>> 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.

(light upbeat music) >> Hey, everyone. Welcome back to theCUBE's day long coverage of Women in Data Science 2023. Live from Stanford University, I'm Lisa Martin. We've had some amazing conversations today with my wonderful co-host, as you've seen. Tracy Zhang joins me next for a very interesting and inspiring conversation. I know we've been bringing them to you, we're bringing you another one here. Dr. Irene Dankwa-Mullan joins us, the Chief Medical Officer at Marti Health, and a speaker at WIDS. Welcome, Irene, it's great to have you. >> Thank you. I'm delighted to be here. Thank you so much for this opportunity. >> So you have an MD and a Master of Public Health. Covid must have been an interesting time for you, with an MPH? >> Very much so. >> Yeah, talk a little bit about you, your background, and Marti Health? This is interesting. This is a brand new startup. This is a digital health equity startup. >> Yes, yes. So, I'll start with my story a little bit about myself. So I was actually born in Ghana. I finished high school there and came here for college. What would I say? After I finished my undergraduate, I went to medical school at Dartmouth and I always knew I wanted to go into public health as well as medicine. So my medical education was actually five years. I did the MPH and my medical degree, at the same time, I got my MPH from Yale School of Public Health. And after I finished, I trained in internal medicine, Johns Hopkins, and after that I went into public health. I am currently living in Maryland, so I'm in Bethesda, Maryland, and that's where I've been. And really enjoyed public health, community health, combining that aspect of sort of prevention and wellness and also working in making sure that we have community health clinics and safety net clinics. So a great experience there. I also had the privilege, after eight years in public health, I went to the National Institute of Health. >> Oh, wow. >> Where I basically worked in clinical research, basically on minority health and health disparities. So, I was in various leadership roles and helped to advance the science of health equity, working in collaboration with a lot of scientists and researchers at the NIH, really to advance the science. >> Where did your interest in health equity come from? Was there a defining moment when you were younger and you thought "There's a lot of inequities here, we have to do something about this." Where did that interest start? >> That's a great question. I think this influence was basically maybe from my upbringing as well as my family and also what I saw around me in Ghana, a lot of preventable diseases. I always say that my grandfather on my father's side was a great influence, inspired me and influenced my career because he was the only sibling, really, that went to school. And as a result, he was able to earn enough money and built, you know, a hospital. >> Oh wow. >> In their hometown. >> Oh my gosh! >> It started as a 20 bed hospital and now it's a 350 bed hospital. >> Oh, wow, that's amazing! >> In our hometown. And he knew that education was important and vital as well for wellbeing. And so he really inspired, you know, his work inspired me. And I remember in residency I went with a group of residents to this hospital in Ghana just to help over a summer break. So during a summer where we went and helped take care of the sick patients and actually learned, right? What it is like to care for so many patients and- >> Yeah. >> It was really a humbling experience. But that really inspired me. I think also being in this country. And when I came to the U.S. and really saw firsthand how patients are treated differently, based on their background or socioeconomic status. I did see firsthand, you know, that kind of unconscious bias. And, you know, drew me to the field of health disparities research and wanted to learn more and do more and contribute. >> Yeah. >> Yeah. So, I was curious. Just when did the data science aspect tap in? Like when did you decide that, okay, data science is going to be a problem solving tool to like all the problems you just said? >> Yeah, that's a good question. So while I was at the NIH, I spent eight years there, and precision medicine was launched at that time and there was a lot of heightened interest in big data and how big data could help really revolutionize medicine and healthcare. And I got the opportunity to go, you know, there was an opportunity where they were looking for physicians or deputy chief health officer at IBM. And so I went to IBM, Watson Health was being formed as a new business unit, and I was one of the first deputy chief health officers really to lead the data and the science evidence. And that's where I realized, you know, we could really, you know, the technology in healthcare, there's been a lot of data that I think we are not really using or optimizing to make sure that we're taking care of our patients. >> Yeah. >> And so that's how I got into data science and making sure that we are building technologies using the right data to advance health equity. >> Right, so talk a little bit about health equity? We mentioned you're with Marti Health. You've been there for a short time, but Marti Health is also quite new, just a few months old. Digital health equity, talk about what Marti's vision is, what its mission is to really help start dialing down a lot of the disparities that you talked about that you see every day? >> Yeah, so, I've been so privileged. I recently joined Marti Health as their Chief Medical Officer, Chief Health Officer. It's a startup that is actually trying to promote a value-based care, also promote patient-centered care for patients that are experiencing a social disadvantage as a result of their race, ethnicity. And were starting to look at and focused on patients that have sickle cell disease. >> Okay. >> Because we realize that that's a population, you know, we know sickle cell disease is a genetic disorder. It impacts a lot of patients that are from areas that are endemic malaria. >> Yeah. >> Yeah. >> And most of our patients here are African American, and when, you know, they suffer so much stigma and discrimination in the healthcare system and complications from their sickle cell disease. And so what we want to do that we feel like sickle cell is a litmus test for disparities. And we want to make sure that they get in patient-centered care. We want to make sure that we are leveraging data and the research that we've done in sickle cell disease, especially on the continent of Africa. >> Okay. >> And provide, promote better quality care for the patients. >> That's so inspiring. You know, we've heard so many great stories today. Were you able to watch the keynote this morning? >> Yes. >> I loved how it always inspires me. This conference is always, we were talking about this all day, how you walk in the Arrillaga Alumni Center here where this event is held every year, the vibe is powerful, it's positive, it's encouraging. >> Inspiring, yeah. >> Absolutely. >> Inspiring. >> Yeah, yeah. >> It's a movement, WIDS is a movement. They've created this community where you feel, I don't know, kind of superhuman. "Why can't I do this? Why not me?" We heard some great stories this morning about data science in terms of applications. You have a great application in terms of health equity. We heard about it in police violence. >> Yes. >> Which is an epidemic in this country for sure, as we know. This happens too often. How can we use data and data science as a facilitator of learning more about that, so that that can stop? I think that's so important for more people to understand all of the broad applications of data science, whether it's police violence or climate change or drug discovery or health inequities. >> Irene: Yeah. >> The potential, I think we're scratching the surface. But the potential is massive. >> Tracy: It is. >> And this is an event that really helps women and underrepresented minorities think, "Why not me? Why can't I get involved in that?" >> Yeah, and I always say we use data to make an make a lot of decisions. And especially in healthcare, we want to be careful about how we are using data because this is impacting the health and outcomes of our patients. And so science evidence is really critical, you know? We want to make sure that data is inclusive and we have quality data. >> Yes. >> And it's transparent. Our clinical trials, I always say are not always diverse and inclusive. And if that's going to form the evidence base or data points then we're doing more harm than good for our patients. And so data science, it's huge. I mean, we need a robust, responsible, trustworthy data science agenda. >> "Trust" you just brought up "trust." >> Yeah. >> I did. >> When we talk about data, we can't not talk about security and privacy and ethics but trust is table stakes. We have to be able to evaluate the data and trust in it. >> Exactly. >> And what it says and the story that can be told from it. So that trust factor is, I think, foundational to data science. >> We all see what happened with Covid, right? I mean, when the pandemic came out- >> Absolutely. >> Everyone wanted information. We wanted data, we wanted data we could trust. There was a lot of hesitancy even with the vaccine. >> Yeah. >> Right? And so public health, I mean, like you said, we had to do a lot of work making sure that the right information from the right data was being translated or conveyed to the communities. And so you are totally right. I mean, data and good information, relevant data is always key. >> Well- >> Is there any- Oh, sorry. >> Go ahead. >> Is there anything Marti Health is doing in like ensuring that you guys get the right data that you can put trust in it? >> Yes, absolutely. And so this is where we are, you know, part of it would be getting data, real world evidence data for patients who are being seen in the healthcare system with sickle cell disease, so that we can personalize the data to those patients and provide them with the right treatment, the right intervention that they need. And so part of it would be doing predictive modeling on some of the data, risk, stratifying risk, who in the sickle cell patient population is at risk of progressing. Or getting, you know, they all often get crisis, vaso-occlusive crisis because the cells, you know, the blood cell sickles and you want to avoid those chest crisis. And so part of what we'll be doing is, you know, using predictive modeling to target those at risk of the disease progressing, so that we can put in preventive measures. It's all about prevention. It's all about making sure that they're not being, you know, going to the hospital or the emergency room where sometimes they end up, you know, in pain and wanting pain medicine. And so. >> Do you see AI as being a critical piece in the transformation of healthcare, especially where inequities are concerned? >> Absolutely, and and when you say AI, I think it's responsible AI. >> Yes. >> And making sure that it's- >> Tracy: That's such a good point. >> Yeah. >> Very. >> With the right data, with relevant data, it's definitely key. I think there is so much data points that healthcare has, you know, in the healthcare space there's fiscal data, biological data, there's environmental data and we are not using it to the full capacity and full potential. >> Tracy: Yeah. >> And I think AI can do that if we do it carefully, and like I said, responsibly. >> That's a key word. You talked about trust, responsibility. Where data science, AI is concerned- >> Yeah. >> It has to be not an afterthought, it has to be intentional. >> Tracy: Exactly. >> And there needs to be a lot of education around it. Most people think, "Oh, AI is just for the technology," you know? >> Yeah, right. >> Goop. >> Yes. >> But I think we're all part, I mean everyone needs to make sure that we are collecting the right amount of data. I mean, I think we all play a part, right? >> We do. >> We do. >> In making sure that we have responsible AI, we have, you know, good data, quality data. And the data sciences is a multi-disciplinary field, I think. >> It is, which is one of the things that's exciting about it is it is multi-disciplinary. >> Tracy: Exactly. >> And so many of the people that we've talked to in data science have these very non-linear paths to get there, and so I think they bring such diversity of thought and backgrounds and experiences and thoughts and voices. That helps train the AI models with data that's more inclusive. >> Irene: Yes. >> Dropping down the volume on the bias that we know is there. To be successful, it has to. >> Definitely, I totally agree. >> What are some of the things, as we wrap up here, that you're looking forward to accomplishing as part of Marti Health? Like, maybe what's on the roadmap that you can share with us for Marti as it approaches the the second half of its first year? >> Yes, it's all about promoting health equity. It's all about, I mean, there's so much, well, I would start with, you know, part of the healthcare transformation is making sure that we are promoting care that's based on value and not volume, care that's based on good health outcomes, quality health outcomes, and not just on, you know, the quantity. And so Marti Health is trying to promote that value-based care. We are envisioning a world in which everyone can live their full life potential. Have the best health outcomes, and provide that patient-centered precision care. >> And we all want that. We all want that. We expect that precision and that personalized experience in our consumer lives, why not in healthcare? Well, thank you, Irene, for joining us on the program today. >> Thank you. >> Talking about what you're doing to really help drive the volume up on health equity, and raise awareness for the fact that there's a lot of inequities in there we have to fix. We have a long way to go. >> We have, yes. >> Lisa: But people like you are making an impact and we appreciate you joining theCUBE today and sharing what you're doing, thank you. >> Thank you. >> Thank you- >> Thank you for having me here. >> Oh, our pleasure. For our guest and Tracy Zhang, this is Lisa Martin from WIDS 2023, the eighth Annual Women in Data Science Conference brought to you by theCUBE. Stick around, our show wrap will be in just a minute. Thanks for watching. (light upbeat music)

(upbeat music) >> Morning guys and girls, welcome back to theCUBE's live coverage of Women in Data Science WIDS 2023 live at Stanford University. Lisa Martin here with my co-host for this segment, Tracy Zhang. We're really excited to be talking with a great female rockstar. You're going to learn a lot from her next, Jacqueline Kuo, solutions engineer at Dataiku. Welcome, Jacqueline. Great to have you. >> Thank you so much. >> Thank for being here. >> I'm so excited to be here. >> So one of the things I have to start out with, 'cause my mom Kathy Dahlia is watching, she's a New Yorker. You are a born and raised New Yorker and I learned from my mom and others. If you're born in New York no matter how long you've moved away, you are a New Yorker. There's you guys have like a secret club. (group laughs) >> I am definitely very proud of being born and raised in New York. My family immigrated to New York, New Jersey from Taiwan. So very proud Taiwanese American as well. But I absolutely love New York and I can't imagine living anywhere else. >> Yeah, yeah. >> I love it. >> So you studied, I was doing some research on you you studied mechanical engineering at MIT. >> Yes. >> That's huge. And you discovered your passion for all things data-related. You worked at IBM as an analytics consultant. Talk to us a little bit about your career path. Were you always interested in engineering STEM-related subjects from the time you were a child? >> I feel like my interests were ranging in many different things and I ended up landing in engineering, 'cause I felt like I wanted to gain a toolkit like a toolset to make some sort of change with or use my career to make some sort of change in this world. And I landed on engineering and mechanical engineering specifically, because I felt like I got to, in my undergrad do a lot of hands-on projects, learn every part of the engineering and design process to build products which is super-transferable and transferable skills sort of is like the trend in my career so far. Where after undergrad I wanted to move back to New York and mechanical engineering jobs are kind of few and fall far in between in the city. And I ended up landing at IBM doing analytics consulting, because I wanted to understand how to use data. I knew that data was really powerful and I knew that working with it could allow me to tell better stories to influence people across different industries. And that's also how I kind of landed at Dataiku to my current role, because it really does allow me to work across different industries and work on different problems that are just interesting. >> Yeah, I like the way that, how you mentioned building a toolkit when doing your studies at school. Do you think a lot of skills are still very relevant to your job at Dataiku right now? >> I think that at the core of it is just problem solving and asking questions and continuing to be curious or trying to challenge what is is currently given to you. And I think in an engineering degree you get a lot of that. >> Yeah, I'm sure. >> But I think that we've actually seen that a lot in the panels today already, that you get that through all different types of work and research and that kind of thoughtfulness comes across in all different industries too. >> Talk a little bit about some of the challenges, that data science is solving, because every company these days, whether it's an enterprise in manufacturing or a small business in retail, everybody has to be data-driven, because the end user, the end customer, whoever that is whether it's a person, an individual, a company, a B2B, expects to have a personalized custom experience and that comes from data. But you have to be able to understand that data treated properly, responsibly. Talk about some of the interesting projects that you're doing at Dataiku or maybe some that you've done in the past that are really kind of transformative across things climate change or police violence, some of the things that data science really is impacting these days. >> Yeah, absolutely. I think that what I love about coming to these conferences is that you hear about those really impactful social impact projects that I think everybody who's in data science wants to be working on. And I think at Dataiku what's great is that we do have this program called Ikig.AI where we work with nonprofits and we support them in their data and analytics projects. And so, a project I worked on was with the Clean Water, oh my goodness, the Ocean Cleanup project, Ocean Cleanup organization, which was amazing, because it was sort of outside of my day-to-day and it allowed me to work with them and help them understand better where plastic is being aggregated across the world and where it appears, whether that's on beaches or in lakes and rivers. So using data to help them better understand that. I feel like from a day-to-day though, we, in terms of our customers, they're really looking at very basic problems with data. And I say basic, not to diminish it, but really just to kind of say that it's high impact, but basic problems around how do they forecast sales better? That's a really kind of, sort of basic problem, but it's actually super-complex and really impactful for people, for companies when it comes to forecasting how much headcount they need to have in the next year or how much inventory to have if they're retail. And all of those are going to, especially for smaller companies, make a huge impact on whether they make profit or not. And so, what's great about working at Dataiku is you get to work on these high-impact projects and oftentimes I think from my perspective, I work as a solutions engineer on the commercial team. So it's just, we work generally with smaller customers and sometimes talking to them, me talking to them is like their first introduction to what data science is and what they can do with that data. And sort of using our platform to show them what the possibilities are and help them build a strategy around how they can implement data in their day-to-day. >> What's the difference? You were a data scientist by title and function, now you're a solutions engineer. Talk about the ascendancy into that and also some of the things that you and Tracy will talk about as those transferable, those transportable skills that probably maybe you learned in engineering, you brought data science now you're bringing to solutions engineering. >> Yeah, absolutely. So data science, I love working with data. I love getting in the weeds of things and I love, oftentimes that means debugging things or looking line by line at your code and trying to make it better. I found that on in the data science role, while those things I really loved, sometimes it also meant that I didn't, couldn't see or didn't have visibility into the broader picture of well like, well why are we doing this project? And who is it impacting? And because oftentimes your day-to-day is very much in the weeds. And so, I moved into sales or solutions engineering at Dataiku to get that perspective, because what a sales engineer does is support the sale from a technical perspective. And so, you really truly understand well, what is the customer looking for and what is going to influence them to make a purchase? And how do you tell the story of the impact of data? Because oftentimes they need to quantify well, if I purchase a software like Dataiku then I'm able to build this project and make this X impact on the business. And that is really powerful. That's where the storytelling comes in and that I feel like a lot of what we've been hearing today about connecting data with people who can actually do something with that data. That's really the bridge that we as sales engineers are trying to connect in that sales process. >> It's all about connectivity, isn't it? >> Yeah, definitely. We were talking about this earlier that it's about making impact and it's about people who we are analyzing data is like influencing. And I saw that one of the keywords or one of the biggest thing at Dataiku is everyday AI, so I wanted to just ask, could you please talk more about how does that weave into the problem solving and then day-to-day making an impact process? >> Yes, so I started working on Dataiku around three years ago and I fell in love with the product itself. The product that we have is we allow for people with different backgrounds. If you're coming from a data analyst background, data science, data engineering, maybe you are more of like a business subject matter expert, to all work in one unified central platform, one user interface. And why that's powerful is that when you're working with data, it's not just that data scientist working on their own and their own computer coding. We've heard today that it's all about connecting the data scientists with those business people, with maybe the data engineers and IT people who are actually going to put that model into production or other folks. And so, they all use different languages. Data scientists might use Python and R, your business people are using PowerPoint and Excel, everyone's using different tools. How do we bring them all in one place so that you can have conversations faster? So the business people can understand exactly what you're building with the data and can get their hands on that data and that model prediction faster. So that's what Dataiku does. That's the product that we have. And I completely forgot your question, 'cause I got so invested in talking about this. Oh, everyday AI. Yeah, so the goal of of Dataiku is really to allow for those maybe less technical people with less traditional data science backgrounds. Maybe they're data experts and they understand the data really well and they've been working in SQL for all their career. Maybe they're just subject matter experts and want to get more into working with data. We allow those people to do that through our no and low-code tools within our platform. Platform is very visual as well. And so, I've seen a lot of people learn data science, learn machine learning by working in the tool itself. And that's sort of, that's where everyday AI comes in, 'cause we truly believe that there are a lot of, there's a lot of unutilized expertise out there that we can bring in. And if we did give them access to data, imagine what we could do in the kind of work that they can do and become empowered basically with that. >> Yeah, we're just scratching the surface. I find data science so fascinating, especially when you talk about some of the real world applications, police violence, health inequities, climate change. Here we are in California and I don't know if you know, we're experiencing an atmospheric river again tomorrow. Californians and the rain- >> Storm is coming. >> We are not good... And I'm a native Californian, but we all know about climate change. People probably don't associate all of the data that is helping us understand it, make decisions based on what's coming what's happened in the past. I just find that so fascinating. But I really think we're truly at the beginning of really understanding the impact that being data-driven can actually mean whether you are investigating climate change or police violence or health inequities or your a grocery store that needs to become data-driven, because your consumer is expecting a personalized relevant experience. I want you to offer me up things that I know I was doing online grocery shopping, yesterday, I just got back from Europe and I was so thankful that my grocer is data-driven, because they made the process so easy for me. And but we have that expectation as consumers that it's going to be that easy, it's going to be that personalized. And what a lot of folks don't understand is the data the democratization of data, the AI that's helping make that a possibility that makes our lives easier. >> Yeah, I love that point around data is everywhere and the more we have, the actually the more access we actually are providing. 'cause now compute is cheaper, data is literally everywhere, you can get access to it very easily. And so, I feel like more people are just getting themselves involved and that's, I mean this whole conference around just bringing more women into this industry and more people with different backgrounds from minority groups so that we get their thoughts, their opinions into the work is so important and it's becoming a lot easier with all of the technology and tools just being open source being easier to access, being cheaper. And that I feel really hopeful about in this field. >> That's good. Hope is good, isn't it? >> Yes, that's all we need. But yeah, I'm glad to see that we're working towards that direction. I'm excited to see what lies in the future. >> We've been talking about numbers of women, percentages of women in technical roles for years and we've seen it hover around 25%. I was looking at some, I need to AnitaB.org stats from 2022 was just looking at this yesterday and the numbers are going up. I think the number was 26, 27.6% of women in technical roles. So we're seeing a growth there especially over pre-pandemic levels. Definitely the biggest challenge that still seems to be one of the biggest that remains is attrition. I would love to get your advice on what would you tell your younger self or the previous prior generation in terms of having the confidence and the courage to pursue engineering, pursue data science, pursue a technical role, and also stay in that role so you can be one of those females on stage that we saw today? >> Yeah, that's the goal right there one day. I think it's really about finding other people to lift and mentor and support you. And I talked to a bunch of people today who just found this conference through Googling it, and the fact that organizations like this exist really do help, because those are the people who are going to understand the struggles you're going through as a woman in this industry, which can get tough, but it gets easier when you have a community to share that with and to support you. And I do want to definitely give a plug to the WIDS@Dataiku team. >> Talk to us about that. >> Yeah, I was so fortunate to be a WIDS ambassador last year and again this year with Dataiku and I was here last year as well with Dataiku, but we have grown the WIDS effort so much over the last few years. So the first year we had two events in New York and also in London. Our Dataiku's global. So this year we additionally have one in the west coast out here in SF and another one in Singapore which is incredible to involve that team. But what I love is that everyone is really passionate about just getting more women involved in this industry. But then also what I find fortunate too at Dataiku is that we have a strong female, just a lot of women. >> Good. >> Yeah. >> A lot of women working as data scientists, solutions engineer and sales and all across the company who even if they aren't doing data work in a day-to-day, they are super-involved and excited to get more women in the technical field. And so. that's like our Empower group internally that hosts events and I feel like it's a really nice safe space for all of us to speak about challenges that we encounter and feel like we're not alone in that we have a support system to make it better. So I think from a nutrition standpoint every organization should have a female ERG to just support one another. >> Absolutely. There's so much value in a network in the community. I was talking to somebody who I'm blanking on this may have been in Barcelona last week, talking about a stat that showed that a really high percentage, 78% of people couldn't identify a female role model in technology. Of course, Sheryl Sandberg's been one of our role models and I thought a lot of people know Sheryl who's leaving or has left. And then a whole, YouTube influencers that have no idea that the CEO of YouTube for years has been a woman, who has- >> And she came last year to speak at WIDS. >> Did she? >> Yeah. >> Oh, I missed that. It must have been, we were probably filming. But we need more, we need to be, and it sounds like Dataiku was doing a great job of this. Tracy, we've talked about this earlier today. We need to see what we can be. And it sounds like Dataiku was pioneering that with that ERG program that you talked about. And I completely agree with you. That should be a standard program everywhere and women should feel empowered to raise their hand ask a question, or really embrace, "I'm interested in engineering, I'm interested in data science." Then maybe there's not a lot of women in classes. That's okay. Be the pioneer, be that next Sheryl Sandberg or the CTO of ChatGPT, Mira Murati, who's a female. We need more people that we can see and lean into that and embrace it. I think you're going to be one of them. >> I think so too. Just so that young girls like me like other who's so in school, can see, can look up to you and be like, "She's my role model and I want to be like her. And I know that there's someone to listen to me and to support me if I have any questions in this field." So yeah. >> Yeah, I mean that's how I feel about literally everyone that I'm surrounded by here. I find that you find role models and people to look up to in every conversation whenever I'm speaking with another woman in tech, because there's a journey that has had happen for you to get to that place. So it's incredible, this community. >> It is incredible. WIDS is a movement we're so proud of at theCUBE to have been a part of it since the very beginning, since 2015, I've been covering it since 2017. It's always one of my favorite events. It's so inspiring and it just goes to show the power that data can have, the influence, but also just that we're at the beginning of uncovering so much. Jacqueline's been such a pleasure having you on theCUBE. Thank you. >> Thank you. >> For sharing your story, sharing with us what Dataiku was doing and keep going. More power to you girl. We're going to see you up on that stage one of these years. >> Thank you so much. Thank you guys. >> Our pleasure. >> Our pleasure. >> For our guests and Tracy Zhang, this is Lisa Martin, you're watching theCUBE live at WIDS '23. #EmbraceEquity is this year's International Women's Day theme. Stick around, our next guest joins us in just a minute. (upbeat music)

>> Narrator: "theCUBE's" live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (intro music) >> Good afternoon, everyone. Welcome back to "theCUBE's" day three coverage of MWC23. Lisa Martin here in Spain, Barcelona, Spain with Dave Nicholson. We're going to have a really interesting conversation next. We're going to really dig into MWC, it's history, where it's going, some of the controversy here. Please welcome our guests. We have Jim Harris, International Best Selling Author of "Blindsided." And Carolina Milanese is here, President and Principle Analyst of creative strategies. Welcome to "theCUBE" guys. Thank you. >> Thanks. So great to be here. >> So this is day three. 80,000 people or so. You guys have a a lot of history up at this event. Caroline, I want to start with you. Talk a little bit about that. This obviously the biggest one in, in quite a few years. People are ready to be back, but there's been some, a lot of news here, but some controversy going on. Give us the history, and your perspective on some of the news that's coming out from this week's event. >> It feels like a very different show. I don't know if I would say growing up show, because we are still talking about networks and mobility, but there's so much more now around what the networks actually empower, versus the network themselves. And a little bit of maybe that's where some of the controversy is coming from, carriers still trying to find their identity, right, of, of what their role is in all there is to do with a connected world. I go back a long way. I go back to when Mobile World Congress was called, was actually called GSM, and it was in Khan. So, you know, we went from France to Spain. But just looking at the last full Mobile World Congress here in Barcelona, in pre-pandemic to now, very different show. We went from a show that was very much focused on mobility and smartphones, to a show that was all about cars. You know, we had cars everywhere, 'cause we were talking about smart cities and connected cars, to now a show this year that is very much focused on B2B. And so a lot of companies that are here to either work with the carriers, or also talk about sustainability for instance, or enable what is the next future evolution of computing with XR and VR. >> So Jim, talk to us a little bit about your background. You, I was doing a little sleuthing on you. You're really focusing on disruptive innovation. We talk about disruption a lot in different industries. We're seeing a lot of disruption in telco. We're seeing a lot of frenemies going on. Give us your thoughts about what you're seeing at this year's event. >> Well, there's some really exciting things. I listened to the keynote from Orange's CEO, and she was complaining that 55% of the traffic on her network is from five companies. And then the CEO of Deutsche Telecom got up, and he was complaining that 60% of the traffic on his network is from six entities. So do you think they coordinated pre, pre-show? But really what they're saying is, these OTT, you know, Netflix and YouTube, they should be paying us for access. Now, this is killer funny. The front page today of the show, "Daily," the CO-CEO of Netflix says, "Hey, we make less profit than the telcos, "so you should be paying us, "not the other way around." You know, we spend half of the money we make just on developing content. So, this is really interesting. The orange CEO said, "We're not challenging net neutrality. "We don't want more taxes." But boom. So this is disruptive. Huge pressure. 67% of all mobile traffic is video, right? So it's a big hog bandwidth wise. So how are they going to do this? Now, I look at it, and the business model for the, the telcos, is really selling sim cards and smartphones. But for every dollar of revenue there, there's five plus dollars in apps, and consulting and everything else. So really, but look at how they're structured. They can't, you know, take somebody who talks to the public and sells sim cards, and turn 'em in, turn 'em in to an app developer. So how are they going to square this circle? So I see some, they're being disrupted because they're sticking to what they've historically done. >> But it's interesting because at the end of the day, the conversation that we are having right now is the conversation that we had 10 years ago, where carriers don't want to just be a dumb pipe, right? And that's what they are now returning to. They tried to be media as well, but that didn't work out for most carriers, right? It is a little bit better in the US. We've seen, you know, some success there. But, but here has been more difficult. And I think that's the, the concern, that even for the next, you know, evolution, that's the, their role. >> So how do they, how do they balance this dumb pipe idea, with the fact that if you make the toll high enough, being a dumb pipe is actually a pretty good job. You know, sit back, collect check, go to the beach, right? So where, where, where, where does this end up? >> Well, I think what's going to happen is, if you see five to 15 X the revenue on top of a pipe, you know, the hyperscalers are going to start going after the business. The consulting companies like PWC, McKinsey, the app developers, they're... So how do you engage those communities as a telco to get more revenue? I think this is a question that they really need to look at. But we tend to stick within our existing business model. I'll just give you one stat that blows me away. Uber is worth more than every taxi cab company in North America added together. And so the taxi industry owns billions in assets in cars and limousines. Uber doesn't own a single vehicle. So having a widely distributed app, is a huge multiplier on valuation. And I look to a company like Safari in Kenya, which developed M-Pesa, which Pesa means mo, it's mobile money in Swahili. And 25% of the country's GDP is facilitated by M-Pesa. And that's not even on smartphones. They're feature phones, Nokia phones. I call them dumb phones, but Nokia would call them "feature phones." >> Yeah. >> So think about that. Like 25, now transactions are very small, and the cut is tiny. But when you're facilitating 25% of a country's GDP, >> Yeah. >> Tiny, over billions of transactions is huge. But that's not the way telcos have historically thought or worked. And so M-Pesa and Safari shows the way forward. What do you think on that? >> I, I think that the experience, and what they can layer on top from a services perspective, especially in the private sector, is also important. I don't, I never believe that a carrier, given how they operate, is the best media company in the world, right? It is a very different world. But I do think that there's opportunity, first of all, to, to actually tell their story in a different way. If you're thinking about everything that a network actually empowers, there's a, there's a lot there. There's a lot that is good for us as, as society. There's a lot that is good for business. What can they do to start talking about differently about their services, and then layer on top of what they offer? A better way to actually bring together private and public network. It's not all about cellular, wifi and cellular coming together. We're talking a lot about satellite here as well. So, there's definitely more there about quality of service. Is, is there though, almost a biological inevitability that prevents companies from being able to navigate that divide? >> Hmm. >> Look at, look at when, when, when we went from high definition 720P, very exciting, 1080P, 4K. Everybody ran out and got a 4K TV. Well where was the, where was the best 4K content coming from? It wasn't, it wasn't the networks, it wasn't your cable operator, it was YouTube. It was YouTube. If you had suggested that 10 years before, that that would happen, people would think that you were crazy. Is it possible for folks who are now leading their companies, getting up on stage, and daring to say, "This content's coming over, "and I want to charge you more "for using my pipes." It's like, "Really? Is that your vision? "That's the vision that you want to share with us here?" I hear the sound of dead people walking- (laughing) when I hear comments like that. And so, you know, my students at Wharton in the CTO program, who are constantly looking at this concept of disruption, would hear that and go, "Ooh, gee, did the board hear what that person said?" I, you know, am I being too critical of people who could crush me like a bug? (laughing) >> I mean, it's better that they ask the people with money than not consumers to pay, right? 'Cause we've been through a phase where the carriers were actually asking for more money depending on critical things. Like for instance, if you're doing business email, then were going to charge you more than if you were a consumer. Or if you were watching video, they would charge you more for that. Then they understood that a consumer would walk away and go somewhere else. So they stopped doing that. But to your point, I think, and, and very much to what you focus from a disruption perspective, look at what Chat GTP and what Microsoft has been doing. Not much talk about this here at the show, which is interesting, but the idea that now as a consumer, I can ask new Bing to get me the 10 best restaurants in Barcelona, and I no longer go to Yelp, or all the other businesses where I was going to before, to get their recommendation, what happens to them? You're, you're moving away, and you're taking eyeballs away from those websites. And, and I think that, that you know, your point is exactly right. That it's, it's about how, from a revenue perspective, you are spending a lot of money to facilitate somebody else, and what's in it for you? >> Yeah. And to be clear, consumers pay for everything. >> Always. Always. (laughs) >> Taxpayers and consumers always pay for everything. So there is no, "Well, we're going to make them pay, so you don't have to pay." >> And if you are not paying, you are the product. Exactly. >> Yes. (laughing) >> Carolina, talk a little bit about what you're seeing at the event from some of the infrastructure players, the hyperscalers, obviously a lot of enterprise focus here at this event. What are some of the things that you're seeing? Are you impressed with, with their focus in telco, their focus to partner, build an ecosystem? What are you seeing? >> I'm seeing also talk about sustainability, and enabling telco to be more sustainable. You know, there, there's a couple of things that are a little bit different from the US where I live, which is that telcos in Europe, have put money into sustainability through bonds. And so they use the money that they then get from the bonds that they create, to, to supply or to fuel their innovation in sustainability. And so there's a dollar amount on sustainability. There's also an opportunity obviously from a growth perspective. And there's a risk mitigation, right? Especially in Europe, more and more you're going to be evaluated based on how sustainable you are. So there are a lot of companies here, if you're thinking about the Ciscos of the world. Dell, IBM all talking about sustainability and how to help carriers measure, and then obviously be more sustainable with their consumption and, and power. >> Going to be interesting to see where that goes over the years, as we talk to, every company we talk to at whatever show, has an ESG sustainability initiative, and only, well, many of them only want to work with other companies who have the same types of initiative. So a lot of, great that there's focus on sustainability, but hopefully we'll see more action down the road. Wanted to ask you about your book, "Blind," the name is interesting, "Blindsided." >> Well, I just want to tag on to this. >> Sure. >> One of the most exciting things for me is fast charging technology. And Shalmie, cell phone, or a smartphone maker from China, just announced yesterday, a smartphone that charges from 0 to 100% in five minutes. Now this is using GAN FEST technology. And the leader in the market is a company called Navitas. And this has profound implications. You know, it starts with the smartphone, right? But then it moves to the laptops. And then it'll move to EV's. So, as we electrify the $10 trillion a year transportation industry, there's a huge opportunity. People want charging faster. There's also a sustainability story that, to Carolina's point, that it uses less electricity. So, if we electrify the grid in order to support transportation, like the Tesla Semi's coming out, there are huge demands over a period. We need energy efficiency technologies, like this GAN FEST technology. So to me, this is humongous. And it, we only see it here in the show, in Shalmie, saying, "Five minutes." And everybody, the consumers go, "Oh, that's cool." But let's look at the bigger story, which is electrifying transportation globally. And this is going to be big. >> Yeah. And, and to, and to double click on that a little bit, to be clear, when we talk about fast charging today, typically it's taking the battery from a, not a zero state of charge, but a relatively low state of charge to 80%. >> Yep. >> Then it tapers off dramatically. And that translates into less range in an EV, less usable time on any other device, and there's that whole linkage between the power in, and the battery's ability to be charged, and how much is usable. And from a sustainability perspective, we are going to have an avalanche of batteries going into secondary use cases over time. >> They don't get tossed into landfills contrary to what people might think. >> Yep. >> In fact, they are used in a variety of ways after their primary lifespan. But that, that is, that in and of itself is a revolutionary thing. I'm interested in each of your thoughts on the China factor. Glaringly absent here, from my perspective, as sort of an Apple fanboy, where are they? Why aren't they talking about their... They must, they must feel like, "Well we just don't need to." >> We don't need to. We just don't need to. >> Absolutely. >> And then you walk around and you see these, these company names that are often anglicized, and you don't necessarily immediately associate them with China, but it's like, "Wait a minute, "that looks better than what I have, "and I'm not allowed to have access to that thing." What happens in the future there geopolitically? >> It's a pretty big question for- >> Its is. >> For a short little tech show. (Caroline laughs) But what happens as we move forward? When is the entire world going to be able to leverage in a secure way, some of the stuff that's coming out of, if they're not the largest economy in the world yet, they shortly will be. >> What's the story there? >> Well, it's interesting that you mentioned First Apple that has never had a presence at Mobile World Congress. And fun enough, I'm part of the GSMA judges for the GLOMO Awards, and last night I gave out Best Mobile Phone for last year, and it was to the iPhone4 Team Pro. and best disruptive technology, which was for the satellite function feature on, on the new iPhone. So, Apple might not be here, but they are. >> Okay. >> And, and so that's the first thing. And they are as far as being top of mind to every competitor in the smartphone market still. So a lot of the things that, even from a design perspective that you see on some of the Chinese brands, really remind you of, of Apple. What is interesting for me, is how there wouldn't be, with the exception of Samsung and Motorola, there's no one else here that is non-Chinese from a smartphone point of view. So that's in itself, is something that changed dramatically over the years, especially for somebody like me that still remember Nokia being the number one in the market. >> Huh. >> So. >> Guys, we could continue this conversation. We are unfortunately out of time. But thank you so much for joining Dave and me, talking about your perspectives on the event, the industry, the disruptive forces. It's going to be really interesting to see where it goes. 'Cause at the end of the day, it's the consumers that just want to make sure I can connect wherever I am 24 by seven, and it just needs to work. Thank you so much for your insights. >> Thank you. >> Lisa, it's been great. Dave, great. It's a pleasure. >> Our pleasure. For our guests, and for Dave Nicholson, I'm Lisa Martin. You're watching, "theCUBE," the leader in live and emerging tech coverage coming to you day three of our coverage of MWC 23. Stick around. Our next guest joins us momentarily. (outro music)

>> Narrator: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (light airy music) >> Hi, everybody, welcome back to the Fira in Barcelona. My name is Dave Vellante. I'm here with my co-host Dave Nicholson. Lisa Martin is in the house. John Furrier is pounding the news from our Palo Alto studio. We are super excited to be talking about cloud at the edge, what that means. Charles Tsai is here. He's the Senior Director of product management at Dell Technologies and Peter Fetterolf is the Chief Technology Officer at ACG Business Analytics, a firm that goes deep into the TCO and the telco space, among other things. Gents, welcome to theCUBE. Thanks for coming on. Thank you. >> Good to be here. >> Yeah, good to be here. >> So I've been in search all week of the elusive next wave of monetization for the telcos. We know they make great money on connectivity, they're really good at that. But they're all talking about how they can't let this happen again. Meaning we can't let the over the top vendors yet again, basically steal our cookies. So we're going to not mess it up this time. We're going to win in the monetization. Charles, where are those monetization opportunities? Obviously at the edge, the telco cloud at the edge. What is that all about and where's the money? >> Well, Dave, I think from a Dell's perspective, what we want to be able to enable operators is a solution that enable them to roll out services much quicker, right? We know there's a lot of innovation around IoT, MEG and so on and so forth, but they continue to rely on traditional technology and way of operations is going to take them years to enable new services. So what Dell is doing is now, creating the entire vertical stack from the hardware through CAST and automation that enable them, not only to push out services very quickly, but operating them using cloud principles. >> So it's when you say the entire vertical stack, it's the integrated hardware components with like, for example, Red Hat on top- >> Right. >> Or a Wind River? >> That's correct. >> Okay, and then open API, so the developers can create workloads, I presume data companies. We just had a data conversation 'cause that was part of the original stack- >> That's correct. >> So through an open ecosystem, you can actually sort of recreate that value, correct? >> That's correct. >> Okay. >> So one thing Dell is doing, is we are offering an infrastructure block where we are taking over the overhead of certifying every release coming from the Red Hat or the Wind River of the world, right? We want telcos to spend their resources on what is going to generate them revenue. Not the overhead of creating this cloud stack. >> Dave, I remember when we went through this in the enterprise and you had companies like, you know, IBM with the AS400 and the mainframe saying it's easier to manage, which it was, but it's still, you know, it was subsumed by the open systems trend. >> Yeah, yeah. And I think that's an important thing to probe on, is this idea of what is, what exactly does it mean to be cloud at the edge in the telecom space? Because it's a much used term. >> Yeah. >> When we talk about cloud and edge, in sort of generalized IT, but what specifically does it mean? >> Yeah, so when we talk about telco cloud, first of all it's kind of different from what you're thinking about public cloud today. And there's a couple differences. One, if you look at the big hyperscaler public cloud today, they tend to be centralized in huge data centers. Okay, telco cloud, there are big data centers, but then there's also regional data centers. There are edge data centers, which are your typical like access central offices that have turned data centers, and then now even cell sites are becoming mini data centers. So it's distributed. I mean like you could have like, even in a country like say Germany, you'd have 30,000 soul sites, each one of them being a data center. So it's a very different model. Now the other thing I want to go back to the question of monetization, okay? So how do you do monetization? The only way to do that, is to be able to offer new services, like Charles said. How do you offer new services? You have to have an open ecosystem that's going to be very, very flexible. And if we look at where telcos are coming from today, they tend to be very inflexible 'cause they're all kind of single vendor solutions. And even as we've moved to virtualization, you know, if you look at packet core for instance, a lot of them are these vertical stacks of say a Nokia or Ericson or Huawei where you know, you can't really put any other vendors or any other solutions into that. So basically the idea is this kind of horizontal architecture, right? Where now across, not just my central data centers, but across my edge data centers, which would be traditionally my access COs, as well as my cell sites. I have an open environment. And we're kind of starting with, you know, packet core obviously with, and UPFs being distributed, but now open ran or virtual ran, where I can have CUs and DUs and I can split CUs, they could be at the soul site, they could be in edge data centers. But then moving forward, we're going to have like MEG, which are, you know, which are new kinds of services, you know, could be, you know, remote cars it could be gaming, it could be the Metaverse. And these are going to be a multi-vendor environment. So one of the things you need to do is you need to have you know, this cloud layer, and that's what Charles was talking about with the infrastructure blocks is helping the service providers do that, but they still own their infrastructure. >> Yeah, so it's still not clear to me how the service providers win that game but we can maybe come back to that because I want to dig into TCO a little bit. >> Sure. >> Because I have a lot of friends at Dell. I don't have a lot of friends at HPE. I've always been critical when they take an X86 server put a name on it that implies edge and they throw it over the fence to the edge, that's not going to work, okay? We're now seeing, you know we were just at the Dell booth yesterday, you did the booth crawl, which was awesome. Purpose-built servers for this environment. >> Charles: That's right. >> So there's two factors here that I want to explore in TCO. One is, how those next gen servers compare to the previous gen, especially in terms of power consumption but other factors and then how these sort of open ran, open ecosystem stacks compared to proprietary stacks. Peter, can you help us understand those? >> Yeah, sure. And Charles can comment on this as well. But I mean there, there's a couple areas. One is just moving the next generation. So especially on the Intel side, moving from Ice Lake to the Sapphire Rapids is a big deal, especially when it comes to the DU. And you know, with the radios, right? There's the radio unit, the RU, and then there's the DU the distributed unit, and the CU. The DU is really like part of the radio, but it's virtualized. When we moved from Ice lake to Sapphire Rapids, which is third generation intel to fourth generation intel, we're literally almost doubling the performance in the DU. And that's really important 'cause it means like almost half the number of servers and we're talking like 30, 40, 50,000 servers in some cases. So, you know, being able to divide that by two, that's really big, right? In terms of not only the the cost but all the TCO and the OpEx. Now another area that's really important, when I was talking moving from these vertical silos to the horizontal, the issue with the vertical silos is, you can't place any other workloads into those silos. So it's kind of inefficient, right? Whereas when we have the horizontal architecture, now you can place workloads wherever you want, which basically also means less servers but also more flexibility, more service agility. And then, you know, I think Charles can comment more, specifically on the XR8000, some things Dell's doing, 'cause it's really exciting relative to- >> Sure. >> What's happening in there. >> So, you know, when we start looking at putting compute at the edge, right? We recognize the first thing we have to do is understand the environment we are going into. So we spend with a lot of time with telcos going to the south side, going to the edge data center, looking at operation, how do the engineer today deal with maintenance replacement at those locations? Then based on understanding the operation constraints at those sites, we create innovation and take a traditional server, remodel it to make sure that we minimize the disruption to the operations, right? Just because we are helping them going from appliances to open compute, we do not want to disrupt what is have been a very efficient operation on the remote sites. So we created a lot of new ideas and develop them on general compute, where we believe we can save a lot of headache and disruptions and still provide the same level of availability, resiliency, and redundancy on an open compute platform. >> So when we talk about open, we don't mean generic? Fair? See what I mean? >> Open is more from the software workload perspective, right? A Dell server can run any type of workload that customer intend. >> But it's engineered for this? >> Environment. >> Environment. >> That's correct. >> And so what are some of the environmental issues that are dealt with in the telecom space that are different than the average data center? >> The most basic one, is in most of the traditional cell tower, they are deployed within cabinets instead of racks. So they are depth constraints that you just have no access to the rear of the chassis. So that means on a server, is everything you need to access, need to be in the front, nothing should be in the back. Then you need to consider how labor union come into play, right? There's a lot of constraint on who can go to a cell tower and touch power, who can go there and touch compute, right? So we minimize all that disruption through a modular design and make it very efficient. >> So when we took a look at XR8000, literally right here, sitting on the desk. >> Uh-huh. >> Took it apart, don't panic, just pulled out some sleds and things. >> Right, right. >> One of the interesting demonstrations was how it compared to the size of a shoe. Now apparently you hired someone at Dell specifically because they wear a size 14 shoe, (Charles laughs) so it was even more dramatic. >> That's right. >> But when you see it, and I would suggest that viewers go back and take a look at that segment, specifically on the hardware. You can see exactly what you just referenced. This idea that everything is accessible from the front. Yeah. >> So I want to dig in a couple things. So I want to push back a little bit on what you were saying about the horizontal 'cause there's the benefit, if you've got the horizontal infrastructure, you can run a lot more workloads. But I compare it to the enterprise 'cause I, that was the argument, I've made that argument with converged infrastructure versus say an Oracle vertical stack, but it turned out that actually Oracle ran Oracle better, okay? Is there an analog in telco or is this new open architecture going to be able to not only service the wide range of emerging apps but also be as resilient as the proprietary infrastructure? >> Yeah and you know, before I answer that, I also want to say that we've been writing a number of white papers. So we have actually three white papers we've just done with Dell looking at infrastructure blocks and looking at vertical versus horizontal and also looking at moving from the previous generation hardware to the next generation hardware. So all those details, you can find the white papers, and you can find them either in the Dell website or at the ACG research website >> ACGresearch.com? >> ACG research. Yeah, if you just search ACG research, you'll find- >> Yeah. >> Lots of white papers on TCO. So you know, what I want to say, relative to the vertical versus horizontal. Yeah, obviously in the vertical side, some of those things will run well, I mean it won't have issues. However, that being said, as we move to cloud native, you know, it's very high performance, okay? In terms of the stack, whether it be a Red Hat or a VMware or other cloud layers, that's really become much more mature. It now it's all CNF base, which is really containerized, very high performance. And so I don't think really performance is an issue. However, my feeling is that, if you want to offer new services and generate new revenue, you're not going to do it in vertical stacks, period. You're going to be able to do a packet core, you'll be able to do a ran over here. But now what if I want to offer a gaming service? What if I want to do metaverse? What if I want to do, you have to have an environment that's a multi-vendor environment that supports an ecosystem. Even in the RAN, when we look at the RIC, and the xApps and the rApps, these are multi-vendor environments that's going to create a lot of flexibility and you can't do that if you're restricted to, I can only have one vendor running on this hardware. >> Yeah, we're seeing these vendors work together and create RICs. That's obviously a key point, but what I'm hearing is that there may be trade offs, but the incremental value is going to overwhelm that. Second question I have, Peter is, TCO, I've been hearing a lot about 30%, you know, where's that 30% come from? Is it Op, is it from an OpEx standpoint? Is it labor, is it power? Is it, you mentioned, you know, cutting the number of servers in half. If I can unpack the granularity of that TCO, where's the benefit coming from? >> Yeah, the answer is yes. (Peter and Charles laugh) >> Okay, we'll do. >> Yeah, so- >> One side that, in terms of, where is the big bang for the bucks? >> So I mean, so you really need to look at the white paper to see details, but definitely power, definitely labor, definitely reducing the number of servers, you know, reducing the CapEx. The other thing is, is as you move to this really next generation horizontal telco cloud, there's the whole automation and orchestration, that is a key component as well. And it's enabled by what Dell is doing. It's enabled by the, because the thing is you're not going to have end-to-end automation if you have all this legacy stuff there or if you have these vertical stacks where you can't integrate. I mean you can automate that part and then you have separate automation here, you separate. you need to have integrated automation and orchestration across the whole thing. >> One other point I would add also, right, on the hardware perspective, right? With the customized hardware, what we allow operator to do is, take out the existing appliance and push a edge optimized server without reworking the entire infrastructure. There is a significant saving where you don't have to rethink about what is my power infrastructure, right? What is my security infrastructure? The server is designed to leverage the existing, what is already there. >> How should telco, Charles, plan for this transformation? Are there specific best practices that you would recommend in terms of the operational model? >> Great question. I think first thing is do an inventory of what you have. Understand what your constraints are and then come to Dell, we will love to consult with you, based on our experience on the best practices. We know how to minimize additional changes. We know how to help your support engineer, understand how to shift appliance based operation to a cloud-based operation. >> Is that a service you offer? Is that a pre-sales freebie? What is maybe both? >> It's both. >> Yeah. >> It's both. >> Yeah. >> Guys- >> Just really quickly. >> We're going to wrap. >> The, yeah. Dave loves the TCO discussion. I'm always thinking in terms of, well how do you measure TCO when you're comparing something where you can't do something to an environment where you're going to be able to do something new? And I know that that's always the challenge in any kind of emerging market where things are changing, any? >> Well, I mean we also look at, not only TCO, but we look at overall business case. So there's basically service at GLD and revenue and then there's faster time to revenues. Well, and actually ACG, we actually have a platform called the BAE or Business Analytics Engine that's a very sophisticated simulation cloud-based platform, where we can actually look at revenue month by month. And we look at what's the impact of accelerating revenue by three months. By four months. >> So you're looking into- >> By six months- >> So you're forward looking. You're just not consistently- >> So we're not just looking at TCO, we're looking at the overall business case benefit. >> Yeah, exactly right. There's the TCO, which is the hard dollars. >> Right. >> CFO wants to see that, he or she needs to see that. But you got to, you can convince that individual, that there's a business case around it. >> Peter: Yeah. >> And then you're going to sign up for that number. >> Peter: Yeah. >> And they're going to be held to it. That's the story the world wants. >> At the end of the day, telcos have to be offered new services 'cause look at all the money that's been spent. >> Dave: Yeah, that's right. >> On investment on 5G and everything else. >> 0.5 trillion over the next seven years. All right, guys, we got to go. Sorry to cut you off. >> Okay, thank you very much. >> But we're wall to wall here. All right, thanks so much for coming on. >> Dave: Fantastic. >> All right, Dave Vellante, for Dave Nicholson. Lisa Martin's in the house. John Furrier in Palo Alto Studios. Keep it right there. MWC 23 live from the Fira in Barcelona. (light airy music)

>> Narrator: TheCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music intro) (logo background tingles) >> Hi everybody, welcome back to day three of MWC23, my name is Dave Vellante and we're here live at the Theater of Barcelona, Lisa Martin, David Nicholson, John Furrier's in our studio in Palo Alto. Lot of buzz at the show, the Mobile World Daily Today, front page, Netflix chief hits back in fair share row, Greg Peters, the co-CEO of Netflix, talking about how, "Hey, you guys want to tax us, the telcos want to tax us, well, maybe you should help us pay for some of the content. Your margins are higher, you have a monopoly, you know, we're delivering all this value, you're bundling Netflix in, from a lot of ISPs so hold on, you know, pump the brakes on that tax," so that's the big news. Lockheed Martin, FOSS issues, AI guidelines, says, "AI's not going to take over your job anytime soon." Although I would say, your job's going to be AI-powered for the next five years. We're going to talk about data, we've been talking about the disaggregation of the telco stack, part of that stack is a data layer. John Kreisa is here, the CMO of Couchbase, John, you know, we've talked about all week, the disaggregation of the telco stacks, they got, you know, Silicon and operating systems that are, you know, real time OS, highly reliable, you know, compute infrastructure all the way up through a telemetry stack, et cetera. And that's a proprietary block that's really exploding, it's like the big bang, like we saw in the enterprise 20 years ago and we haven't had much discussion about that data layer, sort of that horizontal data layer, that's the market you play in. You know, Couchbase obviously has a lot of telco customers- >> John: That's right. >> We've seen, you know, Snowflake and others launch telco businesses. What are you seeing when you talk to customers at the show? What are they doing with that data layer? >> Yeah, so they're building applications to drive and power unique experiences for their users, but of course, it all starts with where the data is. So they're building mobile applications where they're stretching it out to the edge and you have to move the data to the edge, you have to have that capability to deliver that highly interactive experience to their customers or for their own internal use cases out to that edge, so seeing a lot of that with Couchbase and with our customers in telco. >> So what do the telcos want to do with data? I mean, they've got the telemetry data- >> John: Yeah. >> Now they frequently complain about the over-the-top providers that have used that data, again like Netflix, to identify customer demand for content and they're mopping that up in a big way, you know, certainly Amazon and shopping Google and ads, you know, they're all using that network. But what do the telcos do today and what do they want to do in the future? They're all talking about monetization, how do they monetize that data? >> Yeah, well, by taking that data, there's insight to be had, right? So by usage patterns and what's happening, just as you said, so they can deliver a better experience. It's all about getting that edge, if you will, on their competition and so taking that data, using it in a smart way, gives them that edge to deliver a better service and then grow their business. >> We're seeing a lot of action at the edge and, you know, the edge can be a Home Depot or a Lowe's store, but it also could be the far edge, could be a, you know, an oil drilling, an oil rig, it could be a racetrack, you know, certainly hospitals and certain, you know, situations. So let's think about that edge, where there's maybe not a lot of connectivity, there might be private networks going in, in the future- >> John: That's right. >> Private 5G networks. What's the data flow look like there? Do you guys have any customers doing those types of use cases? >> Yeah, absolutely. >> And what are they doing with the data? >> Yeah, absolutely, we've got customers all across, so telco and transportation, all kinds of service delivery and healthcare, for example, we've got customers who are delivering healthcare out at the edge where they have a remote location, they're able to deliver healthcare, but as you said, there's not always connectivity, so they need to have the applications, need to continue to run and then sync back once they have that connectivity. So it's really having the ability to deliver a service, reliably and then know that that will be synced back to some central server when they have connectivity- >> So the processing might occur where the data- >> Compute at the edge. >> How do you sync back? What is that technology? >> Yeah, so there's, so within, so Couchbase and Couchbase's case, we have an autonomous sync capability that brings it back to the cloud once they get back to whether it's a private network that they want to run over, or if they're doing it over a public, you know, wifi network, once it determines that there's connectivity and, it can be peer-to-peer sync, so different edge apps communicating with each other and then ultimately communicating back to a central server. >> I mean, the other theme here, of course, I call it the software-defined telco, right? But you got to have, you got to run on something, got to have hardware. So you see companies like AWS putting Outposts, out to the edge, Outposts, you know, doesn't really run a lot of database to mind, I mean, it runs RDS, you know, maybe they're going to eventually work with companies like... I mean, you're a partner of AWS- >> John: We are. >> Right? So do you see that kind of cloud infrastructure that's moving to the edge? Do you see that as an opportunity for companies like Couchbase? >> Yeah, we do. We see customers wanting to push more and more of that compute out to the edge and so partnering with AWS gives us that opportunity and we are certified on Outpost and- >> Oh, you are? >> We are, yeah. >> Okay. >> Absolutely. >> When did that, go down? >> That was last year, but probably early last year- >> So I can run Couchbase at the edge, on Outpost? >> Yeah, that's right. >> I mean, you know, Outpost adoption has been slow, we've reported on that, but are you seeing any traction there? Are you seeing any nibbles? >> Starting to see some interest, yeah, absolutely. And again, it has to be for the right use case, but again, for service delivery, things like healthcare and in transportation, you know, they're starting to see where they want to have that compute, be very close to where the actions happen. >> And you can run on, in the data center, right? >> That's right. >> You can run in the cloud, you know, you see HPE with GreenLake, you see Dell with Apex, that's essentially their Outposts. >> Yeah. >> They're saying, "Hey, we're going to take our whole infrastructure and make it as a service." >> Yeah, yeah. >> Right? And so you can participate in those environments- >> We do. >> And then so you've got now, you know, we call it supercloud, you've got the on-prem, you've got the, you can run in the public cloud, you can run at the edge and you want that consistent experience- >> That's right. >> You know, from a data layer- >> That's right. >> So is that really the strategy for a data company is taking or should be taking, that horizontal layer across all those use cases? >> You do need to think holistically about it, because you need to be able to deliver as a, you know, as a provider, wherever the customer wants to be able to consume that application. So you do have to think about any of the public clouds or private networks and all the way to the edge. >> What's different John, about the telco business versus the traditional enterprise? >> Well, I mean, there's scale, I mean, one thing they're dealing with, particularly for end user-facing apps, you're dealing at a very very high scale and the expectation that you're going to deliver a very interactive experience. So I'd say one thing in particular that we are focusing on, is making sure we deliver that highly interactive experience but it's the scale of the number of users and customers that they have, and the expectation that your application's always going to work. >> Speaking of applications, I mean, it seems like that's where the innovation is going to come from. We saw yesterday, GSMA announced, I think eight APIs telco APIs, you know, we were talking on theCUBE, one of the analysts was like, "Eight, that's nothing," you know, "What do these guys know about developers?" But you know, as Daniel Royston said, "Eight's better than zero." >> Right? >> So okay, so we're starting there, but the point being, it's all about the apps, that's where the innovation's going to come from- >> That's right. >> So what are you seeing there, in terms of building on top of the data app? >> Right, well you have to provide, I mean, have to provide the APIs and the access because it is really, the rubber meets the road, with the developers and giving them the ability to create those really rich applications where they want and create the experiences and innovate and change the way that they're giving those experiences. >> Yeah, so what's your relationship with developers at Couchbase? >> John: Yeah. >> I mean, talk about that a little bit- >> Yeah, yeah, so we have a great relationship with developers, something we've been investing more and more in, in terms of things like developer relations teams and community, Couchbase started in open source, continue to be based on open source projects and of course, those are very developer centric. So we provide all the consistent APIs for developers to create those applications, whether it's something on Couchbase Lite, which is our kind of edge-based database, or how they can sync that data back and we actually automate a lot of that syncing which is a very difficult developer task which lends them to one of the developer- >> What I'm trying to figure out is, what's the telco developer look like? Is that a developer that comes from the enterprise and somebody comes from the blockchain world, or AI or, you know, there really doesn't seem to be a lot of developer talk here, but there's a huge opportunity. >> Yeah, yeah. >> And, you know, I feel like, the telcos kind of remind me of, you know, a traditional legacy company trying to get into the developer world, you know, even Oracle, okay, they bought Sun, they got Java, so I guess they have developers, but you know, IBM for years tried with Bluemix, they had to end up buying Red Hat, really, and that gave them the developer community. >> Yep. >> EMC used to have a thing called EMC Code, which was a, you know, good effort, but eh. And then, you know, VMware always trying to do that, but, so as you move up the stack obviously, you have greater developer affinity. Where do you think the telco developer's going to come from? How's that going to evolve? >> Yeah, it's interesting, and I think they're... To kind of get to your first question, I think they're fairly traditional enterprise developers and when we break that down, we look at it in terms of what the developer persona is, are they a front-end developer? Like they're writing that front-end app, they don't care so much about the infrastructure behind or are they a full stack developer and they're really involved in the entire application development lifecycle? Or are they living at the backend and they're really wanting to just focus in on that data layer? So we lend towards all of those different personas and we think about them in terms of the APIs that we create, so that's really what the developers are for telcos is, there's a combination of those front-end and full stack developers and so for them to continue to innovate they need to appeal to those developers and that's technology, like Couchbase, is what helps them do that. >> Yeah and you think about the Apples, you know, the app store model or Apple sort of says, "Okay, here's a developer kit, go create." >> John: Yeah. >> "And then if it's successful, you're going to be successful and we're going to take a vig," okay, good model. >> John: Yeah. >> I think I'm hearing, and maybe I misunderstood this, but I think it was the CEO or chairman of Ericsson on the day one keynotes, was saying, "We are going to monetize the, essentially the telemetry data, you know, through APIs, we're going to charge for that," you know, maybe that's not the best approach, I don't know, I think there's got to be some innovation on top. >> John: Yeah. >> Now maybe some of these greenfield telcos are going to do like, you take like a dish networks, what they're doing, they're really trying to drive development layers. So I think it's like this wild west open, you know, community that's got to be formed and right now it's very unclear to me, do you have any insights there? >> I think it is more, like you said, Wild West, I think there's no emerging standard per se for across those different company types and sort of different pieces of the industry. So consequently, it does need to form some more standards in order to really help it grow and I think you're right, you have to have the right APIs and the right access in order to properly monetize, you have to attract those developers or you're not going to be able to monetize properly. >> Do you think that if, in thinking about your business and you know, you've always sold to telcos, but now it's like there's this transformation going on in telcos, will that become an increasingly larger piece of your business or maybe even a more important piece of your business? Or it's kind of be steady state because it's such a slow moving industry? >> No, it is a big and increasing piece of our business, I think telcos like other enterprises, want to continue to innovate and so they look to, you know, technologies like, Couchbase document database that allows them to have more flexibility and deliver the speed that they need to deliver those kinds of applications. So we see a lot of migration off of traditional legacy infrastructure in order to build that new age interface and new age experience that they want to deliver. >> A lot of buzz in Silicon Valley about open AI and Chat GPT- >> Yeah. >> You know, what's your take on all that? >> Yeah, we're looking at it, I think it's exciting technology, I think there's a lot of applications that are kind of, a little, sort of innovate traditional interfaces, so for example, you can train Chat GPT to create code, sample code for Couchbase, right? You can go and get it to give you that sample app which gets you a headstart or you can actually get it to do a better job of, you know, sorting through your documentation, like Chat GPT can do a better job of helping you get access. So it improves the experience overall for developers, so we're excited about, you know, what the prospect of that is. >> So you're playing around with it, like everybody is- >> Yeah. >> And potentially- >> Looking at use cases- >> Ways tO integrate, yeah. >> Hundred percent. >> So are we. John, thanks for coming on theCUBE. Always great to see you, my friend. >> Great, thanks very much. >> All right, you're welcome. All right, keep it right there, theCUBE will be back live from Barcelona at the theater. SiliconANGLE's continuous coverage of MWC23. Go to siliconangle.com for all the news, theCUBE.net is where all the videos are, keep it right there. (cheerful upbeat music outro)

>> Announcer: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Hi everybody. Welcome back to Barcelona. We're here at the Fira Live, theCUBE's ongoing coverage of day two of MWC 23. Back in 2021 was my first Mobile World Congress. And you know what? It was actually quite an experience because there was nobody there. I talked to my friend, who's now my co-host, Chris Lewis about what to expect. He said, Dave, I don't think a lot of people are going to be there, but Danielle Royston is here and she's the CEO of Totoge. And that year when Erickson tapped out of its space she took out 60,000 square feet and built out Cloud City. If it weren't for Cloud City, there would've been no Mobile World Congress in June and July of 2021. DR is back. Great to see you. Thanks for coming on. >> It's great to see you. >> Chris. Awesome to see you. >> Yeah, Chris. Yep. >> Good to be back. Yep. >> You guys remember the narrative back then. There was this lady running around this crazy lady that I met at at Google Cloud next saying >> Yeah. Yeah. >> the cloud's going to take over Telco. And everybody's like, well, this lady's nuts. The cloud's been leaning in, you know? >> Yeah. >> So what do you think, I mean, what's changed since since you first caused all those ripples? >> I mean, I have to say that I think that I caused a lot of change in the industry. I was talking to leaders over at AWS yesterday and they were like, we've never seen someone push like you have and change so much in a short period of time. And Telco moves slow. It's known for that. And they're like, you are pushing buttons and you're getting people to change and thank you and keep going. And so it's been great. It's awesome. >> Yeah. I mean, it was interesting, Chris, we heard on the keynotes we had Microsoft, Satya came in, Thomas Curian came in. There was no AWS. And now I asked CMO of GSMA about that. She goes, hey, we got a great relationship with it, AWS. >> Danielle: Yeah. >> But why do you think they weren't here? >> Well, they, I mean, they are here. >> Mean, not here. Why do you think they weren't profiled? >> They weren't on the keynote stage. >> But, you know, at AWS, a lot of the times they want to be the main thing. They want to be the main part of the show. They don't like sharing the limelight. I think they just didn't want be on the stage with the Google CLoud guys and the these other guys, what they're doing they're building out, they're doing so much stuff. As Danielle said, with Telcos change in the ecosystem which is what's happening with cloud. Cloud's making the Telcos think about what the next move is, how they fit in with the way other people do business. Right? So Telcos never used to have to listen to anybody. They only listened to themselves and they dictated the way things were done. They're very successful and made a lot of money but they're now having to open up they're having to leverage the cloud they're having to leverage the services that (indistinct words) and people out provide and they're changing the way they work. >> So, okay in 2021, we talked a lot about the cloud as a potential disruptor, and your whole premise was, look you got to lean into the cloud, or you're screwed. >> Danielle: Yeah. >> But the flip side of that is, if they lean into the cloud too much, they might be screwed. >> Danielle: Yeah. >> So what's that equilibrium? Have they been able to find it? Are you working with just the disruptors or how's that? >> No I think they're finding it right. So my talk at MWC 21 was all about the cloud is a double-edged sword, right? There's two sides to it, and you definitely need to proceed through it with caution, but also I don't know that you have a choice, right? I mean, the multicloud, you know is there another industry that spends more on CapEx than Telco? >> No. >> Right. The hyperscalers are doing it right. They spend, you know, easily approaching over a $100 billion in CapEx that rivals this industry. And so when you have a player like that an industry driving, you know and investing so much Telco, you're always complaining how everyone's riding your coattails. This is the opportunity to write someone else's coattails. So jump on, right? I think you don't have a choice especially if other Telco competitors are using hyperscalers and you don't, they're going to be left behind. >> So you advise these companies all the time, but >> I mean, the issue is they're all they're all using all the hyperscalers, right? So they're the multi, the multiple relationships. And as Danielle said, the multi-layer of relationship they're using the hyperscalers to change their own internal operational environments to become more IT-centric to move to that software centric Telco. And they're also then with the hyperscalers going to market in different ways sometimes with them, sometimes competing with them. What what it means from an analyst point of view is you're suddenly changing the dynamic of a market where we used to have nicely well defined markets previously. Now they're, everyone's in it together, you know, it's great. And, and it's making people change the way they think about services. What I, what I really hope it changes more than anything else is the way the customers at the end of the, at the end of the supply, the value chain think this is what we can get hold of this stuff. Now we can go into the network through the cloud and we can get those APIs. We can draw on the mechanisms we need to to run our personal lives, to run our business lives. And frankly, society as a whole. It's really exciting. >> Then your premise is basically you were saying they should ride on the top over the top of the cloud vendor. >> Yeah. Right? >> No. Okay. But don't they lose the, all the data if they do that? >> I don't know. I mean, I think the hyperscalers are not going to take their data, right? I mean, that would be a really really bad business move if Google Cloud and Azure and and AWS start to take over that, that data. >> But they can't take it. >> They can't. >> From regulate, from sovereignty and regulation. >> They can't because of regulation, but also just like business, right? If they started taking their data and like no enterprises would use them. So I think, I think the data is safe. I think you, obviously every country is different. You got to understand the different rules and regulations for data privacy and, and how you keep it. But I think as we look at the long term, right and we always talk about 10 and 20 years there's going to be a hyperscaler region in every country right? And there will be a way for every Telco to use it. I think their data will be safe. And I think it just, you're going to be able to stand on on the shoulders of someone else for once and use the building blocks of software that these guys provide to make better experiences for subscribers. >> You guys got to explain this to me because when I say data I'm not talking about, you know, personal information. I'm talking about all the telemetry, you know, all the all the, you know the plumbing. >> Danielle: Yeah. >> Data, which is- >> It will increasingly be shared because you need to share it in order to deliver the services in the streamline efficient way that needs to be deliver. >> Did I hear the CEO of Ericsson Wright where basically he said, we're going to charge developers for access to that data through APIs. >> What the Ericsson have done, obviously with the Vage acquisition is they want to get into APIs. So the idea is you're exposing features, quality policy on demand type features for example, or even pulling we still use that a lot of SMS, right? So pulling those out using those APIs. So it will be charged in some way. Whether- >> Man: Like Twitter's charging me for APIs, now I API calls, you >> Know what it is? I think it's Twilio. >> Man: Oh, okay. >> Right. >> Man: No, no, that's sure. >> There's no reason why telcos couldn't provide a Twilio like service itself. >> It's a horizontal play though right? >> Danielle: Correct because developers need to be charged by the API. >> But doesn't there need to be an industry standard to do that as- >> Well. I think that's what they just announced. >> Industry standard. >> Danielle: I think they just announced that. Yeah. Right now I haven't looked at that API set, right? >> There's like eight of them. >> There's eight of them. Twilio has, it's a start you got to start somewhere Dave. (crosstalk) >> And there's all, the TM forum is all the other standard >> Right? Eight is better than zero- >> Right? >> Haven't got plenty. >> I mean for an industry that didn't really understand APIs as a feature, as a product as a service, right? For Mats Granryd, the deputy general of GSMA to stand on the keynote stage and say we partnered and we're unveiling, right. Pay by the use APIs. I was for it. I was like, that is insane. >> I liked his keynote actually, because I thought he was going to talk about how many attendees and how much economic benefiting >> Danielle: We're super diverse. >> He said, I would usually talk about that and you know greening in the network by what you did talk about a little bit. But, but that's, that surprised me. >> Yeah. >> But I've seen in the enterprise this is not my space as, you know, you guys don't live this but I've seen Oracle try to get developers. IBM had to pay $35 billion trying to get for Red Hat to get developers, right? EMC used to have a thing called EMC code, failed. >> I mean they got to do something, right? So 4G they didn't really make the business case the ROI on the investment in the network. Here we are with 5G, same discussion is having where's the use case? How are we going to monetize and make the ROI on this massive investment? And now they're starting to talk about 6G. Same fricking problem is going to happen again. And so I think they need to start experimenting with new ideas. I don't know if it's going to work. I don't know if this new a API network gateway theme that Mats talked about yesterday will work. But they need to start unbundling that unlimited plan. They need to start charging people who are using the network more, more money. Those who are using it less, less. They need to figure this out. This is a crisis for them. >> Yeah our own CEO, I mean she basically said, Hey, I'm for net neutrality, but I want to be able to charge the people that are using it more and more >> To make a return on, on a capital. >> I mean it costs billions of dollars to build these networks, right? And they're valuable. We use them and we talked about this in Cloud City 21, right? The ability to start building better metaverses. And I know that's a buzzword and everyone hates it, but it's true. Like we're working from home. We need- there's got to be a better experience in Zoom in 2D, right? And you need a great network for that metaverse to be awesome. >> You do. But Danielle, you don't need cellular for doing that, do you? So the fixed network is as important. >> Sure. >> And we're at mobile worlds. But actually what we beginning to hear and Crystal Bren did say this exactly, it's about the comp the access is sort of irrelevant. Fixed is better because it's more the cost the return on investment is better from fiber. Mobile we're going to change every so many years because we're a new generation. But we need to get the mechanism in place to deliver that. I actually don't agree that we should everyone should pay differently for what they use. It's a universal service. We need it as individuals. We need to make it sustainable for every user. Let's just not go for the biggest user. It's not, it's not the way to build it. It won't work if you do that you'll crash the system if you do that. And, and the other thing which I disagree on it's not about standing on the shoulders and benefiting from what- It's about cooperating across all levels. The hyperscalers want to work with the telcos as much as the telcos want to work with the hyperscalers. There's a lot of synergy there. There's a lot of ways they can work together. It's not one or the other. >> But I think you're saying let the cloud guys do the heavy lifting and I'm - >> Yeah. >> Not at all. >> And so you don't think so because I feel like the telcos are really good at pipes. They've always been good at pipes. They're engineers. >> Danielle: Yeah. >> Are they hanging on to the to the connectivity or should they let that go and well and go toward the developer. >> I mean AWS had two announcements on the 21st a week before MWC. And one was that telco network builder. This is literally being able to deploy a network capability at AWS with keystrokes. >> As a managed service. >> Danielle: Correct. >> Yeah. >> And so I don't know how the telco world I felt the shock waves, right? I was like, whoa, that seems really big. Because they're taking something that previously was like bread and butter. This is what differentiates each telco and now they've standardized it and made it super easy so anyone can do it. Now do I think the five nines of super crazy hardcore network criteria will be built on AWS this way? Probably not, but no >> It's not, it's not end twin. So you can't, no. >> Right. But private networks could be built with this pretty easily, right? And so telcos that don't have as much funding, right. Smaller, more experiments. I think it's going to change the way we think about building networks in telcos >> And those smaller telcos I think are going to be more developer friendly. >> Danielle: Yeah. >> They're going to have business models that invite those developers in. And that's, it's the disruption's going to come from the ISVs and the workloads that are on top of that. >> Well certainly what Dish is trying to do, right? Dish is trying to build a- they launched it reinvent a developer experience. >> Dave: Yeah. >> Right. Built around their network and you know, again I don't know, they were not part of this group that designed these eight APIs but I'm sure they're looking with great intent on what does this mean for them. They'll probably adopt them because they want people to consume the network as APIs. That's their whole thing that Mark Roanne is trying to do. >> Okay, and then they're doing open ran. But is it- they're not really cons- They're not as concerned as Rakuten with the reliability and is that the right play? >> In this discussion? Open RAN is not an issue. It really is irrelevant. It's relevant for the longer term future of the industry by dis aggregating and being able to share, especially ran sharing, for example, in the short term in rural environments. But we'll see some of that happening and it will change, but it will also influence the way the other, the existing ran providers build their services and offer their value. Look you got to remember in the relationship between the equipment providers and the telcos are very dramatically. Whether it's Ericson, NOKIA, Samsung, Huawei, whoever. So those relations really, and the managed services element to that depends on what skills people have in-house within the telco and what service they're trying to deliver. So there's never one size fits all in this industry. >> You're very balanced in your analysis and I appreciate that. >> I try to be. >> But I am not. (chuckles) >> So when Dr went off, this is my question. When Dr went off a couple years ago on the cloud's going to take over the world, you were skeptical. You gave a approach. Have you? >> I still am. >> Have you moderated your thoughts on that or- >> I believe the telecom industry is is a very strong industry. It's my industry of course I love it. But the relationship it is developing much different relationships with the ecosystem players around it. You mentioned developers, you mentioned the cloud players the equipment guys are changing there's so many moving parts to build the telco of the future that every country needs a very strong telco environment to be able to support the site as a whole. People individuals so- >> Well I think two years ago we were talking about should they or shouldn't they, and now it's an inevitability. >> I don't think we were Danielle. >> All using the hyperscalers. >> We were always going to need to transform the telcos from the conservative environments in which they developed. And they've had control of everything in order to reduce if they get no extra revenue at all, reducing the cost they've got to go on a cloud migration path to do that. >> Amenable. >> Has it been harder than you thought? >> It's been easier than I thought. >> You think it's gone faster than >> It's gone way faster than I thought. I mean pushing on this flywheel I thought for sure it would take five to 10 years it is moving. I mean the maths comp thing the AWS announcements last week they're putting in hyperscalers in Saudi Arabia which is probably one of the most sort of data private places in the world. It's happening really fast. >> What Azure's doing? >> I feel like I can't even go to sleep. Because I got to keep up with it. It's crazy. >> Guys. >> This is awesome. >> So awesome having you back on. >> Yeah. >> Chris, thanks for co-hosting. Appreciate you stay here. >> Yep. >> Danielle, amazing. We'll see you. >> See you soon. >> A lot of action here. We're going to come out >> Great. >> Check out your venue. >> Yeah the Togi buses that are outside. >> The big buses. You got a great setup there. We're going to see you on Wednesday. Thanks again. >> Awesome. Thanks. >> All right. Keep it right there. We'll be back to wrap up day two from MWC 23 on theCUBE. (upbeat music)

(soft music) >> Announcer: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Welcome back to Spain, everybody. We're here at the Fira in MWC23. Is just an amazing day. This place is packed. They said 80,000 people. I think it might even be a few more walk-ins. I'm Dave Vellante, Lisa Martin is here, David Nicholson. But right now we have the Analyst Hot Takes with three friends of theCUBE. Chris Lewis is back again with me in the co-host seat. Zeus Kerravala, analyst extraordinaire. Great to see you, Z. and Sarbjeet SJ Johal. Good to see you again, theCUBE contributor. And that's my new name for him. He says that is his nickname. Guys, thanks for coming back on. We got the all male panel, sorry, but it is what it is. So Z, is this the first time you've been on it at MWC. Take aways from the show, Hot Takes. What are you seeing? Same wine, new bottle? >> In a lot of ways, yeah. I mean, I was talking to somebody this earlier that if you had come from like MWC five years ago to this year, a lot of the themes are the same. Telco transformation, cloud. I mean, 5G is a little new. Sustainability is certainly a newer theme here. But I think it highlights just the difficulty I think the telcos have in making this transformation. And I think, in some ways, I've been unfair to them in some degree 'cause I've picked on them in the past for not moving fast enough. These are, you know, I think these kind of big transformations almost take like a perfect storm of things that come together to happen, right? And so, in the past, we had technologies that maybe might have lowered opex, but they're hard to deploy. They're vertically integrated. We didn't have the software stacks. But it appears today that between the cloudification of, you know, going to cloud native, the software stacks, the APIs, the ecosystems, I think we're actually in a position to see this industry finally move forward. >> Yeah, and Chris, I mean, you have served this industry for a long time. And you know, when you, when you do that, you get briefed as an analyst, you actually realize, wow, there's a lot of really smart people here, and they're actually, they have challenges, they're working through it. So Zeus was saying he's been tough on the industry. You know, what do you think about how the telcos have evolved in the last five years? >> I think they've changed enormously. I think the problem we have is we're always looking for the great change, the big step change, and there is no big step change in a way. What telcos deliver to us as individuals, businesses, society, the connectivity piece, that's changed. We get better and better and more reliable connectivity. We're shunting a load more capacity through. What I think has really changed is their attitude to their suppliers, their attitude to their partners, and their attitude to the ecosystem in which they play. Understanding that connectivity is not the end game. Connectivity is part of the emerging end game where it will include storage, compute, connect, and analytics and everything else. So I think the realization that they are not playing their own game anymore, it's a much more open game. And some things they will continue to do, some things they'll stop doing. We've seen them withdraw from moving into adjacent markets as much as we used to see. So a lot of them in the past went off to try and do movies, media, and a lot went way way into business IT stuff. They've mainly pulled back from that, and they're focusing on, and let's face it, it's not just a 5G show. The fixed environment is unbelievably important. We saw that during the pandemic. Having that fixed broadband connection using wifi, combining with cellular. We love it. But the problem as an industry is that the users often don't even know the connectivity's there. They only know when it doesn't work, right? >> If it's not media and it's not business services, what is it? >> Well, in my view, it will be enabling third parties to deliver the services that will include media, that will include business services. So embedding the connectivity all the way into the application that gets delivered or embedding it so the quality mechanism deliver the gaming much more accurately or, I'm not a gamer, so I can't comment on that. But no, the video quality if you want to have a high quality video will come through better. >> And those cohorts will pay for that value? >> Somebody will pay somewhere along the line. >> Seems fuzzy to me. >> Me too. >> I do think it's use case dependent. Like you look at all the work Verizon did at the Super Bowl this year, that's a perfect case where they could have upsold. >> Explain that. I'm not familiar with it. >> So Verizon provided all the 5G in the Super Bowl. They provided a lot of, they provided private connectivity for the coaches to talk to the sidelines. And that's a mission critical application, right? In the NFL, if one side can't talk, the other side gets shut down. You can't communicate with the quarterback or the coaches. There's a lot of risk at that. So, but you know, there's a case there, though, I think where they could have even made that fan facing. Right? And if you're paying 2000 bucks to go to a game, would you pay 50 bucks more to have a higher tier of bandwidth so you can post things on social? People that go there, they want people to know they were there. >> Every football game you go to, you can't use your cell. >> Analyst: Yeah, I know, right? >> All right, let's talk about developers because we saw the eight APIs come out. I think ISVs are going to be a big part of this. But it's like Dee Arthur said. Hey, eight's better than zero, I guess. Okay, so, but so the innovation is going to come from ISVs and developers, but what are your hot takes from this show and now day two, we're a day and a half in, almost two days in. >> Yeah, yeah. There's a thing that we have talked, I mentioned many times is skills gravity, right? Skills have gravity, and also, to outcompete, you have to also educate. That's another theme actually of my talks is, or my research is that to puts your technology out there to the practitioners, you have to educate them. And that's the only way to democratize your technology. What telcos have been doing is they have been stuck to the proprietary software and proprietary hardware for too long, from Nokia's of the world and other vendors like that. So now with the open sourcing of some of the components and a few others, right? And they're open source space and antenna, you know? Antennas are becoming software now. So with the invent of these things, which is open source, it helps us democratize that to the other sort of skirts of the practitioners, if you will. And that will bring in more applications first into the IOT space, and then maybe into the core sort of California, if you will. >> So what does a telco developer look like? I mean, all the blockchain developers and crypto developers are moving into generative AI, right? So maybe those worlds come together. >> You'd like to think though that the developers would understand everything's network centric today. So you'd like to think they'd understand that how the network responds, you know, you'd take a simple app like Zoom or something. If it notices the bandwidth changes, it should knock down the resolution. If it goes up it, then you can add different features and things and you can make apps a lot smarter that way. >> Well, G2 was saying today that they did a deal with Mercedes, you know this probably better than I do, where they're going to embed WebEx in the car. And if you're driving, it'll shut off the camera. >> Of course. >> I'm like, okay. >> I'll give you a better example though. >> But that's my point. Like, isn't there more that we can do? >> You noticed down on the SKT stand the little helicopter. That's a vertical lift helicopter. So it's an electric vertical lift helicopter. Just think of that for a second. And then think of the connectivity to control that, to securely control that. And then I was recently at an event with Zeus actually where we saw an air traffic control system where there was no people manning the tower. It was managed by someone remotely with all the cameras around them. So managing all of those different elements, we call it IOT, but actually it's way more than what we thought of as IOT. All those components connecting, communicating securely and safely. 'Cause I don't want that helicopter to come down on my head, do you? (men laugh) >> Especially if you're in there. (men laugh) >> Okay, so you mentioned sustainability. Everybody's talking about power. I don't know if you guys have a lot of experience around TCO, but I'm trying to get to, well, is this just because energy costs are so high, and then when the energy becomes cheap again, nobody's going to pay any attention to it? Or is this the real deal? >> So one of the issues around the, if we want to experience all that connectivity locally or that helicopter wants to have that connectivity, we have to ultimately build denser, more reliable networks. So there's a CapEx, we're going to put more base stations in place. We need more fiber in the ground to support them. Therefore, the energy consumption will go up. So we need to be more efficient in the use of energy. Simple as that. >> How much of the operating expense is energy? Like what percent of it? Is it 10%? Is it 20%? Is it, does anybody know? >> It depends who you ask and it depends on the- >> I can't get an answer to that. I mean, in the enterprise- >> Analyst: The data centers? >> Yeah, the data centers. >> We have the numbers. I think 10 to 15%. >> It's 10 to 12%, something like that. Is it much higher? >> I've got feeling it's 30%. >> Okay, so if it's 30%, that's pretty good. >> I do think we have to get better at understanding how to measure too. You know, like I was talking with John Davidson at Sysco about this that every rev of silicon they come out with uses more power, but it's a lot more dense. So at the surface, you go, well, that's using a lot more power. But you can consolidate 10 switches down to two switches. >> Well, Intel was on early and talking about how they can intelligently control the cores. >> But it's based off workload, right? That's the thing. So what are you running over it? You know, and so, I don't think our industry measures that very well. I think we look at things kind of boxed by box versus look at total consumption. >> Well, somebody else in theCUBE was saying they go full throttle. That the networks just say just full throttle everything. And that obviously has to change from the power consumption standpoint. >> Obviously sustainability and sensory or sensors from IOT side, they go hand in hand. Just simple examples like, you know, lights in the restrooms, like in public areas. Somebody goes in there and just only then turns. The same concept is being applied to servers and compute and storage and every aspects and to networks as well. >> Cell tower. >> Yeah. >> Cut 'em off, right? >> Like the serverless telco? (crosstalk) >> Cell towers. >> Well, no, I'm saying, right, but like serverless, you're not paying for the compute when you're not using it, you know? >> It is serverless from the economics point of view. Yes, it's like that, you know? It goes to the lowest level almost like sleep on our laptops, sleep level when you need more power, more compute. >> I mean, some of that stuff's been in networking equipment for a long time, it just never really got turned on. >> I want to ask you about private networks. You wrote a piece, Athenet was acquired by HPE right after Dell announced a relationship with Athenet, which was kind of, that was kind of funny. And so a good move, good judo move by by HP. I asked Dell about it, and they said, look, we're open. They said the right things. We'll see, but I think it's up to HP. >> Well, and the network inside Dell is. >> Yeah, okay, so. Okay, cool. So, but you said something in that article you wrote on Silicon Angle that a lot of people feel like P5G is going to basically replace wireless or cannibalize wireless. You said you didn't agree with that. Explain why? >> Analyst: Wifi. >> Wifi, sorry, I said wireless. >> No, that's, I mean that's ridiculous. Pat Gelsinger said that in his last VMware, which I thought was completely irresponsible. >> That it was going to cannibalize? >> Cannibalize wifi globally is what he said, right? Now he had Verizon on stage with him, so. >> Analyst: Wifi's too inexpensive and flexible. >> Wifi's cheap- >> Analyst: It's going to embed really well. Embedded in that. >> It's reached near ubiquity. It's unlicensed. So a lot of businesses don't want to manage their own spectrum, right? And it's great for this, right? >> Analyst: It does the job. >> For casual connectivity. >> Not today. >> Well, it does for the most part. Right now- >> For the most part. But never at these events. >> If it's engineered correctly, it will. Right? Where you need private 5G is when reliability is an absolute must. So, Chris, you and I visited the Port of Rotterdam, right? So they're putting 5G, private 5G there, but there's metal containers everywhere, right? And that's going to disrupt it. And so there are certain use cases where it makes sense. >> I've been in your basement, and you got some pretty intense equipment in there. You have private 5G in there. >> But for carpeted offices, it does not make sense to bring private. The economics don't make any sense. And you know, it runs hot. >> So where's it going to be used? Give us some examples of where we should be looking for. >> The early ones are obviously in mining, and you say in ports, in airports. It broadens cities because you've got so many moving parts in there, and always think about it, very expensive moving parts. The cranes in the port are normally expensive piece of kits. You're moving that, all that logistics around. So managing that over a distance where the wifi won't work over the distance. And in mining, we're going to see enormous expensive trucks moving around trying to- >> I think a great new use case though, so the Cleveland Browns actually the first NFL team to use it for facial recognition to enter the stadium. So instead of having to even pull your phone out, it says, hey Dave Vellante. You've got four tickets, can we check you all in? And you just walk through. You could apply that to airports. You could do put that in a hotel. You could walk up and check in. >> Analyst: Retail. >> Yeah, retail. And so I think video, realtime video analytics, I think it's a perfect use case for that. >> But you don't need 5G to do that. You could do that through another mechanism, couldn't you? >> You could do wire depending on how mobile you want to do it. Like in a stadium, you're pulling those things in and out all the time. You're moving 'em around and things, so. >> Yeah, but you're coming in at a static point. >> I'll take the contrary view here. >> See, we can't even agree on that. (men laugh) >> Yeah, I love it. Let's go. >> I believe the reliability of connection is very important, right? And the moving parts. What are the moving parts in wifi? We have the NIC card, you know, the wifi card in these suckers, right? In a machine, you know? They're bigger in size, and the radios for 5G are smaller in size. So neutralization is important part of the whole sort of progress to future, right? >> I think 5G costs as well. Yes, cost as well. But cost, we know that it goes down with time, right? We're already talking about 60, and the 5G stuff will be good. >> Actually, sorry, so one of the big boom areas at the moment is 4G LTE because the component price has come down so much, so it is affordable, you can afford to bring it all together. People don't, because we're still on 5G, if 5G standalone everywhere, you're not going to get a consistent service. So those components are unbelievably important. The skillsets of the people doing integration to bring them all together, unbelievably important. And the business case within the business. So I was talking to one of the heads of one of the big retail outlets in the UK, and I said, when are you going to do 5G in the stores? He said, well, why would I tear out all the wifi? I've got perfectly functioning wifi. >> Yeah, that's true. It's already there. But I think the technology which disappears in front of you, that's the best technology. Like you don't worry about it. You don't think it's there. Wifi, we think we think about that like it's there. >> And I do think wifi 5G switching's got to get easier too. Like for most users, you don't know which is better. You don't even know how to test it. And to your point, it does need to be invisible where the user doesn't need to think about it, right? >> Invisible. See, we came back to invisible. We talked about that yesterday. Telecom should be invisible. >> And it should be, you know? You don't want to be thinking about telecom, but at the same time, telecoms want to be more visible. They want to be visible like Netflix, don't they? I still don't see the path. It's fuzzy to me the path of how they're not going to repeat what happened with the over the top providers if they're invisible. >> Well, if you think about what telcos delivers to consumers, to businesses, then extending that connectivity into your home to help you support secure and extend your connection into Zeus's basement, whatever it is. Obviously that's- >> His awesome setup down there. >> And then in the business environment, there's a big change going on from the old NPLS networks, the old rigid structures of networks to SD1 where the control point is moved outside, which can be under control of the telco, could be under the control of a third party integrator. So there's a lot changing. I think we obsess about the relative role of the telco. The demand is phenomenal for connectivity. So address that, fulfill that. And if they do that, then they'll start to build trust in other areas. >> But don't you think they're going to address that and fulfill that? I mean, they're good at it. That's their wheelhouse. >> And it's a 1.6 trillion market, right? So it's not to be sniffed at. That's fixed on mobile together, obviously. But no, it's a big market. And do we keep changing? As long as the service is good, we don't move away from it. >> So back to the APIs, the eight APIs, right? >> I mean- >> Eight APIs is a joke actually almost. I think they released it too early. The release release on the main stage, you know? Like, what? What is this, right? But of course they will grow into hundreds and thousands of APIs. But they have to spend a lot of time and effort in that sort of context. >> I'd actually like to see the GSMA work with like AWS and Microsoft and VMware and software companies and create some standardization across their APIs. >> Yeah. >> I spoke to them yes- >> We're trying to reinvent them. >> Is that not what they're doing? >> No, they said we are not in the business of a defining standards. And they used a different term, not standard. I mean, seriously. I was like, are you kidding me? >> Let's face it, there aren't just eight APIs out there. There's so many of them. The TM forum's been defining when it's open data architecture. You know, the telcos themselves are defining them. The standards we talked about too earlier with Danielle. There's a lot of APIs out there, but the consistency of APIs, so we can bring them together, to bring all the different services together that will support us in our different lives is really important. I think telcos will do it, it's in their interest to do it. >> All right, guys, we got to wrap. Let's go around the horn here, starting with Chris, Zeus, and then Sarbjeet, just bring us home. Number one hot take from Mobile World Congress MWC23 day two. >> My favorite hot take is the willingness of all the participants who have been traditional telco players who looked inwardly at the industry looking outside for help for partnerships, and to build an ecosystem, a more open ecosystem, which will address our requirements. >> Zeus? >> Yeah, I was going to talk about ecosystem. I think for the first time ever, when I've met with the telcos here, I think they're actually, I don't think they know how to get there yet, but they're at least aware of the fact that they need to understand how to build a big ecosystem around them. So if you think back like 50 years ago, IBM and compute was the center of everything in your company, and then the ecosystem surrounded it. I think today with digital transformation being network centric, the telcos actually have the opportunity to be that center of excellence, and then build an ecosystem around them. I think the SIs are actually in a really interesting place to help them do that 'cause they understand everything top to bottom that I, you know, pre pandemic, I'm not sure the telcos were really understand. I think they understand it today, I'm just not sure they know how to get there. . >> Sarbjeet? >> I've seen the lot of RN demos and testing companies and I'm amazed by it. Everything is turning into software, almost everything. The parts which are not turned into software. I mean every, they will soon. But everybody says that we need the hardware to run something, right? But that hardware, in my view, is getting miniaturized, and it's becoming smaller and smaller. The antennas are becoming smaller. The equipment is getting smaller. That means the cost on the physicality of the assets is going down. But the cost on the software side will go up for telcos in future. And telco is a messy business. Not everybody can do it. So only few will survive, I believe. So that's what- >> Software defined telco. So I'm on a mission. I'm looking for the monetization path. And what I haven't seen yet is, you know, you want to follow the money, follow the data, I say. So next two days, I'm going to be looking for that data play, that potential, the way in which this industry is going to break down the data silos I think there's potential goldmine there, but I haven't figured out yet. >> That's a subject for another day. >> Guys, thanks so much for coming on. You guys are extraordinary partners of theCUBE friends, and great analysts and congratulations and thank you for all you do. Really appreciate it. >> Analyst: Thank you. >> Thanks a lot. >> All right, this is a wrap on day two MWC 23. Go to siliconangle.com for all the news. Where Rob Hope and team are just covering all the news. John Furrier is in the Palo Alto studio. We're rocking all that news, taking all that news and putting it on video. Go to theCUBE.net, you'll see everything on demand. Thanks for watching. This is a wrap on day two. We'll see you tomorrow. (soft music)

>> theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (ambient music) >> Lisa: Hey everyone, welcome back to Barcelona, Spain. It's theCUBE Live at MWC 23. Lisa Martin with Dave Vellante. This is day two of four days of cube coverage but you know that, because you've already been watching yesterday and today. We're going to have a great conversation next with EnterpriseWeb and Red Hat. We've had great conversations the last day and a half about the Telco industry, the challenges, the opportunities. We're going to unpack that from this lens. Please welcome Dave Duggal, founder and CEO of EnterpriseWeb and Azhar Sayeed is here, Senior Director Solution Architecture at Red Hat. >> Guys, it's great to have you on the program. >> Yes. >> Thank you Lisa, >> Great being here with you. >> Dave let's go ahead and start with you. Give the audience an overview of EnterpriseWeb. What kind of business is it? What's the business model? What do you guys do? >> Okay so, EnterpriseWeb is reinventing middleware, right? So the historic middleware was to build vertically integrated stacks, right? And those stacks are now such becoming the rate limiters for interoperability for so the end-to-end solutions that everybody's looking for, right? Red Hat's talking about the unified platform. You guys are talking about Supercloud, EnterpriseWeb addresses that we've built middleware based on serverless architecture, so lightweight, low latency, high performance middleware. And we're working with the world's biggest, we sell through channels and we work through partners like Red Hat Intel, Fortnet, Keysight, Tech Mahindra. So working with some of the biggest players that have recognized the value of our innovation, to deliver transformation to the Telecom industry. >> So what are you guys doing together? Is this, is this an OpenShift play? >> Is it? >> Yeah. >> Yeah, so we've got two projects right her on the floor at MWC throughout the various partners, where EnterpriseWeb is actually providing an application layer, sorry application middleware over Red Hat's, OpenShift and we're essentially generating operators so Red Hat operators, so that all our vendors, and, sorry vendors that we onboard into our catalog can be deployed easily through the OpenShift platform. And we allow those, those vendors to be flexibly composed into network services. So the real challenge for operators historically is that they, they have challenges onboarding the vendors. It takes a long time. Each one of them is a snowflake. They, you know, even though there's standards they don't all observe or follow the same standards. So we make it easier using models, right? For, in a model driven process to on boards or streamline that onboarding process, compose functions into services deploy those services seamlessly through Red Hat's OpenShift, and then manage the, the lifecycle, like the quality of service and the SLAs for those services. >> So Red Hat obviously has pretty prominent Telco business has for a while. Red Hat OpenStack actually is is pretty popular within the Telco business. People thought, "Oh, OpenStack, that's dead." Actually, no, it's actually doing quite well. We see it all over the place where for whatever reason people want to build their own cloud. And, and so, so what's happening in the industry because you have the traditional Telcos we heard in the keynotes that kind of typical narrative about, you know, we can't let the over the top vendors do this again. We're, we're going to be Apifi everything, we're going to monetize this time around, not just with connectivity but the, but the fact is they really don't have a developer community. >> Yes. >> Yet anyway. >> Then you have these disruptors over here that are saying "Yeah, we're going to enable ISVs." How do you see it? What's the landscape look like? Help us understand, you know, what the horses on the track are doing. >> Sure. I think what has happened, Dave, is that the conversation has moved a little bit from where they were just looking at IS infrastructure service with virtual machines and OpenStack, as you mentioned, to how do we move up the value chain and look at different applications. And therein comes the rub, right? You have applications with different requirements, IT network that have various different requirements that are there. So as you start to build those cloud platform, as you start to modernize those set of applications, you then start to look at microservices and how you build them. You need the ability to orchestrate them. So some of those problem statements have moved from not just refactoring those applications, but actually now to how do you reliably deploy, manage in a multicloud multi cluster way. So this conversation around Supercloud or this conversation around multicloud is very >> You could say Supercloud. That's okay >> (Dave Duggal and Azhar laughs) >> It's absolutely very real though. The reason why it's very real is, if you look at transformations around Telco, there are two things that are happening. One, Telco IT, they're looking at partnerships with hybrid cloud, I mean with public cloud players to build a hybrid environment. They're also building their own Telco Cloud environment for their network functions. Now, in both of those spaces, they end up operating two to three different environments themselves. Now how do you create a level of abstraction across those? How do you manage that particular infrastructure? And then how do you orchestrate all of those different workloads? Those are the type of problems that they're actually beginning to solve. So they've moved on from really just putting that virtualizing their application, putting it on OpenStack to now really seriously looking at "How do I build a service?" "How do I leverage the catalog that's available both in my private and public and build an overall service process?" >> And by the way what you just described as hybrid cloud and multicloud is, you know Supercloud is what multicloud should have been. And what, what it originally became is "I run on this cloud and I run on this cloud" and "I run on this cloud and I have a hybrid." And, and Supercloud is meant to create a common experience across those clouds. >> Dave Duggal: Right? >> Thanks to, you know, Supercloud middleware. >> Yeah. >> Right? And, and so that's what you guys do. >> Yeah, exactly. Exactly. Dave, I mean, even the name EnterpriseWeb, you know we started from looking from the application layer down. If you look at it, the last 10 years we've looked from the infrastructure up, right? And now everybody's looking northbound saying "You know what, actually, if I look from the infrastructure up the only thing I'll ever build is silos, right?" And those silos get in the way of the interoperability and the agility the businesses want. So we take the perspective as high level abstractions, common tools, so that if I'm a CXO, I can look down on my environments, right? When I'm really not, I honestly, if I'm an, if I'm a CEO I don't really care or CXO, I don't really care so much about my infrastructure to be honest. I care about my applications and their behavior. I care about my SLAs and my quality of service, right? Those are the things I care about. So I really want an EnterpriseWeb, right? Something that helps me connect all my distributed applications all across all of the environments. So I can have one place a consistency layer that speaks a common language. We know that there's a lot of heterogeneity down all those layers and a lot of complexity down those layers. But the business doesn't care. They don't want to care, right? They want to actually take their applications deploy them where they're the most performant where they're getting the best cost, right? The lowest and maybe sustainability concerns, all those. They want to address those problems, meet their SLAs meet their quality service. And you know what, if it's running on Amazon, great. If it's running on Google Cloud platform, great. If it, you know, we're doing one project right here that we're demonstrating here is with with Amazon Tech Mahindra and OpenShift, where we took a disaggregated 5G core, right? So this is like sort of latest telecom, you know net networking software, right? We're deploying pulling elements of that network across core, across Amazon EKS, OpenShift on Red Hat ROSA, as well as just OpenShift for cloud. And we, through a single pane of deployment and management, we deployed the elements of the 5G core across them and then connected them in an end-to-end process. That's Telco Supercloud. >> Dave Vellante: So that's an O-RAN deployment. >> Yeah that's >> So, the big advantage of that, pardon me, Dave but the big advantage of that is the customer really doesn't care where the components are being served from for them. It's a 5G capability. It happens to sit in different locations. And that's, it's, it's about how do you abstract and how do you manage all those different workloads in a cohesive way? And that's exactly what EnterpriseWeb is bringing to the table. And what we do is we abstract the underlying infrastructure which is the cloud layer. So if, because AWS operating environment is different then private cloud operating environment then Azure environment, you have the networking is set up is different in each one of them. If there is a way you can abstract all of that and present it in a common operating model it becomes a lot easier than for anybody to be able to consume. >> And what a lot of customers tell me is the way they deal with multicloud complexity is they go with mono cloud, right? And so they'll lose out on some of the best services >> Absolutely >> If best of, so that's not >> that's not ideal, but at the end of the day, agree, developers don't want to muck with all the plumbing >> Dave Duggal: Yep. >> They want to write code. >> Azhar: Correct. >> So like I come back to are the traditional Telcos leaning in on a way that they're going to enable ISVs and developers to write on top of those platforms? Or are there sort of new entrance and disruptors? And I know, I know the answer is both >> Dave Duggal: Yep. >> but I feel as though the Telcos still haven't, traditional Telcos haven't tuned in to that developer affinity, but you guys sell to them. >> What, what are you seeing? >> Yeah, so >> What we have seen is there are Telcos fall into several categories there. If you look at the most mature ones, you know they are very eager to move up the value chain. There are some smaller very nimble ones that have actually doing, they're actually doing something really interesting. For example, they've provided sandbox environments to developers to say "Go develop your applications to the sandbox environment." We'll use that to build an net service with you. I can give you some interesting examples across the globe that, where that is happening, right? In AsiaPac, particularly in Australia, ANZ region. There are a couple of providers who have who have done this, but in, in, in a very interesting way. But the challenges to them, why it's not completely open or public yet is primarily because they haven't figured out how to exactly monetize that. And, and that's the reason why. So in the absence of that, what will happen is they they have to rely on the ISV ecosystem to be able to build those capabilities which they can then bring it on as part of the catalog. But in Latin America, I was talking to one of the providers and they said, "Well look we have a public cloud, we have our own public cloud, right?" What we want do is use that to offer localized services not just bring everything in from the top >> But, but we heard from Ericson's CEO they're basically going to monetize it by what I call "gouge", the developers >> (Azhar laughs) >> access to the network telemetry as opposed to saying, "Hey, here's an open platform development on top of it and it will maybe create something like an app store and we'll take a piece of the action." >> So ours, >> to be is a better model. >> Yeah. So that's perfect. Our second project that we're showing here is with Intel, right? So Intel came to us cause they are a reputation for doing advanced automation solutions. They gave us carte blanche in their labs. So this is Intel Network Builders they said pick your partners. And we went with the Red Hat, Fort Net, Keysite this company KX doing AIML. But to address your DevX, here's Intel explicitly wants to get closer to the developers by exposing their APIs, open APIs over their infrastructure. Just like Red Hat has APIs, right? And so they can expose them northbound to developers so developers can leverage and tune their applications, right? But the challenge there is what Intel is doing at the low level network infrastructure, right? Is fundamentally complex, right? What you want is an abstraction layer where develop and this gets to, to your point Dave where you just said like "The developers just want to get their job done." or really they want to focus on the business logic and accelerate that service delivery, right? So the idea here is an EnterpriseWeb they can literally declaratively compose their services, express their intent. "I want this to run optimized for low latency. I want this to run optimized for energy consumption." Right? And that's all they say, right? That's a very high level statement. And then the run time translates it between all the elements that are participating in that service to realize the developer's intent, right? No hands, right? Zero touch, right? So that's now a movement in telecom. So you're right, it's taking a while because these are pretty fundamental shifts, right? But it's intent based networking, right? So it's almost two parts, right? One is you have to have the open APIs, right? So that the infrastructure has to expose its capabilities. Then you need abstractions over the top that make it simple for developers to take, you know, make use of them. >> See, one of the demonstrations we are doing is around AIOps. And I've had literally here on this floor, two conversations around what I call as network as a platform. Although it sounds like a cliche term, that's exactly what Dave was describing in terms of exposing APIs from the infrastructure and utilizing them. So once you get that data, then now you can do analytics and do machine learning to be able to build models and figure out how you can orchestrate better how you can monetize better, how can how you can utilize better, right? So all of those things become important. It's just not about internal optimization but it's also about how do you expose it to third party ecosystem to translate that into better delivery mechanisms or IOT capability and so on. >> But if they're going to charge me for every API call in the network I'm going to go broke (team laughs) >> And I'm going to get really pissed. I mean, I feel like, I'm just running down, Oracle. IBM tried it. Oracle, okay, they got Java, but they don't they don't have developer jobs. VMware, okay? They got Aria. EMC used to have a thing called code. IBM had to buy Red Hat to get to the developer community. (Lisa laughs) >> So I feel like the telcos don't today have those developer shops. So, so they have to partner. [Azhar] Yes. >> With guys like you and then be more open and and let a zillion flowers bloom or else they're going to get disrupted in a big way and they're going to it's going to be a repeat of the over, over the top in, in in a different model that I can't predict. >> Yeah. >> Absolutely true. I mean, look, they cannot be in the connectivity business. Telcos cannot be just in the connectivity business. It's, I think so, you know, >> Dave Vellante: You had a fry a frozen hand (Dave Daggul laughs) >> off that, you know. >> Well, you know, think about they almost have to go become over the top on themselves, right? That's what the cloud guys are doing, right? >> Yeah. >> They're riding over their backbone that by taking a creating a high level abstraction, they in turn abstract away the infrastructure underneath them, right? And that's really the end game >> Right? >> Dave Vellante: Yeah. >> Is because now, >> they're over the top it's their network, it's their infrastructure, right? They don't want to become bid pipes. >> Yep. >> Now you, they can take OpenShift, run that in any cloud. >> Yep. >> Right? >> You can run that in hybrid cloud, enterprise web can do the application layer configuration and management. And together we're running, you know, OSI layers one through seven, east to west, north to south. We're running across the the RAN, the core and the transport. And that is telco super cloud, my friend. >> Yeah. Well, >> (Dave Duggal laughs) >> I'm dominating the conversation cause I love talking super cloud. >> I knew you would. >> So speaking of super superpowers, when you're in customer or prospective customer conversations with providers and they've got, obviously they're they're in this transformative state right now. How, what do you describe as the superpower between Red Hat and EnterpriseWeb in terms of really helping these Telcos transforms. But at the end of the day, the connectivity's there the end user gets what they want, which is I want this to work wherever I am. >> Yeah, yeah. That's a great question, Lisa. So I think the way you could look at it is most software has, has been evolved to be specialized, right? So in Telcos' no different, right? We have this in the enterprise, right? All these specialized stacks, all these components that they wire together in the, in you think of Telco as a sort of a super set of enterprise problems, right? They have all those problems like magnified manyfold, right? And so you have specialized, let's say orchestrators and other tools for every Telco domain for every Telco layer. Now you have a zoo of orchestrators, right? None of them were designed to work together, right? They all speak a specific language, let's say quote unquote for doing a specific purpose. But everything that's interesting in the 21st century is across layers and across domains, right? If a siloed static application, those are dead, right? Nobody's doing those anymore. Even developers don't do those developers are doing composition today. They're not doing, nobody wants to hear about a 6 million lines of code, right? They want to hear, "How did you take these five things and bring 'em together for productive use?" >> Lisa: Right. How did you deliver faster for my enterprise? How did you save me money? How did you create business value? And that's what we're doing together. >> I mean, just to add on to Dave, I was talking to one of the providers, they have more than 30,000 nodes in their infrastructure. When I say no to your servers running, you know, Kubernetes,running open stack, running different components. If try managing that in one single entity, if you will. Not possible. You got to fragment, you got to segment in some way. Now the question is, if you are not exposing that particular infrastructure and the appropriate KPIs and appropriate things, you will not be able to efficiently utilize that across the board. So you need almost a construct that creates like a manager of managers, a hierarchical structure, which would allow you to be more intelligent in terms of how you place those, how you manage that. And so when you ask the question about what's the secret sauce between the two, well this is exactly where EnterpriseWeb brings in that capability to analyze information, be more intelligent about it. And what we do is provide an abstraction of the cloud layer so that they can, you know, then do the right job in terms of making sure that it's appropriate and it's consistent. >> Consistency is key. Guys, thank you so much. It's been a pleasure really digging through EnterpriseWeb. >> Thank you. >> What you're doing >> with Red Hat. How you're helping the organization transform and Supercloud, we can't forget Supercloud. (Dave Vellante laughs) >> Fight Supercloud. Guys, thank you so much for your time. >> Thank you so much Lisa. >> Thank you. >> Thank you guys. >> Very nice. >> Lisa: We really appreciate it. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live tech coverage coming to you live from MWC 23. We'll be back after a short break.

(upbeat music) >> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies: creating technologies that drive human progress. (upbeat music) >> Welcome back to the Fira in Barcelona. You're watching theCUBE's Coverage day two of MWC 23. Check out SiliconANGLE.com for all the news, John Furrier in our Palo Alto studio, breaking that down. But we're here live Dave Vellante, Dave Nicholson and Lisa Martin. We're really excited. We're going to talk qubits. Vanessa Diaz is here. She's CEO of LuxQuanta And Antonio Acin is a professor of ICFO. Folks, welcome to theCUBE. We're going to talk quantum. Really excited about that. >> Vanessa: Thank you guys. >> What does quantum have to do with the network? Tell us. >> Right, so we are actually leaving the second quantum revolution. So the first one actually happened quite a few years ago. It enabled very much the communications that we have today. So in this second quantum revolution, if in the first one we learn about some very basic properties of quantum physics now our scientific community is able to actually work with the systems and ask them to do things. So quantum technologies mean right now, three main pillars, no areas of exploration. The first one is quantum computing. Everybody knows about that. Antonio knows a lot about that too so he can explain further. And it's about computers that now can do wonder. So the ability of of these computers to compute is amazing. So they'll be able to do amazing things. The other pillar is quantum communications but in fact it's slightly older than quantum computer, nobody knows that. And we are the ones that are coming to actually counteract the superpowers of quantum computers. And last but not least quantum sensing, that's the the application of again, quantum physics to measure things that were impossible to measure in with such level of quality, of precision than before. So that's very much where we are right now. >> Okay, so I think I missed the first wave of quantum computing Because, okay, but my, our understanding is ones and zeros, they can be both and the qubits aren't that stable, et cetera. But where are we today, Antonio in terms of actually being able to apply quantum computing? I'm inferring from what Vanessa said that we've actually already applied it but has it been more educational or is there actual work going on with quantum? >> Well, at the moment, I mean, typical question is like whether we have a quantum computer or not. I think we do have some quantum computers, some machines that are able to deal with these quantum bits. But of course, this first generation of quantum computers, they have noise, they're imperfect, they don't have many qubits. So we have to understand what we can do with these quantum computers today. Okay, this is science, but also technology working together to solve relevant problems. So at this moment is not clear what we can do with present quantum computers but we also know what we can do with a perfect quantum computer without noise with many quantum bits, with many qubits. And for instance, then we can solve problems that are out of reach for our classical computers. So the typical example is the problem of factorization that is very connected to what Vanessa does in her company. So we have identified problems that can be solved more efficiently with a quantum computer, with a very good quantum computer. People are working to have this very good quantum computer. At the moment, we have some imperfect quantum computers, we have to understand what we can do with these imperfect machines. >> Okay. So for the first wave was, okay, we have it working for a little while so we see the potential. Okay, and we have enough evidence almost like a little experiment. And now it's apply it to actually do some real work. >> Yeah, so now there is interest by companies so because they see a potential there. So they are investing and they're working together with scientists. We have to identify use cases, problems of relevance for all of us. And then once you identify a problem where a quantum computer can help you, try to solve it with existing machines and see if you can get an advantage. So now the community is really obsessed with getting a quantum advantage. So we really hope that we will get a quantum advantage. This, we know we will get it. We eventually have a very good quantum computer. But we want to have it now. And we're working on that. We have some results, there were I would say a bit academic situation in which a quantum advantage was proven. But to be honest with you on a really practical problem, this has not happened yet. But I believe the day that this happens and I mean it will be really a game changing. >> So you mentioned the word efficiency and you talked about the quantum advantage. Is the quantum advantage a qualitative advantage in that it is fundamentally different? Or is it simply a question of greater efficiency, so therefore a quantitative advantage? The example in the world we're used to, think about a card system where you're writing information on a card and putting it into a filing cabinet and then you want to retrieve it. Well, the information's all there, you can retrieve it. Computer system accelerates that process. It's not doing something that is fundamentally different unless you accept that the speed with which these things can be done gives it a separate quality. So how would you characterize that quantum versus non quantum? Is it just so much horse power changes the game or is it fundamentally different? >> Okay, so from a fundamental perspective, quantum physics is qualitatively different from classical physics. I mean, this year the Nobel Prize was given to three experimentalists who made experiments that proved that quantum physics is qualitatively different from classical physics. This is established, I mean, there have been experiments proving that. Now when we discuss about quantum computation, it's more a quantitative difference. So we have problems that you can solve, in principle you can solve with the classical computers but maybe the amount of time you need to solve them is we are talking about centuries and not with your laptop even with a classic super computer, these machines that are huge, where you have a building full of computers there are some problems for which computers take centuries to solve them. So you can say that it's quantitative, but in practice you may even say that it's impossible in practice and it will remain impossible. And now these problems become feasible with a quantum computer. So it's quantitative but almost qualitative I would say. >> Before we get into the problems, 'cause I want to understand some of those examples, but Vanessa, so your role at LuxQuanta is you're applying quantum in the communication sector for security purposes, correct? >> Vanessa: Correct. >> Because everybody talks about how quantum's going to ruin our lives in terms of taking all our passwords and figuring everything out. But can quantum help us defend against quantum and is that what you do? >> That's what we do. So one of the things that Antonio's explaining so our quantum computer will be able to solve in a reasonable amount of time something that today is impossible to solve unless you leave a laptop or super computer working for years. So one of those things is cryptography. So at the end, when use send a message and you want to preserve its confidentiality what you do is you destroy it but following certain rules which means they're using some kind of key and therefore you can send it through a public network which is the case for every communication that we have, we go through the internet and then the receiver is going to be able to reassemble it because they have that private key and nobody else has. So that private key is actually made of computational problems or mathematical problems that are very, very hard. We're talking about 40 years time for a super computer today to be able to hack it. However, we do not have the guarantee that there is already very smart mind that already have potentially the capacity also of a quantum computer even with enough, no millions, but maybe just a few qubits, it's enough to actually hack this cryptography. And there is also the fear that somebody could actually waiting for quantum computing to finally reach out this amazing capacity we harvesting now which means capturing all this confidential information storage in it. So when we are ready to have the power to unlock it and hack it and see what's behind. So we are talking about information as delicate as governmental, citizens information related to health for example, you name it. So what we do is we build a key to encrypt the information but it's not relying on a mathematical problem it's relying on the laws of quantum physics. So I'm going to have a channel that I'm going to pump photons there, light particles of light. And that quantum channel, because of the laws of physics is going to allow to detect somebody trying to sneak in and seeing the key that I'm establishing. If that happens, I will not create a key if it's clean and nobody was there, I'll give you a super key that nobody today or in the future, regardless of their computational power, will be able to hack. >> So it's like super zero trust. >> Super zero trust. >> Okay so but quantum can solve really challenging mathematical problems. If you had a quantum computer could you be a Bitcoin billionaire? >> Not that I know. I think people are, okay, now you move me a bit of my comfort zone. Because I know people have working on that. I don't think there is a lot of progress at least not that I am aware of. Okay, but I mean, in principle you have to understand that our society is based on information and computation. Computers are a key element in our society. And if you have a machine that computes better but much better than our existing machines, this gives you an advantage for many things. I mean, progress is locked by many computational problems we cannot solve. We can want to have better materials better medicines, better drugs. I mean this, you have to solve hard computational problems. If you have machine that gives you machine learning, big data. I mean, if you have a machine that gives you an advantage there, this may be a really real change. I'm not saying that we know how to do these things with a quantum computer. But if we understand how this machine that has been proven more powerful in some context can be adapted to some other context. I mean having a much better computer machine is an advantage. >> When? When are we going to have, you said we don't really have it today, we want it today. Are we five years away, 10 years away? Who's working on this? >> There are already quantum computers are there. It's just that the capacity that they have of right now is the order of a few hundred qubits. So people are, there are already companies harvesting, they're actually the companies that make these computers they're already putting them. People can access to them through the cloud and they can actually run certain algorithms that have been tailor made or translated to the language of a quantum computer to see how that performs there. So some people are already working with them. There is billions of investment across the world being put on different flavors of technologies that can reach to that quantum supremacy that we are talking about. The question though that you're asking is Q day it sounds like doomsday, you know, Q day. So depending on who you talk to, they will give you a different estimation. So some people say, well, 2030 for example but perhaps we could even think that it could be a more aggressive date, maybe 2027. So it is yet to be the final, let's say not that hard deadline but I think that the risk, that it can actually bring is big enough for us to pay attention to this and start preparing for it. So the end times of cryptography that's what quantum is doing is we have a system here that can actually prevent all your communications from being hacked. So if you think also about Q day and you go all the way back. So whatever tools you need to protect yourself from it, you need to deploy them, you need to see how they fit in your organization, evaluate the benefits, learn about it. So that, how close in time does that bring us? Because I believe that the time to start thinking about this is now. >> And it's likely it'll be some type of hybrid that will get us there, hybrid between existing applications. 'Cause you have to rewrite or write new applications and that's going to take some time. But it sounds like you feel like this decade we will see Q day. What probability would you give that? Is it better than 50/50? By 2030 we'll see Q day. >> But I'm optimistic by nature. So yes, I think it's much higher than 50. >> Like how much higher? >> 80, I would say yes. I'm pretty confident. I mean, but what I want to say also usually when I think there is a message here so you have your laptop, okay, in the past I had a Spectrum This is very small computer, it was more or less the same size but this machine is much more powerful. Why? Because we put information on smaller scales. So we always put information in smaller and smaller scale. This is why here you have for the same size, you have much more information because you put on smaller scales. So if you go small and small and small, you'll find the quantum word. So this is unavoidable. So our information devices are going to meet the quantum world and they're going to exploit it. I'm fully convinced about this, maybe not for the quantum computer we're imagining now but they will find it and they will use quantum effects. And also for cryptography, for me, this is unavoidable. >> And you brought the point there are several companies working on that. I mean, I can get quantum computers on in the cloud and Amazon and other suppliers. IBM of course is. >> The underlying technology, there are competing versions of how you actually create these qubits. pins of electrons and all sorts of different things. Does it need to be super cooled or not? >> Vanessa: There we go. >> At a fundamental stage we'd be getting ground. But what is, what does ChatGPT look like when it can leverage the quantum realm? >> Well, okay. >> I Mean are we all out of jobs at that point? Should we all just be planning for? >> No. >> Not you. >> I think all of us real estate in Portugal, should we all be looking? >> No, actually, I mean in machine learning there are some hopes about quantum competition because usually you have to deal with lots of data. And we know that in quantum physics you have a concept that is called superposition. So we, there are some hopes not in concrete yet but we have some hopes that these superpositions may allow you to explore this big data in a more efficient way. One has to if this can be confirmed. But one of the hopes creating this lots of qubits in this superpositions that you will have better artificial intelligence machines but, okay, this is quite science fiction what I'm saying now. >> At this point and when you say superposition, that's in contrast to the ones and zeros that we're used to. So when someone says it could be a one or zero or a one and a zero, that's referencing the concept of superposition. And so if this is great for encryption, doesn't that necessarily mean that bad actors can leverage it in a way that is now unhackable? >> I mean our technologies, again it's impossible to hack because it is the laws of physics what are allowing me to detect an intruder. So that's the beauty of it. It's not something that you're going to have to replace in the future because there will be a triple quantum computer, it is not going to affect us in any way but definitely the more capacity, computational capacity that we see out there in quantum computers in particular but in any other technologies in general, I mean, when we were coming to talk to you guys, Antonio and I, he was the one saying we do not know whether somebody has reached some relevant computational power already with the technologies that we have. And they've been able to hack already current cryptography and then they're not telling us. So it's a bit of, the message is a little bit like a paranoid message, but if you think about security that the amount of millions that means for a private institution know when there is a data breach, we see it every day. And also the amount of information that is relevant for the wellbeing of a country. Can you really put a reasonable amount of paranoid to that? Because I believe that it's worth exploring whatever tool is going to prevent you from putting any of those piece of information at risk. >> Super interesting topic guys. I know you're got to run. Thanks for stopping by theCUBE, it was great to have you on. >> Thank you guys. >> All right, so this is the SiliconANGLE theCUBE's coverage of Mobile World Congress, MWC now 23. We're live at the Fira Check out silicon SiliconANGLE.com and theCUBE.net for all the videos. Be right back, right after this short break. (relaxing 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. >> 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)

(upbeat instrumental) >> Hello and welcome to this CUBE conversation. I'm John Furrier, host of theCUBE, here in Palo Alto, California. Got a great conversation with Robert Nishihara who's the co-founder and CEO of Anyscale. Robert, great to have you on this CUBE conversation. It's great to see you. We did your first Ray Summit a couple years ago and congratulations on your venture. Great to have you on. >> Thank you. Thanks for inviting me. >> So you're first time CEO out of Berkeley in Data. You got the Databricks is coming out of there. You got a bunch of activity coming from Berkeley. It's like a, it really is kind of like where a lot of innovations going on data. Anyscale has been one of those startups that has risen out of that scene. Right? You look at the success of what the Data lakes are now. Now you've got the generative AI. This has been a really interesting innovation market. This new wave is coming. Tell us what's going on with Anyscale right now, as you guys are gearing up and getting some growth. What's happening with the company? >> Yeah, well one of the most exciting things that's been happening in computing recently, is the rise of AI and the excitement about AI, and the potential for AI to really transform every industry. Now of course, one of the of the biggest challenges to actually making that happen is that doing AI, that AI is incredibly computationally intensive, right? To actually succeed with AI to actually get value out of AI. You're typically not just running it on your laptop, you're often running it and scaling it across thousands of machines, or hundreds of machines or GPUs, and to, so organizations and companies and businesses that do AI often end up building a large infrastructure team to manage the distributed systems, the computing to actually scale these applications. And that's a, that's a, a huge software engineering lift, right? And so, one of the goals for Anyscale is really to make that easy. To get to the point where, developers and teams and companies can succeed with AI. Can build these scalable AI applications, without really you know, without a huge investment in infrastructure with a lot of, without a lot of expertise in infrastructure, where really all they need to know is how to program on their laptop, how to program in Python. And if you have that, then that's really all you need to succeed with AI. So that's what we've been focused on. We're building Ray, which is an open source project that's been starting to get adopted by tons of companies, to actually train these models, to deploy these models, to do inference with these models, you know, to ingest and pre-process their data. And our goals, you know, here with the company are really to make Ray successful. To grow the Ray community, and then to build a great product around it and simplify the development and deployment, and productionization of machine learning for, for all these businesses. >> It's a great trend. Everyone wants developer productivity seeing that, clearly right now. And plus, developers are voting literally on what standards become. As you look at how the market is open source driven, a lot of that I love the model, love the Ray project love the, love the Anyscale value proposition. How big are you guys now, and how is that value proposition of Ray and Anyscale and foundational models coming together? Because it seems like you guys are in a perfect storm situation where you guys could get a real tailwind and draft off the the mega trend that everyone's getting excited. The new toy is ChatGPT. So you got to look at that and say, hey, I mean, come on, you guys did all the heavy lifting. >> Absolutely. >> You know how many people you are, and what's the what's the proposition for you guys these days? >> You know our company's about a hundred people, that a bit larger than that. Ray's been going really quickly. It's been, you know, companies using, like OpenAI uses Ray to train their models, like ChatGPT. Companies like Uber run all their deep learning you know, and classical machine learning on top of Ray. Companies like Shopify, Spotify, Netflix, Cruise, Lyft, Instacart, you know, Bike Dance. A lot of these companies are investing heavily in Ray for their machine learning infrastructure. And I think it's gotten to the point where, if you're one of these, you know type of businesses, and you're looking to revamp your machine learning infrastructure. If you're looking to enable new capabilities, you know make your teams more productive, increase, speed up the experimentation cycle, you know make it more performance, like build, you know, run applications that are more scalable, run them faster, run them in a more cost efficient way. All of these types of companies are at least evaluating Ray and Ray is an increasingly common choice there. I think if they're not using Ray, if many of these companies that end up not using Ray, they often end up building their own infrastructure. So Ray has been, the growth there has been incredibly exciting over the, you know we had our first in-person Ray Summit just back in August, and planning the next one for, for coming September. And so when you asked about the value proposition, I think there's there's really two main things, when people choose to go with Ray and Anyscale. One reason is about moving faster, right? It's about developer productivity, it's about speeding up the experimentation cycle, easily getting their models in production. You know, we hear many companies say that they, you know they, once they prototype a model, once they develop a model, it's another eight weeks, or 12 weeks to actually get that model in production. And that's a reason they talk to us. We hear companies say that, you know they've been training their models and, and doing inference on a single machine, and they've been sort of scaling vertically, like using bigger and bigger machines. But they, you know, you can only do that for so long, and at some point you need to go beyond a single machine and that's when they start talking to us. Right? So one of the main value propositions is around moving faster. I think probably the phrase I hear the most is, companies saying that they don't want their machine learning people to have to spend all their time configuring infrastructure. All this is about productivity. >> Yeah. >> The other. >> It's the big brains in the company. That are being used to do remedial tasks that should be automated right? I mean that's. >> Yeah, and I mean, it's hard stuff, right? It's also not these people's area of expertise, and or where they're adding the most value. So all of this is around developer productivity, moving faster, getting to market faster. The other big value prop and the reason people choose Ray and choose Anyscale, is around just providing superior infrastructure. This is really, can we scale more? You know, can we run it faster, right? Can we run it in a more cost effective way? We hear people saying that they're not getting good GPU utilization with the existing tools they're using, or they can't scale beyond a certain point, or you know they don't have a way to efficiently use spot instances to save costs, right? Or their clusters, you know can't auto scale up and down fast enough, right? These are all the kinds of things that Ray and Anyscale, where Ray and Anyscale add value and solve these kinds of problems. >> You know, you bring up great points. Auto scaling concept, early days, it was easy getting more compute. Now it's complicated. They're built into more integrated apps in the cloud. And you mentioned those companies that you're working with, that's impressive. Those are like the big hardcore, I call them hardcore. They have a good technical teams. And as the wave starts to move from these companies that were hyper scaling up all the time, the mainstream are just developers, right? So you need an interface in, so I see the dots connecting with you guys and I want to get your reaction. Is that how you see it? That you got the alphas out there kind of kicking butt, building their own stuff, alpha developers and infrastructure. But mainstream just wants programmability. They want that heavy lifting taken care of for them. Is that kind of how you guys see it? I mean, take us through that. Because to get crossover to be democratized, the automation's got to be there. And for developer productivity to be in, it's got to be coding and programmability. >> That's right. Ultimately for AI to really be successful, and really you know, transform every industry in the way we think it has the potential to. It has to be easier to use, right? And that is, and being easier to use, there's many dimensions to that. But an important one is that as a developer to do AI, you shouldn't have to be an expert in distributed systems. You shouldn't have to be an expert in infrastructure. If you do have to be, that's going to really limit the number of people who can do this, right? And I think there are so many, all of the companies we talk to, they don't want to be in the business of building and managing infrastructure. It's not that they can't do it. But it's going to slow them down, right? They want to allocate their time and their energy toward building their product, right? To building a better product, getting their product to market faster. And if we can take the infrastructure work off of the critical path for them, that's going to speed them up, it's going to simplify their lives. And I think that is critical for really enabling all of these companies to succeed with AI. >> Talk about the customers you guys are talking to right now, and how that translates over. Because I think you hit a good thread there. Data infrastructure is critical. Managed services are coming online, open sources continuing to grow. You have these people building their own, and then if they abandon it or don't scale it properly, there's kind of consequences. 'Cause it's a system you mentioned, it's a distributed system architecture. It's not as easy as standing up a monolithic app these days. So when you guys go to the marketplace and talk to customers, put the customers in buckets. So you got the ones that are kind of leaning in, that are pretty peaked, probably working with you now, open source. And then what's the customer profile look like as you go mainstream? Are they looking to manage service, looking for more architectural system, architecture approach? What's the, Anyscale progression? How do you engage with your customers? What are they telling you? >> Yeah, so many of these companies, yes, they're looking for managed infrastructure 'cause they want to move faster, right? Now the kind of these profiles of these different customers, they're three main workloads that companies run on Anyscale, run with Ray. It's training related workloads, and it is serving and deployment related workloads, like actually deploying your models, and it's batch processing, batch inference related workloads. Like imagine you want to do computer vision on tons and tons of, of images or videos, or you want to do natural language processing on millions of documents or audio, or speech or things like that, right? So the, I would say the, there's a pretty large variety of use cases, but the most common you know, we see tons of people working with computer vision data, you know, computer vision problems, natural language processing problems. And it's across many different industries. We work with companies doing drug discovery, companies doing you know, gaming or e-commerce, right? Companies doing robotics or agriculture. So there's a huge variety of the types of industries that can benefit from AI, and can really get a lot of value out of AI. And, but the, but the problems are the same problems that they all want to solve. It's like how do you make your team move faster, you know succeed with AI, be more productive, speed up the experimentation, and also how do you do this in a more performant way, in a faster, cheaper, in a more cost efficient, more scalable way. >> It's almost like the cloud game is coming back to AI and these foundational models, because I was just on a podcast, we recorded our weekly podcast, and I was just riffing with Dave Vellante, my co-host on this, were like, hey, in the early days of Amazon, if you want to build an app, you just, you have to build a data center, and then you go to now you go to the cloud, cloud's easier, pay a little money, penny's on the dollar, you get your app up and running. Cloud computing is born. With foundation models in generative AI. The old model was hard, heavy lifting, expensive, build out, before you get to do anything, as you mentioned time. So I got to think that you're pretty much in a good position with this foundational model trend in generative AI because I just looked at the foundation map, foundation models, map of the ecosystem. You're starting to see layers of, you got the tooling, you got platform, you got cloud. It's filling out really quickly. So why is Anyscale important to this new trend? How do you talk to people when they ask you, you know what does ChatGPT mean for Anyscale? And how does the financial foundational model growth, fit into your plan? >> Well, foundational models are hugely important for the industry broadly. Because you're going to have these really powerful models that are trained that you know, have been trained on tremendous amounts of data. tremendous amounts of computes, and that are useful out of the box, right? That people can start to use, and query, and get value out of, without necessarily training these huge models themselves. Now Ray fits in and Anyscale fit in, in a number of places. First of all, they're useful for creating these foundation models. Companies like OpenAI, you know, use Ray for this purpose. Companies like Cohere use Ray for these purposes. You know, IBM. If you look at, there's of course also open source versions like GPTJ, you know, created using Ray. So a lot of these large language models, large foundation models benefit from training on top of Ray. And, but of course for every company training and creating these huge foundation models, you're going to have many more that are fine tuning these models with their own data. That are deploying and serving these models for their own applications, that are building other application and business logic around these models. And that's where Ray also really shines, because Ray you know, is, can provide common infrastructure for all of these workloads. The training, the fine tuning, the serving, the data ingest and pre-processing, right? The hyper parameter tuning, the and and so on. And so where the reason Ray and Anyscale are important here, is that, again, foundation models are large, foundation models are compute intensive, doing you know, using both creating and using these foundation models requires tremendous amounts of compute. And there there's a big infrastructure lift to make that happen. So either you are using Ray and Anyscale to do this, or you are building the infrastructure and managing the infrastructure yourself. Which you can do, but it's, it's hard. >> Good luck with that. I always say good luck with that. I mean, I think if you really need to do, build that hardened foundation, you got to go all the way. And I think this, this idea of composability is interesting. How is Ray working with OpenAI for instance? Take, take us through that. Because I think you're going to see a lot of people talking about, okay I got trained models, but I'm going to have not one, I'm going to have many. There's big debate that OpenAI is going to be the mother of all LLMs, but now, but really people are also saying that to be many more, either purpose-built or specific. The fusion and these things come together there's like a blending of data, and that seems to be a value proposition. How does Ray help these guys get their models up? Can you take, take us through what Ray's doing for say OpenAI and others, and how do you see the models interacting with each other? >> Yeah, great question. So where, where OpenAI uses Ray right now, is for the training workloads. Training both to create ChatGPT and models like that. There's both a supervised learning component, where you're pre-training this model on doing supervised pre-training with example data. There's also a reinforcement learning component, where you are fine-tuning the model and continuing to train the model, but based on human feedback, based on input from humans saying that, you know this response to this question is better than this other response to this question, right? And so Ray provides the infrastructure for scaling the training across many, many GPUs, many many machines, and really running that in an efficient you know, performance fault tolerant way, right? And so, you know, open, this is not the first version of OpenAI's infrastructure, right? They've gone through iterations where they did start with building the infrastructure themselves. They were using tools like MPI. But at some point, you know, given the complexity, given the scale of what they're trying to do, you hit a wall with MPI and that's going to happen with a lot of other companies in this space. And at that point you don't have many other options other than to use Ray or to build your own infrastructure. >> That's awesome. And then your vision on this data interaction, because the old days monolithic models were very rigid. You couldn't really interface with them. But we're kind of seeing this future of data fusion, data interaction, data blending at large scale. What's your vision? How do you, what's your vision of where this goes? Because if this goes the way people think. You can have this data chemistry kind of thing going on where people are integrating all kinds of data with each other at large scale. So you need infrastructure, intelligence, reasoning, a lot of code. Is this something that you see? What's your vision in all this? Take us through. >> AI is going to be used everywhere right? It's, we see this as a technology that's going to be ubiquitous, and is going to transform every business. I mean, imagine you make a product, maybe you were making a tool like Photoshop or, or whatever the, you know, tool is. The way that people are going to use your tool, is not by investing, you know, hundreds of hours into learning all of the different, you know specific buttons they need to press and workflows they need to go through it. They're going to talk to it, right? They're going to say, ask it to do the thing they want it to do right? And it's going to do it. And if it, if it doesn't know what it's want, what it's, what's being asked of it. It's going to ask clarifying questions, right? And then you're going to clarify, and you're going to have a conversation. And this is going to make many many many kinds of tools and technology and products easier to use, and lower the barrier to entry. And so, and this, you know, many companies fit into this category of trying to build products that, and trying to make them easier to use, this is just one kind of way it can, one kind of way that AI will will be used. But I think it's, it's something that's pretty ubiquitous. >> Yeah. It'll be efficient, it'll be efficiency up and down the stack, and will change the productivity equation completely. You just highlighted one, I don't want to fill out forms, just stand up my environment for me. And then start coding away. Okay well this is great stuff. Final word for the folks out there watching, obviously new kind of skill set for hiring. You guys got engineers, give a plug for the company, for Anyscale. What are you looking for? What are you guys working on? Give a, take the last minute to put a plug in for the company. >> Yeah well if you're interested in AI and if you think AI is really going to be transformative, and really be useful for all these different industries. We are trying to provide the infrastructure to enable that to happen, right? So I think there's the potential here, to really solve an important problem, to get to the point where developers don't need to think about infrastructure, don't need to think about distributed systems. All they think about is their application logic, and what they want their application to do. And I think if we can achieve that, you know we can be the foundation or the platform that enables all of these other companies to succeed with AI. So that's where we're going. I think something like this has to happen if AI is going to achieve its potential, we're looking for, we're hiring across the board, you know, great engineers, on the go-to-market side, product managers, you know people who want to really, you know, make this happen. >> Awesome well congratulations. I know you got some good funding behind you. You're in a good spot. I think this is happening. I think generative AI and foundation models is going to be the next big inflection point, as big as the pc inter-networking, internet and smartphones. This is a whole nother application framework, a whole nother set of things. So this is the ground floor. Robert, you're, you and your team are right there. Well done. >> Thank you so much. >> All right. Thanks for coming on this CUBE conversation. I'm John Furrier with theCUBE. Breaking down a conversation around AI and scaling up in this new next major inflection point. This next wave is foundational models, generative AI. And thanks to ChatGPT, the whole world's now knowing about it. So it really is changing the game and Anyscale is right there, one of the hot startups, that is in good position to ride this next wave. Thanks for watching. (upbeat instrumental)

>> 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)

(gentle music) >> Okay, welcome back to Supercloud 2, live here in Palo Alto, California for our live stage performance. Supercloud 2 is our second Supercloud event. We're going to get these out as fast as we can every couple months. It's our second one, you'll see two and three this year. I'm John Furrier, my co-host, Dave Vellante. A panel here to break down the Supercloud momentum, the wave, and the developer impact that we bringing back Vittorio Viarengo, who's a VP for Cross-Cloud Services at VMware. Sarbjeet Johal, industry influencer and Analyst at StackPayne, his company, Cube alumni and Influencer. Sarbjeet, great to see you. Vittorio, thanks for coming back. >> Nice to be here. >> My pleasure. >> Vittorio, you just gave a keynote where we unpacked the cross-cloud services, what VMware is doing, how you guys see it, not just from VMware's perspective, but VMware looking out broadly at the industry and developers came up and you were like, "Developers, developer, developers", kind of a goof on the Steve Ballmer famous meme that everyone's seen. This is a huge star, sorry, I mean a big piece of it. The developers are the canary in the coal mines. They're the ones who are being asked to code the digital transformation, which is fully business transformation and with the market the way it is right now in terms of the accelerated technology, every enterprise grade business model's changing. The technology is evolving, the builders are kind of, they want go faster. I'm saying they're stuck in a way, but that's my opinion, but there's a lot of growth. >> Yeah. >> The impact, they got to get released up and let it go. Those developers need to accelerate faster. It's been a big part of productivity, and the conversations we've had. So developer impact is huge in Supercloud. What's your, what do you guys think about this? We'll start with you, Sarbjeet. >> Yeah, actually, developers are the masons of the digital empires I call 'em, right? They lay every brick and build all these big empires. On the left side of the SDLC, or the, you know, when you look at the system operations, developer is number one cost from economic side of things, and from technology side of things, they are tech hungry people. They are developers for that reason because developer nights are long, hours are long, they forget about when to eat, you know, like, I've been a developer, I still code. So you want to keep them happy, you want to hug your developers. We always say that, right? Vittorio said that right earlier. The key is to, in this context, in the Supercloud context, is that developers don't mind mucking around with platforms or APIs or new languages, but they hate the infrastructure part. That's a fact. They don't want to muck around with servers. It's friction for them, it is like they don't want to muck around even with the VMs. So they want the programmability to the nth degree. They want to automate everything, so that's how they think and cloud is the programmable infrastructure, industrialization of infrastructure in many ways. So they are happy with where we are going, and we need more abstraction layers for some developers. By the way, I have this sort of thinking frame for last year or so, not all developers are same, right? So if you are a developer at an ISV, you behave differently. If you are a developer at a typical enterprise, you behave differently or you are forced to behave differently because you're not writing software.- >> Well, developers, developers have changed, I mean, Vittorio, you and I were talking earlier on the keynote, and this is kind of the key point is what is a developer these days? If everything is software enabled, I mean, even hardware interviews we do with Nvidia, and Amazon and other people building silicon, they all say the same thing, "It's software on a chip." So you're seeing the role of software up and down the stack and the role of the stack is changing. The old days of full stack developer, what does that even mean? I mean, the cloud is a half a stack kind of right there. So, you know, developers are certainly more agile, but cloud native, I mean VMware is epitome of operations, IT operations, and the Tan Zoo initiative, you guys started, you went after the developers to look at them, and ask them questions, "What do you need?", "How do you transform the Ops from virtualization?" Again, back to your point, so this hardware abstraction, what is software, what is cloud native? It's kind of messy equation these days. How do you guys grokel with that? >> I would argue that developers don't want the Supercloud. I dropped that up there, so, >> Dave: Why not? >> Because developers, they, once they get comfortable in AWS or Google, because they're doing some AI stuff, which is, you know, very trendy right now, or they are in IBM, any of the IPA scaler, professional developers, system developers, they love that stuff, right? Yeah, they don't, the infrastructure gets in the way, but they're just, the problem is, and I think the Supercloud should be driven by the operators because as we discussed, the operators have been left behind because they're busy with day-to-day jobs, and in most cases IT is centralized, developers are in the business units. >> John: Yeah. >> Right? So they get the mandate from the top, say, "Our bank, they're competing against". They gave teenagers or like young people the ability to do all these new things online, and Venmo and all this integration, where are we? "Oh yeah, we can do it", and then build it, and then deploy it, "Okay, we caught up." but now the operators are back in the private cloud trying to keep the backend system running and so I think the Supercloud is needed for the primarily, initially, for the operators to get in front of the developers, fit in the workflow, but lay the foundation so it is secure.- >> So, so I love this thinking because I love the rift, because the rift points to what is the target audience for the value proposition and if you're a developer, Supercloud enables you so you shouldn't have to deal with Supercloud. >> Exactly. >> What you're saying is get the operating environment or operating system done properly, whether it's architecture, building the platform, this comes back to architecture platform conversations. What is the future platform? Is it a vendor supplied or is it customer created platform? >> Dave: So developers want best to breed, is what you just said. >> Vittorio: Yeah. >> Right and operators, they, 'cause developers don't want to deal with governance, they don't want to deal with security, >> No. >> They don't want to deal with spinning up infrastructure. That's the role of the operator, but that's where Supercloud enables, to John's point, the developer, so to your question, is it a platform where the platform vendor is responsible for the architecture, or there is it an architectural standard that spans multiple clouds that has to emerge? Based on what you just presented earlier, Vittorio, you are the determinant of the architecture. It's got to be open, but you guys determine that, whereas the nirvana is, "Oh no, it's all open, and it just kind of works." >> Yeah, so first of all, let's all level set on one thing. You cannot tell developers what to do. >> Dave: Right, great >> At least great developers, right? Cannot tell them what to do. >> Dave: So that's what, that's the way I want to sort of, >> You can tell 'em what's possible. >> There's a bottle on that >> If you tell 'em what's possible, they'll test it, they'll look at it, but if you try to jam it down their throat, >> Yeah. >> Dave: You can't tell 'em how to do it, just like your point >> Let me answer your answer the question. >> Yeah, yeah. >> So I think we need to build an architect, help them build an architecture, but it cannot be proprietary, has to be built on what works in the cloud and so what works in the cloud today is Kubernetes, is you know, number of different open source project that you need to enable and then provide, use this, but when I first got exposed to Kubernetes, I said, "Hallelujah!" We had a runtime that works the same everywhere only to realize there are 12 different distributions. So that's where we come in, right? And other vendors come in to say, "Hey, no, we can make them all look the same. So you still use Kubernetes, but we give you a place to build, to set those operation policy once so that you don't create friction for the developers because that's the last thing you want to do." >> Yeah, actually, coming back to the same point, not all developers are same, right? So if you're ISV developer, you want to go to the lowest sort of level of the infrastructure and you want to shave off the milliseconds from to get that performance, right? If you're working at AWS, you are doing that. If you're working at scale at Facebook, you're doing that. At Twitter, you're doing that, but when you go to DMV and Kansas City, you're not doing that, right? So your developers are different in nature. They are given certain parameters to work with, certain sort of constraints on the budget side. They are educated at a different level as well. Like they don't go to that end of the degree of sort of automation, if you will. So you cannot have the broad stroking of developers. We are talking about a citizen developer these days. That's a extreme low, >> You mean Low-Code. >> Yeah, Low-Code, No-code, yeah, on the extreme side. On one side, that's citizen developers. On the left side is the professional developers, when you say developers, your mind goes to the professional developers, like the hardcore developers, they love the flexibility, you know, >> John: Well app, developers too, I mean. >> App developers, yeah. >> You're right a lot of, >> Sarbjeet: Infrastructure platform developers, app developers, yes. >> But there are a lot of customers, its a spectrum, you're saying. >> Yes, it's a spectrum >> There's a lot of customers don't want deal with that muck. >> Yeah. >> You know, like you said, AWS, Twitter, the sophisticated developers do, but there's a whole suite of developers out there >> Yeah >> That just want tools that are abstracted. >> Within a company, within a company. Like how I see the Supercloud is there shouldn't be anything which blocks the developers, like their view of the world, of the future. Like if you're blocked as a developer, like something comes in front of you, you are not developer anymore, believe me, (John laughing) so you'll go somewhere else >> John: First of all, I'm, >> You'll leave the company by the way. >> Dave: Yeah, you got to quit >> Yeah, you will quit, you will go where the action is, where there's no sort of blockage there. So like if you put in front of them like a huge amount of a distraction, they don't like it, so they don't, >> Well, the idea of a developer, >> Coming back to that >> Let's get into 'cause you mentioned platform. Get year in the term platform engineering now. >> Yeah. >> Platform developer. You know, I remember back in, and I think there's still a term used today, but when I graduated my computer science degree, we were called "Software engineers," right? Do people use that term "Software engineering", or is it "Software development", or they the same, are they different? >> Well, >> I think there's a, >> So, who's engineering what? Are they engineering or are they developing? Or both? Well, I think it the, you made a great point. There is a factor of, I had the, I was blessed to work with Adam Bosworth, that is the guy that created some of the abstraction layer, like Visual Basic and Microsoft Access and he had so, he made his whole career thinking about this layer, and he always talk about the professional developers, the developers that, you know, give him a user manual, maybe just go at the APIs, he'll build anything, right, from system engine, go down there, and then through obstruction, you get the more the procedural logic type of engineers, the people that used to be able to write procedural logic and visual basic and so on and so forth. I think those developers right now are a little cut out of the picture. There's some No-code, Low-Code environment that are maybe gain some traction, I caught up with Adam Bosworth two weeks ago in New York and I asked him "What's happening to this higher level developers?" and you know what he is told me, and he is always a little bit out there, so I'm going to use his thought process here. He says, "ChapGPT", I mean, they will get to a point where this high level procedural logic will be written by, >> John: Computers. >> Computers, and so we may not need as many at the high level, but we still need the engineers down there. The point is the operation needs to get in front of them >> But, wait, wait, you seen the ChatGPT meme, I dunno if it's a Dilbert thing where it's like, "Time to tic" >> Yeah, yeah, yeah, I did that >> "Time to develop the code >> Five minutes, time to decode", you know, to debug the codes like five hours. So you know, the whole equation >> Well, this ChatGPT is a hot wave, everyone's been talking about it because I think it illustrates something that's NextGen, feels NextGen, and it's just getting started so it's going to get better. I mean people are throwing stones at it, but I think it's amazing. It's the equivalent of me seeing the browser for the first time, you know, like, "Wow, this is really compelling." This is game-changing, it's not just keyword chat bots. It's like this is real, this is next level, and I think the Supercloud wave that people are getting behind points to that and I think the question of Ops and Dev comes up because I think if you limit the infrastructure opportunity for a developer, I think they're going to be handicapped. I mean that's a general, my opinion, the thesis is you give more aperture to developers, more choice, more capabilities, more good things could happen, policy, and that's why you're seeing the convergence of networking people, virtualization talent, operational talent, get into the conversation because I think it's an infrastructure engineering opportunity. I think this is a seminal moment in a new stack that's emerging from an infrastructure, software virtualization, low-code, no-code layer that will be completely programmable by things like the next Chat GPT or something different, but yet still the mechanics and the plumbing will still need engineering. >> Sarbjeet: Oh yeah. >> So there's still going to be more stuff coming on. >> Yeah, we have, with the cloud, we have made the infrastructure programmable and you give the programmability to the programmer, they will be very creative with that and so we are being very creative with our infrastructure now and on top of that, we are being very creative with the silicone now, right? So we talk about that. That's part of it, by the way. So you write the code to the particle's silicone now, and on the flip side, the silicone is built for certain use cases for AI Inference and all that. >> You saw this at CES? >> Yeah, I saw at CES, the scenario is this, the Bosch, I spoke to Bosch, I spoke to John Deere, I spoke to AWS guys, >> Yeah. >> They were showcasing their technology there and I was spoke to Azure guys as well. So the Bosch is a good example. So they are building, they are right now using AWS. I have that interview on camera, I will put it some sometime later on there online. So they're using AWS on the back end now, but Bosch is the number one, number one or number two depending on what day it is of the year, supplier of the componentry to the auto industry, and they are creating a platform for our auto industry, so is Qualcomm actually by the way, with the Snapdragon. So they told me that customers, their customers, BMW, Audi, all the manufacturers, they demand the diversity of the backend. Like they don't want all, they, all of them don't want to go to AWS. So they want the choice on the backend. So whatever they cook in the middle has to work, they have to sprinkle the data for the data sovereign side because they have Chinese car makers as well, and for, you know, for other reasons, competitive reasons and like use. >> People don't go to, aw, people don't go to AWS either for political reasons or like competitive reasons or specific use cases, but for the most part, generally, I haven't met anyone who hasn't gone first choice with either, but that's me personally. >> No, but they're building. >> Point is the developer wants choice at the back end is what I'm hearing, but then finish that thought. >> Their developers want the choice, they want the choice on the back end, number one, because the customers are asking for, in this case, the customers are asking for it, right? But the customers requirements actually drive, their economics drives that decision making, right? So in the middle they have to, they're forced to cook up some solution which is vendor neutral on the backend or multicloud in nature. So >> Yeah, >> Every >> I mean I think that's nirvana. I don't think, I personally don't see that happening right now. I mean, I don't see the parody with clouds. So I think that's a challenge. I mean, >> Yeah, true. >> I mean the fact of the matter is if the development teams get fragmented, we had this chat with Kit Colbert last time, I think he's going to come on and I think he's going to talk about his keynote in a few, in an hour or so, development teams is this, the cloud is heterogenous, which is great. It's complex, which is challenging. You need skilled engineering to manage these clouds. So if you're a CIO and you go all in on AWS, it's hard. Then to then go out and say, "I want to be completely multi-vendor neutral" that's a tall order on many levels and this is the multicloud challenge, right? So, the question is, what's the strategy for me, the CIO or CISO, what do I do? I mean, to me, I would go all in on one and start getting hedges and start playing and then look at some >> Crystal clear. Crystal clear to me. >> Go ahead. >> If you're a CIO today, you have to build a platform engineering team, no question. 'Cause if we agree that we cannot tell the great developers what to do, we have to create a platform engineering team that using pieces of the Supercloud can build, and let's make this very pragmatic and give examples. First you need to be able to lay down the run time, okay? So you need a way to deploy multiple different Kubernetes environment in depending on the cloud. Okay, now we got that. The second part >> That's like table stakes. >> That are table stake, right? But now what is the advantage of having a Supercloud service to do that is that now you can put a policy in one place and it gets distributed everywhere consistently. So for example, you want to say, "If anybody in this organization across all these different buildings, all these developers don't even know, build a PCI compliant microservice, They can only talk to PCI compliant microservice." Now, I sleep tight. The developers still do that. Of course they're going to get their hands slapped if they don't encrypt some messages and say, "Oh, that should have been encrypted." So number one. The second thing I want to be able to say, "This service that this developer built over there better satisfy this SLA." So if the SLA is not satisfied, boom, I automatically spin up multiple instances to certify the SLA. Developers unencumbered, they don't even know. So this for me is like, CIO build a platform engineering team using one of the many Supercloud services that allow you to do that and lay down. >> And part of that is that the vendor behavior is such, 'cause the incentive is that they don't necessarily always work together. (John chuckling) I'll give you an example, we're going to hear today from Western Union. They're AWS shop, but they want to go to Google, they want to use some of Google's AI tools 'cause they're good and maybe they're even arguably better, but they're also a Snowflake customer and what you'll hear from them is Amazon and Snowflake are working together so that SageMaker can be integrated with Snowflake but Google said, "No, you want to use our AI tools, you got to use BigQuery." >> Yeah. >> Okay. So they say, "Ah, forget it." So if you have a platform engineering team, you can maybe solve some of that vendor friction and get competitive advantage. >> I think that the future proximity concept that I talk about is like, when you're doing one thing, you want to do another thing. Where do you go to get that thing, right? So that is very important. Like your question, John, is that your point is that AWS is ahead of the pack, which is true, right? They have the >> breadth of >> Infrastructure by a lot >> infrastructure service, right? They breadth of services, right? So, how do you, When do you bring in other cloud providers, right? So I believe that you should standardize on one cloud provider, like that's your primary, and for others, bring them in on as needed basis, in the subsection or sub portfolio of your applications or your platforms, what ever you can. >> So yeah, the Google AI example >> Yeah, I mean, >> Or the Microsoft collaboration software example. I mean there's always or the M and A. >> Yeah, but- >> You're going to get to run Windows, you can run Windows on Amazon, so. >> By the way, Supercloud doesn't mean that you cannot do that. So the perfect example is say that you're using Azure because you have a SQL server intensive workload. >> Yep >> And you're using Google for ML, great. If you are using some differentiated feature of this cloud, you'll have to go somewhere and configure this widget, but what you can abstract with the Supercloud is the lifecycle manage of the service that runs on top, right? So how does the service get deployed, right? How do you monitor performance? How do you lifecycle it? How you secure it that you can abstract and that's the value and eventually value will win. So the customers will find what is the values, obstructing in making it uniform or going deeper? >> How about identity? Like take identity for instance, you know, that's an opportunity to abstract. Whether I use Microsoft Identity or Okta, and I can abstract that. >> Yeah, and then we have APIs and standards that we can use so eventually I think where there is enough pain, the right open source will emerge to solve that problem. >> Dave: Yeah, I can use abstract things like object store, right? That's pretty simple. >> But back to the engineering question though, is that developers, developers, developers, one thing about developers psychology is if something's not right, they say, "Go get fixing. I'm not touching it until you fix it." They're very sticky about, if something's not working, they're not going to do it again, right? So you got to get it right for developers. I mean, they'll maybe tolerate something new, but is the "juice worth the squeeze" as they say, right? So you can't go to direct say, "Hey, it's, what's a work in progress? We're going to get our infrastructure together and the world's going to be great for you, but just hang tight." They're going to be like, "Get your shit together then talk to me." So I think that to me is the question. It's an Ops question, but where's that value for the developer in Supercloud where the capabilities are there, there's less friction, it's simpler, it solves the complexity problem. I don't need these high skilled labor to manage Amazon. I got services exposed. >> That's what we talked about earlier. It's like the Walmart example. They basically, they took away from the developer the need to spin up infrastructure and worry about all the governance. I mean, it's not completely there yet. So the developer could focus on what he or she wanted to do. >> But there's a big, like in our industry, there's a big sort of flaw or the contention between developers and operators. Developers want to be on the cutting edge, right? And operators want to be on the stability, you know, like we want governance. >> Yeah, totally. >> Right, so they want to control, developers are like these little bratty kids, right? And they want Legos, like they want toys, right? Some of them want toys by way. They want Legos, they want to build there and they want make a mess out of it. So you got to make sure. My number one advice in this context is that do it up your application portfolio and, or your platform portfolio if you are an ISV, right? So if you are ISV you most probably, you're building a platform these days, do it up in a way that you can say this portion of our applications and our platform will adhere to what you are saying, standardization, you know, like Kubernetes, like slam dunk, you know, it works across clouds and in your data center hybrid, you know, whole nine yards, but there is some subset on the next door systems of innovation. Everybody has, it doesn't matter if you're DMV of Kansas or you are, you know, metaverse, right? Or Meta company, right, which is Facebook, they have it, they are building something new. For that, give them some freedom to choose different things like play with non-standard things. So that is the mantra for moving forward, for any enterprise. >> Do you think developers are happy with the infrastructure now or are they wanting people to get their act together? I mean, what's your reaction, or you think. >> Developers are happy as long as they can do their stuff, which is running code. They want to write code and innovate. So to me, when Ballmer said, "Developer, develop, Developer, what he meant was, all you other people get your act together so these developers can do their thing, and to me the Supercloud is the way for IT to get there and let developer be creative and go fast. Why not, without getting in trouble. >> Okay, let's wrap up this segment with a super clip. Okay, we're going to do a sound bite that we're going to make into a short video for each of you >> All right >> On you guys summarizing why Supercloud's important, why this next wave is relevant for the practitioners, for the industry and we'll turn this into an Instagram reel, YouTube short. So we'll call it a "Super clip. >> Alright, >> Sarbjeet, you want, you want some time to think about it? You want to go first? Vittorio, you want. >> I just didn't mind. (all laughing) >> No, okay, okay. >> I'll do it again. >> Go back. No, we got a fresh one. We'll going to already got that one in the can. >> I'll go. >> Sarbjeet, you go first. >> I'll go >> What's your super clip? >> In software systems, abstraction is your friend. I always say that. Abstraction is your friend, even if you're super professional developer, abstraction is your friend. We saw from the MFC library from C++ days till today. Abstract, use abstraction. Do not try to reinvent what's already being invented. Leverage cloud, leverage the platform side of the cloud. Not just infrastructure service, but platform as a service side of the cloud as well, and Supercloud is a meta platform built on top of these infrastructure services from three or four or five cloud providers. So use that and embrace the programmability, embrace the abstraction layer. That's the key actually, and developers who are true developers or professional developers as you said, they know that. >> Awesome. Great super clip. Vittorio, another shot at the plate here for super clip. Go. >> Multicloud is awesome. There's a reason why multicloud happened, is because gave our developers the ability to innovate fast and ever before. So if you are embarking on a digital transformation journey, which I call a survival journey, if you're not innovating and transforming, you're not going to be around in business three, five years from now. You have to adopt the Supercloud so the developer can be developer and keep building great, innovating digital experiences for your customers and IT can get in front of it and not get in trouble together. >> Building those super apps with Supercloud. That was a great super clip. Vittorio, thank you for sharing. >> Thanks guys. >> Sarbjeet, thanks for coming on talking about the developer impact Supercloud 2. On our next segment, coming up right now, we're going to hear from Walmart enterprise architect, how they are building and they are continuing to innovate, to build their own Supercloud. Really informative, instructive from a practitioner doing it in real time. Be right back with Walmart here in Palo Alto. Thanks for watching. (gentle music)