perfect 100% of the time against 100% of the bad actors. Let's keep out what we can, but then recognize and have that ability to recover when necessary. >> Yeah, thank you. So, Andrew, I like what Jim was saying about living off the land, of course, meaning using your own tooling against you, kind of hiding in plain sight, if you will. And as Jim was saying, you can't be perfect, but so, given that, what's your perspective on what good cybersecurity hygiene looks like? >> Yeah, so you have to understand what your crown-jewel data looks like, what a good copy of a recoverable asset looks like when you look at an attack, if it were to occur, right? How you get that copy of data back into production. And not only that, but what that golden image actually entails. So, whether it's networking, storage, some copy of a source code, intellectual property, maybe SIM2B data, or an Active Directory, or DNS dump, right? Understanding what your data actually entails, so that you can protect it, and that you can build out your recovery plan for it. >> So, and where's that live? Where's that gold copy? You put in a yellow sticky? You know, it's got to be somewhere safe, right? So, you have to think about that chain as well, right? >> Absolutely. Yeah. So, a lot of folks have not gone through the exercise of identifying what that golden copy looks like. Everyone has a DR scenario, everyone has a DR strategy, but actually identifying what that golden crown-jewel data, let's call it, actually entails is one aspect of it, and then where to put it, how to protect it, how to make it immutable and isolated, that's the other portion of it. >> If I go back to sort of earlier part of last decade, cybersecurity was kind of a check-off item, and then as you got toward the middle part of the decade, and I'd say clearly by 2016, security became a boardroom issue, it was on the agenda every quarter at the board meetings. So, compliance is no longer the driver is my point. The driver is business risk, real loss of reputation, or data, or money, etc. What are the business implications of not having your cyber house in order today? >> They're extreme, Dave. I mean, the bad actors are good at what they do, these losses by organizations tens, hundreds of millions into the billions, sometimes, plus the reputational damage that's difficult to really measure. There haven't been a lot of organizations that have actually been put out of business by an attack, at least not directly, if they're larger organizations. But that's also on the table too. So, you can't just rely on, oh, we need to do A, B and C because our regulators require it. You need to look at what the actual risk is to the business, and then come up with the strategy from there. >> Jim, staying with you. One of the most common targets we hear of attackers is to go after the backup corpus. So, how should customers think about protecting themselves from that tactic? >> Well, Dave, you hit on it before, right? Everybody's had the backup and DR strategies for a long time going back to requirements that we had in place for physical disaster or human error. And that's a great starting point for a resilience capability. But that's all it is, is a starting point. Because the bad actors will, they also understand that you have those capabilities, and they've adapted to that. In every sophisticated attack that we see, the backup is a target, the bad actors want to take it out, or corrupt it, or do something else to that backup so that it's not available to you. That's not to say they're always successful, and it's still a good control to have in place because maybe it will survive. But you have to plan beyond that. So, the capabilities that we talk about with resilience, let's harden that backup infrastructure, you've already got it in place, let's use the capabilities that are there like immutability and other controls to make it more difficult for the bad actors to get to. But then, as Andrew said, that gold copy, that critical systems, you need to protect that in something that's more secure, which commonly we might say a cyber vault, or there's a lot of different capabilities for cyber vaulting, some far better than others. And that's some of the things that we focus on. >> You know, it's interesting, but I've talked to a lot of CIOs about this prior to the pandemic, they had their, as you're pointing out, Jim, they had their DR strategy in place, but they felt like they weren't business-resilient, and they realized that when we had the forced march to digital. So, Andrew, are there solutions out there to help with this problem? Do you guys have an answer to this? >> Yeah, absolutely. So, I'm glad you brought up resiliency. We take a position that to be cyber resilient, it includes operational resiliency, it includes understanding at the C level what the implication of an attack means, as we stated, and then how to recover back into production. When you look at protecting that data, not only do you want to put it into what we call a vault, which is a Dell technology that is an offline immutable copy of your crown-jewel data, but also how to recover it in real time. So, DXC offers a, I don't want to call it a turnkey solution, since we architect these specific to each client needs, right? When we look at what client data entails, their recovery point, objectives, recovery time objectives, what we call quality of the restoration, but, when we architect these out, we look at not only how to protect the data, but how to alert and monitor for attacks in realtime. How to understand what we should do when a breach is in progress. Putting together with our security operations centers a forensic and recovery plan and a runbook for the client. And then being able to cleanse and remediate, so that we can get that data back into production. These are all services that DXC offers in conjunction with the Dell solution to protect and recover and keep bad actors out. And if we can't keep 'em out, to ensure that we are back into production in short order. >> This discussion we've been having about DR kind of versus resilience, and you were just talking about RPO and RTO, I mean, it used to be that a lot of firms wouldn't even test their recovery, 'cause it was too risky, or maybe they tested it on July 4th or something like that, but I'm inferring that's changed. I wonder if we could double-click on recovery, how hard is it to test that recovery, and how quickly are you seeing organizations recover from attacks? >> So, it depends, right? On the industry vertical, what kind of data, again, financial services client compared to a manufacturing client are going to be two separate conversations. We've seen it as quickly as being able to recover in six hours, in 12 hours, in some instances we have the grace period of a day to a couple days, we do offer the ability to run scenarios once a quarter where we can stand up in our systems, the production data that we are protecting to ensure that we have a good recoverable copy. But it depends on the client. >> I really like the emphasis here, Dave, that you're raising and that Andrew's talking about, it's not on the technology of how the data gets protected, it's focused on the recovery. That's all that we want to do. And so, the solution with DXC really focuses on generating that recovery for customers. I think where people get a little bit twisted up on their testing capability is you have to think about different scenarios. So, there are scenarios where the attack might be small, it might be limited to a database or an application. It might be really broadly based, like the NotPetya attacks from a few years ago. In the regulatory environment we call those attacks severe but plausible. So, you can't necessarily test everything with the infrastructure, but you can test some things with the infrastructure, others, you might sit around on a tabletop exercise, or walk through what that looks like to really get that recovery kind of muscle memory, so that people know what to do when those things occur. But the key to it, as Andrew said before, have to focus down what are those critical applications. What do we need? What's most important? What has to come back first? And that really will go a long way towards having the right recovery points and recovery times from a cyber disaster. >> Yeah, makes sense. Understanding the value of that data is going to inform you how to respond and how to prioritize. Andrew, one of the things that we hear a lot on theCUBE, especially lately, is around IOT, IIOT, Industry 4.0, the whole OT security piece of it. And the problem being that, traditionally, operations technologies have been air gapped, often by design, but as businesses increasingly they're driving initiatives like Industry 4.0, and they're connecting these OT systems to IT systems. They're driving efficiency, preventative maintenance, etc. So, a lot of data flowing through the pipes, if you will. What are you seeing in terms of the threats to critical infrastructure, and how should customers think about addressing these issues? >> Yeah. So, bad actors can come in many forms, we've seen instances of social engineering, we've seen USB stick dropped in a warehouse. That data that is flowing through the IOT device is as sensitive now as your core mainframe infrastructure data. So, when you look at it from a protection standpoint, conceptually, it's not dissimilar from what we've been talking about, where you want to understand, again, what the most critical data is. Looking at IOT data and applications is no different than your core systems now, right? Depending on what your business is, right? So, when we're looking at protecting these, yes, we want firewalls, yes, we want air gap solutions, yes, we want front end protection, but we're looking at it from a resiliency perspective. Putting that data, understanding what data entails to put in the vault from an IOT perspective is just as critical as it is for your core systems. >> Jim, anything you can add to this topic? >> Yeah, I think you hit on the key points there. Everything is interconnected. So, even in the days where maybe people thought the OT systems weren't online, oftentimes the IT systems are talking to them, or controlling them SCADA systems, or perhaps supporting them. Think back to the pipeline attack of last year. All the public testimony was that the OT systems didn't get attacked directly, but there was uncertainty around that, and the IT systems hadn't been secured. So, that caused the OT systems to have to shut down. It certainly is a different recovery when you're shutting them down on your own versus being attacked, but the outcome was the same, that the business couldn't operate. So, you really have to take all of those into account, and I think that does go back to exactly what Andrew's saying, understanding your critical business services, and then the applications and data, and other components that support those and drive those, and making sure those are protected, you understand them, you have the ability to recover them if necessary. >> So guys, I mean, you made the point, I mean, you're right. The adversary is highly capable, they're motivated, 'cause the ROI is so lucrative. It's like this never-ending battle that cybersecurity pros go through, it really is kind of frontline sort of technical heroes, if you will. But sometimes it just feels daunting. Why are you optimistic about the future of cyber from the good guys' perspective? >> I think we're coming at the problem the right way, Dave, so that focus, I'm so pleased with the idea that we are planning that the systems aren't going to be 100% capable every single time and let's figure that out, right? That's real-world stuff. So, just as the bad actors continue to adapt and expand, so do we. And I think the differences there, the common criminals, it's getting harder and harder for them. The more sophisticated ones, they're tough to beat all the time, and, of course, you've raised the question of some nation states and other activities, but there's a lot more information sharing, there's a lot more focus from the business side of the house, and not just the IT side of the house that we need to figure these things out. >> Yeah. To add to that, I think furthering education for the client base is important. You brought up a point earlier, it used to be a boardroom conversation due to compliance reasons. Now, as we have been in the market for a while, we continue to mature the offerings, it's further education for not only the business itself, but for the IT systems and how they interconnect, and working together so that these systems can be protected, and continue to be evolved, and continue to be protected through multiple frameworks as opposed to seeing it as another check-the-box item that the board has to adhere to. >> All right, guys. We got to go. Thank you so much. Great conversation on a really important topic. Keep keep up the good work. Appreciate it. >> Thanks, Dave. >> Thank you. >> All right. And thank you for watching. Stay tuned for more excellent discussions around the partnership between Dell Technologies and DXC Technology. We're talking about solving real-world problems, how this partnership has evolved over time, really meeting the changing enterprise landscape challenges. Keep it right there.

>> From the Cube Studios in Palo Alto in Boston bringing you data-driven insights from the Cube and ETR. This is Breaking Analysis with Dave Vellante. >> The unraveling of market enthusiasm continued in Q4 of 2022 with the earnings reports from the US hyperscalers, the big three now all in. As we said earlier this year, even the cloud is an immune from the macro headwinds and the cracks in the armor that we saw from the data that we shared last summer, they're playing out into 2023. For the most part actuals are disappointing beyond expectations including our own. It turns out that our estimates for the big three hyperscaler's revenue missed by 1.2 billion or 2.7% lower than we had forecast from even our most recent November estimates. And we expect continued decelerating growth rates for the hyperscalers through the summer of 2023 and we don't think that's going to abate until comparisons get easier. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, we share our view of what's happening in cloud markets not just for the hyperscalers but other firms that have hitched a ride on the cloud. And we'll share new ETR data that shows why these trends are playing out tactics that customers are employing to deal with their cost challenges and how long the pain is likely to last. You know, riding the cloud wave, it's a two-edged sword. Let's look at the players that have gone all in on or are exposed to both the positive and negative trends of cloud. Look the cloud has been a huge tailwind for so many companies like Snowflake and Databricks, Workday, Salesforce, Mongo's move with Atlas, Red Hats Cloud strategy with OpenShift and so forth. And you know, the flip side is because cloud is elastic what comes up can also go down very easily. Here's an XY graphic from ETR that shows spending momentum or net score on the vertical axis and market presence in the dataset on the horizontal axis provision or called overlap. This is data from the January 2023 survey and that the red dotted lines show the positions of several companies that we've highlighted going back to January 2021. So let's unpack this for a bit starting with the big three hyperscalers. The first point is AWS and Azure continue to solidify their moat relative to Google Cloud platform. And we're going to get into this in a moment, but Azure and AWS revenues are five to six times that of GCP for IaaS. And at those deltas, Google should be gaining ground much faster than the big two. The second point on Google is notice the red line on GCP relative to its starting point. While it appears to be gaining ground on the horizontal axis, its net score is now below that of AWS and Azure in the survey. So despite its significantly smaller size it's just not keeping pace with the leaders in terms of market momentum. Now looking at AWS and Microsoft, what we see is basically AWS is holding serve. As we know both Google and Microsoft benefit from including SaaS in their cloud numbers. So the fact that AWS hasn't seen a huge downward momentum relative to a January 2021 position is one positive in the data. And both companies are well above that magic 40% line on the Y-axis, anything above 40% we consider to be highly elevated. But the fact remains that they're down as are most of the names on this chart. So let's take a closer look. I want to start with Snowflake and Databricks. Snowflake, as we reported from several quarters back came down to Earth, it was up in the 80% range in the Y-axis here. And it's still highly elevated in the 60% range and it continues to move to the right, which is positive but as we'll address in a moment it's customers can dial down consumption just as in any cloud. Now, Databricks is really interesting. It's not a public company, it never made it to IPO during the sort of tech bubble. So we don't have the same level of transparency that we do with other companies that did make it through. But look at how much more prominent it is on the X-axis relative to January 2021. And it's net score is basically held up over that period of time. So that's a real positive for Databricks. Next, look at Workday and Salesforce. They've held up relatively well, both inching to the right and generally holding their net scores. Same from Mongo, which is the brown dot above its name that says Elastic, it says a little gets a little crowded which Elastic's actually the blue dot above it. But generally, SaaS is harder to dial down, Workday, Salesforce, Oracles, SaaS and others. So it's harder to dial down because commitments have been made in advance, they're kind of locked in. Now, one of the discussions from last summer was as Mongo, less discretionary than analytics i.e. Snowflake. And it's an interesting debate but maybe Snowflake customers, you know, they're also generally committed to a dollar amount. So over time the spending is going to be there. But in the short term, yeah maybe Snowflake customers can dial down. Now that highlighted dotted red line, that bolded one is Datadog and you can see it's made major strides on the X-axis but its net score has decelerated quite dramatically. Openshift's momentum in the survey has dropped although IBM just announced that OpenShift has a a billion dollar ARR and I suspect what's happening there is IBM consulting is bundling OpenShift into its modernization projects. It's got a, that sort of captive base if you will. And as such it's probably not as top of mind to the respondents but I'll bet you the developers are certainly aware of it. Now the other really notable call out here is CloudFlare, We've reported on them earlier. Cloudflare's net score has held up really well since January of 2021. It really hasn't seen the downdraft of some of these others, but it's making major major moves to the right gaining market presence. We really like how CloudFlare is performing. And the last comment is on Oracle which as you can see, despite its much, much lower net score continues to gain ground in the market and thrive from a profitability standpoint. But the data pretty clearly shows that there's a downdraft in the market. Okay, so what's happening here? Let's dig deeper into this data. Here's a graphic from the most recent ETR drill down asking customers that said they were going to cut spending what technique they're using to do so. Now, as we've previously reported, consolidating redundant vendors is by far the most cited approach but there's two key points we want to make here. One is reducing excess cloud resources. As you can see in the bars is the second most cited technique and it's up from the previous polling period. The second we're not showing, you know directly but we've got some red call outs there. Reducing cloud costs jumps to 29% and 28% respectively in financial services and tech telco. And it's much closer to second. It's basically neck and neck with consolidating redundant vendors in those two industries. So they're being really aggressive about optimizing cloud cost. Okay, so as we said, cloud is great 'cause you can dial it up but it's just as easy to dial down. We've identified six factors that customers tell us are affecting their cloud consumption and there are probably more, if you got more we'd love to hear them but these are the ones that are fairly prominent that have hit our radar. First, rising mortgage rates mean banks are processing fewer loans means less cloud. The crypto crash means less trading activity and that means less cloud resources. Third lower ad spend has led companies to reduce not only you know, their ad buying but also their frequency of running their analytics and their calculations. And they're also often using less data, maybe compressing the timeframe of the corpus down to a shorter time period. Also very prominent is down to the bottom left, using lower cost compute instances. For example, Graviton from AWS or AMD chips and tiering storage to cheaper S3 or deep archived tiers. And finally, optimizing based on better pricing plans. So customers are moving from, you know, smaller companies in particular moving maybe from on demand or other larger companies that are experimenting using on demand or they're moving to spot pricing or reserved instances or optimized savings plans. That all lowers cost and that means less cloud resource consumption and less cloud revenue. Now in the days when everything was on prem CFOs, what would they do? They would freeze CapEx and IT Pros would have to try to do more with less and often that meant a lot of manual tasks. With the cloud it's much easier to move things around. It still takes some thinking and some effort but it's dramatically simpler to do so. So you can get those savings a lot faster. Now of course the other huge factor is you can cut or you can freeze. And this graphic shows data from a recent ETR survey with 159 respondents and you can see the meaningful uptick in hiring freezes, freezing new IT deployments and layoffs. And as we've been reporting, this has been trending up since earlier last year. And note the call out, this is especially prominent in retail sectors, all three of these techniques jump up in retail and that's a bit of a concern because oftentimes consumer spending helps the economy make a softer landing out of a pullback. But this is a potential canary in the coal mine. If retail firms are pulling back it's because consumers aren't spending as much. And so we're keeping a close eye on that. So let's boil this down to the market data and what this all means. So in this graphic we show our estimates for Q4 IaaS revenues compared to the "actual" IaaS revenues. And we say quote because AWS is the only one that reports, you know clean revenue and IaaS, Azure and GCP don't report actuals. Why would they? Because it would make them look even, you know smaller relative to AWS. Rather, they bury the figures in overall cloud which includes their, you know G-Suite for Google and all the Microsoft SaaS. And then they give us little tidbits about in Microsoft's case, Azure, they give growth rates. Google gives kind of relative growth of GCP. So, and we use survey data and you know, other data to try to really pinpoint and we've been covering this for, I don't know, five or six years ever since the cloud really became a thing. But looking at the data, we had AWS growing at 25% this quarter and it came in at 20%. So a significant decline relative to our expectations. AWS announced that it exited December, actually, sorry it's January data showed about a 15% mid-teens growth rate. So that's, you know, something we're watching. Azure was two points off our forecast coming in at 38% growth. It said it exited December in the 35% growth range and it said that it's expecting five points of deceleration off of that. So think 30% for Azure. GCP came in three points off our expectation coming in 35% and Alibaba has yet to report but we've shaved a bid off that forecast based on some survey data and you know what maybe 9% is even still not enough. Now for the year, the big four hyperscalers generated almost 160 billion of revenue, but that was 7 billion lower than what what we expected coming into 2022. For 2023, we're expecting 21% growth for a total of 193.3 billion. And while it's, you know, lower, you know, significantly lower than historical expectations it's still four to five times the overall spending forecast that we just shared with you in our predictions post of between 4 and 5% for the overall market. We think AWS is going to come in in around 93 billion this year with Azure closing in at over 71 billion. This is, again, we're talking IaaS here. Now, despite Amazon focusing investors on the fact that AWS's absolute dollar growth is still larger than its competitors. By our estimates Azure will come in at more than 75% of AWS's forecasted revenue. That's a significant milestone. AWS is operating margins by the way declined significantly this past quarter, dropping from 30% of revenue to 24%, 30% the year earlier to 24%. Now that's still extremely healthy and we've seen wild fluctuations like this before so I don't get too freaked out about that. But I'll say this, Microsoft has a marginal cost advantage relative to AWS because one, it has a captive cloud on which to run its massive software estate. So it can just throw software at its own cloud and two software marginal costs. Marginal economics despite AWS's awesomeness in high degrees of automation, software is just a better business. Now the upshot for AWS is the ecosystem. AWS is essentially in our view positioning very smartly as a platform for data partners like Snowflake and Databricks, security partners like CrowdStrike and Okta and Palo Alto and many others and SaaS companies. You know, Microsoft is more competitive even though AWS does have competitive products. Now of course Amazon's competitive to retail companies so that's another factor but generally speaking for tech players, Amazon is a really thriving ecosystem that is a secret weapon in our view. AWS happy to spin the meter with its partners even though it sells competitive products, you know, more so in our view than other cloud players. Microsoft, of course is, don't forget is hyping now, we're hearing a lot OpenAI and ChatGPT we reported last week in our predictions post. How OpenAI is shot up in terms of market sentiment in ETR's emerging technology company surveys and people are moving to Azure to get OpenAI and get ChatGPT that is a an interesting lever. Amazon in our view has to have a response. They have lots of AI and they're going to have to make some moves there. Meanwhile, Google is emphasizing itself as an AI first company. In fact, Google spent at least five minutes of continuous dialogue, nonstop on its AI chops during its latest earnings call. So that's an area that we're watching very closely as the buzz around large language models continues. All right, let's wrap up with some assumptions for 2023. We think SaaS players are going to continue to be sticky. They're going to be somewhat insulated from all these downdrafts because they're so tied in and customers, you know they make the commitment up front, you've got the lock in. Now having said that, we do expect some backlash over time on the onerous and generally customer unfriendly pricing models of most large SaaS companies. But that's going to play out over a longer period of time. Now for cloud generally and the hyperscalers specifically we do expect accelerating growth rates into Q3 but the amplitude of the demand swings from this rubber band economy, we expect to continue to compress and become more predictable throughout the year. Estimates are coming down, CEOs we think are going to be more cautious when the market snaps back more cautious about hiring and spending and as such a perhaps we expect a more orderly return to growth which we think will slightly accelerate in Q4 as comps get easier. Now of course the big risk to these scenarios is of course the economy, the FED, consumer spending, inflation, supply chain, energy prices, wars, geopolitics, China relations, you know, all the usual stuff. But as always with our partners at ETR and the Cube community, we're here for you. We have the data and we'll be the first to report when we see a change at the margin. Okay, that's a wrap for today. I want to thank Alex Morrison who's on production and manages the podcast, Ken Schiffman as well out of our Boston studio getting this up on LinkedIn Live. Thank you for that. Kristen Martin also and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our Editor-in-Chief over at siliconangle.com. He does some great editing for us. Thank you all. Remember all these episodes are available as podcast. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com, at siliconangle.com where you can see all the data and you want to get in touch. Just all you can do is email me david.vellante@siliconangle.com or DM me @dvellante if you if you got something interesting, I'll respond. If you don't, it's either 'cause I'm swamped or it's just not tickling me. You can comment on our LinkedIn post as well. And please check out ETR.ai for the best survey data in the enterprise tech business. This is Dave Vellante for the Cube Insights powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (gentle upbeat music)

(lively music) >> Welcome back to our coverage of Cloud Native Security Con. I'm Dave Vellante, here in our Boston studio. We're connecting today, throughout the day, with Palo Alto on the ground in Seattle. And right now I'm here with Michael Foster with Red Hat. He's on the ground in Seattle. We're going to discuss the trends and containers and security and everything that's going on at the show in Seattle. Michael, good to see you, thanks for coming on. >> Good to see you, thanks for having me on. >> Lot of market momentum for Red Hat. The IBM earnings call the other day, announced OpenShift is a billion-dollar ARR. So it's quite a milestone, and it's not often, you know. It's hard enough to become a billion-dollar software company and then to have actually a billion-dollar product alongside. So congratulations on that. And let's start with the event. What's the buzz at the event? People talking about shift left, obviously supply chain security is a big topic. We've heard a little bit about or quite a bit about AI. What are you hearing on the ground? >> Yeah, so the last event I was at that I got to see you at was three months ago, with CubeCon and the talk was supply chain security. Nothing has really changed on that front, although I do think that the conversation, let's say with the tech companies versus what customers are actually looking at, is slightly different just based on the market. And, like you said, thank you for the shout-out to a billion-dollar OpenShift, and ACS is certainly excited to be part of that. We are seeing more of a consolidation, I think, especially in security. The money's still flowing into security, but people want to know what they're running. We've allowed, had some tremendous growth in the last couple years and now it's okay. Let's get a hold of the containers, the clusters that we're running, let's make sure everything's configured. They want to start implementing policies effectively and really get a feel for what's going on across all their workloads, especially with the bigger companies. I think bigger companies allow some flexibility in the security applications that they can deploy. They can have different groups that manage different ones, but in the mid to low market, you're seeing a lot of consolidation, a lot of companies that want basically one security tool to manage them all, so to speak. And I think that the features need to somewhat accommodate that. We talk supply chain, I think most people continue to care about network security, vulnerability management, shifting left and enabling developers. That's the general trend I see. Still really need to get some hands on demos and see some people that I haven't seen in a while. >> So a couple things on, 'cause, I mean, we talk about the macroeconomic climate all the time. We do a lot of survey data with our partners at ETR, and their recent data shows that in terms of cost savings, for those who are actually cutting their budgets, they're looking to consolidate redundant vendors. So, that's one form of consolidation. The other theme, of course, is there's so many tools out in the security market that consolidating tools is something that can help simplify, but then at the same time, you see opportunities open up, like IOT security. And so, you have companies that are starting up to just do that. So, there's like these countervailing trends. I often wonder, Michael, will this ever end? It's like the universe growing and tooling, what are your thoughts? >> I mean, I completely agree. It's hard to balance trying to grow the company in a time like this, at the same time while trying to secure it all, right? So you're seeing the consolidation but some of these applications and platforms need to make some promises to say, "Hey, we're going to move into this space." Right, so when you have like Red Hat who wants to come out with edge devices and help manage the IOT devices, well then, you have a security platform that can help you do that, that's built in. Then the messaging's easy. When you're trying to do that across different cloud providers and move into IOT, it becomes a little bit more challenging. And so I think that, and don't take my word for this, some of those IOT startups, you might see some purchasing in the next couple years in order to facilitate those cloud platforms to be able to expand into that area. To me it makes sense, but I don't want to hypothesize too much from the start. >> But I do, we just did our predictions post and as a security we put up the chart of candidates, and there's like dozens, and dozens, and dozens. Some that are very well funded, but I mean, you've seen some down, I mean, down rounds everywhere, but these many companies have raised over a billion dollars and it's like uh-oh, okay, so they're probably okay, maybe. But a lot of smaller firms, I mean there's just, there's too many tools in the marketplace, but it seems like there is misalignment there, you know, kind of a mismatch between, you know, what customers would like to have happen and what actually happens in the marketplace. And that just underscores, I think, the complexities in security. So I guess my question is, you know, how do you look at Cloud Native Security, and what's different from traditional security approaches? >> Okay, I mean, that's a great question, and it's something that we've been talking to customers for the last five years about. And, really, it's just a change in mindset. Containers are supposed to unleash developer speed, and if you don't have a security tool to help do that, then you're basically going to inhibit developers in some form or another. I think managing that, while also giving your security teams the ability to tell the message of we are being more secure. You know, we're limiting vulnerabilities in our cluster. We are seeing progress because containers, you know, have a shorter life cycle and there is security and speed. Having that conversation with the C-suites is a little different, especially when how they might be used to virtual machines and managing it through that. I mean, if it works, it works from a developer's standpoint. You're not taking advantage of those containers and the developer's speed, so that's the difference. Now doing that and then first challenge is making that pitch. The second challenge is making that pitch to then scale it, so you can get onboard your developers and get your containers up and running, but then as you bring in new groups, as you move over to Kubernetes or you get into more container workloads, how do you onboard your teams? How do you scale? And I tend to see a general trend of a big investment needed for about two years to make that container shift. And then the security tools come in and really blossom because once that core separation of responsibilities happens in the organization, then the security tools are able to accelerate the developer workflow and not inhibit it. >> You know, I'm glad you mentioned, you know, separation of responsibilities. We go to a lot of shows, as you know, with theCUBE, and many of them are cloud shows. And in the one hand, Cloud has, you know, obviously made the world, you know, more interesting and better in so many different ways and even security, but it's like new layers are forming. You got the cloud, you got the shared responsibility model, so the cloud is like the first line of defense. And then you got the CISO who is relying heavily on devs to, you know, the whole shift left thing. So we're asking developers to do a lot and then you're kind of behind them. I guess you have audit is like the last line of defense, but my question to you is how can software developers really ensure that cloud native tools that they're using are secure? What steps can they take to improve security and specifically what's Red Hat doing in that area? >> Yeah, well I think there's, I would actually move away from that being the developer responsibility. I think the job is the operators' and the security people. The tools to give them the ability to see. The vulnerabilities they're introducing. Let's say signing their images, actually verifying that the images that's thrown in the cloud, are the ones that they built, that can all be done and it can be done open source. So we have a DevSecOps validated pattern that Red Hat's pushed out, and it's all open source tools in the cloud native space. And you can sign your builds and verify them at runtime and make sure that you're doing that all for free as one option. But in general, I would say that the hope is that you give the developer the information to make responsible choices and that there's a dialogue between your security and operations and developer teams but security, we should not be pushing that on developer. And so I think with ACS and our tool, the goal is to get in and say, "Let's set some reasonable policies, have a conversation, let's get a security liaison." Let's say in the developer team so that we can make some changes over time. And the more we can automate that and the more we can build and have that conversation, the better that you'll, I don't say the more security clusters but I think that the more you're on your path of securing your environment. >> How much talk is there at the event about kind of recent high profile incidents? We heard, you know, Log4j, of course, was mentioned in the Keynote. Somebody, you know, I think yelled out from the audience, "We're still dealing with that." But when you think about these, you know, incidents when looking back, what lessons do you think we've learned from these events? >> Oh, I mean, I think that I would say, if you have an approach where you're managing your containers, managing the age and using containers to accelerate, so let's say no images that are older than 90 days, for example, you're going to avoid a lot of these issues. And so I think people that are still dealing with that aspect haven't set up the proper, let's say, disclosure between teams and update strategy and so on. So I don't want to, I think the Log4j, if it's still around, you know, something's missing there but in general you want to be able to respond quickly and to do that and need the tools and policies to be able to tell people how to fix that issue. I mean, the Log4j fix was seven days after, so your developers should have been well aware of that. Your security team should have been sending the messages out. And I remember even fielding all the calls, all the fires that we had to put out when that happened. But yeah. >> I thought Brian Behlendorf's, you know, talk this morning was interesting 'cause he was making an attempt to say, "Hey, here's some things that you might not be thinking about that are likely to occur." And I wonder if you could, you know, comment on them and give us your thoughts as to how the industry generally, maybe Red Hat specifically, are thinking about dealing with them. He mentioned ChatGPT or other GPT to automate Spear phishing. He said the identity problem is still not fixed. Then he talked about free riders sniffing repos essentially for known vulnerabilities that are slow to fix. He talked about regulations that might restrict shipping code. So these are things that, you know, essentially, we can, they're on the radar, but you know, we're kind of putting out, you know, yesterday's fire. What are your thoughts on those sort of potential issues that we're facing and how are you guys thinking about it? >> Yeah, that's a great question, and I think it's twofold. One, it's brought up in front of a lot of security leaders in the space for them to be aware of it because security, it's a constant battle, constant war that's being fought. ChatGPT lowers the barrier of entry for a lot of them, say, would-be hackers or people like that to understand systems and create, let's say, simple manifests to leverage Kubernetes or leverage a misconfiguration. So as the barrier drops, we as a security team in security, let's say group organization, need to be able to respond and have our own tools to be able to combat that, and we do. So a lot of it is just making sure that we shore up our barriers and that people are aware of these threats. The harder part I think is educating the public and that's why you tend to see maybe the supply chain trend be a little bit ahead of the implementation. I think they're still, for example, like S-bombs and signing an attestation. I think that's still, you know, a year, two years, away from becoming, let's say commonplace, especially in something like a production environment. Again, so, you know, stay bleeding edge, and then make sure that you're aware of these issues and we'll be constantly coming to these calls and filling you in on what we're doing and make sure that we're up to speed. >> Yeah, so I'm hearing from folks like yourself that the, you know, you think of the future of Cloud Native Security. We're going to see continued emphasis on, you know, better integration of security into the DevSecOps. You're pointing out it's really, you know, the ops piece, that runtime that we really need to shore up. You can't just put it on the shoulders of the devs. And, you know, using security focused tools and best practices. Of course you hear a lot about that and the continued drive toward automation. My question is, you know, automation, machine learning, how, where are we in that maturity cycle? How much of that is being adopted? Sometimes folks are, you know, they embrace automation but it brings, you know, unknown, unintended consequences. Are folks embracing that heavily? Are there risks associated around that, or are we kind of through that knothole in your view? >> Yeah, that's a great question. I would compare it to something like a smart home. You know, we sort of hit a wall. You can automate so much, but it has to actually be useful to your teams. So when we're going and deploying ACS and using a cloud service, like one, you know, you want something that's a service that you can easily set up. And then the other thing is you want to start in inform mode. So you can't just automate everything, even if you're doing runtime enforcement, you need to make sure that's very, very targeted to exactly what you want and then you have to be checking it because people start new workloads and people get onboarded every week or month. So it's finding that balance between policies where you can inform the developer and the operations teams and that they give them the information to act. And that worst case you can step in as a security team to stop it, you know, during the onboarding of our ACS cloud service. We have an early access program and I get on-calls, and it's not even security team, it's the operations team. It starts with the security product, you know, and sometimes it's just, "Hey, how do I, you know, set this policy so my developers will find this vulnerability like a Log4Shell and I just want to send 'em an email, right?" And these are, you know, they have the tools and they can do that. And so it's nice to see the operations take on some security. They can automate it because maybe you have a NetSec security team that doesn't know Kubernetes or containers as well. So that shared responsibility is really useful. And then just again, making that automation targeted, even though runtime enforcement is a constant thing that we talk about, the amount that we see it in the wild where people are properly setting up admission controllers and it's acting. It's, again, very targeted. Databases, cubits x, things that are basically we all know is a no-go in production. >> Thank you for that. My last question, I want to go to the, you know, the hardest part and 'cause you're talking to customers all the time and you guys are working on the hardest problems in the world. What is the hardest aspect of securing, I'm going to come back to the software supply chain, hardest aspect of securing the software supply chain from the perspective of a security pro, software engineer, developer, DevSecOps Pro, and then this part b of that is, is how are you attacking that specifically as Red Hat? >> Sure, so as a developer, it's managing vulnerabilities with updates. As an operations team, it's keeping all the cluster, because you have a bunch of different teams working in the same environment, let's say, from a security team. It's getting people to listen to you because there are a lot of things that need to be secured. And just communicating that and getting it actionable data to the people to make the decisions as hard from a C-suite. It's getting the buy-in because it's really hard to justify the dollars and cents of security when security is constantly having to have these conversations with developers. So for ACS, you know, we want to be able to give the developer those tools. We also want to build the dashboards and reporting so that people can see their vulnerabilities drop down over time. And also that they're able to respond to it quickly because really that's where the dollars and cents are made in the product. It's that a Log4Shell comes out. You get immediately notified when the feeds are updated and you have a policy in action that you can respond to it. So I can go to my CISOs and say, "Hey look, we're limiting vulnerabilities." And when this came out, the developers stopped it in production and we were able to update it with the next release. Right, like that's your bread and butter. That's the story that you want to tell. Again, it's a harder story to tell, but it's easy when you have the information to be able to justify the money that you're spending on your security tools. Hopefully that answered your question. >> It does. That was awesome. I mean, you got data, you got communication, you got the people, obviously there's skillsets, you have of course, tooling and technology is a big part of that. Michael, really appreciate you coming on the program, sharing what's happening on the ground in Seattle and can't wait to have you back. >> Yeah. Awesome. Thanks again for having me. >> Yeah, our pleasure. All right. Thanks for watching our coverage of the Cloud Native Security Con. I'm Dave Vellante. I'm in our Boston studio. We're connecting to Palo Alto. We're connecting on the ground in Seattle. Keep it right there for more coverage. Be right back. (lively music)

(upbeat music) >> Hello, this is Dave Vellante, and we're here to talk about the massive $61 billion planned acquisition of VMware by Broadcom. And I'm here with Phil Brotherton of NetApp to discuss the implications for customers, for the industry, and NetApp's particular point of view. Phil, welcome. Good to see you again. >> It's great to see you, Dave. >> So this topic has garnered a lot of conversation. What's your take on this epic event? What does it mean for the industry generally, and customers specifically? >> You know, I think time will tell a little bit, Dave. We're in the early days. We've, you know, so we heard the original announcements and then it's evolved a little bit, as we're going now. I think overall it'll be good for the ecosystem in the end. There's a lot you can do when you start combining what VMware can do with compute and some of the hardware assets of Broadcom. There's a lot of security things that can be brought, for example, to the infrastructure, that are very high-end and cool, and then integrated, so it's easy to do. So I think there's a lot of upside for it. There's obviously a lot of concern about what it means for vendor consolidation and pricing and things like that. So time will tell. >> You know, when this announcement first came out, I wrote a piece, you know, how "Broadcom will tame the VMware beast," I called it. And, you know, looked at Broadcom's history and said they're going to cut, they're going to raise prices, et cetera, et cetera. But I've seen a different tone, certainly, as Broadcom has got into the details. And I'm sure I and others maybe scared a lot of customers, but I think everybody's kind of calming down now. What are you hearing from customers about this acquisition? How are they thinking about it? >> You know, I think it varies. There's, I'd say generally we have like half our installed base, Dave, runs ESX Server, so the bulk of our customers use VMware, and generally they love VMware. And I'm talking mainly on-prem. We're just extending to the cloud now, really, at scale. And there's a lot of interest in continuing to do that, and that's really strong. The piece that's careful is this vendor, the cost issues that have come up. The things that were in your piece, actually. And what does that mean to me, and how do I balance that out? Those are the questions people are dealing with right now. >> Yeah, so there's obviously a lot of talk about the macro, the macro headwinds. Everybody's being a little cautious. The CIOs are tapping the brakes. We all sort of know that story. But we have some data from our partner ETR that ask, they go out every quarter and they survey, you know, 1500 or so IT practitioners, and they ask the ones that are planning to spend less, that are cutting, "How are you going to approach that? What's your primary methodology in terms of achieving, you know, cost optimization?" The number one, by far, answer was to consolidate redundant vendors. It was like, it's now up to about 40%. The second, distant second, was, "We're going to, you know, optimize cloud costs." You know, still significant, but it was really that consolidating the redundant vendors. Do you see that? How does NetApp fit into that? >> Yeah, that is an interesting, that's a very interesting bit of research, Dave. I think it's very right. One thing I would say is, because I've been in the infrastructure business in Silicon Valley now for 30 years. So these ups and downs are, that's a consistent thing in our industry, and I always think people should think of their infrastructure and cost management. That's always an issue, with infrastructure as cost management. What I've told customers forever is that when you look at cost management, our best customers at cost management are typically service providers. There's another aspect to cost management, is you want to automate as much as possible. And automation goes along with vendor consolidation, because how you automate different products, you don't want to have too many vendors in your layers. And what I mean by the layers of ecosystem, there's a storage layer, the network layer, the compute layer, like, the security layer, database layer, et cetera. When you think like that, everybody should pick their partners very carefully, per layer. And one last thought on this is, it's not like people are dumb, and not trying to do this. It's, when you look at what happens in the real world, acquisitions happen, things change as you go. And in these big customers, that's just normal, that things change. But you always have to have this push towards consolidating and picking your vendors very carefully. >> Also, just to follow up on that, I mean, you know, when you think about multi-cloud, and you mentioned, you know, you've got some big customers, they do a lot of M & A, it's kind of been multi-cloud by accident. "Oh, we got all these other tools and storage platforms and whatever it is." So where does NetApp fit in that whole consolidation equation? I'm thinking about, you know, cross-cloud services, which is a big VMware theme, thinking about a consistent experience, on-prem, hybrid, across the three big clouds, out to the edge. Where do you fit? >> So our view has been, and it was this view, and we extend it to the cloud, is that the data layer, so in our software, is called ONTAP, the data layer is a really important layer that provides a lot of efficiency. It only gets bigger, how you do compliance, how you do backup, DR, blah blah blah. All that data layer services needs to operate on-prem and on the clouds. So when you look at what we've done over the years, we've extended to all the clouds, our data layer. We've put controls, management tools, over the top, so that you can manage the entire data layer, on-prem and cloud, as one layer. And we're continuing to head down that path, 'cause we think that data layer is obviously the path to maximum ability to do compliance, maximum cost advantages, et cetera. So we've really been the company that set our sights on managing the data layer. Now, if you look at VMware, go up into the network layer, the compute layer, VMware is a great partner, and that's why we work with them so closely, is they're so perfect a fit for us, and they've been a great partner for 20 years for us, connecting those infrastructural data layers: compute, network, and storage. >> Well, just to stay on that for a second. I've seen recently, you kind of doubled down on your VMware alliance. You've got stuff at re:Invent I saw, with AWS, you're close to Azure, and I'm really talking about ONTAP, which is sort of an extension of what you were just talking about, Phil, which is, you know, it's kind of NetApp's storage operating system, if you will. It's a world class. But so, maybe talk about that relationship a little bit, and how you see it evolving. >> Well, so what we've been seeing consistently is, customers want to use the advantages of the cloud. So, point one. And when you have to completely refactor apps and all this stuff, it limits, it's friction. It limits what you can do, it raises costs. And what we did with VMware, VMware is this great platform for being able to run basically client-server apps on-prem and cloud, the exact same way. The problem is, when you have large data sets in the VMs, there's some cost issues and things, especially on the cloud. That drove us to work together, and do what we did. We GA-ed, we're the, so NetApp is the only independent storage, independent storage, say this right, independent storage platform certified to run with VMware cloud on Amazon. We GA-ed that last summer. We GA-ed with Azure, the Azure VMware service, a couple months ago. And you'll see news coming with GCP soon. And so the idea was, make it easy for customers to basically run in a hybrid model. And then if you back out and go, "What does that mean for you as a customer?", it's not saying you should go to the cloud, necessarily, or stay on-prem, or whatever. But it's giving you the flexibility to cost-optimize where you want to be. And from a data management point of view, ONTAP gives you the consistent data management, whichever way you decide to go. >> Yeah, so I've been following NetApp for decades, when you were Network Appliance, and I saw you go from kind of the workstation space into the enterprise. I saw you lean into virtualization really early on, and you've been a great VMware partner ever since. And you were early in cloud, so, sort of talking about, you know, that cross-cloud, what we call supercloud. I'm interested in what you're seeing in terms of specific actions that customers are taking. Like, I think about ELAs, and I think it's a two-edged sword. You know, should customers, you know, lean into ELAs right now? You know, what are you seeing there? You talked about, you know, sort of modernizing apps with things like Kubernetes, you know, cloud migration. What are some of the techniques that you're advising customers to take in the context of this acquisition? >> You know, so the basics of this are pretty easy. One is, and I think even Raghu, the CEO of VMware, has talked about this. Extending your ELA is probably a good idea. Like I said, customers love VMware, so having a commitment for a time, consistent cost management for a time is a good strategy. And I think that's why you're hearing ELA extensions being discussed. It's a good idea. The second part, and I think it goes to your surveys, that cost optimization point on the cloud is, moving to the cloud has huge advantages, but if you just kind of lift and shift, oftentimes the costs aren't realized the way you'd want. And the term "modernization," changing your app to use more Kubernetes, more cloud-native services, is often a consideration that goes into that. But that requires time. And you know, most companies have hundreds of apps, or thousands of apps, they have to consider modernizing. So you want to then think through the journey, what apps are going to move, what gets modernized, what gets lifted-shifted, how many data centers are you compressing? There's a lot of data center, the term I've been hearing is "data center evacuations," but data center consolidation. So that there's some even energy savings advantages sometimes with that. But the whole point, I mean, back up to my whole point, the whole point is having the infrastructure that gives you the flexibility to make the journey on your cost advantages and your business requirements. Not being forced to it. Like, it's not really a philosophy, it's more of a business optimization strategy. >> When you think about application modernization and Kubernetes, how does NetApp, you know, fit into that, as a data layer? >> Well, so if you kind of think, you said, like our journey, Dave, was, when we started our life, we were doing basically virtualization of volumes and things for technical customers. And the servers were always bare metal servers that we got involved with back then. This is, like, going back 20 years. Then everyone moved to VMs, and, like, it's probably, today, I mean, getting to your question in a second, but today, loosely, 20% bare metal servers, 80% virtual machines today. And containers is growing, now a big growing piece. So, if you will, sort of another level of virtual machines in containers. And containers were historically stateless, meaning the storage didn't have anything to do. Storage is always the stateful area in the architectures. But as containers are getting used more, stateful containers have become a big deal. So we've put a lot of emphasis into a product line we call Astra that is the world's best data management for containers. And that's both a cloud service and used on-prem in a lot of my customers. It's a big growth area. So that's what, when I say, like, one partner that can do data management, just, that's what we have to do. We have to keep moving with our customers to the type of data they want to store, and how do you store it most efficiently? Hey, one last thought on this is, where I really see this happening, there's a booming business right now in artificial intelligence, and we call it modern data analytics, but people combining big data lakes with AI, and that's where some of this, a lot of the container work comes in. We've extended objects, we have a thing we call file-object duality, to make it easy to bridge the old world of files to the new world of objects. Those all go hand in hand with app modernization. >> Yeah, it's a great thing about this industry. It never sits still. And you're right, it's- >> It's why I'm in it. >> Me too. Yeah, it's so much fun. There's always something. >> It is an abstraction layer. There's always going to be another abstraction layer. Serverless is another example. It's, you know, primarily stateless, that's probably going to, you know, change over time. All right, last question. In thinking about this Broadcom acquisition of VMware, in the macro climate, put a sort of bow on where NetApp fits into this equation. What's the value you bring in this context? >> Oh yeah, well it's like I said earlier, I think it's the data layer of, it's being the data layer that gives you what you guys call the supercloud, that gives you the ability to choose which cloud. Another thing, all customers are running at least two clouds, and you want to be able to pick and choose, and do it your way. So being the data layer, VMware is going to be in our infrastructures for at least as long as I'm in the computer business, Dave. I'm getting a little old. So maybe, you know, but "decades" I think is an easy prediction, and we plan to work with VMware very closely, along with our customers, as they extend from on-prem to hybrid cloud operations. That's where I think this will go. >> Yeah, and I think you're absolutely right. Look at the business case for migrating off of VMware. It just doesn't make sense. It works, it's world class, it recover... They've done so much amazing, you know, they used to be called, Moritz called it the software mainframe, right? And that's kind of what it is. I mean, it means it doesn't go down, right? And it supports virtually any application, you know, around the world, so. >> And I think getting back to your original point about your article, from the very beginning, is, I think Broadcom's really getting a sense of what they've bought, and it's going to be, hopefully, I think it'll be really a fun, another fun era in our business. >> Well, and you can drive EBIT a couple of ways. You can cut, okay, fine. And I'm sure there's some redundancies that they'll find. But there's also, you can drive top-line revenue. And you know, we've seen how, you know, EMC and then Dell used that growth from VMware to throw off free cash flow, and it was just, you know, funded so much, you know, innovation. So innovation is the key. Hock Tan has talked about that a lot. I think there's a perception that Broadcom, you know, doesn't invest in R & D. That's not true. I think they just get very focused with that investment. So, Phil, I really appreciate your time. Thanks so much for joining us. >> Thanks a lot, Dave. It's fun being here. >> Yeah, our pleasure. And thank you for watching theCUBE, your leader in enterprise and emerging tech coverage. (upbeat music)

(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. Best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights, powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> OpenAI The company, and ChatGPT have taken the world by storm. Microsoft reportedly is investing an additional 10 billion dollars into the company. But in our view, while the hype around ChatGPT is justified, we don't believe OpenAI will lock up the market with its first mover advantage. Rather, we believe that success in this market will be directly proportional to the quality and quantity of data that a technology company has at its disposal, and the compute power that it could deploy to run its system. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we unpack the excitement around ChatGPT, and debate the premise that the company's early entry into the space may not confer winner take all advantage to OpenAI. And to do so, we welcome CUBE collaborator, alum, Sarbjeet Johal, (chuckles) and John Furrier, co-host of the Cube. Great to see you Sarbjeet, John. Really appreciate you guys coming to the program. >> Great to be on. >> Okay, so what is ChatGPT? Well, actually we asked ChatGPT, what is ChatGPT? So here's what it said. ChatGPT is a state-of-the-art language model developed by OpenAI that can generate human-like text. It could be fine tuned for a variety of language tasks, such as conversation, summarization, and language translation. So I asked it, give it to me in 50 words or less. How did it do? Anything to add? >> Yeah, think it did good. It's large language model, like previous models, but it started applying the transformers sort of mechanism to focus on what prompt you have given it to itself. And then also the what answer it gave you in the first, sort of, one sentence or two sentences, and then introspect on itself, like what I have already said to you. And so just work on that. So it it's self sort of focus if you will. It does, the transformers help the large language models to do that. >> So to your point, it's a large language model, and GPT stands for generative pre-trained transformer. >> And if you put the definition back up there again, if you put it back up on the screen, let's see it back up. Okay, it actually missed the large, word large. So one of the problems with ChatGPT, it's not always accurate. It's actually a large language model, and it says state of the art language model. And if you look at Google, Google has dominated AI for many times and they're well known as being the best at this. And apparently Google has their own large language model, LLM, in play and have been holding it back to release because of backlash on the accuracy. Like just in that example you showed is a great point. They got almost right, but they missed the key word. >> You know what's funny about that John, is I had previously asked it in my prompt to give me it in less than a hundred words, and it was too long, I said I was too long for Breaking Analysis, and there it went into the fact that it's a large language model. So it largely, it gave me a really different answer the, for both times. So, but it's still pretty amazing for those of you who haven't played with it yet. And one of the best examples that I saw was Ben Charrington from This Week In ML AI podcast. And I stumbled on this thanks to Brian Gracely, who was listening to one of his Cloudcasts. Basically what Ben did is he took, he prompted ChatGPT to interview ChatGPT, and he simply gave the system the prompts, and then he ran the questions and answers into this avatar builder and sped it up 2X so it didn't sound like a machine. And voila, it was amazing. So John is ChatGPT going to take over as a cube host? >> Well, I was thinking, we get the questions in advance sometimes from PR people. We should actually just plug it in ChatGPT, add it to our notes, and saying, "Is this good enough for you? Let's ask the real question." So I think, you know, I think there's a lot of heavy lifting that gets done. I think the ChatGPT is a phenomenal revolution. I think it highlights the use case. Like that example we showed earlier. It gets most of it right. So it's directionally correct and it feels like it's an answer, but it's not a hundred percent accurate. And I think that's where people are seeing value in it. Writing marketing, copy, brainstorming, guest list, gift list for somebody. Write me some lyrics to a song. Give me a thesis about healthcare policy in the United States. It'll do a bang up job, and then you got to go in and you can massage it. So we're going to do three quarters of the work. That's why plagiarism and schools are kind of freaking out. And that's why Microsoft put 10 billion in, because why wouldn't this be a feature of Word, or the OS to help it do stuff on behalf of the user. So linguistically it's a beautiful thing. You can input a string and get a good answer. It's not a search result. >> And we're going to get your take on on Microsoft and, but it kind of levels the playing- but ChatGPT writes better than I do, Sarbjeet, and I know you have some good examples too. You mentioned the Reed Hastings example. >> Yeah, I was listening to Reed Hastings fireside chat with ChatGPT, and the answers were coming as sort of voice, in the voice format. And it was amazing what, he was having very sort of philosophy kind of talk with the ChatGPT, the longer sentences, like he was going on, like, just like we are talking, he was talking for like almost two minutes and then ChatGPT was answering. It was not one sentence question, and then a lot of answers from ChatGPT and yeah, you're right. I, this is our ability. I've been thinking deep about this since yesterday, we talked about, like, we want to do this segment. The data is fed into the data model. It can be the current data as well, but I think that, like, models like ChatGPT, other companies will have those too. They can, they're democratizing the intelligence, but they're not creating intelligence yet, definitely yet I can say that. They will give you all the finite answers. Like, okay, how do you do this for loop in Java, versus, you know, C sharp, and as a programmer you can do that, in, but they can't tell you that, how to write a new algorithm or write a new search algorithm for you. They cannot create a secretive code for you to- >> Not yet. >> Have competitive advantage. >> Not yet, not yet. >> but you- >> Can Google do that today? >> No one really can. The reasoning side of the data is, we talked about at our Supercloud event, with Zhamak Dehghani who's was CEO of, now of Nextdata. This next wave of data intelligence is going to come from entrepreneurs that are probably cross discipline, computer science and some other discipline. But they're going to be new things, for example, data, metadata, and data. It's hard to do reasoning like a human being, so that needs more data to train itself. So I think the first gen of this training module for the large language model they have is a corpus of text. Lot of that's why blog posts are, but the facts are wrong and sometimes out of context, because that contextual reasoning takes time, it takes intelligence. So machines need to become intelligent, and so therefore they need to be trained. So you're going to start to see, I think, a lot of acceleration on training the data sets. And again, it's only as good as the data you can get. And again, proprietary data sets will be a huge winner. Anyone who's got a large corpus of content, proprietary content like theCUBE or SiliconANGLE as a publisher will benefit from this. Large FinTech companies, anyone with large proprietary data will probably be a big winner on this generative AI wave, because it just, it will eat that up, and turn that back into something better. So I think there's going to be a lot of interesting things to look at here. And certainly productivity's going to be off the charts for vanilla and the internet is going to get swarmed with vanilla content. So if you're in the content business, and you're an original content producer of any kind, you're going to be not vanilla, so you're going to be better. So I think there's so much at play Dave (indistinct). >> I think the playing field has been risen, so we- >> Risen and leveled? >> Yeah, and leveled to certain extent. So it's now like that few people as consumers, as consumers of AI, we will have a advantage and others cannot have that advantage. So it will be democratized. That's, I'm sure about that. But if you take the example of calculator, when the calculator came in, and a lot of people are, "Oh, people can't do math anymore because calculator is there." right? So it's a similar sort of moment, just like a calculator for the next level. But, again- >> I see it more like open source, Sarbjeet, because like if you think about what ChatGPT's doing, you do a query and it comes from somewhere the value of a post from ChatGPT is just a reuse of AI. The original content accent will be come from a human. So if I lay out a paragraph from ChatGPT, did some heavy lifting on some facts, I check the facts, save me about maybe- >> Yeah, it's productive. >> An hour writing, and then I write a killer two, three sentences of, like, sharp original thinking or critical analysis. I then took that body of work, open source content, and then laid something on top of it. >> And Sarbjeet's example is a good one, because like if the calculator kids don't do math as well anymore, the slide rule, remember we had slide rules as kids, remember we first started using Waze, you know, we were this minority and you had an advantage over other drivers. Now Waze is like, you know, social traffic, you know, navigation, everybody had, you know- >> All the back roads are crowded. >> They're car crowded. (group laughs) Exactly. All right, let's, let's move on. What about this notion that futurist Ray Amara put forth and really Amara's Law that we're showing here, it's, the law is we, you know, "We tend to overestimate the effect of technology in the short run and underestimate it in the long run." Is that the case, do you think, with ChatGPT? What do you think Sarbjeet? >> I think that's true actually. There's a lot of, >> We don't debate this. >> There's a lot of awe, like when people see the results from ChatGPT, they say what, what the heck? Like, it can do this? But then if you use it more and more and more, and I ask the set of similar question, not the same question, and it gives you like same answer. It's like reading from the same bucket of text in, the interior read (indistinct) where the ChatGPT, you will see that in some couple of segments. It's very, it sounds so boring that the ChatGPT is coming out the same two sentences every time. So it is kind of good, but it's not as good as people think it is right now. But we will have, go through this, you know, hype sort of cycle and get realistic with it. And then in the long term, I think it's a great thing in the short term, it's not something which will (indistinct) >> What's your counter point? You're saying it's not. >> I, no I think the question was, it's hyped up in the short term and not it's underestimated long term. That's what I think what he said, quote. >> Yes, yeah. That's what he said. >> Okay, I think that's wrong with this, because this is a unique, ChatGPT is a unique kind of impact and it's very generational. People have been comparing it, I have been comparing to the internet, like the web, web browser Mosaic and Netscape, right, Navigator. I mean, I clearly still remember the days seeing Navigator for the first time, wow. And there weren't not many sites you could go to, everyone typed in, you know, cars.com, you know. >> That (indistinct) wasn't that overestimated, the overhyped at the beginning and underestimated. >> No, it was, it was underestimated long run, people thought. >> But that Amara's law. >> That's what is. >> No, they said overestimated? >> Overestimated near term underestimated- overhyped near term, underestimated long term. I got, right I mean? >> Well, I, yeah okay, so I would then agree, okay then- >> We were off the charts about the internet in the early days, and it actually exceeded our expectations. >> Well there were people who were, like, poo-pooing it early on. So when the browser came out, people were like, "Oh, the web's a toy for kids." I mean, in 1995 the web was a joke, right? So '96, you had online populations growing, so you had structural changes going on around the browser, internet population. And then that replaced other things, direct mail, other business activities that were once analog then went to the web, kind of read only as you, as we always talk about. So I think that's a moment where the hype long term, the smart money, and the smart industry experts all get the long term. And in this case, there's more poo-pooing in the short term. "Ah, it's not a big deal, it's just AI." I've heard many people poo-pooing ChatGPT, and a lot of smart people saying, "No this is next gen, this is different and it's only going to get better." So I think people are estimating a big long game on this one. >> So you're saying it's bifurcated. There's those who say- >> Yes. >> Okay, all right, let's get to the heart of the premise, and possibly the debate for today's episode. Will OpenAI's early entry into the market confer sustainable competitive advantage for the company. And if you look at the history of tech, the technology industry, it's kind of littered with first mover failures. Altair, IBM, Tandy, Commodore, they and Apple even, they were really early in the PC game. They took a backseat to Dell who came in the scene years later with a better business model. Netscape, you were just talking about, was all the rage in Silicon Valley, with the first browser, drove up all the housing prices out here. AltaVista was the first search engine to really, you know, index full text. >> Owned by Dell, I mean DEC. >> Owned by Digital. >> Yeah, Digital Equipment >> Compaq bought it. And of course as an aside, Digital, they wanted to showcase their hardware, right? Their super computer stuff. And then so Friendster and MySpace, they came before Facebook. The iPhone certainly wasn't the first mobile device. So lots of failed examples, but there are some recent successes like AWS and cloud. >> You could say smartphone. So I mean. >> Well I know, and you can, we can parse this so we'll debate it. Now Twitter, you could argue, had first mover advantage. You kind of gave me that one John. Bitcoin and crypto clearly had first mover advantage, and sustaining that. Guys, will OpenAI make it to the list on the right with ChatGPT, what do you think? >> I think categorically as a company, it probably won't, but as a category, I think what they're doing will, so OpenAI as a company, they get funding, there's power dynamics involved. Microsoft put a billion dollars in early on, then they just pony it up. Now they're reporting 10 billion more. So, like, if the browsers, Microsoft had competitive advantage over Netscape, and used monopoly power, and convicted by the Department of Justice for killing Netscape with their monopoly, Netscape should have had won that battle, but Microsoft killed it. In this case, Microsoft's not killing it, they're buying into it. So I think the embrace extend Microsoft power here makes OpenAI vulnerable for that one vendor solution. So the AI as a company might not make the list, but the category of what this is, large language model AI, is probably will be on the right hand side. >> Okay, we're going to come back to the government intervention and maybe do some comparisons, but what are your thoughts on this premise here? That, it will basically set- put forth the premise that it, that ChatGPT, its early entry into the market will not confer competitive advantage to >> For OpenAI. >> To Open- Yeah, do you agree with that? >> I agree with that actually. It, because Google has been at it, and they have been holding back, as John said because of the scrutiny from the Fed, right, so- >> And privacy too. >> And the privacy and the accuracy as well. But I think Sam Altman and the company on those guys, right? They have put this in a hasty way out there, you know, because it makes mistakes, and there are a lot of questions around the, sort of, where the content is coming from. You saw that as your example, it just stole the content, and without your permission, you know? >> Yeah. So as quick this aside- >> And it codes on people's behalf and the, those codes are wrong. So there's a lot of, sort of, false information it's putting out there. So it's a very vulnerable thing to do what Sam Altman- >> So even though it'll get better, others will compete. >> So look, just side note, a term which Reid Hoffman used a little bit. Like he said, it's experimental launch, like, you know, it's- >> It's pretty damn good. >> It is clever because according to Sam- >> It's more than clever. It's good. >> It's awesome, if you haven't used it. I mean you write- you read what it writes and you go, "This thing writes so well, it writes so much better than you." >> The human emotion drives that too. I think that's a big thing. But- >> I Want to add one more- >> Make your last point. >> Last one. Okay. So, but he's still holding back. He's conducting quite a few interviews. If you want to get the gist of it, there's an interview with StrictlyVC interview from yesterday with Sam Altman. Listen to that one it's an eye opening what they want- where they want to take it. But my last one I want to make it on this point is that Satya Nadella yesterday did an interview with Wall Street Journal. I think he was doing- >> You were not impressed. >> I was not impressed because he was pushing it too much. So Sam Altman's holding back so there's less backlash. >> Got 10 billion reasons to push. >> I think he's almost- >> Microsoft just laid off 10000 people. Hey ChatGPT, find me a job. You know like. (group laughs) >> He's overselling it to an extent that I think it will backfire on Microsoft. And he's over promising a lot of stuff right now, I think. I don't know why he's very jittery about all these things. And he did the same thing during Ignite as well. So he said, "Oh, this AI will write code for you and this and that." Like you called him out- >> The hyperbole- >> During your- >> from Satya Nadella, he's got a lot of hyperbole. (group talks over each other) >> All right, Let's, go ahead. >> Well, can I weigh in on the whole- >> Yeah, sure. >> Microsoft thing on whether OpenAI, here's the take on this. I think it's more like the browser moment to me, because I could relate to that experience with ChatG, personally, emotionally, when I saw that, and I remember vividly- >> You mean that aha moment (indistinct). >> Like this is obviously the future. Anything else in the old world is dead, website's going to be everywhere. It was just instant dot connection for me. And a lot of other smart people who saw this. Lot of people by the way, didn't see it. Someone said the web's a toy. At the company I was worked for at the time, Hewlett Packard, they like, they could have been in, they had invented HTML, and so like all this stuff was, like, they just passed, the web was just being passed over. But at that time, the browser got better, more websites came on board. So the structural advantage there was online web usage was growing, online user population. So that was growing exponentially with the rise of the Netscape browser. So OpenAI could stay on the right side of your list as durable, if they leverage the category that they're creating, can get the scale. And if they can get the scale, just like Twitter, that failed so many times that they still hung around. So it was a product that was always successful, right? So I mean, it should have- >> You're right, it was terrible, we kept coming back. >> The fail whale, but it still grew. So OpenAI has that moment. They could do it if Microsoft doesn't meddle too much with too much power as a vendor. They could be the Netscape Navigator, without the anti-competitive behavior of somebody else. So to me, they have the pole position. So they have an opportunity. So if not, if they don't execute, then there's opportunity. There's not a lot of barriers to entry, vis-a-vis say the CapEx of say a cloud company like AWS. You can't replicate that, Many have tried, but I think you can replicate OpenAI. >> And we're going to talk about that. Okay, so real quick, I want to bring in some ETR data. This isn't an ETR heavy segment, only because this so new, you know, they haven't coverage yet, but they do cover AI. So basically what we're seeing here is a slide on the vertical axis's net score, which is a measure of spending momentum, and in the horizontal axis's is presence in the dataset. Think of it as, like, market presence. And in the insert right there, you can see how the dots are plotted, the two columns. And so, but the key point here that we want to make, there's a bunch of companies on the left, is he like, you know, DataRobot and C3 AI and some others, but the big whales, Google, AWS, Microsoft, are really dominant in this market. So that's really the key takeaway that, can we- >> I notice IBM is way low. >> Yeah, IBM's low, and actually bring that back up and you, but then you see Oracle who actually is injecting. So I guess that's the other point is, you're not necessarily going to go buy AI, and you know, build your own AI, you're going to, it's going to be there and, it, Salesforce is going to embed it into its platform, the SaaS companies, and you're going to purchase AI. You're not necessarily going to build it. But some companies obviously are. >> I mean to quote IBM's general manager Rob Thomas, "You can't have AI with IA." information architecture and David Flynn- >> You can't Have AI without IA >> without, you can't have AI without IA. You can't have, if you have an Information Architecture, you then can power AI. Yesterday David Flynn, with Hammersmith, was on our Supercloud. He was pointing out that the relationship of storage, where you store things, also impacts the data and stressablity, and Zhamak from Nextdata, she was pointing out that same thing. So the data problem factors into all this too, Dave. >> So you got the big cloud and internet giants, they're all poised to go after this opportunity. Microsoft is investing up to 10 billion. Google's code red, which was, you know, the headline in the New York Times. Of course Apple is there and several alternatives in the market today. Guys like Chinchilla, Bloom, and there's a company Jasper and several others, and then Lena Khan looms large and the government's around the world, EU, US, China, all taking notice before the market really is coalesced around a single player. You know, John, you mentioned Netscape, they kind of really, the US government was way late to that game. It was kind of game over. And Netscape, I remember Barksdale was like, "Eh, we're going to be selling software in the enterprise anyway." and then, pshew, the company just dissipated. So, but it looks like the US government, especially with Lena Khan, they're changing the definition of antitrust and what the cause is to go after people, and they're really much more aggressive. It's only what, two years ago that (indistinct). >> Yeah, the problem I have with the federal oversight is this, they're always like late to the game, and they're slow to catch up. So in other words, they're working on stuff that should have been solved a year and a half, two years ago around some of the social networks hiding behind some of the rules around open web back in the days, and I think- >> But they're like 15 years late to that. >> Yeah, and now they got this new thing on top of it. So like, I just worry about them getting their fingers. >> But there's only two years, you know, OpenAI. >> No, but the thing (indistinct). >> No, they're still fighting other battles. But the problem with government is that they're going to label Big Tech as like a evil thing like Pharma, it's like smoke- >> You know Lena Khan wants to kill Big Tech, there's no question. >> So I think Big Tech is getting a very seriously bad rap. And I think anything that the government does that shades darkness on tech, is politically motivated in most cases. You can almost look at everything, and my 80 20 rule is in play here. 80% of the government activity around tech is bullshit, it's politically motivated, and the 20% is probably relevant, but off the mark and not organized. >> Well market forces have always been the determining factor of success. The governments, you know, have been pretty much failed. I mean you look at IBM's antitrust, that, what did that do? The market ultimately beat them. You look at Microsoft back in the day, right? Windows 95 was peaking, the government came in. But you know, like you said, they missed the web, right, and >> so they were hanging on- >> There's nobody in government >> to Windows. >> that actually knows- >> And so, you, I think you're right. It's market forces that are going to determine this. But Sarbjeet, what do you make of Microsoft's big bet here, you weren't impressed with with Nadella. How do you think, where are they going to apply it? Is this going to be a Hail Mary for Bing, or is it going to be applied elsewhere? What do you think. >> They are saying that they will, sort of, weave this into their products, office products, productivity and also to write code as well, developer productivity as well. That's a big play for them. But coming back to your antitrust sort of comments, right? I believe the, your comment was like, oh, fed was late 10 years or 15 years earlier, but now they're two years. But things are moving very fast now as compared to they used to move. >> So two years is like 10 Years. >> Yeah, two years is like 10 years. Just want to make that point. (Dave laughs) This thing is going like wildfire. Any new tech which comes in that I think they're going against distribution channels. Lina Khan has commented time and again that the marketplace model is that she wants to have some grip on. Cloud marketplaces are a kind of monopolistic kind of way. >> I don't, I don't see this, I don't see a Chat AI. >> You told me it's not Bing, you had an interesting comment. >> No, no. First of all, this is great from Microsoft. If you're Microsoft- >> Why? >> Because Microsoft doesn't have the AI chops that Google has, right? Google is got so much core competency on how they run their search, how they run their backends, their cloud, even though they don't get a lot of cloud market share in the enterprise, they got a kick ass cloud cause they needed one. >> Totally. >> They've invented SRE. I mean Google's development and engineering chops are off the scales, right? Amazon's got some good chops, but Google's got like 10 times more chops than AWS in my opinion. Cloud's a whole different story. Microsoft gets AI, they get a playbook, they get a product they can render into, the not only Bing, productivity software, helping people write papers, PowerPoint, also don't forget the cloud AI can super help. We had this conversation on our Supercloud event, where AI's going to do a lot of the heavy lifting around understanding observability and managing service meshes, to managing microservices, to turning on and off applications, and or maybe writing code in real time. So there's a plethora of use cases for Microsoft to deploy this. combined with their R and D budgets, they can then turbocharge more research, build on it. So I think this gives them a car in the game, Google may have pole position with AI, but this puts Microsoft right in the game, and they already have a lot of stuff going on. But this just, I mean everything gets lifted up. Security, cloud, productivity suite, everything. >> What's under the hood at Google, and why aren't they talking about it? I mean they got to be freaked out about this. No? Or do they have kind of a magic bullet? >> I think they have the, they have the chops definitely. Magic bullet, I don't know where they are, as compared to the ChatGPT 3 or 4 models. Like they, but if you look at the online sort of activity and the videos put out there from Google folks, Google technology folks, that's account you should look at if you are looking there, they have put all these distinctions what ChatGPT 3 has used, they have been talking about for a while as well. So it's not like it's a secret thing that you cannot replicate. As you said earlier, like in the beginning of this segment, that anybody who has more data and the capacity to process that data, which Google has both, I think they will win this. >> Obviously living in Palo Alto where the Google founders are, and Google's headquarters next town over we have- >> We're so close to them. We have inside information on some of the thinking and that hasn't been reported by any outlet yet. And that is, is that, from what I'm hearing from my sources, is Google has it, they don't want to release it for many reasons. One is it might screw up their search monopoly, one, two, they're worried about the accuracy, 'cause Google will get sued. 'Cause a lot of people are jamming on this ChatGPT as, "Oh it does everything for me." when it's clearly not a hundred percent accurate all the time. >> So Lina Kahn is looming, and so Google's like be careful. >> Yeah so Google's just like, this is the third, could be a third rail. >> But the first thing you said is a concern. >> Well no. >> The disruptive (indistinct) >> What they will do is do a Waymo kind of thing, where they spin out a separate company. >> They're doing that. >> The discussions happening, they're going to spin out the separate company and put it over there, and saying, "This is AI, got search over there, don't touch that search, 'cause that's where all the revenue is." (chuckles) >> So, okay, so that's how they deal with the Clay Christensen dilemma. What's the business model here? I mean it's not advertising, right? Is it to charge you for a query? What, how do you make money at this? >> It's a good question, I mean my thinking is, first of all, it's cool to type stuff in and see a paper get written, or write a blog post, or gimme a marketing slogan for this or that or write some code. I think the API side of the business will be critical. And I think Howie Xu, I know you're going to reference some of his comments yesterday on Supercloud, I think this brings a whole 'nother user interface into technology consumption. I think the business model, not yet clear, but it will probably be some sort of either API and developer environment or just a straight up free consumer product, with some sort of freemium backend thing for business. >> And he was saying too, it's natural language is the way in which you're going to interact with these systems. >> I think it's APIs, it's APIs, APIs, APIs, because these people who are cooking up these models, and it takes a lot of compute power to train these and to, for inference as well. Somebody did the analysis on the how many cents a Google search costs to Google, and how many cents the ChatGPT query costs. It's, you know, 100x or something on that. You can take a look at that. >> A 100x on which side? >> You're saying two orders of magnitude more expensive for ChatGPT >> Much more, yeah. >> Than for Google. >> It's very expensive. >> So Google's got the data, they got the infrastructure and they got, you're saying they got the cost (indistinct) >> No actually it's a simple query as well, but they are trying to put together the answers, and they're going through a lot more data versus index data already, you know. >> Let me clarify, you're saying that Google's version of ChatGPT is more efficient? >> No, I'm, I'm saying Google search results. >> Ah, search results. >> What are used to today, but cheaper. >> But that, does that, is that going to confer advantage to Google's large language (indistinct)? >> It will, because there were deep science (indistinct). >> Google, I don't think Google search is doing a large language model on their search, it's keyword search. You know, what's the weather in Santa Cruz? Or how, what's the weather going to be? Or you know, how do I find this? Now they have done a smart job of doing some things with those queries, auto complete, re direct navigation. But it's, it's not entity. It's not like, "Hey, what's Dave Vellante thinking this week in Breaking Analysis?" ChatGPT might get that, because it'll get your Breaking Analysis, it'll synthesize it. There'll be some, maybe some clips. It'll be like, you know, I mean. >> Well I got to tell you, I asked ChatGPT to, like, I said, I'm going to enter a transcript of a discussion I had with Nir Zuk, the CTO of Palo Alto Networks, And I want you to write a 750 word blog. I never input the transcript. It wrote a 750 word blog. It attributed quotes to him, and it just pulled a bunch of stuff that, and said, okay, here it is. It talked about Supercloud, it defined Supercloud. >> It's made, it makes you- >> Wow, But it was a big lie. It was fraudulent, but still, blew me away. >> Again, vanilla content and non accurate content. So we are going to see a surge of misinformation on steroids, but I call it the vanilla content. Wow, that's just so boring, (indistinct). >> There's so many dangers. >> Make your point, cause we got to, almost out of time. >> Okay, so the consumption, like how do you consume this thing. As humans, we are consuming it and we are, like, getting a nicely, like, surprisingly shocked, you know, wow, that's cool. It's going to increase productivity and all that stuff, right? And on the danger side as well, the bad actors can take hold of it and create fake content and we have the fake sort of intelligence, if you go out there. So that's one thing. The second thing is, we are as humans are consuming this as language. Like we read that, we listen to it, whatever format we consume that is, but the ultimate usage of that will be when the machines can take that output from likes of ChatGPT, and do actions based on that. The robots can work, the robot can paint your house, we were talking about, right? Right now we can't do that. >> Data apps. >> So the data has to be ingested by the machines. It has to be digestible by the machines. And the machines cannot digest unorganized data right now, we will get better on the ingestion side as well. So we are getting better. >> Data, reasoning, insights, and action. >> I like that mall, paint my house. >> So, okay- >> By the way, that means drones that'll come in. Spray painting your house. >> Hey, it wasn't too long ago that robots couldn't climb stairs, as I like to point out. Okay, and of course it's no surprise the venture capitalists are lining up to eat at the trough, as I'd like to say. Let's hear, you'd referenced this earlier, John, let's hear what AI expert Howie Xu said at the Supercloud event, about what it takes to clone ChatGPT. Please, play the clip. >> So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest to get a, you know, another shot to the openAI sort of the level." You know, I did a (indistinct) >> Line up. >> A hundred million dollar is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So a hundred- >> Guys a hundred million dollars, that's an astoundingly low figure. What do you make of it? >> I was in an interview with, I was interviewing, I think he said hundred million or so, but in the hundreds of millions, not a billion right? >> You were trying to get him up, you were like "Hundreds of millions." >> Well I think, I- >> He's like, eh, not 10, not a billion. >> Well first of all, Howie Xu's an expert machine learning. He's at Zscaler, he's a machine learning AI guy. But he comes from VMware, he's got his technology pedigrees really off the chart. Great friend of theCUBE and kind of like a CUBE analyst for us. And he's smart. He's right. I think the barriers to entry from a dollar standpoint are lower than say the CapEx required to compete with AWS. Clearly, the CapEx spending to build all the tech for the run a cloud. >> And you don't need a huge sales force. >> And in some case apps too, it's the same thing. But I think it's not that hard. >> But am I right about that? You don't need a huge sales force either. It's, what, you know >> If the product's good, it will sell, this is a new era. The better mouse trap will win. This is the new economics in software, right? So- >> Because you look at the amount of money Lacework, and Snyk, Snowflake, Databrooks. Look at the amount of money they've raised. I mean it's like a billion dollars before they get to IPO or more. 'Cause they need promotion, they need go to market. You don't need (indistinct) >> OpenAI's been working on this for multiple five years plus it's, hasn't, wasn't born yesterday. Took a lot of years to get going. And Sam is depositioning all the success, because he's trying to manage expectations, To your point Sarbjeet, earlier. It's like, yeah, he's trying to "Whoa, whoa, settle down everybody, (Dave laughs) it's not that great." because he doesn't want to fall into that, you know, hero and then get taken down, so. >> It may take a 100 million or 150 or 200 million to train the model. But to, for the inference to, yeah to for the inference machine, It will take a lot more, I believe. >> Give it, so imagine, >> Because- >> Go ahead, sorry. >> Go ahead. But because it consumes a lot more compute cycles and it's certain level of storage and everything, right, which they already have. So I think to compute is different. To frame the model is a different cost. But to run the business is different, because I think 100 million can go into just fighting the Fed. >> Well there's a flywheel too. >> Oh that's (indistinct) >> (indistinct) >> We are running the business, right? >> It's an interesting number, but it's also kind of, like, context to it. So here, a hundred million spend it, you get there, but you got to factor in the fact that the ways companies win these days is critical mass scale, hitting a flywheel. If they can keep that flywheel of the value that they got going on and get better, you can almost imagine a marketplace where, hey, we have proprietary data, we're SiliconANGLE in theCUBE. We have proprietary content, CUBE videos, transcripts. Well wouldn't it be great if someone in a marketplace could sell a module for us, right? We buy that, Amazon's thing and things like that. So if they can get a marketplace going where you can apply to data sets that may be proprietary, you can start to see this become bigger. And so I think the key barriers to entry is going to be success. I'll give you an example, Reddit. Reddit is successful and it's hard to copy, not because of the software. >> They built the moat. >> Because you can, buy Reddit open source software and try To compete. >> They built the moat with their community. >> Their community, their scale, their user expectation. Twitter, we referenced earlier, that thing should have gone under the first two years, but there was such a great emotional product. People would tolerate the fail whale. And then, you know, well that was a whole 'nother thing. >> Then a plane landed in (John laughs) the Hudson and it was over. >> I think verticals, a lot of verticals will build applications using these models like for lawyers, for doctors, for scientists, for content creators, for- >> So you'll have many hundreds of millions of dollars investments that are going to be seeping out. If, all right, we got to wrap, if you had to put odds on it that that OpenAI is going to be the leader, maybe not a winner take all leader, but like you look at like Amazon and cloud, they're not winner take all, these aren't necessarily winner take all markets. It's not necessarily a zero sum game, but let's call it winner take most. What odds would you give that open AI 10 years from now will be in that position. >> If I'm 0 to 10 kind of thing? >> Yeah, it's like horse race, 3 to 1, 2 to 1, even money, 10 to 1, 50 to 1. >> Maybe 2 to 1, >> 2 to 1, that's pretty low odds. That's basically saying they're the favorite, they're the front runner. Would you agree with that? >> I'd say 4 to 1. >> Yeah, I was going to say I'm like a 5 to 1, 7 to 1 type of person, 'cause I'm a skeptic with, you know, there's so much competition, but- >> I think they're definitely the leader. I mean you got to say, I mean. >> Oh there's no question. There's no question about it. >> The question is can they execute? >> They're not Friendster, is what you're saying. >> They're not Friendster and they're more like Twitter and Reddit where they have momentum. If they can execute on the product side, and if they don't stumble on that, they will continue to have the lead. >> If they say stay neutral, as Sam is, has been saying, that, hey, Microsoft is one of our partners, if you look at their company model, how they have structured the company, then they're going to pay back to the investors, like Microsoft is the biggest one, up to certain, like by certain number of years, they're going to pay back from all the money they make, and after that, they're going to give the money back to the public, to the, I don't know who they give it to, like non-profit or something. (indistinct) >> Okay, the odds are dropping. (group talks over each other) That's a good point though >> Actually they might have done that to fend off the criticism of this. But it's really interesting to see the model they have adopted. >> The wildcard in all this, My last word on this is that, if there's a developer shift in how developers and data can come together again, we have conferences around the future of data, Supercloud and meshs versus, you know, how the data world, coding with data, how that evolves will also dictate, 'cause a wild card could be a shift in the landscape around how developers are using either machine learning or AI like techniques to code into their apps, so. >> That's fantastic insight. I can't thank you enough for your time, on the heels of Supercloud 2, really appreciate it. All right, thanks to John and Sarbjeet for the outstanding conversation today. Special thanks to the Palo Alto studio team. My goodness, Anderson, this great backdrop. You guys got it all out here, I'm jealous. And Noah, really appreciate it, Chuck, Andrew Frick and Cameron, Andrew Frick switching, Cameron on the video lake, great job. And Alex Myerson, he's on production, manages the podcast for us, Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters. Rob Hof is our editor-in-chief over at SiliconANGLE, does some great editing, thanks to all. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast, wherever you listen. Publish each week on wikibon.com and siliconangle.com. Want to get in touch, email me directly, david.vellante@siliconangle.com or DM me at dvellante, or comment on our LinkedIn post. And by all means, check out etr.ai. They got really great survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, We'll see you next time on Breaking Analysis. (electronic music)