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

>>Welcome back here on the Cube. I'm John Walls. We are in Las Vegas at the Venetian, and this is Reinvent 22 in the Executive Summit sponsored by Accenture. Glad to have you with us here as we continue our conversations. I'm joined by Paul Puckett, who's the former director of the Enterprise Cloud Management Services at the US Army. Paul, good to see you sir. Hey, you as well, John. Thank you. And Justin, she who is managing director and cloud go to market lead at Accenture Federal Services. Justin, good morning to you. Good morning, John. Yeah, glad to have you both here on the cube. First time too, I believe, right? Yes sir. Well, welcome. I wish we had some kind of baptism or indoctrination, but I'll see what I can come up with in the next 10 minutes for you. Let's talk about the Army, Paul. So enterprise cloud management, US Army. You know, I can't imagine the scale we're talking about here. I can't imagine the solutions we're talking about. I can't imagine the users we're talking about. Just for our folks at home, paint the picture a little bit of what kind of landscape it is that you have to cover with that kind of title. >>Sure. The United States Army, about 1.4 million people. Obviously a global organization responsible for protecting and defending the United States as part of our sister services in the Department of Defense. And scale often comes up a lot, right? And we talk about any capability to your solution for the United States Army scale is the, the number one thing, but oftentimes people overlook quality first. And actually when you think of the partnership between the Army and Accenture Federal, we thought a lot when it came to establishing the enterprise Cloud management agency that we wanted to deliver quality first when it came to adopting cloud computing and then scale that quality and not so much be afraid of the, the scale of the army and the size that forces us to make bad decisions. Cuz we wanted to make sure that we proved that there was opportunity and value in the cloud first, and then we wanted to truly scale that. And so no doubt, an immense challenge. The organization's been around for now three years, but I think that we've established irreversible momentum when it comes to modernization, leveraging cloud computing >>For the army. So let's back up. You kind of threw it in there, the ecma. So this agency was, was your a collaboration, right? To create from the ground up and it's in three years in existence. So let's just talk about that. What went into that thinking? What went into the planning and then how did you actually get it up and run into the extent that it is today? >>Sure. Well, it was once the enterprise cloud management office. It was a directorate within the, the CIO G six of the United States Army. So at the headquarters, the army, the chief information Officer, and the G six, which is essentially the military arm for all IT capability were once a joint's organization and the ECMO was created to catalyze the adoption of cloud computing. The army had actually been on a, a cloud adoption journey for many years, but there wasn't a lot of value that was actually derived. And so they created the ecma, well, the ECMO at the time brought me in as the director. And so we were responsible for establishing the new strategy for the adoption of cloud. One of the components of that strategy was essentially we needed an opportunity to be able to buy cloud services at scale. And this was part of our buy secure and build model that we had in place. And so part of the buy piece, we put an acquisition strategy together around how we wanted to buy cloud at scale. We called it the cloud account management optimization. OTA >>Just rolls right off the >>Tongue, it just rolls right off the tongue. And for those that love acronyms, camo, >>Which I liked it when I was say cama, I loved that. That was, that was, >>You always have to have like a tundra, a little >>Piece of that. Very good. It was good. >>But at the time it was novetta, no, Nevada's been bought up by afs, but Novea won that agreement. And so we've had this partnership in place now for just about a year and a half for buying cloud computing net scale. >>So let's talk about, about what you deal with on, on the federal services side here, Justin, in terms of the army. So obviously governance, a major issue, compliance, a major issue, security, you know, paramount importance and all that STEM leads up to quality that Paul was talking about. So when you were looking at this and keeping all those factors in, in your mind, right? I mean, how many, like, oh my God, what kind of days did you have? Oh, well, because this was a handful. >>Well, it was, but you could see when we were responding to the acquisition that it was really, you know, forward thinking and forward leaning in terms of how they thought about cloud acquisition and cloud governance and cloud management. And it's really kind of a sleepy area like cloud account acquisition. Everyone's like, oh, it's easy to get in the cloud, you know, run your credit card on Amazon and you're in, in 30 seconds or less. That's really not the case inside the federal government, whether it's the army, the Air Force or whoever, right? Those, those are, they're real challenges in procuring and acquiring cloud. And so it was clear from, you know, Paul's office that they understood those challenges and we were excited to really meet them with them. >>And, and how, I guess from an institutional perspective, before this was right, I I assume very protective, very tight cloistered, right? You, you, in terms of being open to or, or a more open environment, there might have been some pushback was they're not. Right? So dealing with that, what did you find that to be the case? Well, so >>There's kind of a few pieces to unpacking that. There's a lot of fear in trepidation around something you don't understand, right? And so part of it is the teaching and training and the, and the capability and the opportunity in the cloud and the ability to be exceptionally secure when it comes to no doubt, the sensitivity of the information of the Department of Defense, but also from an action acquisition strategy perspective, more from a financial perspective, the DOD is accustomed to buying hardware. We make these big bets of these big things to, to live in today's centers. And so when we talk about consuming cloud as a utility, there's a lot of fear there as well, because they don't really understand how to kind of pay for something by the drink, if you will, because it incentivizes them to be more efficient with their utilization of resources. >>But when you look at the budgeting process of the d od, there really is not that much of incentive for efficiency. The p PPE process, the planning program, budgeting, execution, they care about execution, which is spending money and you can spend a lot of money in the cloud, right? But how are you actually utilizing that? And so what we wanted to do is create that feedback loop and so the utilization is actually fed into our financial systems that help us then estimate into the future. And that's the capability that we partnered with AFS on is establishing the closing of that feedback loop. So now we can actually optimize our utilization of the cloud. And that's actually driving better incentives in the PPE >>Process. You know, when you think about these keywords here, modernized, digitized, data driven, so on, so forth, I, I don't think a lot of people might connect that to the US government in general just because of, you know, it's a large intentionally slow moving bureaucratic machine, right? Is that fair to characterize it that way? It >>Is, but not in this case. Right? So what we done, >>You you totally juxtapose that. Yeah. >>Yeah. So what we've done is we've really enabled data driven decision making as it relates to cloud accounts and cloud governance. And so we have a, a tool called Cloud Tracker. We deployed for the army at a number of different classifications, and you get a full 360 view of all of your cloud utilization and cloud spend, you know, really up to date within 24 hours of it occurring, right? And there a lot of folks, you know, they didn't never went into the console, they never looked at what they were spending in cloud previously. And so now you just go to a simple web portal and see the entire entirety of the army cloud spend right there at your fingertips. So that really enables like better decision making in terms of like purchasing savings plans and reserved instances and other sorts of AWS specific tools to help you save money. >>So Paul, tell me about Cloud Tracker then. Yeah, I mean from the client side then, can you just say this dashboard lays it out for you right? In great detail about what kind of usage, what kind of efficiencies I assume Yeah. What's working, what's not? >>Absolutely. Well, and, and I think a few things to unpack that's really important here is listen, any cloud service provider has a concept. You can see what you're actually spending. But when it comes to money in the United States government, there are different colors of money. There's regulations when it comes to how money is identified for different capabilities or incentives. And you've gotta be very explicit in how you track and how you spend that money from an auditability perspective. Beyond that, there is a move when it comes to the technology business management, which is the actual labeling of what we actually spend money on for different services or labor or software. And what Cloud Tracker allows us to do is speak the language of the different colors of money. It allows us to also get very fine grain in the actual analysis of, from a TBM perspective, what we're spending on. >>But then also it has real time hooks into our financial systems for execution. And so what that really does for us is it allows us to complete the picture, not just be able to see our spend in the cloud, but also be able to able to see that spending context of all things in the P P P E process as well as the execution process that then really empowers the government to make better investments. And all we're seeing is either cost avoidance or cost savings simply because we're able to close that loop, like I said. Yep. And then we're able to redirect those funds, retag them, remove them through our actual financial office within the headquarters of the army, and be able to repurpose that to other modernization efforts that Congress is essentially asking us to invest >>In. Right. So you know how much money you have, basically. Exactly. Right. You know how much you've already spent, you know how you're spending it, and now you how much you have left, >>You can provide a reliable forecast for your spend. >>Right. You know, hey, we're, we're halfway through this quarter, we're halfway through the, the fiscal year, whatever the case might be. >>Exactly. And the focus on expenditures, you know, the government rates you on, you know, how much have you spent, right? So you have a clear total transparency into what you're going to spend through the rest of the fiscal. Sure. >>All right. Let's just talk about the relationship quickly then about going forward then in terms of federal services and then what on, on the, the US Army side. I mean, what now you've laid this great groundwork, right? You have a really solid foundation where now what next? >>We wanna be all things cloud to the army. I mean, we think there's tremendous opportunity to really aid the modernization efforts and governance across the holistic part of the army. So, you know, we just, we want to, we wanna do it all with the Army as much as we can. It's, it's, it's a fantastic >>Opportunity. Yeah. AFS is, is in a very kind of a strategic role. So as part of the ecma, we own the greater strategy and execution for adoption of cloud on behalf of the entire army. Now, when it comes to delivery of individual capabilities for mission here and there, that's all specific to system owners and different organizations. AFS plays a different role in this instance where they're able to more facilitate the greater strategy on the financial side of the house. And what we've done is we've proven the ability to adopt cloud as a utility rather than this fixed thing, kind of predict the future, spend a whole bunch of money and never use the resource. We're seeing the efficiency for the actual utilization of cloud as a utility. This actually came out as one of the previous NDAs. And so how we actually address nda, I believe it was 2018 in the adoption of cloud as a utility, really is now cornerstone of modernization across all of the do d and really feeds into the Jo Warfighting cloud capability, major acquisition on behalf of all of the D O D to establish buying cloud as just a common service for everyone. >>And so we've been fortunate to inform that team of some of our lessons learned, but when it comes to the partnership, we just see camo moving into production. We've been live for now a year and a half. And so there's another two and a half years of runway there. And then AFS also plays a strategic role at part of our cloud enablement division, which is essentially back to that teaching part, helping the Army understand the opportunity of cloud computing, align the architectures to actually leverage those resources and then deliver capabilities that save soldier's >>Lives. Well, you know, we've, we've always known that the Army does its best work on the ground, and you've done all this groundwork for the military, so I'm not surprised, right? It's, it's a winning formula. Thanks to both of you for being with us here in the executive summit. Great conversation. Awesome. Thanks for having us. A good deal. All right. Thank you. All right. You are watching the executive summit sponsored by Accenture here at Reinvent 22, and you're catching it all on the cube, the leader in high tech coverage.

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR, This is "Breaking Analysis with Dave Vellante". >> Black Hat 22 was held in Las Vegas last week, the same time as theCUBE Supercloud event. Unlike AWS re:Inforce where words are carefully chosen to put a positive spin on security, Black Hat exposes all the warts of cyber and openly discusses its hard truths. It's a conference that's attended by technical experts who proudly share some of the vulnerabilities they've discovered, and, of course, by numerous vendors marketing their products and services. Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this "Breaking Analysis", we summarize what we learned from discussions with several people who attended Black Hat and our analysis from reviewing dozens of keynotes, articles, sessions, and data from a recent Black Hat Attendees Survey conducted by Black Hat and Informa, and we'll end with the discussion of what it all means for the challenges around securing the supercloud. Now, I personally did not attend, but as I said at the top, we reviewed a lot of content from the event which is renowned for its hundreds of sessions, breakouts, and strong technical content that is, as they say, unvarnished. Chris Krebs, the former director of Us cybersecurity and infrastructure security agency, CISA, he gave the keynote, and he spoke about the increasing complexity of tech stacks and the ripple effects that that has on organizational risk. Risk was a big theme at the event. Where re:Inforce tends to emphasize, again, the positive state of cybersecurity, it could be said that Black Hat, as the name implies, focuses on the other end of the spectrum. Risk, as a major theme of the event at the show, got a lot of attention. Now, there was a lot of talk, as always, about the expanded threat service, you hear that at any event that's focused on cybersecurity, and tons of emphasis on supply chain risk as a relatively new threat that's come to the CISO's minds. Now, there was also plenty of discussion about hybrid work and how remote work has dramatically increased business risk. According to data from in Intel 471's Mark Arena, the previously mentioned Black Hat Attendee Survey showed that compromise credentials posed the number one source of risk followed by infrastructure vulnerabilities and supply chain risks, so a couple of surveys here that we're citing, and we'll come back to that in a moment. At an MIT cybersecurity conference earlier last decade, theCUBE had a hypothetical conversation with former Boston Globe war correspondent, Charles Sennott, about the future of war and the role of cyber. We had similar discussions with Dr. Robert Gates on theCUBE at a ServiceNow event in 2016. At Black Hat, these discussions went well beyond the theoretical with actual data from the war in Ukraine. It's clear that modern wars are and will be supported by cyber, but the takeaways are that they will be highly situational, targeted, and unpredictable because in combat scenarios, anything can happen. People aren't necessarily at their keyboards. Now, the role of AI was certainly discussed as it is at every conference, and particularly cyber conferences. You know, it was somewhat dissed as over hyped, not surprisingly, but while AI is not a panacea to cyber exposure, automation and machine intelligence can definitely augment, what appear to be and have been stressed out, security teams can do this by recommending actions and taking other helpful types of data and presenting it in a curated form that can streamline the job of the SecOps team. Now, most cyber defenses are still going to be based on tried and true monitoring and telemetry data and log analysis and curating known signatures and analyzing consolidated data, but increasingly, AI will help with the unknowns, i.e. zero-day threats and threat actor behaviors after infiltration. Now, finally, while much lip service was given to collaboration and public-private partnerships, especially after Stuxsnet was revealed early last decade, the real truth is that threat intelligence in the private sector is still evolving. In particular, the industry, mid decade, really tried to commercially exploit proprietary intelligence and, you know, do private things like private reporting and monetize that, but attitudes toward collaboration are trending in a positive direction was one of the sort of outcomes that we heard at Black Hat. Public-private partnerships are being both mandated by government, and there seems to be a willingness to work together to fight an increasingly capable adversary. These things are definitely on the rise. Now, without this type of collaboration, securing the supercloud is going to become much more challenging and confined to narrow solutions. and we're going to talk about that little later in the segment. Okay, let's look at some of the attendees survey data from Black Hat. Just under 200 really serious security pros took the survey, so not enough to slice and dice by hair color, eye color, height, weight, and favorite movie genre, but enough to extract high level takeaways. You know, these strongly agree or disagree survey responses can sometimes give vanilla outputs, but let's look for the ones where very few respondents strongly agree or disagree with a statement or those that overwhelmingly strongly agree or somewhat agree. So it's clear from this that the respondents believe the following, one, your credentials are out there and available to criminals. Very few people thought that that was, you know, unavoidable. Second, remote work is here to stay, and third, nobody was willing to really jinx their firms and say that they strongly disagree that they'll have to respond to a major cybersecurity incident within the next 12 months. Now, as we've reported extensively, COVID has permanently changed the cybersecurity landscape and the CISO's priorities and playbook. Check out this data that queries respondents on the pandemic's impact on cybersecurity, new requirements to secure remote workers, more cloud, more threats from remote systems and remote users, and a shift away from perimeter defenses that are no longer as effective, e.g. firewall appliances. Note, however, the fifth response that's down there highlighted in green. It shows a meaningful drop in the percentage of remote workers that are disregarding corporate security policy, still too many, but 10 percentage points down from 2021 survey. Now, as we've said many times, bad user behavior will trump good security technology virtually every time. Consistent with the commentary from Mark Arena's Intel 471 threat report, fishing for credentials is the number one concern cited in the Black Hat Attendees Survey. This is a people and process problem more than a technology issue. Yes, using multifactor authentication, changing passwords, you know, using unique passwords, using password managers, et cetera, they're all great things, but if it's too hard for users to implement these things, they won't do it, they'll remain exposed, and their organizations will remain exposed. Number two in the graphic, sophisticated attacks that could expose vulnerabilities in the security infrastructure, again, consistent with the Intel 471 data, and three, supply chain risks, again, consistent with Mark Arena's commentary. Ask most CISOs their number one problem, and they'll tell you, "It's a lack of talent." That'll be on the top of their list. So it's no surprise that 63% of survey respondents believe they don't have the security staff necessary to defend against cyber threats. This speaks to the rise of managed security service providers that we've talked about previously on "Breaking Analysis". We've seen estimates that less than 50% of organizations in the US have a SOC, and we see those firms as ripe for MSSP support as well as larger firms augmenting staff with managed service providers. Now, after re:Invent, we put forth this conceptual model that discussed how the cloud was becoming the first line of defense for CISOs, and DevOps was being asked to do more, things like securing the runtime, the containers, the platform, et cetera, and audit was kind of that last line of defense. So a couple things we picked up from Black Hat which are consistent with this shift and some that are somewhat new, first, is getting visibility across the expanded threat surface was a big theme at Black Hat. This makes it even harder to identify risk, of course, this being the expanded threat surface. It's one thing to know that there's a vulnerability somewhere. It's another thing to determine the severity of the risk, but understanding how easy or difficult it is to exploit that vulnerability and how to prioritize action around that. Vulnerability is increasingly complex for CISOs as the security landscape gets complexified. So what's happening is the SOC, if there even is one at the organization, is becoming federated. No longer can there be one ivory tower that's the magic god room of data and threat detection and analysis. Rather, the SOC is becoming distributed following the data, and as we just mentioned, the SOC is being augmented by the cloud provider and the managed service providers, the MSSPs. So there's a lot of critical security data that is decentralized and this will necessitate a new cyber data model where data can be synchronized and shared across a federation of SOCs, if you will, or mini SOCs or SOC capabilities that live in and/or embedded in an organization's ecosystem. Now, to this point about cloud being the first line of defense, let's turn to a story from ETR that came out of our colleague Eric Bradley's insight in a one-on-one he did with a senior IR person at a manufacturing firm. In a piece that ETR published called "Saved by Zscaler", check out this comment. Quote, "As the last layer, we are filtering all the outgoing internet traffic through Zscaler. And when an attacker is already on your network, and they're trying to communicate with the outside to exchange encryption keys, Zscaler is already blocking the traffic. It happened to us. It happened and we were saved by Zscaler." So that's pretty cool. So not only is the cloud the first line of defense, as we sort of depicted in that previous graphic, here's an example where it's also the last line of defense. Now, let's end on what this all means to securing the supercloud. At our Supercloud 22 event last week in our Palo Alto CUBE Studios, we had a session on this topic on supercloud, securing the supercloud. Security, in our view, is going to be one of the most important and difficult challenges for the idea of supercloud to become real. We reviewed in last week's "Breaking Analysis" a detailed discussion with Snowflake co-founder and president of products, Benoit Dageville, how his company approaches security in their data cloud, what we call a superdata cloud. Snowflake doesn't use the term supercloud. They use the term datacloud, but what if you don't have the focus, the engineering depth, and the bank roll that Snowflake has? Does that mean superclouds will only be developed by those companies with deep pockets and enormous resources? Well, that's certainly possible, but on the securing the supercloud panel, we had three technical experts, Gee Rittenhouse of Skyhigh Security, Piyush Sharrma who's the founder of Accurics who sold to Tenable, and Tony Kueh, who's the former Head of Product at VMware. Now, John Furrier asked each of them, "What is missing? What's it going to take to secure the supercloud? What has to happen?" Here's what they said. Play the clip. >> This is the final question. We have one minute left. I wish we had more time. This is a great panel. We'll bring you guys back for sure after the event. What one thing needs to happen to unify or get through the other side of this fragmentation and then the challenges for supercloud? Because remember, the enterprise equation is solve complexity with more complexity. Well, that's not what the market wants. They want simplicity. They want SaaS. They want ease of use. They want infrastructure risk code. What has to happen? What do you think, each of you? >> So I can start, and extending to the previous conversation, I think we need a consortium. We need a framework that defines that if you really want to operate on supercloud, these are the 10 things that you must follow. It doesn't matter whether you take AWS, Slash, or TCP or you have all, and you will have the on-prem also, which means that it has to follow a pattern, and that pattern is what is required for supercloud, in my opinion. Otherwise, security is going everywhere. They're like they have to fix everything, find everything, and so on and so forth. It's not going to be possible. So they need a framework. They need a consortium, and this consortium needs to be, I think, needs to led by the cloud providers because they're the ones who have these foundational infrastructure elements, and the security vendor should contribute on providing more severe detections or severe findings. So that's, in my opinion, should be the model. >> Great, well, thank you, Gee. >> Yeah, I would think it's more along the lines of a business model. We've seen in cloud that the scale matters, and once you're big, you get bigger. We haven't seen that coalesce around either a vendor, a business model, or whatnot to bring all of this and connect it all together yet. So that value proposition in the industry, I think, is missing, but there's elements of it already available. >> I think there needs to be a mindset. If you look, again, history repeating itself. The internet sort of came together around set of IETF, RSC standards. Everybody embraced and extended it, right? But still, there was, at least, a baseline, and I think at that time, the largest and most innovative vendors understood that they couldn't do it by themselves, right? And so I think what we need is a mindset where these big guys, like Google, let's take an example. They're not going to win at all, but they can have a substantial share. So how do they collaborate with the ecosystem around a set of standards so that they can bring their differentiation and then embrace everybody together. >> Okay, so Gee's point about a business model is, you know, business model being missing, it's broadly true, but perhaps Snowflake serves as a business model where they've just gone out and and done it, setting or trying to set a de facto standard by which data can be shared and monetized. They're certainly setting that standard and mandating that standard within the Snowflake ecosystem with its proprietary framework. You know, perhaps that is one answer, but Tony lays out a scenario where there's a collaboration mindset around a set of standards with an ecosystem. You know, intriguing is this idea of a consortium or a framework that Piyush was talking about, and that speaks to the collaboration or lack thereof that we spoke of earlier, and his and Tony's proposal that the cloud providers should lead with the security vendor ecosystem playing a supporting role is pretty compelling, but can you see AWS and Azure and Google in a kumbaya moment getting together to make that happen? It seems unlikely, but maybe a better partnership between the US government and big tech could be a starting point. Okay, that's it for today. I want to thank the many people who attended Black Hat, reported on it, wrote about it, gave talks, did videos, and some that spoke to me that had attended the event, Becky Bracken, who is the EIC at Dark Reading. They do a phenomenal job and the entire team at Dark Reading, the news desk there, Mark Arena, whom I mentioned, Garrett O'Hara, Nash Borges, Kelly Jackson, sorry, Kelly Jackson Higgins, Roya Gordon, Robert Lipovsky, Chris Krebs, and many others, thanks for the great, great commentary and the content that you put out there, and thanks to Alex Myerson, who's on production, and Alex manages the podcasts for us. Ken Schiffman is also in our Marlborough studio as well, outside of Boston. Kristen Martin and Cheryl Knight, they help get the word out on social media and in our newsletters, and Rob Hoff is our Editor-in-Chief at SiliconANGLE and does some great editing and helps with the titles of "Breaking Analysis" quite often. Remember these episodes, they're all available as podcasts, wherever you listen, just search for "Breaking Analysis Podcasts". I publish each on wikibon.com and siliconangle.com, and you could email me, get in touch with me at david.vellante@siliconangle.com or you can DM me @dvellante or comment on my 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, and we'll see you next time on "Breaking Analysis". (upbeat music)

>> Announcer: TheCube presents HPE Discover 2022, brought to you by HPE. >> Welcome back to HPE Discover 2022, theCube's continuous wall to wall coverage, Dave Vellante with John Furrier. Keith Basil is here as the General Manager for the Edge Business Unit at SUSE. Keith, welcome to theCube, man good to see you. >> Great to be here, it's my first time here and I've seen many shows and you guys are the best. >> Thanks you. >> Thank you very much. >> Big fans of SUSE you know, we've had Melissa on several times. >> Yes. >> Let's start with kind of what you guys are doing here at Discover. >> Well, we're here to support our wonderful partner HPE, as you know SUSE's products and services are now being integrated into the GreenLake offering. So that's very exciting for us. >> Yeah. Now tell us about your background. It's quite interesting you've kind of been in the mix in some really cool places. Tell us a little bit about yourself. >> Probably the most relevant was I used to work at Red Hat, I was a Product Manager working in security for OpenStack and OpenShift working with DOD customers in the intelligence community. Left Red Hat to go to Rancher, started out there as VP of Edge Solutions and then transitioned over to VP of Product for all of Rancher. And then obviously we know SUSE acquired Rancher and as of November 1st, of 2020, I think it was. >> Dave: 2020. >> Yeah, yeah time is flying. I came over, I still remained VP of Product for Rancher for Cloud Native Infrastructure. And I was working on the edge strategy for SUSE and about four months ago we internally built three business units, one for the Linux business, one for enterprise container management, basically the Rancher business, and then the newly minted business unit was the Edge business. And I was offered the role to be GM for that business unit and I happily accepted it. >> Very cool. I mean the market dynamics since the 2018 have changed dramatically, IBM bought Red Hat. A lot of customers said, "Hmm let's see what other alternatives are out there." SUSE popped its head up. You know, Melissa's been quite, you know forthcoming about that. And then you acquire Rancher in 2020, IPO in 2021. That kind of gives you another tailwind. So there's a new market when you go from 2018 to 2022, it's a completely changed dynamic. >> Yes and I'm going to answer your question from the Rancher perspective first, because as we were at Rancher, we had experimented with different flavors of the underlying OS underneath Kubernetes or Kubernetes offerings. And we had, as I said, different flavors, we weren't really operating system people for example. And so post-acquisition, you know, one of my internal roles was to bring the two halves of the house together, the philosophies together where you had a cloud native side in the form of Rancher, very progressive leading innovative products with Rancher with K3s for example. And then you had, you know, really strong enterprise roots around compliance and security, secure supply chain with the enterprise grade Linux. And what we found out was SUSE had been building a version of Linux called SLE Micro, and it was perfectly designed for Edge. And so what we've done over that time period since the acquisition is that we've brought those two things together. And now we're using Kubernetes directives and philosophies to manage all the way down to the operating system. And it is a winning strategy for our customers. And we're really excited about that. >> And what does that product look like? Is that a managed service? How are customers consuming that? >> It could be a managed service, it's something that our managed service providers could embrace and offer to their customers. But we have some customers who are very sophisticated who want to do the whole thing themselves. And so they stand up Rancher, you know at a centralized location at cloud GreenLake for example which is why this is very relevant. And then that control plane if you will, manages thousands of downstream clusters that are running K3s at these Edge locations. And so that's what the complete stack looks like. And so when you add the Linux capability to that scenario we can now roll a new operating system, new kernel, CVE updates, build that as an OCI container image registry format, right? Put that into a registry and then have that thing cascade down through all the downstream clusters and up through a rolling window upgrade of the operating system underneath Kubernetes. And it is a tremendous amount of value when you talk to customers that have this massive scale. >> What's the impact of that, just take us through what happens next. Is it faster? Is it more performant? Is it more reliable? Is it processing data at the Edge? What's the impact of the customer? >> Yes, the answer is yes to that. So let's actually talk about one customer that we we highlighted in our keynote, which is Home Depot. So as we know, Kubernetes is on fire, right? It is the technology everybody's after. So by being in demand, the skills needed, the people shortage is real and people are commanding very high, you know, salaries. And so it's hard to attract talent is the bottom line. And so using our software and our solution and our approach it allows people to scale their existing teams to preserve those precious human resources and that human capital. So that now you can take a team of seven people and manage let's say 3000 downstream stores. >> Yeah it's like the old SRE model for DevOps. >> Correct. >> It's not servers they're managing one to many. >> Yes. >> One to many clusters. >> Correct so you've got the cluster, the life cycle of the cluster. You already have the application life cycle with the classic DevOps. And now what we've built and added to the stack is going down one step further, clicking down if you will to managing the life cycle of the operating system. So you have the SUSE enterprise build chain, all the value, the goodness, compliance, security. Again, all of that comes with that build process. And now we're hooking that into a cloud native flow that ends up downstream in our customers. >> So what I'm hearing is your Edge strategy is not some kind of bespoke, "Hey, I'm going after Edge." It connects to the entire value chain. >> Yes, yeah it's a great point. We want to reuse the existing philosophies that are being used today. We don't want to create something net new, cause that's really the point in leverage that we get by having these teams, you know, do these things at scale. Another point I'm going to make here is that we've defined the Edge into three segments. One is the near Edge, which is the realm of the-- >> I was going to ask about this, great. >> The telecommunications companies. So those use cases and profiles look very different. They're almost data center lite, right? So you've had regional locations, central offices where they're standing up gear classic to you machines, right? So things you find from HPE, for example. And then once you get on the other side of the access device right? The cable modem, the router, whatever it is you get into what we call the far Edge. And this is where the majority of the use cases reside. This is where the diversity of use cases presents itself as well. >> Also security challenges. >> Security challenges. Yes and we can talk about that following in a moment. And then finally, if you look at that far Edge as a box, right? Think of it as a layer two domain, a network. Inside that location, on that network you'll have industrial IOT devices. Those devices are too small to run a full blown operating system such as Linux and Kubernetes in the stack but they do have software on them, right? So we need to be able to discover those devices and manage those devices and pull data from those devices and do it in a cloud native way. So that's what we called the tiny Edge. And I stole that name from the folks over at Microsoft. Kate and Edrick are are leading a project upstream called Akri, A-K-R-I, and we are very much heavily involved in Akri because it will discover the industrial IOT devices and plug those into a local Kubernetes cluster running at that location. >> And Home Depot would fit into the near edge is that correct? >> Yes. >> Yeah okay. >> So each Home Depot store, just to bring it home, is a far Edge location and they have over 2,600 of these locations. >> So far Edge? You would put far Edge? >> Keith: Far Edge yes. >> Far edge, okay. >> John: Near edge is like Metro. Think of Metro. >> And Teleco, communication, service providers MSOs, multi-service operators. Those guys are-- >> Near Edge. >> The near edge, yes. >> Don't you think, John's been asking all week about machine learning and AI, in that tiny Edge. We think there's going to be a lot of AI influencing. >> Keith: Oh absolutely. >> Real time. And it actually is going to need some kind of lighter weight you know, platform. How do you fit into that? >> So going on this, like this model I just described if you go back and look at the SUSECON 2022 demo keynote that I did, we actually on stage stood up that exact stack. So we had a single Intel nook running SLE Micro as we mentioned earlier, running K3s and we plugged into that device, a USB camera which was automatically detected and it loaded Akri and gave us a driver to plug it into a container. Now, to answer your question, that is the point in time where we bring in the ML and the AI, the inference and the pattern recognition, because that camera when you showed the SUSE plush doll, it actually recognized it and put a QR code up on the screen. So that's where it all comes together. So we tried to showcase that in a complete demo. >> Last week, I was here in Vegas for an event Amazon and AWS put on called re:Mars, machine learning, automation, robotics, and space. >> Okay. >> Kind of but basically for me was an industrial edge show. Cause The space is the ultimate like glam to edge is like, you're doing stuff in space that's pretty edgy so to speak, pun intended. But the industrial side of the Edge is going to, we think, accelerate with machine learning. >> Keith: Absolutely. >> And with these kinds of new portable I won't say flash compute or just like connected power sources software. The industrial is going to move really fast. We've been kind of in a snails pace at the Edge, in my opinion. What's your reaction to that? Do you think we're going to see a mass acceleration of growth at the Edge industrial, basically physical, the physical world. >> Yes, first I agree with your assessment okay, wholeheartedly, so much so that it's my strategy to go after the tiny Edge space and be a leader in the industrial IOT space from an open source perspective. So yes. So a few things to answer your question we do have K3s in space. We have a customer partner called Hypergiant where they've launched satellites with K3s running in space same model, that's a far Edge location, probably the farthest Edge location we have. >> John: Deep Edge, deep space. >> Here at HPE Discover, we have a business unit called SUSE RGS, Rancher Government Services, which focuses on the US government and DOD and IC, right? So little bit of the world that I used to work in my past career. Brandon Gulla the CTO of of that unit gave a great presentation about what we call the tactical Edge. And so the same technology that we're using on the commercial and the manufacturing side. >> Like the Jedi contract, the tactical military Edge I think. >> Yes so imagine some of these military grade industrial IOT devices in a disconnected environment. The same software stack and technology would apply to that use case as well. >> So basically the tactical Edge is life? We're humans, we're at the Edge? >> Or it's maintenance, right? So maybe it's pulling sensors from aircraft, Humvees, submarines and doing predictive analysis on the maintenance for those items, those assets. >> All these different Edges, they underscore the diversity that you were just talking Keith and we also see a new hardware architecture emerging, a lot of arm based stuff. Just take a look at what Tesla's doing at the tiny Edge. Keith Basil, thanks so much. >> Sure. >> For coming on theCube. >> John: Great to have you. >> Grateful to be here. >> Awesome story. Okay and thank you for watching. This is Dave Vellante for John Furrier. This is day three of HPE Discover 2022. You're watching theCube, the leader in enterprise and emerging tech coverage. We'll be right back. (upbeat music)

>> From "theCUBE" studios in Palo Alto, in Boston, bringing you data driven insights from "theCUBE" and ETR. This is "Breaking Analysis" with Dave Vellante. >> There are very few political issues that get bipartisan support these days, nevermind consensus spanning geopolitical boundaries. But whether we're talking across the aisle or over the pond, there seems to be common agreement that the power of big tech firms should be regulated. But the government's track record when it comes to antitrust aimed at big tech is actually really mixed, mixed at best. History has shown that market forces rather than public policy have been much more effective at curbing monopoly power in the technology industry. Hello, and welcome to this week's "Wikibon CUBE" insights powered by ETR. In this "Breaking Analysis" we welcome in frequent "CUBE" contributor Dave Moschella, author and senior fellow at the Information Technology and Innovation Foundation. Dave, welcome, good to see you again. >> Hey, thanks Dave, good to be here. >> So you just recently published an article, we're going to bring it up here and I'll read the title, "Theory Aside, Antitrust Advocates Should Keep Their "Big Tech" Ambitions Narrow". And in this post you argue that big sweeping changes like breaking apart companies to moderate monopoly power in the tech industry have been ineffective compared to market forces, but you're not saying government shouldn't be involved rather you're suggesting that more targeted measures combined with market forces are the right answer. Can you maybe explain a little bit more the premise behind your research and some of your conclusions? >> Sure, and first let's go back to that title, when I said, theory aside, that is referring to a huge debate that's going on in global antitrust circles these days about whether antitrust should follow the traditional path of being invoked when there's real harm, demonstrable harm to consumers or a new theory that says that any sort of vast monopoly power inevitably will be bad for competition and consumers at some point, so your best to intervene now to avoid harms later. And that school, which was a very minor part of the antitrust world for many, many years is now quite ascendant and the debate goes on doesn't matter which side of that you're on the questions sort of there well, all right, well, if you're going to do something to take on big tech and clearly many politicians, regulators are sort of issuing to do something, what would you actually do? And what are the odds that that'll do more good than harm? And that was really the origins of the piece and trying to take a historical view of that. >> Yeah, I learned a new word, thank you. Neo-brandzian had to look it up, but basically you're saying that traditionally it was proving consumer harm versus being proactive about the possibility or likelihood of consumer harm. >> Correct, and that's a really big shift that a lot of traditional antitrust people strongly object to, but is now sort of the trendy and more send and view. >> Got it, okay, let's look a little deeper into the history of tech monopolies and government action and see what we can learn from that. We put together this slide that we can reference. It shows the three historical targets in the tech business and now the new ones. In 1969, the DOJ went after IBM, Big Blue and it's 13 years later, dropped its suit. And then in 1984 the government broke Ma Bell apart and in the late 1990s, went after Microsoft, I think it was 1998 in the Wintel monopoly. And recently in an interview with tech journalist, Kara Swisher, the FTC chair Lena Khan claimed that the government played a major role in moderating the power of tech giants historically. And I think she even specifically referenced Microsoft or maybe Kara did and basically said the industry and consumers from the dominance of companies like Microsoft. So Dave, let's briefly talk about and Kara by the way, didn't really challenge that, she kind of let it slide. But let's talk about each of these and test this concept a bit. Were the government actions in these instances necessary? What were the outcomes and the consequences? Maybe you could start with IBM and AT&T. >> Yeah, it's a big topic and there's a lot there and a lot of history, but I might just sort of introduce by saying for whatever reasons antitrust has been part of the entire information technology industry history from mainframe to the current period and that slide sort of gives you that. And the reasons for that are I think once that we sort of know the economies of scale, network effects, lock in safe choices, lot of things that explain it, but the good bit about that is we actually have so much history of this and we can at least see what's happened in the past and when you look at IBM and AT&T they both were massive antitrust cases. The one against IBM was dropped and it was dropped in as you say, in 1980. Well, what was going on in at that time, IBM was sort of considered invincible and unbeatable, but it was 1981 that the personal computer came around and within just a couple of years the world could see that the computing paradigm had change from main frames and minis to PCs lines client server and what have you. So IBM in just a couple of years went from being unbeatable, you can't compete with them, we have to break up with them to being incredibly vulnerable and in trouble and never fully recovered and is sort of a shell of what it once was. And so the market took care of that and no action was really necessary just by everybody thinking there was. The case of AT&T, they did act and they broke up the company and I would say, first question is, was that necessary? Well, lots of countries didn't do that and the reality is 1980 breaking it up into long distance and regional may have made some sense, but by the 1990 it was pretty clear that the telecom world was going to change dramatically from long distance and fixed wires services to internet services, data services, wireless services and all of these things that we're going to restructure the industry anyways. But AT& T one to me is very interesting because of the unintended consequences. And I would say that the main unintended consequence of that was America's competitiveness in telecommunications took a huge hit. And today, to this day telecommunications is dominated by European, Chinese and other firms. And the big American sort of players of the time AT&T which Western Electric became Lucent, Lucent is now owned by Nokia and is really out of it completely and most notably and compellingly Bell Labs, the Bell Labs once the world's most prominent research institution now also a shell of itself and as it was part of Lucent is also now owned by the Finnish company Nokia. So that restructuring greatly damaged America's core strength in telecommunications hardware and research and one can argue we've never recovered right through this 5IG today. So it's a very good example of the market taking care of, the big problem, but meddling leading to some unintended consequences that have hurt the American competitiveness and as we'll talk about, probably later, you can see some of that going on again today and in the past with Microsoft and Intel. >> Right, yeah, Bell Labs was an American gem, kind of like Xerox PARC and basically gone now. You mentioned Intel and Microsoft, Microsoft and Intel. As many people know, some young people don't, IBM unwillingly handed its monopoly to Intel and Microsoft by outsourcing the micro processor and operating system, respectively. Those two companies ended up with IBM ironically, agreeing to take OS2 which was its proprietary operating system and giving Intel, Microsoft Windows not realizing that its ability to dominate a new disruptive market like PCs and operating systems had been vaporized to your earlier point by the new Wintel ecosystem. Now Dave, the government wanted to break Microsoft apart and split its OS business from its application software, in the case of Intel, Intel only had one business. You pointed out microprocessors so it couldn't bust it up, but take us through the history here and the consequences of each. >> Well, the Microsoft one is sort of a classic because the antitrust case which was raging in the sort of mid nineties and 1998 when it finally ended, those were the very, once again, everybody said, Bill Gates was unstoppable, no one could compete with Microsoft they'd buy them, destroy them, predatory pricing, whatever they were accusing of the attacks on Netscape all these sort of things. But those the very years where it was becoming clear first that Microsoft basically missed the early big years of the internet and then again, later missed all the early years of the mobile phone business going back to BlackBerrys and pilots and all those sorts of things. So here we are the government making the case that this company is unstoppable and you can't compete with them the very moment they're entirely on the defensive. And therefore wasn't surprising that that suit eventually was dropped with some minor concessions about Microsoft making it a little bit easier for third parties to work with them and treating people a little bit more, even handling perfectly good things that they did. But again, the more market took care of the problem far more than the antitrust activities did. The Intel one is also interesting cause it's sort of like the AT& T one. On the one hand antitrust actions made Intel much more likely and in fact, required to work with AMD enough to keep that company in business and having AMD lowered prices for consumers certainly probably sped up innovation in the personal computer business and appeared to have a lot of benefits for those early years. But when you look at it from a longer point of view and particularly when look at it again from a global point of view you see that, wow, they not so clear because that very presence of AMD meant that there's a lot more pressure on Intel in terms of its pricing, its profitability, its flexibility and its volumes. All the things that have made it harder for them to A, compete with chips made in Taiwan, let alone build them in the United States and therefore that long term effect of essentially requiring Intel to allow AMD to exist has undermined Intel's position globally and arguably has undermined America's position in the long run. And certainly Intel today is far more vulnerable to an ARM and Invidia to other specialized chips to China, to Taiwan all of these things are going on out there, they're less capable of resisting that than they would've been otherwise. So, you thought we had some real benefits with AMD and lower prices for consumers, but the long term unintended consequences are arguably pretty bad. >> Yeah, that's why we recently wrote in Intel two "Strategic To Fail", we'll see, Okay. now we come to 2022 and there are five companies with anti-trust targets on their backs. Although Microsoft seems to be the least susceptible to US government ironically intervention at this this point, but maybe not and we show "The Cincos Comas Club" in a homage to Russ Hanneman of the show "Silicon Valley" Apple, Microsoft, Google, and Amazon all with trillion dollar plus valuations. But meta briefly crossed that threshold like Mr. Hanneman lost a comma and is now well under that market cap probably around five or 600 million, sorry, billion. But under serious fire nonetheless Dave, people often don't realize the immense monopoly power that IBM had which relatively speaking when measured its percent of industry revenue or profit dwarf that of any company in tech ever, but the industry is much smaller then, no internet, no cloud. Does it call for a different approach this time around? How should we think about these five companies their market power, the implications of government action and maybe what you suggested more narrow action versus broad sweeping changes. >> Yeah, and there's a lot there. I mean, if you go back to the old days IBM had what, 70% of the computer business globally and AT&T had 90% or so of the American telecom market. So market shares that today's players can only dream of. Intel and Microsoft had 90% of the personal computer market. And then you look at today the big five and as wealthy and as incredibly successful as they've been, you sort of have almost the argument that's wrong on the face of it. How can five companies all of which compete with each other to at least some degree, how can they all be monopolies? And the reality is they're not monopolies, they're all oligopolies that are very powerful firms, but none of them have an outright monopoly on anything. There are competitors in all the spaces that they're in and increasing and probably increasingly so. And so, yeah, I think people conflate the extraordinary success of the companies with this belief that therefore they are monopolist and I think they're far less so than those in the past. >> Great, all right, I want to do a quick drill down to cloud computing, it's a key component of digital business infrastructure in his book, "Seeing Digital", Dave Moschella coined a term the matrix or the key which is really referred to the key technology platforms on which people are going to build digital businesses. Dave, we joke you should have called it the metaverse you were way ahead of your time. But I want to look at this ETR chart, we show spending momentum or net score on the vertical access market share or pervasiveness in the dataset on the horizontal axis. We show this view a lot, we put a dotted line at the 40% mark which indicates highly elevated spending. And you can sort of see Microsoft in the upper right, it's so far up to the right it's hidden behind the January 22 and AWS is right there. Those two dominate the cloud far ahead of the pack including Google Cloud. Microsoft and to a lesser extent AWS they dominate in a lot of other businesses, productivity, collaboration, database, security, video conferencing. MarTech with LinkedIn PC software et cetera, et cetera, Googles or alphabets of business of course is ads and we don't have similar spending data on Apple and Facebook, but we know these companies dominate their respective business. But just to give you a sense of the magnitude of these companies, here's some financial data that's worth looking at briefly. The table ranks companies by market cap in trillions that's the second column and everyone in the club, but meta and each has revenue well over a hundred billion dollars, Amazon approaching half a trillion dollars in revenue. The operating income and cash positions are just mind boggling and the cash equivalents are comparable or well above the revenues of highly successful tech companies like Cisco, Dell, HPE, Oracle, and Salesforce. They're extremely profitable from an operating income standpoint with the clear exception of Amazon and we'll come back to that in a moment and we show the revenue multiples in the last column, Apple, Microsoft, and Google, just insane. Dave, there are other equally important metrics, CapX is one which kind of sets the stage for future scale and there are other measures. >> Yeah, including our research and development where those companies are spending hundreds of billions of dollars over the years. And I think it's easy to look at those numbers and just say, this doesn't seem right, how can any companies have so much and spend so much? But if you think of what they're actually doing, those companies are building out the digital infrastructure of essentially the entire world. And I remember once meeting some folks at Google, and they said, beyond AI, beyond Search, beyond Android, beyond all the specific things we do, the biggest thing we're actually doing is building a physical infrastructure that can deliver search results on any topic in microseconds and the physical capacity they built costs those sorts of money. And when people start saying, well, we should have lots and lots of smaller companies well, that sounds good, yeah, it's all right, but where are those companies going to get the money to build out what needs to be built out? And every country in the world is trying to build out its digital infrastructure and some are going to do it much better than others. >> I want to just come back to that chart on Amazon for a bit, notice their comparatively tiny operating profit as a percentage of revenue, Amazon is like Bezos giant lifestyle business, it's really never been that profitable like most retail. However, there's one other financial data point around Amazon's business that we want to share and this chart here shows Amazon's operating profit in the blue bars and AWS's in the orange. And the gray line is the percentage of Amazon's overall operating profit that comes from AWS. That's the right most access, so last quarter we were well over a hundred percent underscoring the power of AWS and the horrendous margins in retail. But AWS is essentially funding Amazon's entrance into new markets, whether it's grocery or movies, Bezos moves into space. Dave, a while back you collaborated with us and we asked our audience, what could disrupt Amazon? And we came up with your detailed help, a number of scenarios as shown here. And we asked the audience to rate the likelihood of each scenario in terms of its likelihood of disrupting Amazon with a 10 being highly likely on average the score was six with complacency, arrogance, blindness, you know, self-inflicted wounds really taking the top spot with 6.5. So Dave is breaking up Amazon the right formula in your view, why or why not? >> Yeah, there's a couple of things there. The first is sort of the irony that when people in the sort of regulatory world talk about the power of Amazon, they almost always talk about their power in consumer markets, whether it's books or retail or impact on malls or main street shops or whatever and as you say that they make very little money doing that. The interest people almost never look at the big cloud battle between Amazon, Microsoft and lesser extent Google, Alibaba others, even though that's where they're by far highest market share and pricing power and all those things are. So the regulatory focus is sort of weird, but you know, the consumer stuff obviously gets more appeal to the general public. But that survey you referred to me was interesting because one of the challenges I sort of sent myself I was like okay, well, if I'm going to say that IBM case, AT&T case, Microsoft's case in all those situations the market was the one that actually minimized the power of those firms and therefore the antitrust stuff wasn't really necessary. Well, how true is that going to be again, just cause it's been true in the past doesn't mean it's true now. So what are the possible scenarios over the 2020s that might make it all happen again? And so each of those were sort of questions that we put out to others, but the ones that to me by far are the most likely I mean, they have the traditional one of company cultures sort of getting fat and happy and all, that's always the case, but the more specific ones, first of all by far I think is China. You know, Amazon retail is a low margin business. It would be vulnerable if it didn't have the cloud profits behind it, but imagine a year from now two years from now trade tensions with China get worse and Christmas comes along and China just says, well, you know, American consumers if you want that new exercise bike or that new shoes or clothing, well, anything that we make well, actually that's not available on Amazon right now, but you can get that from Alibaba. And maybe in America that's a little more farfetched, but in many countries all over the world it's not farfetched at all. And so the retail divisions vulnerability to China just seems pretty obvious. Another possible disruption, Amazon has spent billions and billions with their warehouses and their robots and their automated inventory systems and all the efficiencies that they've done there, but you could argue that maybe someday that's not really necessary that you have Search which finds where a good is made and a logistical system that picks that up and delivers it to customers and why do you need all those warehouses anyways? So those are probably the two top one, but there are others. I mean, a lot of retailers as they get stronger online, maybe they start pulling back some of the premium products from Amazon and Amazon takes their cut of whatever 30% or so people might want to keep more of that in house. You see some of that going on today. So the idea that the Amazon is in vulnerable disruption is probably is wrong and as part of the work that I'm doing, as part of stuff that I do with Dave and SiliconANGLE is how's that true for the others too? What are the scenarios for Google or Apple or Microsoft and the scenarios are all there. And so, will these companies be disrupted as they have in the past? Well, you can't say for sure, but the scenarios are certainly plausible and I certainly wouldn't bet against it and that's what history tells us. And it could easily happen once again and therefore, the antitrust should at least be cautionary and humble and realize that maybe they don't need to act as much as they think. >> Yeah, now, one of the things that you mentioned in your piece was felt like narrow remedies, were more logical. So you're not arguing for totally Les Affaire you're pushing for remedies that are more targeted in scope. And while the EU just yesterday announced new rules to limit the power of tech companies and we showed the article, some comments here the regulators they took the social media to announce a victory and they had a press conference. I know you watched that it was sort of a back slapping fest. The comments however, that we've sort of listed here are mixed, some people applauded, but we saw many comments that were, hey, this is a horrible idea, this was rushed together. And these are going to result as you say in unintended consequences, but this is serious stuff they're talking about applying would appear to be to your point or your prescription more narrowly defined restrictions although a lot of them to any company with a market cap of more than 75 billion Euro or turnover of more than 77.5 billion Euro which is a lot of companies and imposing huge penalties for violations up to 20% of annual revenue for repeat offenders, wow. So again, you've taken a brief look at these developments, you watched the press conference, what do you make of this? This is an application of more narrow restrictions, but in your quick assessment did they get it right? >> Yeah, let's break that down a little bit, start a little bit of history again and then get to Europe because although big sweeping breakups of the type that were proposed for IBM, Microsoft and all weren't necessary that doesn't mean that the government didn't do some useful things because they did. In the case of IBM government forces in Europe and America basically required IBM to make it easier for companies to make peripherals type drives, disc drives, printers that worked with IBM mainframes. They made them un-bundle their software pricing that made it easier for database companies and others to sell their of products. With AT&T it was the government that required AT&T to actually allow other phones to connect to the network, something they argued at the time would destroy security or whatever that it was the government that required them to allow MCI the long distance carrier to connect to the AT network for local deliveries. And with that Microsoft and Intel the government required them to at least treat their suppliers more even handly in terms of pricing and policies and support and such things. So the lessons out there is the big stuff wasn't really necessary, but the little stuff actually helped a lot and I think you can see the scenarios and argue in the piece that there's little stuff that can be done today in all the cases for the big five, there are things that you might want to consider the companies aren't saints they take advantage of their power, they use it in ways that sometimes can be reigned in and make for better off overall. And so that's how it brings us to the European piece of it. And to me, the European piece is much more the bad scenario of doing too much than the wiser course of trying to be narrow and specific. What they've basically done is they have a whole long list of narrow things that they're all trying to do at once. So they want Amazon not to be able to share data about its selling partners and they want Apple to open up their app store and they don't want people Google to be able to share data across its different services, Android, Search, Mail or whatever. And they don't want Facebook to be able to, they want to force Facebook to open up to other messaging services. And they want to do all these things for all the big companies all of which are American, and they want to do all that starting next year. And to me that looks like a scenario of a lot of difficult problems done quickly all of which might have some value if done really, really well, but all of which have all kinds of risks for the unintended consequence we've talked before and therefore they seem to me being too much too soon and the sort of problems we've seen in the past and frankly to really say that, I mean, the Europeans would never have done this to the companies if they're European firms, they're doing this because they're all American firms and the sort of frustration of Americans dominance of the European tech industry has always been there going back to IBM, Microsoft, Intel, and all of them. But it's particularly strong now because the tech business is so big. And so I think the politics of this at a time where we're supposedly all this great unity of America and NATO and Europe in regards to Ukraine, having the Europeans essentially go after the most important American industry brings in the geopolitics in I think an unavoidable way. And I would think the story is going to get pretty tense over the next year or so and as you say, the Europeans think that they're taking massive actions, they think they're doing the right thing. They think this is the natural follow on to the GDPR stuff and even a bigger version of that and they think they have more to come and they see themselves as the people taming big tech not just within Europe, but for the world and absent any other rules that they may pull that off. I mean, GDPR has indeed spread despite all of its flaws. So the European thing which it doesn't necessarily get huge attention here in America is certainly getting attention around the world and I would think it would get more, even more going forward. >> And the caution there is US public policy makers, maybe they can provide, they will provide a tailwind maybe it's a blind spot for them and it could be a template like you say, just like GDPR. Okay, Dave, we got to leave it there. Thanks for coming on the program today, always appreciate your insight and your views, thank you. >> Hey, thanks a lot, Dave. >> All right, don't forget these episodes are all available as podcast, wherever you listen. All you got to do is search, "Breaking Analysis Podcast". Check out ETR website, etr.ai. We publish every week on wikibon.com and siliconangle.com. And you can email me david.vellante@siliconangle.com or DM me @davevellante. Comment on my LinkedIn post. This is Dave Vellante for Dave Michelle for "theCUBE Insights" powered by ETR. Have a great week, stay safe, be well and we'll see you next time. (slow tempo 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. >> If it weren't for Pat Gelsinger, Intel's future would be a disaster. Even with his clear vision, fantastic leadership, deep technical and business acumen, and amazing positivity, the company's future is in serious jeopardy. It's the same story we've been telling for years. Volume is king in the semiconductor industry, and Intel no longer is the volume leader. Despite Intel's efforts to change that dynamic With several recent moves, including making another go at its Foundry business, the company is years away from reversing its lagging position relative to today's leading foundries and design shops. Intel's best chance to survive as a leader in our view, will come from a combination of a massive market, continued supply constraints, government money, and luck, perhaps in the form of a deal with apple in the midterm. Hello, and welcome to this week's "Wikibon CUBE Insights, Powered by ETR." In this "Breaking Analysis," we'll update you on our latest assessment of Intel's competitive position and unpack nuggets from the company's February investor conference. Let's go back in history a bit and review what we said in the early 2010s. If you've followed this program, you know that our David Floyer sounded the alarm for Intel as far back as 2012, the year after PC volumes peaked. Yes, they've ticked up a bit in the past couple of years but they pale in comparison to the volumes that the ARM ecosystem is producing. The world has changed from people entering data into machines, and now it's machines that are driving all the data. Data volumes in Web 1.0 were largely driven by keystrokes and clicks. Web 3.0 is going to be driven by machines entering data into sensors, cameras. Other edge devices are going to drive enormous data volumes and processing power to boot. Every windmill, every factory device, every consumer device, every car, will require processing at the edge to run AI, facial recognition, inference, and data intensive workloads. And the volume of this space compared to PCs and even the iPhone itself is about to be dwarfed with an explosion of devices. Intel is not well positioned for this new world in our view. Intel has to catch up on the process, Intel has to catch up on architecture, Intel has to play catch up on security, Intel has to play catch up on volume. The ARM ecosystem has cumulatively shipped 200 billion chips to date, and is shipping 10x Intel's wafer volume. Intel has to have an architecture that accommodates much more diversity. And while it's working on that, it's years behind. All that said, Pat Gelsinger is doing everything he can and more to close the gap. Here's a partial list of the moves that Pat is making. A year ago, he announced IDM 2.0, a new integrated device manufacturing strategy that opened up its world to partners for manufacturing and other innovation. Intel has restructured, reorganized, and many executives have boomeranged back in, many previous Intel execs. They understand the business and have a deep passion to help the company regain its prominence. As part of the IDM 2.0 announcement, Intel created, recreated if you will, a Foundry division and recently acquired Tower Semiconductor an Israeli firm, that is going to help it in that mission. It's opening up partnerships with alternative processor manufacturers and designers. And the company has announced major investments in CAPEX to build out Foundry capacity. Intel is going to spin out Mobileye, a company it had acquired for 15 billion in 2017. Or does it try and get a $50 billion valuation? Mobileye is about $1.4 billion in revenue, and is likely going to be worth more around 25 to 30 billion, we'll see. But Intel is going to maybe get $10 billion in cash from that, that spin out that IPO and it can use that to fund more FABS and more equipment. Intel is leveraging its 19,000 software engineers to move up the stack and sell more subscriptions and high margin software. He got to sell what he got. And finally Pat is playing politics beautifully. Announcing for example, FAB investments in Ohio, which he dubbed Silicon Heartland. Brilliant! Again, there's no doubt that Pat is moving fast and doing the right things. Here's Pat at his investor event in a T-shirt that says, "torrid, bringing back the torrid pace and discipline that Intel is used to." And on the right is Pat at the State of the Union address, looking sharp in shirt and tie and suit. And he has said, "a bet on Intel is a hedge against geopolitical instability in the world." That's just so good. To that statement, he showed this chart at his investor meeting. Basically it shows that whereas semiconductor manufacturing capacity has gone from 80% of the world's volume to 20%, he wants to get it back to 50% by 2030, and reset supply chains in a market that has become important as oil. Again, just brilliant positioning and pushing all the right hot buttons. And here's a slide underscoring that commitment, showing manufacturing facilities around the world with new capacity coming online in the next few years in Ohio and the EU. Mentioning the CHIPS Act in his presentation in The US and Europe as part of a public private partnership, no doubt, he's going to need all the help he can get. Now, we couldn't resist the chart on the left here shows wafer starts and transistor capacity growth. For Intel, overtime speaks to its volume aspirations. But we couldn't help notice that the shape of the curve is somewhat misleading because it shows a two-year (mumbles) and then widens the aperture to three years to make the curve look steeper. Fun with numbers. Okay, maybe a little nitpick, but these are some of the telling nuggets we pulled from the investor day, and they're important. Another nitpick is in our view, wafers would be a better measure of volume than transistors. It's like a company saying we shipped 20% more exabytes or MIPS this year than last year. Of course you did, and your revenue shrank. Anyway, Pat went through a detailed analysis of the various Intel businesses and promised mid to high double digit growth by 2026, half of which will come from Intel's traditional PC they center in network edge businesses and the rest from advanced graphics HPC, Mobileye and Foundry. Okay, that sounds pretty good. But it has to be taken into context that the balance of the semiconductor industry, yeah, this would be a pretty competitive growth rate, in our view, especially for a 70 plus billion dollar company. So kudos to Pat for sticking his neck out on this one. But again, the promise is several years away, at least four years away. Now we want to focus on Foundry because that's the only way Intel is going to get back into the volume game and the volume necessary for the company to compete. Pat built this slide showing the baby blue for today's Foundry business just under a billion dollars and adding in another $1.5 billion for Tower Semiconductor, the Israeli firm that it just acquired. So a few billion dollars in the near term future for the Foundry business. And then by 2026, this really fuzzy blue bar. Now remember, TSM is the new volume leader, and is a $50 billion company growing. So there's definitely a market there that it can go after. And adding in ARM processors to the mix, and, you know, opening up and partnering with the ecosystems out there can only help volume if Intel can win that business, which you know, it should be able to, given the likelihood of long term supply constraints. But we remain skeptical. This is another chart Pat showed, which makes the case that Foundry and IDM 2.0 will allow expensive assets to have a longer useful life. Okay, that's cool. It will also solve the cumulative output problem highlighted in the bottom right. We've talked at length about Wright's Law. That is, for every cumulative doubling of units manufactured, cost will fall by a constant percentage. You know, let's say around 15% in semiconductor world, which is vitally important to accommodate next generation chips, which are always more expensive at the start of the cycle. So you need that 15% cost buffer to jump curves and make any money. So let's unpack this a bit. You know, does this chart at the bottom right address our Wright's Law concerns, i.e. that Intel can't take advantage of Wright's Law because it can't double cumulative output fast enough? Now note the decline in wafer starts and then the slight uptick, and then the flattening. It's hard to tell what years we're talking about here. Intel is not going to share the sausage making because it's probably not pretty, But you can see on the bottom left, the flattening of the cumulative output curve in IDM 1.0 otherwise known as the death spiral. Okay, back to the power of Wright's Law. Now, assume for a second that wafer density doesn't grow. It does, but just work with us for a second. Let's say you produce 50 million units per year, just making a number up. That gets you cumulative output to $100 million in, sorry, 100 million units in the second year to take you two years to get to that 100 million. So in other words, it takes two years to lower your manufacturing cost by, let's say, roughly 15%. Now, assuming you can get wafer volumes to be flat, which that chart showed, with good yields, you're at 150 now in year three, 200 in year four, 250 in year five, 300 in year six, now, that's four years before you can take advantage of Wright's Law. You keep going at that flat wafer start, and that simplifying assumption we made at the start and 50 million units a year, and well, you get to the point. You get the point, it's now eight years before you can get the Wright's Law to kick in, and you know, by then you're cooked. But now you can grow the density of transistors on a chip, right? Yes, of course. So let's come back to Moore's Law. The graphic on the left says that all the growth is in the new stuff. Totally agree with that. Huge term that Pat presented. Now he also said that until we exhaust the periodic table of elements, Moore's Law is alive and well, and Intel is the steward of Moore's Law. Okay, that's cool. The chart on the right shows Intel going from 100 billion transistors today to a trillion by 2030. Hold that thought. So Intel is assuming that we'll keep up with Moore's Law, meaning a doubling of transistors every let's say two years, and I believe it. So bring that back to Wright's Law, in the previous chart, it means with IDM 2.0, Intel can get back to enjoying the benefits of Wright's Law every two years, let's say, versus IDM 1.0 where they were failing to keep up. Okay, so Intel is saved, yeah? Well, let's bring into this discussion one of our favorite examples, Apple's M1 ARM-based chip. The M1 Ultra is a new architecture. And you can see the stats here, 114 billion transistors on a five nanometer process and all the other stats. The M1 Ultra has two chips. They're bonded together. And Apple put an interposer between the two chips. An interposer is a pathway that allows electrical signals to pass through it onto another chip. It's a super fast connection. You can see 2.5 terabytes per second. But the brilliance is the two chips act as a single chip. So you don't have to change the software at all. The way Intel's architecture works is it takes two different chips on a substrate, and then each has its own memory. The memory is not shared. Apple shares the memory for the CPU, the NPU, the GPU. All of it is shared, meaning it needs no change in software unlike Intel. Now Intel is working on a new architecture, but Apple and others are way ahead. Now let's make this really straightforward. The original Apple M1 had 16 billion transistors per chip. And you could see in that diagram, the recently launched M1 Ultra has $114 billion per chip. Now if you take into account the size of the chips, which are increasing, and the increase in the number of transistors per chip, that transistor density, that's a factor of around 6x growth in transistor density per chip in 18 months. Remember Intel, assuming the results in the two previous charts that we showed, assuming they were achievable, is running at 2x every two years, versus 6x for the competition. And AMD and Nvidia are close to that as well because they can take advantage of TSM's learning curve. So in the previous chart with Moore's Law, alive and well, Intel gets to a trillion transistors by 2030. The Apple ARM and Nvidia ecosystems will arrive at that point years ahead of Intel. That means lower costs and significantly better competitive advantage. Okay, so where does that leave Intel? The story is really not resonating with investors and hasn't for a while. On February 18th, the day after its investor meeting, the stock was off. It's rebound a little bit but investors are, you know, they're probably prudent to wait unless they have really a long term view. And you can see Intel's performance relative to some of the major competitors. You know, Pat talked about five nodes in for years. He made a big deal out of that, and he shared proof points with Alder Lake and Meteor Lake and other nodes, but Intel just delayed granite rapids last month that pushed it out from 2023 to 2024. And it told investors that we're going to have to boost spending to turn this ship around, which is absolutely the case. And that delay in chips I feel like the first disappointment won't be the last. But as we've said many times, it's very difficult, actually, it's impossible to quickly catch up in semiconductors, and Intel will never catch up without volume. So we'll leave you by iterating our scenario that could save Intel, and that's if its Foundry business can eventually win back Apple to supercharge its volume story. It's going to be tough to wrestle that business away from TSM especially as TSM is setting up shop in Arizona, with US manufacturing that's going to placate The US government. But look, maybe the government cuts a deal with Apple, says, hey, maybe we'll back off with the DOJ and FTC and as part of the CHIPS Act, you'll have to throw some business at Intel. Would that be enough when combined with other Foundry opportunities Intel could theoretically produce? Maybe. But from this vantage point, it's very unlikely Intel will gain back its true number one leadership position. If it were really paranoid back when David Floyer sounded the alarm 10 years ago, yeah, that might have made a pretty big difference. But honestly, the best we can hope for is Intel's strategy and execution allows it to get competitive volumes by the end of the decade, and this national treasure survives to fight for its leadership position in the 2030s. Because it would take a miracle for that to happen in the 2020s. Okay, that's it for today. Thanks to David Floyer for his contributions to this research. Always a pleasure working with David. Stephanie Chan helps me do much of the background research for "Breaking Analysis," and works with our CUBE editorial team. Kristen Martin and Cheryl Knight to get the word out. And thanks to SiliconANGLE's editor in chief Rob Hof, who comes up with a lot of the great titles that we have for "Breaking Analysis" and gets the word out to the SiliconANGLE audience. Thanks, guys. Great teamwork. Remember, these episodes are all available as podcast wherever you listen. Just search "Breaking Analysis Podcast." You'll want to check out ETR's website @etr.ai. We also publish a full report every week on wikibon.com and siliconangle.com. You could always get in touch with me on email, david.vellante@siliconangle.com or DM me @dvellante, and comment on my LinkedIn posts. This is Dave Vellante for "theCUBE Insights, Powered by ETR." Have a great week. Stay safe, be well, and we'll see you next time. (upbeat music)

(bright upbeat music) >> Hello, welcome to theCUBE, "Unstoppable Domains Showcase." I'm John Furrier, your host of theCUBE. We got a great discussion here called the influencers around what's going on Web 3.0. And also this new sea change, cultural change around this next generation, internet, web, cloud, all happening, Jeremiah Owyang, Industry Analyst and Founding Part of Kaleido Insights. Jeremiah, great to see you thanks for coming on I appreciate it. Ren Besnard, Vice President of Marketing and Unstoppable Domains in the middle of all the action. Gentlemen, thanks for coming on on theCUBE for this showcase. >> Wow, my pleasure. >> Thanks for having us, John. >> Jeremiah, I want to start with you. You've seen many ways refer in all of your work for over a decade now. You've seen the Web 2.0 wave now the Web 3.0 is here. And it's not, I wouldn't say hyped up it's really just ramping up. And you're seeing real practical examples. You're in the middle of all the action. What is this Web 3.0, can you frame for us? I mean, you've seen many webs. What is Web 3.0 mean, what is it all about? >> Well John, you and I worked in the Web 2.0 space and essentially that enabled peer-to-peer media where people could upload their thoughts and ideas and videos without having to rely on centralized media. Unfortunately, that distributed and decentralized movement actually became centralized on the platform which are the big social networks and big tech companies. And this has caused an uproar because the people who are creating the content did not have control, could not control their identities, and could not really monetize or make decisions. So Web 3.0 which is a moniker of a lot of different trends, including crypto, blockchain and sometimes the metaverse. Is to undo the controlling that has become centralized. And the power is now shifting back into the hands of the participants again. And in this movement, they want to have more control over their identities, their governance, the content that they're creating, how they're actually building it, and then how they're monetizing it. So in many ways it's changing the power and it's a new economic model. So that's Web 3.0. Without really even mentioning the technologies. Is that helpful? >> Yeah, it's great. And Ren, we're talking about on theCUBE many times and one notable stat I don't think it's been reported, but it's been more kind of a rumor. I hear that 30% of the Berkeley computer science students are dropping out and going into to crypto or blockchain or decentralized startups. Which means that there's a big wave coming in of talent. You're seeing startups, you're seeing a lot more formation, you're seeing a lot more, I would say it's kind of ramping up of real people, not just people with dream is actual builders out here doing stuff. What's your take on the Web 3.0 movement with all this kind of change happening from people and also the new ideas being refactored? >> I think that the competition for talent is extremely real. And we start looking at the stats, we see that there is an enormous draft of people that are moving into this space. People that are fascinated by technology and are embracing the ethos of Web 3.0. And at this stage I think it's not only engineers and developers, but we have moved into a second phase where we see that a lot of supporting functions, you know, marketing being one of them, sales, business development are being built up quite rapidly. It's not without actually reminding me of the mid 2000s, you know. When I started working with Google, at that point in time the walled gardens rightly absorbing vast, vast cohorts of young graduates and more experienced professionals that were passionate and moving into the web environment. And I think we are seeing a movement right now, which is not entirely similar except faster. >> Yeah, Jeremiah, you've seen the conversations of the cloud, I call the cloud kind of revolution. You had mobile in 2007. But you got Amazon Web Services changed the application space on how people developed in the cloud. And again, that created a lot of value. Now you're seeing the role of data as a huge part of how people are scaling and the decentralized movements. So you've got cloud which is kind of classic today, state of the art enterprise and or app developers. And you've got now decentralized wave coming, okay. You're seeing apps being developed on that architecture. Data is central in all this, right. So how, how do you view this as someone who's watching the landscape, you know, these walled gardens are hoarding all the data I mean, LinkedIn, Facebook. They're not sharing that data with anyone they're using it for themselves. So as- >> That's right. >> They can control back comes to the forefront. How do you see this market with the applications and what comes out of that? >> So the thing that we seen out of the five things that I had mentioned that are decentralizing. (Jeremiah coughing) Are the ones that have been easier to move across. Have been the ability to monetize and to build. But the data aspect has actually stayed pretty much central, frankly. What has decentralized is that the contracts, the blockchain ledgers, those have decentralized. But the funny thing is often a big portion of these blockchain networks are on Amazon 63 to 70%, same thing with (indistinct). So they're still using the Web 2.0 architectures. However, we're also seeing other forms like IPFS where the data could be spread across a wider range of folks. But right now we're still dependent on what Web 2.0. So the vision and the promise Web 3.0 when it to full decentralization is not here by any means. I'd say we're at a Web 2.25. >> Pre-Web 3.0 no, but actions there. How do you guys see the dangers, 'cause there's a lot of negative press but also there's a lot of positive press. You're seeing a lot of fraud, we've seen a lot of the crypto fraud over the past years. You've seen a lot of now positive. It's almost a self-governance thing and environment, the way the culture is. But what are the dangers, how do you guys educate people, what should people pay attention to, what should people look for to understand, you know, where to position themselves? >> Yes, so we've learned a lot from Web 1.0, Web 2.0, the sharing economy. And we are walking into Web 3.0 with eyes wide open. So people have rightfully put forth a number of challenges, the sustainability issues with excess using of computing and mining the excessive amount of scams that are happening in part due to unknown identities. Also the architecture breaks DAOn in some periods and there's a lack of regulation. This is something different though. In the last periods that we've gone through, we didn't really know what was going to happen. And we walked and think this is going to be great. The sharing economy, the gig economy, the social media's going to change the world around. It's very different now. People are a little bit jaded. So I think that's a change. And so I think we're going to see that sorted out in suss out just like we've seen with other trends. It's still very much in the early years. >> Ren, I got to get your take on this whole should influencers and should people be anonymous or should they be docs out there? You saw the board, eight guys that did that were kind of docs a little bit there. And that went viral. This is an issue, right? Because we just had a problem of fake news, fake people, fake information. And now you have a much more secure environment imutability is a wonderful thing. It's a feature, not a bug, right? So how is this all coming down? And I know you guys are in the middle of it with NFTs as authentication. Take us, what's your take on this because this is a big issue. >> Look, I think first I am extremely optimistic about technology in general. So I'm super, super bullish about this. And yet, you know, I think that while crypto has so many upsides, it's important to be super conscious and aware of the downsides that come with it to, you know. If you think about every Fortune 500 company there is always training required by all employees on internet safety, reporting of potential attacks and so on. In Web 3.0, we don't have that kind of standard reporting mechanisms yet for bad actors in that space. And so when you think about influencers in particular, they do have a responsibility to educate people about the potential, but also the dangers of the technology of Web 3.0 of crypto basically. Whether you're talking about hacks or online safety, the need for hardware, wallet, impersonators on discord, you know, security storing your seed phrase. So every actor influencer or else has got a role to play. I think that in that context to your point, it's very hard to tell whether influencers should be anonymous, oxydemous or fully docked. The decentralized nature of Web 3.0 will probably lead us to see a combination of those anonymity levels so to speak. And the movements that we've seen around some influencers identities become public are particularly interesting. I think there's probably a convergence of Web 2.O and Web 3.0 at play here, you know. Maybe occurring on the notion of 2.5. But for now I think in Web 2.0, all business founders and employees are known and they held accountable for their public comments and their actions. If Web 3.0 enables us to be anonymous, if DAOs have voting control, you know. What happens if people make comments and there is no way to know who they are, basically. What if the DAO doesn't take appropriate action? I think eventually there will be an element of community self-regulation where influencers will be acting in the best interest of their reputation. And I believe that the communities will self-regulate themselves and will create natural boundaries around what can be said or not said. >> I think that's a really good point about influencers and reputation because. Jeremiah, does it matter that you're anonymous have an icon that could be a NFT or a picture. But if I have an ongoing reputation I have trust, to this trust there. It's not like just a bot that was created just to spam someone. You know I'm starting to getting into this new way. >> You're right, and that word you said trust, that's what really this is about. But we've seen that public docs, people with their full identities have made mistakes. They have pulled the hood over people's faces and really scammed them out of a lot of money. We've seen that in the, that doesn't change anything in human behavior. So I think over time that we will see a new form of a reputation system emerge even for pseudonym and perhaps for people that are just anonymous that only show their potential wallet, address a series of numbers and letters. That form might take a new form of a Web 3.0 FICO Score. And you could look at their behaviors. Did they transact, you know, how did they behave? Were they involved in projects that were not healthy? And because all of that information is public on the chain and you can go back in time and see that. We might see a new form of a scoring emerge, of course. Who controls that scoring? That's a whole nother topic gone on controling and trust. So right now, John we do see that there's a number of projects, new NFT projects, where the founders will claim and use this as a point of differentiation that they are fully docs. So you know who they are and in their names. Secondly, we're seeing a number of products or platforms that require KYC, you know, your customers. So that's self-identification often with a government ID or credit card in order to bridge out your coins and turn that into fiat. In some cases that's required in some of these marketplaces. So we're seeing a collision here between our full names and pseudonyms and being anonymous. >> That's awesome. And I think this is the new, again, a whole new form of governance. Ren, you mentioned some comments about DAO. I want to get your thoughts again. You know, Jeremiah we've become historians over the years. We're getting old I'm a little bit older than you. (Jeremiah laughs) But we've seen the- >> You're young men. You know, I remember breaking in the business when the computer standards bodies were built to be more organic and then they became much more of a, kind of an anti-innovation environment where people, the companies would get involved, the standards organization just to slow things DAO and mark things up a little bit. So, you know, you look at DAOs like, hmm, is DAO a good thing or a bad thing. The answer is from people I talk to is, it depends. So I'd love to get your thoughts on getting momentum and becoming defacto with value, a value proposition, vis-a-vis just a DAO for the sake of having a DAO. This has been a conversation that's been kind of in the inside the baseball here, inside the ropes of the industry, but there's trade offs. Can you guys share your thoughts on when to do a DAO and when not to do a DAO and the benefits and trade offs of that? >> Sure, maybe I'll start off with a definition and then we'll go to, Ren. So a DAO, a decentralized autonomous organization, the best way to think about this It's a digital cooperative. and we've heard of worker cooperatives before. The difference is that they're using blockchain technologies in order to do three things, identity, governance, and rewards and mechanisms. They're relying on Web 2.0 tools and technologies like discord and Telegram and social networks to communicate. And as a cooperative they're trying to come up with a common goal. Ren, what's your take, that's the setup. >> So, you know for me when I started my journey into crypto and Web 3.0, I had no idea about what DAO actually meant. And an easy way for me to think of it and to grasp the nature of it was about the comparison between a DAO and perhaps a more traditional company structure, you know. In the traditional company structure, you have (indistinct), the company's led by a CEO and other executives. The DAO is a flat structure, and it's very much led by a group of core contributors. So to Jeremiah's point, you know, you get that notion of a cooperative type of structure. The decision making is very different, you know. We're talking about a super high level of transparency proposals getting submitted and voting systems using (indistinct) as opposed to, you know, management, making decisions behind closed doors. I think that speaks to a totally new form of governance. And I think we have hardly, hardly scratched the surface. We have seen recently very interesting moments in Web 3.0 culture. And we have seen how DAO suddenly have to make certain decisions and come to moments of claiming responsibility in order to police behavior of some of the members. I think that's important. I think it's going to redefine how we're thinking about that particularly new governance models. And I think it's going to pave the way for a lot of super interesting structure in the near future. >> Yeah and that's a great point. >> Go ahead, Jeremiah. >> That's a great point, Ren. Around the transparency for governance. So, John you post the question, does this make things faster or slower? And right now in the most doubts are actually pretty slow because they're set up as a flat organization. So as a response to that they're actually shifting to become representative democracies. Does that sound familiar? Or you can appoint delegates and use tokens to vote for them and they have a decision power. Almost like a committee and they can function. And so we've seen actually there sometimes are hierarchy except the person at the top is voted by those that have the tokens. In some cases, the people at the top had the most tokens. But that's a whole nother topic. So we're seeing a wide variety of governance structures. >> You know, Ren I was talking with Matt G, the Founder of Unstoppable. And I was telling him about the Domain Name System. And one little trivia note that many people don't know about is that the US government 'cause the internet was started by the US. The Department of Commerce kept that on tight leash because the international telecommunications wanted to get their hands on it because of ccTLDs and other things. So at that time, 'cause the innovation yet was isn't yet baked out. It was organically growing the governance, the rules of the road, keeping it very stable versus melding with it. So there's certain technologies that require, Jeremiah that let's keep an eye on as a community let's not formalize anything. Like the government did with the Domain Name System. Let's keep it tight and then finally released it. I think multiple years after 2004, I think it went over to the ITU. But this is a big point. I mean, if you get too structured, organic innovation can't go. What's you guys reaction to that? >> So I think, you know to take the stab at it. We have as a business, you know, thinking of Unstoppable Domains, a strong incentive to innovate. And this is what is going to be determining long-term value growth for the organization, for partners, for users, for customers. So you know the degree of formalization actually gives us a sense of purpose and a sense of action. And if you compare that to DAO, for instance, you can see how some of the upsides and downsides can pan out either way. It's not to say that there is a perfect solution. I think one of the advantages of the DAO is that you can let more people contribute. You can probably remove buyers quite effectively and you can have a high level of participation and involvement in decisions and own the upside in many ways. You know as a company, it's a slightly different setup. We have the opportunity to coordinate a very diverse and part-time workforce in a very you a different way. And we do not have to deal with the inefficiencies that might be inherent to some form of extreme decentralization. So there is a balance from an organizational structure that comes either side. >> Awesome. Jeremiah, I want to get your thoughts on a trend that you've been involved in, we've both been involved in. And you're seeing it now with the kind of social media world, the world of the role of an influencer. It's kind of moved from what was open source and influencer was a connect to someone who shared, created content enabled things to much more of a vanity. You update the photo on Instagram and having a large audience. So is there a new influencer model with Web 3.0 or is it, I control the audience I'm making money that way. Is there a shift in the influencer role or ideas that you see that should be in place for what is the role of an influencer? 'Cause as Web 3.0 comes you're going to see that role become instrumental. We've seen it in open source projects. Influencers, you know, the people who write code or ship code. So what's your take on that? Because this has been a conversation. People have been having the word influencer and redefining and reframing it. >> Sure, the influence model really hasn't changed that much, but the way that they're behaving has when it comes to Web 3.0. In this market, I mean there's a couple of things. Some of the influencers are investors. And so when you see their name on a project or a new startup, that's an indicator there's a higher level of success. You might want to pay more attention to it or not. Secondly, influencers themselves are launching their own NFT projects. So, Gary Vaynerchuk, a number of celebrities, Paris Hilton is involved. They are also doing theirs as well. Steve Aok, famous DJ launched his as well. So they're going head first and participating in building in this model. And their communities are coming around them and they're building economy. Now the difference is it's not I speak as an influencer to the fans. The difference is that the fans are now part of the community and they literally hold and own some of the economic value, whether it's tokens or the NFTs. So it's a collaborative economy, if you will, where they're all benefiting together. And that's a big difference as well. >> Can you see- >> Lastly, there's one little tactic we're seeing where marketers are air dropping NFTs, branded NFTs influencers wallet. So you can see it in there. So there's new tactics that are forming as well. Back to you. >> That's super exciting. Ren, what's your reaction to that? Because he just hit on a whole new way of how engagement's happening, how people are closed looping their votes, their votes of confidence or votes with their wallet. And the brands which are artists now influencers. I mean, this is a whole game changing instrumentation level. >> I think that what we are seeing right now is super reinvigorating as a marketeer who's been around for a few years, basically. I think that the shift in the way brands are going to communicate and engage with their audiences is profound. It's probably as revolutionary and even more revolutionary than the movement for brands in getting into digital. And you have that sentiment of a gold rush right now with a lot of brands that are trying to understand NFTs and how to actually engage with those communities and those audiences. There are many levels in which brands and influencers are going to engage. There are many influencers that actually advance the message and the mission because the explosion of content on Web 3.0 has been crazy. Part of that is due to the network effect nature of crypto. Because as Jaremiah mentioned, people are incentivized to promote projects. Holders of an NFT are also incentivized to promote it. So you end up with a fly wheel which is pretty unique of people that are hyping their project and that are educating other people about it and commenting on the ecosystem with IP right being given to NFT holders. You're going to see people promote brands instead of the brands actually having to. And so the notion of brands are gaining and delivering elements of the value to their fans is something that's super attractive, extremely interesting. And I think again, we have hardly scratched the surface of all that is possible in that particular space. >> That's interesting. You guys are bringing some great insight here. Jeremiah, the old days the word authentic was a kind of a cliche and brands like tried to be authentic. And they didn't really know what to do they called it organic, right? And now you have the trust concept with authenticity and environment like Web 3.0 where you can actually measure it and monetize it and capture it if you're actually authentic and trustworthy. >> That's right, and be because it's on blockchain, you can see how somebody's behaved with their economic behavior in the past. Of course, big corporations aren't going to have that type of trail on blockchain just yet. But individuals and executives who participate in this market might be. And we'll also see new types of affinity. Do executives do they participate in these NFT communities, do they purchase them or numerous brands like Adidas to acquire, you know, different NFT projects to participate. And of course the big brands are grabbing their domains. Of course you could talk to, Ren about that because it's owning your own name is a part of this trust and being found. >> That's awesome. Great insight guys. Closing comments, takeaways for the audience here. Each of you take a minute to share your thoughts on what you think is happening now where it goes, all right, where's it going to go? Jeremiah, we'll start with you. >> Sure, I think the vision of Web 3.0 where full decentralization happens, where the power is completely shifted to the edges. I don't think it's going to happen. I think we will reach Web 2.5. And I've been through so many tech trends where we said that the power's going to shift completely to of the end, it just doesn't. In part there's two reasons. One is the venture capital are the ones who tend to own the programs in the first place. And secondly, the startups themselves end up becoming the one-percenter. We see Airbnb and Uber are one-percenter now. So that trend happens over and over and over. Now with that said, the world will be in a better place. We will have more transparency. We will see economic power shifted to the people, the participants. And so they will have more control over the internet that they are building. >> Awesome, Ren final comments. >> I'm fully aligned with Jeremiah on the notion of control being returned to users, the notion of ownership and the notion of redistribution of the economic value that is created across all the different chains that we are going to see and all those ecosystems. I believe that we are going to witness two parallel movements of expansion. One that is going to be very lateral. When you think of crypto and Web 3.0 essentially you think of a few 100 tribes. And I think that more projects are going to be a more coalitions of individuals and entities, and those are going to exist around those projects. So you're going to see, you know, an increase in the number of tribes that one might join. And I also think that we're going to progress rapidly from the low 100 millions of crypto and NFT holders into the big hands basically. And that's going to be extreme interesting. I think that the next waves of crypto users, NFT fans are going to look very different from the early adopters that we had witnessed in the very early days. So it's not going to be your traditional model of technology adoption curves. I think the demographics are going to shift and the motivations are going to be different as well, which is going to be a wonderful time to educate and engage with new community members. >> All right, Ren and Jeremiah, thank you both for that great insight great segment breaking down Web 3.0 or Web 2.5 as Jeremiah says but we're in a better place. This is a segment with the influencers. As part of theCUBE and the Unstoppable Domain Showcase. I'm John Furrie, your host. Thanks for watching. (bright upbeat music)

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante nvidia wants to completely transform enterprise computing by making data centers run 10x faster at one tenth the cost and video's ceo jensen wang is crafting a strategy to re-architect today's on-prem data centers public clouds and edge computing installations with a vision that leverages the company's strong position in ai architectures the keys to this end-to-end strategy include a clarity of vision massive chip design skills a new arm-based architecture approach that integrates memory processors i o and networking and a compelling software consumption model even if nvidia is unsuccessful at acquiring arm we believe it will still be able to execute on this strategy by actively participating in the arm ecosystem however if its attempts to acquire arm are successful we believe it will transform nvidia from the world's most valuable chip company into the world's most valuable supplier of integrated computing architectures hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll explain why we believe nvidia is in the right position to power the world's computing centers and how it plans to disrupt the grip that x86 architectures have had on the data center for decades the data center market is in transition like the universe the cloud is expanding at an accelerated pace no longer is the cloud an opaque set of remote services i always say somewhere out there sitting in a mega data center no rather the cloud is extending to on-premises data centers data centers are moving into the cloud and they're connecting through adjacent locations that create hybrid interactions clouds are being meshed together across regions and eventually will stretch to the far edge this new definition or view of cloud will be hyper distributed and run by software kubernetes is changing the world of software development and enabling workloads to run anywhere open apis external applications expanding the digital supply chains and this expanding cloud they all increase the threat surface and vulnerability to the most sensitive information that resides within the data center and around the world zero trust has become a mandate we're also seeing ai being injected into every application and it's the technology area that we see with the most momentum coming out of the pandemic this new world will not be powered by general purpose x86 processors rather it will be supported by an ecosystem of arm-based providers in our opinion that are affecting an unprecedented increase in processor performance as we have been reporting and nvidia in our view is sitting in the poll position and is currently the favorite to dominate the next era of computing architecture for global data centers public clouds as well as the near and far edge let's talk about jensen wang's clarity of vision for this new world here's a chart that underscores some of the fundamental assumptions that he's leveraging to expand his market the first is that there's a lot of waste in the data center he claims that only half of the cpu cores deployed in the data center today actually support applications the other half are processing the infrastructure all around the applications that run the software defined data center and they're terribly under utilized nvidia's blue field three dpu the data processing unit was described in a blog post on siliconangle by analyst zias caravala as a complete mini server on a card i like that with software defined networking storage and security acceleration built in this product has the bandwidth and according to nvidia can replace 300 general purpose x86 cores jensen believes that every network chip will be intelligent programmable and capable of this type of acceleration to offload conventional cpus he believes that every server node will have this capability and enable every packed of every packet and every application to be monitored in real time all the time for intrusion and as servers move to the edge bluefield will be included as a core component in his view and this last statement by jensen is critical in our opinion he says ai is the most powerful force of our time whether you agree with that or not it's relevant because ai is everywhere an invidious position in ai and the architectures the company is building are the fundamental linchpin of its data center enterprise strategy so let's take a look at some etr spending data to see where ai fits on the priority list here's a set of data in a view that we often like to share the horizontal axis is market share or pervasiveness in the etr data but we want to call your attention to the vertical axis that's really really what really we want to pay attention today that's net score or spending momentum exiting the pandemic we've seen ai capture the number one position in the last two surveys and we think this dynamic will continue for quite some time as ai becomes the staple of digital transformations and automations an ai will be infused in every single dot you see on this chart nvidia's architectures it just so happens are tailor made for ai workloads and that is how it will enter these markets let's quantify what that means and lay out our view of how nvidia with the help of arm will go after the enterprise market here's some data from wikibon research that depicts the percent of worldwide spending on server infrastructure by workload type here are the key points first the market last year was around 78 billion dollars worldwide and is expected to approach 115 billion by the end of the decade this might even be a conservative figure and we've split the market into three broad workload categories the blue is ai and other related applications what david floyer calls matrix workloads the orange is general purpose think things like erp supply chain hcm collaboration basically oracle saps and microsoft work that's being supported today and of course many other software providers and the gray that's the area that jensen was referring to is about being wasted the offload work for networking and storage and all the software defined management in the data centers around the world okay you can see the squeeze that we think compute infrastructure is gonna gonna occur around that orange area that general-purpose workloads that we think is going to really get squeezed in the next several years on a percentage basis and on an absolute basis it's really not growing nearly as fast as the other two and video with arm in our view is well positioned to attack that blue area and the gray area those those workload offsets and the new emerging ai applications but even the orange as we've reported is under pressure as for example companies like aws and oracle they use arm-based designs to service general purpose workloads why are they doing that cost is the reason because x86 generally and intel specifically are not delivering the price performance and efficiency required to keep up with the demands to reduce data center costs and if intel doesn't respond which we believe it will but if it doesn't act arm we think will get 50 percent of the general purpose workloads by the end of the decade and with nvidia it will dominate the blue the ai and the gray the offload work when we say dominate we're talking like capture 90 percent of the available market if intel doesn't respond now intel they're not just going to sit back and let that happen pat gelsinger is well aware of this in moving intel to a new strategy but nvidia and arm are way ahead in the game in our view and as we've reported this is going to be a real challenge for intel to catch up now let's take a quick look at what nvidia is doing with relevant parts of its pretty massive portfolio here's a slide that shows nvidia's three chip strategy the company is shifting to arm-based architectures which we'll describe in more detail in a moment the slide shows at the top line nvidia's ampere architecture not to be confused with the company ampere computing nvidia is taking a gpu centric approach no surprise obvious reasons there that's their sort of stronghold but we think over time it may rethink this a little bit and lean more into npus the neural processing unit we look at what apple's doing what tesla are doing we see opportunities for companies like nvidia to really sort of go after that but we'll save that for another day nvidia has announced its grace cpu a nod to the famous computer scientist grace hopper grace is a new architecture that doesn't rely on x86 and much more efficiently uses memory resources we'll again describe this in more detail later and the bottom line there that roadmap line shows the bluefield dpu which we described is essentially a complete server on a card in this approach using arm will reduce the elapsed time to go from chip design to production by 50 we're talking about shaving years down to 18 months or less we don't have time to do a deep dive into nvidia's portfolio it's large but we want to share some things that we think are important and this next graphic is one of them this shows some of the details of nvidia's jetson architecture which is designed to accelerate those ai plus workloads that we showed earlier and the reason is that this is important in our view is because the same software supports from small to very large including edge systems and we think this type of architecture is very well suited for ai inference at the edge as well as core data center applications that use ai and as we've said before a lot of the action in ai is going to happen at the edge so this is a good example of leveraging an architecture across a wide spectrum of performance and cost now we want to take a moment to explain why the moved arm-based architectures is so critical to nvidia one of the biggest cost challenges for nvidia today is keeping the gpu utilized typical utilization of gpu is well below 20 percent here's why the left hand side of this chart shows essentially racks if you will of traditional compute and the bottlenecks that nvidia faces the processor and dram they're tied together in separate blocks imagine there are thousands thousands of cores in a rack and every time you need data that lives in another processor you have to send a request and go retrieve it it's very overhead intensive now technologies like rocky are designed to help but it doesn't solve the fundamental architectural bottleneck every gpu shown here also has its own dram and it has to communicate with the processors to get the data i.e they can't communicate with each other efficiently now the right hand side side shows where nvidia is headed start in the middle with system on chip socs cpus are packaged in with npus ipu's that's the image processing unit you know x dot dot dot x pu's the the alternative processors they're all connected with sram which is think of that as a high speed layer like an layer one cache the os for the system on a chip lives inside of this and that's where nvidia has this killer software model what they're doing is they're licensing the consumption of the operating system that's running this system on chip in this entire system and they're affecting a new and really compelling subscription model you know maybe they should just give away the chips and charge for the software like a razer blade model talk about disruptive now the outer layer is the the dpu and the shared dram and other resources like the ampere computing the company this time cpus ssds and other resources these are the processors that will manage the socs together this design is based on nvidia's three chip approach using bluefield dpu leveraging melanox that's the networking component the network enables shared dram across the cpus which will eventually be all arm based grace lives inside the system on a chip and also on the outside layers and of course the gpu lives inside the soc in a scaled-down version like for instance a rendering gpu and we show some gpus on the outer layer as well for ai workloads at least in the near term you know eventually we think they may reside solely in the system on chip but only time will tell okay so you as you can see nvidia is making some serious moves and by teaming up with arm and leaning into the arm ecosystem it plans to take the company to its next level so let's talk about how we think competition for the next era of compute stacks up here's that same xy graph that we love to show market share or pervasiveness on the horizontal tracking against next net score on the vertical net score again is spending velocity and we've cut the etr data to capture players that are that are big in compute and storage and networking we've plugged in a couple of the cloud players these are the guys that we feel are vying for data center leadership around compute aws is a very strong position we believe that more than half of its revenues comes from compute you know ec2 we're talking about more than 25 billion on a run rate basis that's huge the company designs its own silicon graviton 2 etc and is working with isvs to run general purpose workloads on arm-based graviton chips microsoft and google they're going to follow suit they're big consumers of compute they sell a lot but microsoft in particular you know they're likely to continue to work with oem partners to attack that on-prem data center opportunity but it's really intel that's the provider of compute to the likes of hpe and dell and cisco and the odms which are the odms are not shown here now hpe let's talk about them for a second they have architectures and i hate to bring it up but remember the machine i know it's the butt of many jokes especially from competitors it had been you know frankly hpe and hp they deserve some of that heat for all the fanfare and then that they they put out there and then quietly you know pulled the machine or put it out the pasture but hpe has a strong position in high performance computing and the work that it did on new computing architectures with the machine and shared memories that might be still kicking around somewhere inside of hp and could come in handy for some day in the future so hpe has some chops there plus hpe has been known hp historically has been known to design its own custom silicon so i would not count them out as an innovator in this race cisco is interesting because it not only has custom silicon designs but its entry into the compute business with ucs a decade ago was notable and they created a new way to think about integrating resources particularly compute and networking with partnerships to add in the storage piece initially it was within within emc prior to the dell acquisition but you know it continues with netapp and pure and others cisco invests they spend money investing in architectures and we expect the next generation of ucs oh ucs2 ucs 2.0 will mark another notable milestone in the company's data center business dell just had an amazing quarterly earnings report the company grew top line revenue by around 12 percent and it wasn't because of an easy compare to last year dells is simply executing despite continued softness in the legacy emc storage business laptop the laptop demand continued to soar in dell server business it's growing again but we don't see dell as an architectural innovator per se in compute rather we think the company will be content to partner with suppliers whether it's intel nvidia arm-based partners or all of the above dell we think will rely on its massive portfolio its excellent supply chain and execution ethos to compete now ibm is notable for historical reasons with its mainframe ibm created the first great compute monopoly before it unwind and wittingly handed it to intel along with microsoft we don't see ibm necessarily aspiring to retake that compute platform mantle that once once held with mainframes rather red hat in the march to hybrid cloud is the path that we think in our view is ibm's approach now let's get down to the elephants in the room intel nvidia and china inc china is of course relevant because of companies like alibaba and huawei and the chinese chinese government's desire to be self-sufficient in semiconductor technology and technology generally but our premise here is that the trends are favoring nvidia over intel in this picture because nvidia is making moves to further position itself for new workloads in the data center and compete for intel's stronghold intel is going to attempt to remake itself but it should have been doing this seven years ago what pat gelsinger is doing today intel is simply far behind and it's going to take at least a couple years for them to really start to to make inroads in this new model let's stay on the nvidia v intel comparison for a moment and take a snapshot of the two companies here's a quick chart that we put together with some basic kpis some of these figures are approximations or they're rounded so don't stress over it too much but you can see intel is an 80 billion dollar company 4x the size of nvidia but nvidia's market cap far exceeds that of intel why is that of course growth in our view it's justified due to that growth and nvidia's strategic positioning intel used to be the gross margin king but nvidia has much higher gross margins interesting now when it comes down to free cash flow intel is still dominant as it pertains to the balance sheet intel is way more capital intensive than nvidia and as it starts to build out its foundries that's going to eat into intel's cash position now what we did is we put together a little pro forma on the third column of nvidia plus arm circa let's say the end of 2022. we think they could get to a run rate that is about half the size of intel and that can propel the company's market cap to well over half a trillion dollars if they get any credit for arm they're paying 40 billion dollars for arm a company that's you know sub 2 billion the risk is that because of the arm because the arm deal is based on cash plus tons of stock it could put pressure on the market capitalization for some time arm has 90 percent gross margins because it pretty much has a pure license model so it helps the gross margin line a little bit for this in this pro forma and the balance sheet is a swag arm has said that it's not going to take on debt to do the transaction but we haven't had time to really dig into that and figure out how they're going to structure it so we took a took a swag in in what we would do with this low interest rate environment but but take that with a grain of salt we'll do more research in there the point is given the momentum and growth of nvidia its strategic position in ai is in its deep engineering they're aimed at all the right places and its potential to unlock huge value with arm on paper it looks like the horse to beat if it can execute all right let's wrap up here's a summary look the architectures on which nvidia is building its dominant ai business are evolving and nvidia is well positioned to drive a truck right to the enterprise in our view the power has shifted from intel to the arm ecosystem and nvidia is leaning in big time whereas intel it has to preserve its current business while recreating itself at the same time this is going to take a couple of years but intel potentially has the powerful backing of the us government too strategic to fail the wild card is will nvidia be successful in acquiring arm certain factions in the uk and eu are fighting the deal because they don't want the u.s dictating to whom arm can sell its technology for example the restrictions placed on huawei for many suppliers of arm-based chips based on u.s sanctions nvidia's competitors like broadcom qualcomm at all are nervous that if nvidia gets armed they will be at a competitive disadvantage they being invidious competitors and for sure china doesn't want nvidia controlling arm for obvious reasons and it will do what it can to block the deal and or put handcuffs on how business can be done in china we can see a scenario where the u.s government pressures the uk and eu regulators to let this deal go through look ai and semiconductors you can't get much more strategic than that for the u.s military and the u.s long-term competitiveness in exchange for maybe facilitating the deal the government pressures nvidia to guarantee some feed to the intel foundry business while at the same time imposing conditions that secure access to arm-based technology for nvidia's competitors and maybe as we've talked about before having them funnel business to intel's foundry actually we've talked about the us government enticing apple to do so but it could also entice nvidia's competitors to do so propping up intel's foundry business which is clearly starting from ground zero and is going to need help outside of intel's own semiconductor manufacturing internally look we don't have any inside information as to what's happening behind the scenes with the us government and so forth but on its earning call on its earnings call nvidia said they're working with regulators that are on track to complete the deal in early 2022. we'll see okay that's it for today thank you to david floyer who co-created this episode with me and remember i publish each week on wikibon.com and siliconangle.com these episodes they're all available as podcasts all you're going to do is search breaking analysis podcast and you can always connect with me on twitter at dvalante or email me at david.valante siliconangle.com i always appreciate the comments on linkedin and in the clubhouse please follow me so you can be notified when we start a room and riff on these topics and don't forget to check out etr.plus for all the survey data this is dave vellante for the cube insights powered by etr be well and we'll see you next time [Music] you

(upbeat music) >> In 1946, the acerbic manager of the Dodgers, Leo the Lip Durocher famously said of baseball, great Mel Ott who was player manager of the Giants at the time. You know what happens to nice guys. They finished in last place. The phrase nice guys finish last was born. It became popular outside of baseball. Well joining me today is someone who was a consummate gentlemen and a nice guy who proves that idiom absolutely isn't true at all. He's also written a new book "Play nice and Win" Michael Dell chairman and CEO of Dell technologies, welcome back to the CUBE. >> Thank you very much, Dave, always great to be with you. Wonderful to be on the CUBE and thanks for your great coverage of Dell technologies world. >> Yeah. We're very excited to be covering the virtual version this year, next year we're back face to face I'm Sure. And we're going to talk about your book but I want to start by asking you to comment on the past 12 months, how are you going to remember 2020? >> I'm going to remember it by the resiliency of the world and our team, the adaptability the acceleration of digital transformation which is pretty amazing around the world. The vital role that technology played in addressing some of the biggest challenges, whether it was the creation of vaccines or, you know, decoding the virus itself or just addressing all the challenges that the world had. You know, I think it's a game changer in terms of disease identification and how we prevent these kinds of things going forward. You know, there's still a long way to go in terms of how do we get 7.5 billion people vaccinated and safe. I also think it exposed, you know some of the fault lines in our society. And that's a great learning for all of us in terms of access to healthcare and education and, you know, the digital resources that power the world. And so, yeah, those are some of the things that really stand out for me. >> Well, I mean, I think leaders like yourself and position of influence, absolutely passionate about some of those changes that we see coming in society. So hopefully we'll have time to talk about that but I wanted to get into the business. I think a lot of people, myself included felt that 2020 was going to be a down year for big tech companies like yours and that relied heavily on selling products that data centers and central offices but the remote work trend and the laptop, boom offset, some of those on-prem softness and headwinds combined with VMware the financial performance of Dell technologies was actually quite amazing. Why were you able to do so well last year? >> Well, first of all, you're right. We did, we had record pretty much everything record revenues, record operating income, record cashflow and be also paid down a record amount of debt. And so I think the strength and resiliency of our supply chain, as well as the broad diversified nature of what we provide our customers continue to serve us very well as they moved to this sort of do anything from anywhere in the world. And it continues the first part of this year, business is very strong >> You know, a few weeks ago, of course you officially announced the spinoff of Dell technologies. Wasn't a huge surprise but the 81% equity ownership of VMware are you worried about untethering VMware from Dell or maybe you can share more on what this means for the future of, your two companies and your customers. >> Right? So, I think this will drive additional growth opportunities for both Dell Tech and VMware, while it unlocks a lot of value for our stakeholders. What we've done is to formalize the commercial relationship into a series of agreements and those are unique and differentiated and they provide lots of flexibility and we've driven a tremendous amount of innovation together and that's going to continue and it will, one of the things we said back in 2015 you'll remember is our commitment to keep the VMware ecosystem open and independent and working across the whole industry. We've done that. You'll continue to see us innovate together with Edge solutions, certainly all the great work we've done with VxRail SD LAN, you know Tanzu creates this platform to modernize applications and VMware Cloud and Dell technologies are the easy path to a multi-cloud architecture. And, that continues to work super well and is not going to be slowed down at all. So... and of course, I'll continue to be a chairman of both companies and we're not selling VMware we're distributing our ownership to our shareholders. >> Well, of course, Dell is the largest sort channel if you will, for VMware. So that's ... you guys got a tight relationship but I want to ask you about digital transformation and everybody talked about it pre COVID but nobody really knew exactly what it was but COVID sort of brought that into focus very quickly. If you weren't a digital business, you were out of business. So going forward, how do you see that whole digital transformation playing out? >> You know I think the plot of any company is to figure out how it can use its data and turn that into insights and outcomes and better results and ultimately competitive advantage faster. And as you said, you know, if it's not able to do that, it's probably going to go out of business. And that agenda just got massively accelerated because it was kind of digital was sort of the only thing that worked during this, this past period. So every organization has figured out that technology is not the IT department, it's actually the fulcrum of progress in the entire company. And so we're seeing sort of across the board a dramatic acceleration in the investment in digital technologies, you know, Edge is growing very fast. I think 5G just accelerates this and, you know you're seeing it in all the demand trends. It's quite positive and, you know, I think you'll see even a more rapid separation from those companies that are able to take advantage of this and quickly adjust their businesses their organizations, and those that are >> You better hop on board or get left behind, you know, the Edge. You mentioned the Edge it's a little bit like digital transformation, you know kind of pre COVID and even post COVID. It means a lot of things to a lot of different people but the telecoms transformation and 5G they have there certainly real. How do you see the Edge? >> You know, the Edges is ... think of it as actually the real world, right? It's, not a data center sitting in the center of the universe somewhere. And look today, you know only 10% of data is processed outside of the data center, but, you know, it's estimated by 2025 you got 75% of enterprise data will be processed outside of a traditional data center or a Cloud. And so as everything becomes intelligent connected 5G accelerates that it's going to be a huge acceleration of this whole process of digital transformation. And you know, again, think about this. I mean, the cost of making something intelligent used to be really expensive. Now it's asymptotically approaching zero. And of course all those things are connected. They're talking to each other and exactly what does this mean for every industry. Nobody's really quite sure and not everything is going to work, but, you know we're seeing it in manufacturing, in retail, in healthcare and the growth on the Edge is really accelerating in a meaningful way. And it's not so much about, you know people talking people with machines, we know how to do that. Now it's about the thing right And, you know you've got like 200 billion arm processors, you know out there in the last couple of years, all those things talking to the other things, generating data it happens in the real world. That's what the Edge is. >> Yeah as you know, we're a big fans of the arm model. And I think it just presents huge opportunities for companies like Dell. I want to ask you about Cloud. And I have to say, I think, you know companies like Dell have been maybe a little bit defensive over the last several years when it comes to Cloud but I think you starting to see the Cloud as a gift with all that CAPEX that's being built out by these hyperscalers. You know, thank you. It seems to me, you can build on top of that. How are you thinking about the Cloud as an opportunity for you and your customers especially as the definition of Cloud evolves? >> Well, first, you know, what we see is and the Edge is kind of the third place or the third premise, right? You got Clouds in the public form, you've got the Colo which is really growing fast and, you know the private hybrid Clouds, and now you've got the Edge. And so you've got infrastructure all over the place with Edge being the fastest growing. You know, one of the big things we see is that customers want a consistent way to operate and execute across that whole platform. And, you know, one of the other things that we've been focused on at Dell technologies is how can we move our business to more of a service and subscription on demand and provide customers that flexibility to to pay as they consume. And so, to some extent this is an evolution of, you know, products to services to managed services, to everything as a service. And so, you know, looking at our balance sheet you'll see over $40 billion in remaining performance obligations as we moved the business to that kind of model and it's been growing double digits for several quarters in a row. And so, you know, we're embracing Cloud and on-demand, and as a service, and obviously here at Dell technologies world we're talking a lot about Apex and our continuing initiatives to move our whole business in that direction. >> Yeah. Apex is a real accelerator for that model. I want to switch topics a little bit. I got a long list of things I want to talk about ESG, sustainability, inclusion, you know, is another topic that, that I'm interested in. I want it. And I said before, people like yourself in a position of influence to influence public policy and obviously the employees and your ecosystem why is it not just the right thing to do? Why is... why are those things good business, Michael? >> Well, it's good business because people want to be part of something that is important and purposeful. You know, it's not just make a profit and earn a living right? You know, people want to be inspired and feel that they're part of something special. And look, I think if you look at the positive changes that have occurred in the world certainly you could turn on the news and see the horrible things that happened in the last 24 hours or something like that. But if you step back and think about the amazing progress that's happened in the last several decades, you know a lot of it's been driven by technology and by businesses that have stepped up and made a difference and made commitments. And, you know, we're one of those companies that has made a series of commitments you know, 10 years ago, we set out with our 2020 goals. We accomplished significant majority of those retired those. Now we set out our progress made real 2030 goals all around the ESD themes. And it's not only the right thing to do but it is good for business. It inspires our team members, our customers and I think initiatives like progress made real at Dell and thousands of other companies. Ultimately, those are the things that are going to drive progress forward. I believe, you know, more so than government edicts or regulation, those can play a role. But I think, companies voluntarily driving things like the circular economy and how we include everyone in our business and provide opportunities for everyone to succeed no matter where they come from. I think those are the things that are really going to drive the world forward. >> Well, I want to ask you about public policy because as you say, it's not just the government, but of course sometimes the government can get in the way. You're seeing a lot of vitriol around Val break up big tech but the same time, you're seeing the US government and the EU very willing to help out with the semiconductor competitiveness in the like I know you were tapped with the new administration President Biden, tapping, you know, the best minds in tech and you were asked to part sort of participate give feedback. What can you tell us about, you know your advice to the US government? >> Well, you know, lots of great discussion with the new administration and it's a delight to see that they're focused on semiconductors and sort of the industries of the future. This is a big deal. I mean, you know, we've got some big global competitors out there other nations that are with a deterministic strategy very focused on the industries of the future. But US, you know if you think about the atomic age and, you know the Apollo missions that created the whole semiconductor industry ARPANET and ultimately the Internet and that kind of stopped right there, you know, there wasn't as much government investment in some of those big R and D initiatives that really drove an enormous creation of industries and success for the United States and its citizens. And so I think focusing on semiconductors and how you build the infrastructure of the future really important for the United States to continue to be a leader in that you know, we were, you know, producing a one point about 37% of the world's semiconductors. It's now down to 12% and dropping and really important that more investments are made in that area. It's a combination of capital, talent, you know education knowledge, and also, you know, the policies that promote the development of these kinds of businesses. >> Yah well, Pat's got a very big challenge ahead of them. And so that's why but we've said Intel's too strategic to fail in our view but I wanted to plug your book a little bit. My former boss, you and I have talked about this. He was also a gentleman who proved Leo Durocher wrong. He was very nice guy, but also a winner, Play Nice But Win, why did you decide to write another book? >> Well, you know, Dave, a lot has happened in the last 20 years and especially the last nine or so years since we went private and, you know merged with EMC and VMware and went public again. And, you know, I'd say we... first of all, you know when I wrote the first book in 1998 I wasn't comfortable disclosing a lot. And, and I wasn't vulnerable enough and didn't feel, you know, able to do that. Now I do, you know, I'm older, you know hopefully a little wiser. And so I think everybody's going to like hearing some of the fun stories about not only my childhood but you know, the dorm room and beyond, and leading up to, you know the pivotal changes that have occurred the last decade my alligator wrestling with Carl Icahn and other, you know there's lots of fun stories in there. I got arrested one time. It was only for speeding tickets, don't worry but you know, lots of fun. I'm really looking forward to the book coming out and being able to talk about it. >> I can't wait. You know, I've said many times anybody who could beat the great icon is interesting to me. I wanted to ask you, I mentioned my old boss, Pat McGovern. I used to say to them all the time, "Pat how come you don't buy more companies?" And he'd say," Dave, you know the vast majority of acquisitions and mergers they failed to meet their objectives." Did you ever imagine, I mean... I did the EMC acquisition. Did... how could it not have exceeded your expectations? I wonder if you could give us your final thoughts on that. >> You know, and I talk about this a lot in the book. I mean, these are kind of the ultimate considered decisions. And in the case of the EMC combination it was something that we had thought about going back to 2008, 2009. And then, you know, started thinking about it in 2014 worked on it for a full year before it got announced in 2015 and finally closed in 2016. But yeah, I mean, you know, we thought it would be great. It turned out to be even better than We thought the revenue synergies were far greater. The teams were quite energized. Customers liked what we were providing and you know it's ... and, of course the markets were supportive Right? You know, we were paying close attention to interest rates and how we could structure the merger in a attractive way. And, you know, thank goodness, lots of hard work lots of determination, you know, it's worked out quite well. >> Yeah, great commitment from the Dell team as well. Congratulations on that. Go ahead, please. >> And any adventure continues right? It's...( both chuckles) >> I can't wait to see the next chapter and I can't wait to get the book, but congratulations on that, all your tremendous success you're you are a winner and a gentleman and a friend of the CUBE, Michael Dell. Thanks so much. >> Thank you so much Dave. >> And thank you for watching. And this is the CUBE continuous coverage of Dell tech world 2021, the virtual edition. Keep it right there, right back. (upbeat music)

>> From theCUBE studios in Palo Alto, in Boston bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante >> The latest Arm Neoverse announcement further cements our opinion that it's architecture business model and ecosystem execution are defining a new era of computing and leaving Intel in it's dust. We believe the company and its partners have at least a two year lead on Intel and are currently in a far better position to capitalize on a major waves that are driving the technology industry and its innovation. To compete our view is that Intel needs a new strategy. Now, Pat Gelsinger is bringing that but they also need financial support from the US and the EU governments. Pat Gelsinger was just noted as asking or requesting from the EU government $9 billion, sorry, 8 billion euros in financial support. And very importantly, Intel needs a volume for its new Foundry business. And that is where Apple could be a key. Hello, everyone. And welcome to this week's weekly bond Cube insights powered by ETR. In this breaking analysis will explain why Apple could be the key to saving Intel and America's semiconductor industry leadership. We'll also further explore our scenario of the evolution of computing and what will happen to Intel if it can't catch up. Here's a hint it's not pretty. Let's start by looking at some of the key assumptions that we've made that are informing our scenarios. We've pointed out many times that we believe Arm wafer volumes are approaching 10 times those of x86 wafers. This means that manufacturers of Arm chips have a significant cost advantage over Intel. We've covered that extensively, but we repeat it because when we see news reports and analysis and print it's not a factor that anybody's highlighting. And this is probably the most important issue that Intel faces. And it's why we feel that Apple could be Intel's savior. We'll come back to that. We've projected that the chip shortage will last no less than three years, perhaps even longer. As we reported in a recent breaking analysis. Well, Moore's law is waning. The result of Moore's law, I.e the doubling of processor performance every 18 to 24 months is actually accelerating. We've observed and continue to project a quadrupling of performance every two years, breaking historical norms. Arm is attacking the enterprise and the data center. We see hyperscalers as the tip of their entry spear. AWS's graviton chip is the best example. Amazon and other cloud vendors that have engineering and software capabilities are making Arm-based chips capable of running general purpose applications. This is a huge threat to x86. And if Intel doesn't quickly we believe Arm will gain a 50% share of an enterprise semiconductor spend by 2030. We see the definition of Cloud expanding. Cloud is no longer a remote set of services, in the cloud, rather it's expanding to the edge where the edge could be a data center, a data closet, or a true edge device or system. And Arm is by far in our view in the best position to support the new workloads and computing models that are emerging as a result. Finally geopolitical forces are at play here. We believe the U S government will do, or at least should do everything possible to ensure that Intel and the U S chip industry regain its leadership position in the semiconductor business. If they don't the U S and Intel could fade to irrelevance. Let's look at this last point and make some comments on that. Here's a map of the South China sea in a way off in the Pacific we've superimposed a little pie chart. And we asked ourselves if you had a hundred points of strategic value to allocate, how much would you put in the semiconductor manufacturing bucket and how much would go to design? And our conclusion was 50, 50. Now it used to be because of Intel's dominance with x86 and its volume that the United States was number one in both strategic areas. But today that orange slice of the pie is dominated by TSMC. Thanks to Arm volumes. Now we've reported extensively on this and we don't want to dwell on it for too long but on all accounts cost, technology, volume. TSMC is the clear leader here. China's president Xi has a stated goal of unifying Taiwan by China's Centennial in 2049, will this tiny Island nation which dominates a critical part of the strategic semiconductor pie, go the way of Hong Kong and be subsumed into China. Well, military experts say it was very hard for China to take Taiwan by force, without heavy losses and some serious international repercussions. The US's military presence in the Philippines and Okinawa and Guam combined with support from Japan and South Korea would make it even more difficult. And certainly the Taiwanese people you would think would prefer their independence. But Taiwanese leadership, it ebbs and flows between those hardliners who really want to separate and want independence and those that are more sympathetic to China. Could China for example, use cyber warfare to over time control the narrative in Taiwan. Remember if you control the narrative you can control the meme. If you can crawl the meme you control the idea. If you control the idea, you control the belief system. And if you control the belief system you control the population without firing a shot. So is it possible that over the next 25 years China could weaponize propaganda and social media to reach its objectives with Taiwan? Maybe it's a long shot but if you're a senior strategist in the U S government would you want to leave that to chance? We don't think so. Let's park that for now and double click on one of our key findings. And that is the pace of semiconductor performance gains. As we first reported a few weeks ago. Well, Moore's law is moderating the outlook for cheap dense and efficient processing power has never been better. This slideshows two simple log lines. One is the traditional Moore's law curve. That's the one at the bottom. And the other is the current pace of system performance improvement that we're seeing measured in trillions of operations per second. Now, if you calculate the historical annual rate of processor performance improvement that we saw with x86, the math comes out to around 40% improvement per year. Now that rate is slowing. It's now down to around 30% annually. So we're not quite doubling every 24 months anymore with x86 and that's why people say Moore's law is dead. But if you look at the (indistinct) effects of packaging CPU's, GPU's, NPUs accelerators, DSPs and all the alternative processing power you can find in SOC system on chip and eventually system on package it's growing at more than a hundred percent per annum. And this means that the processing power is now quadrupling every 24 months. That's impressive. And the reason we're here is Arm. Arm has redefined the core process of model for a new era of computing. Arm made an announcement last week which really recycle some old content from last September, but it also put forth new proof points on adoption and performance. Arm laid out three components and its announcement. The first was Neoverse version one which is all about extending vector performance. This is critical for high performance computing HPC which at one point you thought that was a niche but it is the AI platform. AI workloads are not a niche. Second Arm announced the Neoverse and two platform based on the recently introduced Arm V9. We talked about that a lot in one of our earlier Breaking Analysis. This is going to performance boost of around 40%. Now the third was, it was called CMN-700 Arm maybe needs to work on some of its names, but Arm said this is the industry's most advanced mesh interconnect. This is the glue for the V1 and the N2 platforms. The importance is it allows for more efficient use and sharing of memory resources across components of the system package. We talked about this extensively in previous episodes the importance of that capability. Now let's share with you this wheel diagram underscores the completeness of the Arm platform. Arms approach is to enable flexibility across an open ecosystem, allowing for value add at many levels. Arm has built the architecture in design and allows an open ecosystem to provide the value added software. Now, very importantly, Arm has created the standards and specifications by which they can with certainty, certify that the Foundry can make the chips to a high quality standard, and importantly that all the applications are going to run properly. In other words, if you design an application, it will work across the ecosystem and maintain backwards compatibility with previous generations, like Intel has done for years but Arm as we'll see next is positioning not only for existing workloads but also the emerging high growth applications. To (indistinct) here's the Arm total available market as we see it, we think the end market spending value of just the chips going into these areas is $600 billion today. And it's going to grow to 1 trillion by 2030. In other words, we're allocating the value of the end market spend in these sectors to the marked up value of the Silicon as a percentage of the total spend. It's enormous. So the big areas are Hyperscale Clouds which we think is around 20% of this TAM and the HPC and AI workloads, which account for about 35% and the Edge will ultimately be the largest of all probably capturing 45%. And these are rough estimates and they'll ebb and flow and there's obviously some overlap but the bottom line is the market is huge and growing very rapidly. And you see that little red highlighted area that's enterprise IT. Traditional IT and that's the x86 market in context. So it's relatively small. What's happening is we're seeing a number of traditional IT vendors, packaging x86 boxes throwing them over the fence and saying, we're going after the Edge. And what they're doing is saying, okay the edge is this aggregation point for all these end point devices. We think the real opportunity at the Edge is for AI inferencing. That, that is where most of the activity and most of the spending is going to be. And we think Arm is going to dominate that market. And this brings up another challenge for Intel. So we've made the point a zillion times that PC volumes peaked in 2011. And we saw that as problematic for Intel for the cost reasons that we've beat into your head. And lo and behold PC volumes, they actually grew last year thanks to COVID and we'll continue to grow it seems for a year or so. Here's some ETR data that underscores that fact. This chart shows the net score. Remember that's spending momentum it's the breakdown for Dell's laptop business. The green means spending is accelerating and the red is decelerating. And the blue line is net score that spending momentum. And the trend is up and to the right now, as we've said this is great news for Dell and HP and Lenovo and Apple for its laptops, all the laptops sellers but it's not necessarily great news for Intel. Why? I mean, it's okay. But what it does is it shifts Intel's product mix toward lower margin, PC chips and it squeezes Intel's gross margins. So the CFO has to explain that margin contraction to wall street. Imagine that the business that got Intel to its monopoly status is growing faster than the high margin server business. And that's pulling margins down. So as we said, Intel is fighting a war on multiple fronts. It's battling AMD in the core x86 business both PCs and servers. It's watching Arm mop up in mobile. It's trying to figure out how to reinvent itself and change its culture to allow more flexibility into its designs. And it's spinning up a Foundry business to compete with TSMC. So it's got to fund all this while at the same time propping up at stock with buybacks Intel last summer announced that it was accelerating it's $10 billion stock buyback program, $10 billion. Buy stock back, or build a Foundry which do you think is more important for the future of Intel and the us semiconductor industry? So Intel, it's got to protect its past while building his future and placating wall street all at the same time. And here's where it gets even more dicey. Intel's got to protect its high-end x86 business. It is the cash cow and funds their operation. Who's Intel's biggest customer Dell, HP, Facebook, Google Amazon? Well, let's just say Amazon is a big customer. Can we agree on that? And we know AWS is biggest revenue generator is EC2. And EC2 was powered by microprocessors made from Intel and others. We found this slide in the Arm Neoverse deck and it caught our attention. The data comes from a data platform called lifter insights. The charts show, the rapid growth of AWS is graviton chips which are they're custom designed chips based on Arm of course. The blue is that graviton and the black vendor A presumably is Intel and the gray is assumed to be AMD. The eye popper is the 2020 pie chart. The instant deployments, nearly 50% are graviton. So if you're Pat Gelsinger, you better be all over AWS. You don't want to lose this customer and you're going to do everything in your power to keep them. But the trend is not your friend in this account. Now the story gets even gnarlier and here's the killer chart. It shows the ISV ecosystem platforms that run on graviton too, because AWS has such good engineering and controls its own stack. It can build Arm-based chips that run software designed to run on general purpose x86 systems. Yes, it's true. The ISV, they got to do some work, but large ISV they have a huge incentives because they want to ride the AWS wave. Certainly the user doesn't know or care but AWS cares because it's driving costs and energy consumption down and performance up. Lower cost, higher performance. Sounds like something Amazon wants to consistently deliver, right? And the ISV portfolio that runs on our base graviton and it's just going to continue to grow. And by the way, it's not just Amazon. It's Alibaba, it's Oracle, it's Marvell. It's 10 cents. The list keeps growing Arm, trotted out a number of names. And I would expect over time it's going to be Facebook and Google and Microsoft. If they're not, are you there? Now the last piece of the Arm architecture story that we want to share is the progress that they're making and compare that to x86. This chart shows how Arm is innovating and let's start with the first line under platform capabilities. Number of cores supported per die or, or system. Now die is what ends up as a chip on a small piece of Silicon. Think of the die as circuit diagram of the chip if you will, and these circuits they're fabricated on wafers using photo lithography. The wafers then cut up into many pieces each one, having a chip. Each of these pieces is the chip. And two chips make up a system. The key here is that Arm is quadrupling the number of cores instead of increasing thread counts. It's giving you cores. Cores are better than threads because threads are shared and cores are independent and much easier to virtualize. This is particularly important in situations where you want to be as efficient as possible sharing massive resources like the Cloud. Now, as you can see in the right hand side of the chart under the orange Arm is dramatically increasing the amount of capabilities compared to previous generations. And one of the other highlights to us is that last line that CCIX and CXL support again Arm maybe needs to name these better. These refer to Arms and memory sharing capabilities within and between processors. This allows CPU's GPU's NPS, et cetera to share resources very often efficiently especially compared to the way x86 works where everything is currently controlled by the x86 processor. CCIX and CXL support on the other hand will allow designers to program the system and share memory wherever they want within the system directly and not have to go through the overhead of a central processor, which owns the memory. So for example, if there's a CPU, GPU, NPU the CPU can say to the GPU, give me your results at a specified location and signal me when you're done. So when the GPU is finished calculating and sending the results, the GPU just signals the operation is complete. Versus having to ping the CPU constantly, which is overhead intensive. Now composability in that chart means the system it's a fixed. Rather you can programmatically change the characteristics of the system on the fly. For example, if the NPU is idle you can allocate more resources to other parts of the system. Now, Intel is doing this too in the future but we think Arm is way ahead. At least by two years this is also huge for Nvidia, which today relies on x86. A major problem for Nvidia has been coherent memory management because the utilization of its GPU is appallingly low and it can't be easily optimized. Last week, Nvidia announced it's intent to provide an AI capability for the data center without x86 I.e using Arm-based processors. So Nvidia another big Intel customer is also moving to Arm. And if it's successful acquiring Arm which is still a long shot this trend is only going to accelerate. But the bottom line is if Intel can't move fast enough to stem the momentum of Arm we believe Arm will capture 50% of the enterprise semiconductor spending by 2030. So how does Intel continue to lead? Well, it's not going to be easy. Remember we said, Intel, can't go it alone. And we posited that the company would have to initiate a joint venture structure. We propose a triumvirate of Intel, IBM with its power of 10 and memory aggregation and memory architecture And Samsung with its volume manufacturing expertise on the premise that it coveted in on US soil presence. Now upon further review we're not sure the Samsung is willing to give up and contribute its IP to this venture. It's put a lot of money and a lot of emphasis on infrastructure in South Korea. And furthermore, we're not convinced that Arvind Krishna who we believe ultimately made the call to Jettisons. Jettison IBM's micro electronics business wants to put his efforts back into manufacturing semi-conductors. So we have this conundrum. Intel is fighting AMD, which is already at seven nanometer. Intel has a fall behind in process manufacturing which is strategically important to the United States it's military and the nation's competitiveness. Intel's behind the curve on cost and architecture and is losing key customers in the most important market segments. And it's way behind on volume. The critical piece of the pie that nobody ever talks about. Intel must become more price and performance competitive with x86 and bring in new composable designs that maintain x86 competitive. And give the ability to allow customers and designers to add and customize GPU's, NPUs, accelerators et cetera. All while launching a successful Foundry business. So we think there's another possibility to this thought exercise. Apple is currently reliant on TSMC and is pushing them hard toward five nanometer, in fact sucking up a lot of that volume and TSMC is maybe not servicing some other customers as well as it's servicing Apple because it's a bit destructive, it is distracted and you have this chip shortage. So Apple because of its size gets the lion's share of the attention but Apple needs a trusted onshore supplier. Sure TSMC is adding manufacturing capacity in the US and Arizona. But back to our precarious scenario in the South China sea. Will the U S government and Apple sit back and hope for the best or will they hope for the best and plan for the worst? Let's face it. If China gains control of TSMC, it could block access to the latest and greatest process technology. Apple just announced that it's investing billions of dollars in semiconductor technology across the US. The US government is pressuring big tech. What about an Apple Intel joint venture? Apple brings the volume, it's Cloud, it's Cloud, sorry. It's money it's design leadership, all that to the table. And they could partner with Intel. It gives Intel the Foundry business and a guaranteed volume stream. And maybe the U S government gives Apple a little bit of breathing room and the whole big up big breakup, big tech narrative. And even though it's not necessarily specifically targeting Apple but maybe the US government needs to think twice before it attacks big tech and thinks about the long-term strategic ramifications. Wouldn't that be ironic? Apple dumps Intel in favor of Arm for the M1 and then incubates, and essentially saves Intel with a pipeline of Foundry business. Now back to IBM in this scenario, we've put a question mark on the slide because maybe IBM just gets in the way and why not? A nice clean partnership between Intel and Apple? Who knows? Maybe Gelsinger can even negotiate this without giving up any equity to Apple, but Apple could be a key ingredient to a cocktail of a new strategy under Pat Gelsinger leadership. Gobs of cash from the US and EU governments and volume from Apple. Wow, still a long shot, but one worth pursuing because as we've written, Intel is too strategic to fail. Okay, well, what do you think? You can DM me @dvellante or email me at david.vellante@siliconangle.com or comment on my LinkedIn post. Remember, these episodes are all available as podcasts so please subscribe wherever you listen. I publish weekly on wikibon.com and siliconangle.com. And don't forget to check out etr.plus for all the survey analysis. And I want to thank my colleague, David Floyer for his collaboration on this and other related episodes. This is Dave Vellante for theCUBE insights powered by ETR. Thanks for watching, be well, and we'll see you next time. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is Braking Analysis with Dave Vellante. >> Intel's big announcement this week underscores the threat that the United States faces from China. The US needs to lead in semiconductor design and manufacturing. And that lead is slipping because Intel has been fumbling the ball over the past several years, a mere two months into the job, new CEO Pat Gelsinger wasted no time in setting a new course for perhaps, the most strategically important American technology company. We believe that Gelsinger has only shown us part of his plan. This is the beginning of a long and highly complex journey. Despite Gelsinger's clear vision, his deep understanding of technology and execution ethos, in order to regain its number one position, Intel we believe we'll need to have help from partners, competitors and very importantly, the US government. Hello everyone and welcome to this week's Wikibon CUBE insights powered by ETR. In this breaking analysis we'll peel the onion Intel's announcement of this week and explain why we're perhaps not as sanguine as was Wall Street on Intel's prospects. And we'll lay out what we think needs to take place for Intel to once again, become top gun and for us to gain more confidence. By the way this is the first time we're broadcasting live with Braking Analysis. We're broadcasting on the CUBE handles on Twitch, Periscope and YouTube and going forward we'll do this regularly as a live program and we'll bring in the community perspective into the conversation through chat. Now you may recall that in January, we kind of dismissed analysis that said Intel didn't have to make any major strategic changes to its business when they brought on Pat Gelsinger. Rather we said the exact opposite. Our view at time was that the root of Intel's problems could be traced to the fact that it wasn't no longer the volume leader. Because mobile volumes dwarf those of x86. As such we said that Intel couldn't go up the learning curve for next gen technologies as fast as its competitors and it needed to shed its dogma of being highly vertically integrated. We said Intel needed to more heavily leverage outsourced foundries. But more specifically, we suggested that in order for Intel to regain its volume lead, it needed to, we said at the time, spin out its manufacturing, create a joint venture sure with a volume leader, leveraging Intel's US manufacturing presence. This, we still believe with some slight refreshes to our thinking based on what Gelsinger has announced. And we'll talk about that today. Now specifically there were three main pieces and a lot of details to Intel's announcement. Gelsinger made it clear that Intel is not giving up its IDM or integrated device manufacturing ethos. He called this IDM 2.0, which comprises Intel's internal manufacturing, leveraging external Foundries and creating a new business unit called Intel Foundry Services. It's okay. Gelsinger said, "We are not giving up on integrated manufacturing." However, we think this is somewhat nuanced. Clearly Intel can't, won't and shouldn't give up on IDM. However, we believe Intel is entering a new era where it's giving designers more choice. This was not explicitly stated. However we feel like Intel's internal manufacturing arm will have increased pressure to serve its designers in a more competitive manner. We've already seen this with Intel finally embracing EUV or extreme ultraviolet lithography. Gelsinger basically said that Intel didn't lean into EUV early on and that it created more complexity in its 10 nanometer process, which dominoed into seven nanometer and as you know the rest of the story and Intel's delays. But since mid last year, it's embraced the technology. Now as a point of reference, Samsung started applying EUV for its seven nanometer technology in 2018. And it began shipping in early 2020. So as you can see, it takes years to get this technology into volume production. The point is that Intel realizes it needs to be more competitive. And we suspect, it will give more freedom to designers to leverage outsource manufacturing. But Gelsinger clearly signaled that IDM is not going away. But the really big news is that Intel is setting up a new division with a separate PNL that's going to report directly to Pat. Essentially it's hanging out a shingle and saying, we're open for business to make your chips. Intel is building two new Fabs in Arizona and investing $20 billion as part of this initiative. Now well Intel has tried this before earlier last decade. Gelsinger says that this time we're serious and we're going to do it right. We'll come back to that. This organizational move while not a spin out or a joint venture, it's part of the recipe that we saw as necessary for Intel to be more competitive. Let's talk about why Intel is doing this. Look at lots has changed in the world of semiconductors. When you think about it back when Pat was at Intel in the '90s, Intel was the volume leader. It crushed the competition with x86. And the competition at the time was coming from risk chips. And when Apple changed the game with iPod and iPhone and iPad, the volume equation flipped to mobile. And that led to big changes in the industry. Specifically, the world started to separate design from manufacturing. We now see firms going from design to tape out in 12 months versus taking three years. A good example is Tesla and his deal with ARM and Samsung. And what's happened is Intel has gone from number one in Foundry in terms of clock speed, wafer density, volume, lowest cost, highest margin to falling behind. TSMC, Samsung and alternative processor competitors like NVIDIA. Volume is still the maker of kings in this business. That hasn't changed and it confers advantage in terms of cost, speed and efficiency. But ARM wafer volumes, we estimate are 10x those of x86. That's a big change since Pat left Intel more than a decade ago. There's also a major chip shortage today. But you know this time, it feels a little different than the typical semiconductor boom and bust cycles. Semiconductor consumption is entering a new era and new use cases emerging from automobiles to factories, to every imaginable device piece of equipment, infrastructure, silicon is everywhere. But the biggest threat of all is China. China wants to be self-sufficient in semiconductors by 2025. It's putting approximately $60 billion into new chip Fabs, and there's more to come. China wants to be the new economic leader of the world and semiconductors are critical to that goal. Now there are those poopoo the China threat. This recent article from Scott Foster lays out some really good information. But the one thing that caught our attention is a statement that China's semiconductor industry is nowhere near being a major competitor in the global market. Let alone an existential threat to the international order and the American way of life. I think Scotty is stuck in the engine room and can't see the forest of the trees, wake up. Sure. You can say China is way behind. Let's take an example. NAND. Today China is at about 64 3D layers whereas Micron they're at 172. By 2022 China's going to be at 128. Micron, it's going to be well over 200. So what's the big deal? We say talk to us in 2025 because we think China will be at parody. That's just one example. Now the type of thinking that says don't worry about China and semi's reminds me of the epic lecture series that Clay Christiansen gave as a visiting professor at Oxford University on the history of, and the economics of the steel industry. Now if you haven't watched this series, you should. Basically Christiansen took the audience through the dynamics of steel production. And he asked the question, "Who told the steel manufacturers that gross margin was the number one measure of profitability? Was it God?" he joked. His point was, when new entrance came into the market in the '70s, they were bottom feeders going after the low margin, low quality, easiest to make rebar sector. And the incumbents nearly pulled back and their mix shifted to higher margin products and their gross margins went up and life was good. Until they lost the next layer. And then the next, and then the next, until it was game over. Now, one of the things that got lost in Pat's big announcement on the 23rd of March was that Intel guided the street below consensus on revenue and earnings. But the stock went up the next day. Now when asked about gross margin in the Q&A segment of the announcement, yes, gross margin is a if not the key metric in semi's in terms of measuring profitability. When asked Intel CFO George Davis explained that with the uptick in PCs last year there was a product shift to the lower margin PC sector and that put pressure on gross margins. It was a product mix thing. And revenue because PC chips are less expensive than server chips was affected as were margins. Now we shared this chart in our last Intel update showing, spending momentum over time for Dell's laptop business from ETR. And you can see in the inset, the unit growth and the market data from IDC, yes, Dell's laptop business is growing, everybody's laptop business is growing. Thank you COVID. But you see the numbers from IDC, Gartner, et cetera. Now, as we pointed out last time, PC volumes had peaked in 2011 and that's when the long arm of rights law began to eat into Intel's dominance. Today ARM wafer production as we said is far greater than Intel's and well, you know the story. Here's the irony, the very bucket that conferred volume adventures to Intel PCs, yes, it had a slight uptick last year, which was great news for Dell. But according to Intel it pulled down its margins. The point is Intel is loving the high end of the market because it's higher margin and more profitable. I wonder what Clay Christensen would say to that. Now there's more to this story. Intel's CFO blame the supply constraints on Intel's revenue and profit pressures yet AMD's revenue and profits are booming. So RTSMCs. Only Intel can't seem to thrive when there's this massive chip shortage. Now let's get back to Pat's announcement. Intel is for sure, going forward investing $20 billion in two new US-based fabrication facilities. This chart shows Intel's investments in US R&D, US CapEx and the job growth that's created as a result, as well as R&D and CapEx investments in Ireland and Israel. Now we added the bar on the right hand side from a Wall Street journal article that compares TSMC CapEx in the dark green to that of Intel and the light green. You can see TSMC surpass the CapEx investment of Intel in 2015, and then Intel took the lead back again. And in 2017 was, hey it on in 2018. But last year TSMC took the lead, again. And appears to be widening that lead quite substantially. Leading us to our conclusion that this will not be enough. These moves by Intel will not be enough. They need to do more. And a big part of this announcement was partnerships and packaging. Okay. So here's where it gets interesting. Intel, as you may know was late to the party with SOC system on a chip. And it's going to use its packaging prowess to try and leap frog the competition. SOC bundles things like GPU, NPU, DSUs, accelerators caches on a single chip. So better use the real estate if you will. Now Intel wants to build system on package which will dis-aggregate memory from compute. Now remember today, memory is very poorly utilized. What Intel is going to do is to create a package with literally thousands of nodes comprising small processors, big processors, alternative processors, ARM processors, custom Silicon all sharing a pool of memory. This is a huge innovation and we'll come back to this in a moment. Now as part of the announcement, Intel trotted out some big name customers, prospects and even competitors that it wants to turn into prospects and customers. Amazon, Google, Satya Nadella gave a quick talk from Microsoft to Cisco. All those guys are designing their own chips as does Ericsson and look even Qualcomm is on the list, a competitor. Intel wants to earn the right to make chips for these firms. Now many on the list like Microsoft and Google they'd be happy to do so because they want more competition. And Qualcomm, well look if Intel can do a good job and be a strong second sourced, why not? Well, one reason is they compete aggressively with Intel but we don't like Intel so much but it's very possible. But the two most important partners on this slide are one IBM and two, the US government. Now many people were going to gloss over IBM in this announcement, but we think it's one of the most important pieces of the puzzle. Yes. IBM and semiconductors. IBM actually has some of the best semiconductor technology in the world. It's got great architecture and is two to three years ahead of Intel with POWER10. Yes, POWER. IBM is the world's leader in terms of dis-aggregating compute from memory with the ability to scale to thousands of nodes, sound familiar? IBM leads in power density, efficiency and it can put more stuff closer together. And it's looking now at a 20x increase in AI inference performance. We think Pat has been thinking about this for a while and he said, how can I leave leap frog system on chip. And we think he thought and said, I'll use our outstanding process manufacturing and I'll tap IBM as a partner for R&D and architectural chips to build the next generation of systems that are more flexible and performant than anything that's out there. Now look, this is super high end stuff. And guess who needs really high end massive supercomputing capabilities? Well, the US military. Pat said straight up, "We've talked to the government and we're honored to be competing for the government/military chips boundary." I mean, look Intel in my view was going to have to fall down into face not win this business. And by making the commitment to Foundry Services we think they will get a huge contract from the government, as large, perhaps as $10 billion or more to build a secure government Foundry and serve the military for decades to come. Now Pat was specifically asked in the Q&A section is this Foundry strategy that you're embarking on viable without the help of the US government? Kind of implying that it was a handout or a bailout. And Pat of course said all the right things. He said, "This is the right thing for Intel. Independent of the government, we haven't received any commitment or subsidies or anything like that from the US government." Okay, cool. But they have had conversations and I have no doubt, and Pat confirmed this, that those conversations were very, very positive that Intel should head in this direction. Well, we know what's happening here. The US government wants Intel to win. It needs Intel to win and its participation greatly increases the probability of success. But unfortunately, we still don't think it's enough for Intel to regain its number one position. Let's look at that in a little bit more detail. The headwinds for Intel are many. Look it can't just flick a switch and catch up on manufacturing leadership. It's going to take four years. And lots can change in that time. It tells market momentum as well as we pointed out earlier is headed in the wrong direction from a financial perspective. Moreover, where is the volume going to come from? It's going to take years for Intel to catch up for ARMS if it never can. And it's going to have to fight to win that business from its current competitors. Now I have no doubt. It will fight hard under Pat's excellent leadership. But the Foundry business is different. Consider this, Intel's annual CapEx expenditures, if you divide that by their yearly revenue it comes out to about 20% of revenue. TSMC spends 50% of its revenue each year on CapEx. This is a different animal, very service oriented. So look, we're not pounding the table saying Intel's worst days are over. We don't think they are. Now, there are some positives, I'm showing those in the right-hand side. Pat Gelsinger was born for this job. He proved that the other day, even though we already knew it. I have never seen him more excited and more clearheaded. And we agreed that the chip demand dynamic is going to have legs in this decade and beyond with Digital, Edge, AI and new use cases that are going to power that demand. And Intel is too strategic to fail. And the US government has huge incentives to make sure that it succeeds. But it's still not enough in our opinion because like the steel manufacturers Intel's real advantage today is increasingly in the high end high margin business. And without volume, China is going to win this battle. So we continue to believe that a new joint venture is going to emerge. Here's our prediction. We see a triumvirate emerging in a new joint venture that is led by Intel. It brings x86, that volume associated with that. It brings cash, manufacturing prowess, R&D. It brings global resources, so much more than we show in this chart. IBM as we laid out brings architecture, it's R&D, it's longstanding relationships. It's deal flow, it can funnel its business to the joint venture as can of course, parts of Intel. We see IBM getting a nice licensed deal from Intel and or the JV. And it has to get paid for its contribution and we think it'll also get a sweet deal and the manufacturing fees from this Intel Foundry. But it's still not enough to beat China. Intel needs volume. And that's where Samsung comes in. It has the volume with ARM, has the experience and a complete offering across products. We also think that South Korea is a more geographically appealing spot in the globe than Taiwan with its proximity to China. Not to mention that TSMC, it doesn't need Intel. It's already number one. Intel can get a better deal from number two, Samsung. And together these three we think, in this unique structure could give it a chance to become number one by the end of the decade or early in the 2030s. We think what's happening is our take, is that Intel is going to fight hard to win that government business, put itself in a stronger negotiating position and then cut a deal with some supplier. We think Samsung makes more sense than anybody else. Now finally, we want to leave you with some comments and some thoughts from the community. First, I want to thank David Foyer. His decade plus of work and knowledge of this industry along with this collaboration made this work possible. His fingerprints are all over this research in case you didn't notice. And next I want to share comments from two of my colleagues. The first is Serbjeet Johal. He sent this to me last night. He said, "We are not in our grandfather's compute era anymore. Compute is getting spread into every aspect of our economy and lives. The use of processors is getting more and more specialized and will intensify with the rise in edge computing, AI inference and new workloads." Yes, I totally agree with Sarbjeet. And that's the dynamic which Pat is betting and betting big. But the bottom line is summed up by my friend and former IDC mentor, Dave Moschella. He says, "This is all about China. History suggests that there are very few second acts, you know other than Microsoft and Apple. History also will say that the antitrust pressures that enabled AMD to thrive are the ones, the very ones that starved Intel's cash. Microsoft made the shift it's PC software cash cows proved impervious to competition. The irony is the same government that attacked Intel's monopoly now wants to be Intel's protector because of China. Perhaps it's a cautionary tale to those who want to break up big tech." Wow. What more can I add to that? Okay. That's it for now. Remember I publish each week on wikibon.com and siliconangle.com. These episodes are all available as podcasts. All you got to do is search for Braking Analysis podcasts and you can always connect with me on Twitter @dvellante or email me at david.vellante, siliconangle.com As always I appreciate the comments on LinkedIn and in clubhouse please follow me so that you're notified when we start a room and start riffing on these topics. And don't forget to check out etr.plus for all the survey data. This is Dave Vellante for theCUBE insights powered by ETR, be well, and we'll see you next time. (upbeat music)

>> From theCube studios in Palo Alto in Boston, bringing you data-driven insights from theCube in ETR. This is Breaking Analysis with Dave Vellante. >> New data suggests the tech spending will be higher than we previously thought for 2021. COVID learnings, a faster than expected vaccine rollout, productivity gains in the last 10 months, and broad-based cloud leverage lead us to raise our outlook for next year. We now expect a three to 5% increase in 2021 technology spending, roughly double our previously forecasted growth rate of 2%. Hello everyone and welcome to this week's we keep on Cube Insights powered by ETR. In this breaking analysis, we're going to share new spending data from ETR partners and take a preliminary look at which sectors and which companies are showing momentum heading into next year. Let's get right into it. The data is pointing to a strong 2021 rebound. A latest survey from ETR and the information from theCube Community suggests that the accelerated pace of the vaccine rollout pent up demand for normalcy and learnings from COVID will boost 2021 tech spending higher than previously anticipated. Now a key factor we've cited is that the forced March to digital transformation due to the pandemic created a massive proof of concept for what works and what doesn't in a digital business. CIOs are planning to bet on those sure things to drive continued productivity improvements and new business opportunities. Now, speaking of productivity, nearly 80% of respondents in the latest ETR survey indicate that productivity either stayed the same or improved over the past three months. Now of those, the vast majority, more than 80% cited improvements in productivity. This has been a common theme throughout the year. As well, the expectation among CIOs is that many workers will return to the office in the second half of the year, which we expect will drive new spending in the infrastructure needs of company HQs, which have been neglected over the past 10 months. Now, despite the expectation that many workers will return to the office, 2020 has shown us that working remotely, hey, it's here to stay, and a much larger number of employees are going to be permanently remote working than pre pandemic. ETR survey data shows that that number is going to be approximately double over the longterm. We'll look at some of that specific data. In addition, cloud computing, it became the staple of business viability in 2020. Those that were up the cloud adoption ramp, well, they benefited greatly, those that weren't well, they had to learn fast. Now, along with remote work cloud necessitated new thinking around network security, and as we've reported identity access management, endpoint security and cloud security with the beneficiaries. Companies like Okta, CrowdStrike, Zscaler, a number of others continue to ride this wave. Larger established security companies like Cisco, Palo Alto Networks, F5, Fortunate and others, they have major portions of their business that are benefiting from the tailwinds in the shift and network traffic, as a result of cloud and remote work. Now, despite all the momentum in the market and the expect of improvements in 2021, these tailwinds are not expected to be evenly distributed, far from it. We think Q4 is going to remain soft relative to last year and Q1 2021 is going to be flat, maybe up slightly. Remember the COVID impact was definitely felt in March of this year. So based on the earnings that we saw, there may be some upside in Q1, given that organizations are still being cautious in Q4, and really there's still some uncertainty in Q1. Let's look at some of the survey responses and you'll see why we're more optimistic than we've previously reported. This chart shows the responses to key questions around spending trajectories from the March, June, September, and December surveys of this year. Now it's no surprise that there's been little change in remote workers and limiting business travel. But look at the other categories, seeing a dramatic reduction in hiring freezes. The percentage of companies freezing new IT deployments continues to drop throughout the year. And then conversely, the percentage of companies accelerating new it deployments that's sharply up to 34% from the March low of 12%. And look at the headcount trends. The percentage of companies instituting layoffs. It continues its downward trajectory while accelerated hiring is now up to 17%. So there's a lot to be excited about in these results. Now let's look the remote worker trend. How do CIO see that shift in the near to midterm? This chart shows the work from home data and it's amazingly consistent from the September survey drill down. You can see CIO's is indicate that on average, 15 to 60% of workers were remote prior to the pandemic, and that jumped up to 72 to 73% currently, and is expected to stay in the high fifties until the summer of 2021. Thereafter, organizations expect that the number of employees that work remotely on a permanent basis is going to more than double to 34% long term. By the way, I've talked to a number of executives, CEOs, CIOs, and CFOs that expect that number to be higher than these especially in the technology sector. They expect more than half of their workers to be remote and are looking to consolidate facilities cost to save money. As we've said, cloud computing has been the most significant contributor to business resilience and digital transformation this year. So let's look at cloud strategies and see how CIOs expect those to evolve. This chart shows responses to how organizations see multi-cloud evolving. It's interesting to note the ETR call-out, which concludes that the narrative around multi-cloud multi-cloud is real, and it is. But I want to talk to you about a flip side to this notion in that, as many customers have, or are planning to increasingly concentrate workloads in the cloud. This actually makes some sense. Sure, virtually every major company uses multiple clouds, but more often than not, it concentrate work on a primary cloud. CIO strategies, they're not generally evenly distributed across clouds. The data shows that this is the case for less than 20% of the respondents, rather organizations are typically going to apply an 80, 20 or a 70, 30 rule for their multi-cloud approach. Meaning they pick a primary cloud on which most work is done, and then they use alternative clouds as either a hedge or maybe for specific workloads or maybe even data protection purposes. Now, if you think about it, optimizing on a primary cloud allows organizations to simplify their security and governance and consolidate their skills. At this point in the cloud evolution, it seems CIOs feel there's more value that is going to come from leveraging the cloud to change their operating models, and maybe broadly spreading the wealth to reduce risk or maybe cut costs, or maybe even to tap specialized capabilities. What's more in thinking about AWS and Microsoft respectively. Each can make a very strong case from MANO cloud. AWS has more features than any other cloud, and as such can handle most workloads. Microsoft can make a similar argument for its customers that have an affinity and a largest state of Microsoft software. The key for multi-cloud in our view will be the degree to which technology vendors can abstract the underlying cloud complexity and create a layer that floats above the clouds and adds incremental value. Snowflakes data cloud is one of the best examples of this, and we've covered that pretty extensively. Now, clearly VMware and Red Hat have aspirations at the infrastructure layer in a similar fashion. Pure storage, and NetApp are a couple of the largest storage players with similar visions. And then Qumulo and Clumio are two other examples with promising technologies, but they have a much smaller install base. Take a look at Cisco, Dell, IBM and HPE. They have a lot to gain and a lot to lose in this cloud game. So multi-cloud is an imperative for these leaders, but for them it's much more complicated because of the complexity and vastness of their portfolios. And notably Dell has VMware and IBM of course has Red Hat, which are key assets that can be leveraged for this multi-cloud game. HPE has a channel and a large install base, but all of these firms, they have to spread R&D much more thinly than some of these other companies that we mentioned for example. The bottom line is that multi-cloud has to be more than just plugging into an operating well on any of the clouds. It require... Which is by the way, this is mostly where we are today. It requires an incremental value proposition that solves a clear problem, and at the same time runs efficiently, meaning it takes advantage of cloud native services at scale. What sectors are showing momentum heading into 2021? And who are some of the names that are looking strong? We've reported a lot that cloud containers and container orchestration, machine intelligence and automation are by far the hottest sectors, the biggest areas of investment with the greatest spending momentum. Now we measure this in ETR parlance, remember by net score. But here's the good news, almost every other sector in the ETR taxonomy with the notable exception of IT outsourcing and IT consulting is showing positive spending momentum relative to previous surveys this year. Yeah, maybe not, it's not a shock, but it appears that the tech spending recovery will be broad-based. It's also worth noting that there are several vendors that stand out and we show a number of them here. CrowdStrike, Microsoft has had consistent performance in the dataset throughout this year. Okta, we called out those guys last year and they've clearly performed as you can see in their earnings reports. Pure storage, interestingly, big acceleration and a turnaround from last quarter in the dataset, and of course, snowflake has been off the charts as we reported many times. These guys are all seeing highly accelerated momentum. UiPath just announced its intent to IPO, AWS, Google, Zscaler, SailPoint, ServiceNow, and Elastic, these all continue to trend up. And so, there are some real positives that we're looking for a member of the ETR surveys, they're forward-looking. So we'll see, as we catch up next quarter. Now, before we wrap, I want to say a few words on security, and maybe it's a bit of a non-sequitur here, but I think it's relevant to the trends that we've been discussing, especially as we talk about moving to the cloud. And as you know, we've reported many times on the security space, basically updating you quarterly with our scenarios and the spending and the technology trends and highlighting our four-star companies. Four-star company's insecurity on those with both momentum and significant market presence. And last year we put CrowdStrike, Okta and Zscaler, and some others on the radar. And we've closely track the cyber business of larger companies with a security portfolio like Palo Alto and Cisco, and more recently, VMware has made some acquisitions. Now the government hacked that became news this week. It really underscores the importance of security. It remains the most challenging area for organizations because well, failure's not an option, skills are short, tools are abundant, the adversaries are very well-funded and extremely capable yet failure is common as we saw this week. And there's a misconception that cloud solves the security problem, and it's important to point out that it does not. Cloud is a shared responsibility model, meaning the cloud provider is going to secure the infrastructure for example, but it's up to you as the customer to configure things properly and deal with application security. It's ultimately on you. And the example of S3 is instructive because we've seen a number S3 breaches over the years where the customer didn't properly configure the S3 bucket. We're talking about companies like Honda and Capital One, not just small businesses that don't have the SecOps resources. And generally it was because a non-security person was configuring things. Maybe they were Or developers who are not focused on security, and perhaps permission set too broadly, and access was given to far too many people. Whatever the issue, it took some breaches and subsequent education to increase awareness of this problem and tighten it up. We see some similar trends occurring with new workloads, especially in cloud databases. It's becoming so easy to spin up new data warehouses for example, and we believe that there are exposures out there due the lack of awareness or inconsistent corporate governance being applied to these new data stores. As well, even though important areas like threat intelligence and database security are important, SecOps budgets are stretched thin. And when you ask companies where the priorities are, these fall lower down the list, these areas specifically have taken a back seat, the endpoint, identity and cloud security. And we bring this up because it's a potential blind spot as we saw this week with the US government hack. It was stealthy, it wasn't detected for many, many months. Who knows maybe even years. And not to be a buzzkill, but the point is, cloud enthusiasm has to be concompetent with security vigilant. Enough preaching, let's wrap up here. As we enter 2020, this year, we said the cloud was going to be the force that drove innovation along with data and AI. And as we look in the rear view mirror and put 2020 behind us, I know many of you want to do that, it was the cloud that enabled businesses to not only continue to operate, but to actually increase productivity. Nonetheless, we still see IT spending declines of four to 5% this year with an expectation of a tepid Q4 relative to the last year. We see Q1 slowly rebounding and kind of a swoosh, let me try that again, recovery in the subsequent quarters with tech spending rebounding in 2021 to a positive three to 5%, let's call it 4%. Now supporting us scenario, the pandemic forced a giant Petri dish for digital. And we see some real successes and learnings that organizations will apply in 2021 to bet on sure things. These are cloud, containers, AI, ML, machine intelligence pieces and automation. For sure, along with upticks for virtually every other sector of technology because spending has been so depressed. The two exceptions are outsourcing and IT consulting and related services which continue to be a drag on overall spending. Priorities must be focused on security and governance and further improvements in applying corporate edicts in a cloud world. We also see new data architectures emerging where domain knowledge becomes central to data platforms. We'll be covering this in more detail on top of the work that we've already done in this area. Now, automation is not only an opportunity, it's become a mandate. Yes, RPA, but also broader automation agendas be on point tools. And importantly, we're not talking about paving the cow path here by automating existing processes. Rather we're talking about rethinking processes across the entire organization for a new digital reality where many of these processes are being invented. The work of Erik Brynjolfsson and Andrew McAfee on the second machine age. It was pressured back in 2014 and the conclusions they drew, they're becoming increasingly important in the 2020s, meaning that look machines have always replaced humans throughout time. But for the first time in history, it's happening for cognitive functions, and a huge base of workers is going to be, or as being marginalized, unless they're retrained. Education and public policy that supports this transition is critical. And I for one would like to see a much more productive discussion that goes beyond the cult of break up big tech. Rather I'd like to see governments partner with big tech to truly do good and help drive the re-skilling of workers for the digital age. Now cloud remains the underpinning of the digital business mandate, but the path forward isn't really always crystal clear. This is evidenced by the virtual dead heat between those organizations that are consolidating workloads in a cloud workloads versus those that are hedging bets on a multi-cloud strategy. One thing is clear cloud is the linchpin for our growth scenarios and will continue to be the substrate for innovation in the coming decade. Remember, these episodes, they're all available as podcasts, wherever you listen, all you got to do is search Breaking Analysis podcast, and please subscribe to the series, appreciate that. Check out ETR's website at ETR.plus. We also publish full report every week on wikibond.com and siliconangle.com and get in touch with me at David.vallante, siliconangle.Com, you can DM me at D. Vellante. And please by all means comment on our LinkedIn posts. This is Dave Vellante for theCube Insights powered by ETR. Have a great week everybody, Merry Christmas, happy Hanukkah, happy Kwanzaa, or happy, whatever holiday you celebrate. Stay safe, be well, and we'll see you next time. (upbeat music)

>>welcome to our session on security titled Securing Your Cloud. Everywhere With Me is Brian Langston, senior solutions engineer from Miranda's, who leads security initiatives from Renta's most security conscious customers. Our topic today is security, and we're setting the bar high by talking in some depth about the requirements of the most highly regulated industries. So, Brian four Regulated industries What do you perceive as the benefits of evolution from classic infra za service to container orchestration? >>Yeah, the adoption of container orchestration has given rise to five key benefits. The first is accountability. Think about the evolution of Dev ops and the security focused version of that team. Deb. SEC ops. These two competencies have emerged to provide, among other things, accountability for the processes they oversee. The outputs that they enable. The second benefit is audit ability. Logging has always been around, but the pervasiveness of logging data within container or container environments allows for the definition of audit trails in new and interesting ways. The third area is transparency organizations that have well developed container orchestration pipelines are much more likely to have a higher degree of transparency in their processes. This helps development teams move faster. It helped operations teams operations teams identify and resolve issues easier and help simplify the observation and certification of security operations by security organizations. Next is quality. Several decades ago, Toyota revolutionized the manufacturing industry when they implemented the philosophy of continuous improvement. Included within that philosophy was this dependency and trust in the process as the process was improved so that the quality of the output Similarly, the refinement of the process of container orchestration yields ah, higher quality output. The four things have mentioned ultimately points to a natural outcome, which is speed when you don't have to spend so much time wondering who does what or who did what. When you have the clear visibility to your processes and because you can continuously improve the quality of your work, you aren't wasting time in a process that produces defects or spending time and wasteful rework phases. You can move much faster than we've seen this to be the case with our customers. >>So what is it specifically about? Container orchestration that gives these benefits, I guess. I guess I'm really asking why are these benefits emerging now around these technologies? What's enabling them, >>right? So I think it boils down to four things related to the orchestration pipelines that are also critical components. Two successful security programs for our customers and related industry. The first one is policy. One of the core concepts and container orchestration is this idea of declaring what you want to happen or declaring the way you want things done? One place where declarations air made our policies. So as long as we can define what we want to happen, it's much easier to do complementary activities like enforcement, which is our second enabler. Um, tools that allow you to define a policy typically have a way to enforce that policy. Where this isn't the case, you need to have a way of enforcing and validating the policies objectives. Miranda's tools allow custom policies to be written and also enforce those policies. The third enabler is the idea of a baseline. Having a well documented set of policies and processes allows you to establish a baseline. Um, it allows you to know what's normal. Having a baseline allows you to measure against it as a way of evaluating whether or not you're achieving your objectives with container orchestration. The fourth enabler of benefits is continuous assessment, which is about measuring constantly back to what I said a few minutes ago. With the toilet away measuring constantly helps you see whether your processes and your target and state are being delivered as your output deviates from that baseline, your adjustments can be made more quickly. So these four concepts, I think, could really make or break your compliance status. >>It's a really way interesting way of thinking about compliance. I had thought previously back compliance, mostly as a as a matter of legally declaring and then trying to do something. But at this point, we have methods beyond legal boilerplate for asserting what we wanna happen, as you say, and and this is actually opening up new ways to detect, deviation and and enforce failure to comply. That's really exciting. Um, so you've you've touched on the benefits of container orchestration here, and you've provided some thoughts on what the drivers on enablers are. So what does Miranda's fit in all this? How does how are we helping enable these benefits, >>right? Well, our goal and more antis is ultimately to make the world's most compliant distribution. We we understand what our customers need, and we have developed our product around those needs, and I could describe a few key security aspects about our product. Um, so Miranda's promotes this idea of building and enabling a secure software supply chain. The simplified version of that that pertains directly to our product follows a build ship run model. So at the build stage is doctor trusted registry. This is where images are stored following numerous security best practices. Image scanning is an optional but highly recommended feature to enable within D T R. Image tags can be regularly pruned so that you have the most current validated images available to your developers. And the second or middle stage is the ship stage, where Miranda's enforces policies that also follow industry best practices, as well as custom image promotion policies that our customers can write and align to their own internal security requirements. The third and final stages to run stage. And at this stage, we're talking about the engine itself. Docker Engine Enterprise is the Onley container, run time with 51 40 dash to cryptography and has many other security features built in communications across the cluster across the container platform are all secure by default. So this build ship stage model is one way of how our products help support this idea of a secure supply chain. There are other aspects of the security supply chain that arm or customer specific that I won't go into. But that's kind of how we could help our product. The second big area eso I just touched on the secure supply chain. The second big area is in a Stig certification. Um, a stick is basically an implementation or configuration guide, but it's published by the U. S government for products used by the US government. It's not exclusive to them, but for customers that value security highly, especially in a regulated industry, will understand the significance and value that the Stig certification brings. So in achieving the certification, we've demonstrated compliance or alignment with a very rigid set of guidelines. Our fifth validation, the cryptography and the Stig certification our third party at two stations that our product is secure, whether you're using our product as a government customer, whether you're a customer in a regulated industry or something else, >>I did not understand what the Stig really Waas. It's helpful because this is not something that I think people in the industry by and large talk about. I suspect because these things are hard to get and time consuming to get s so they don't tend to bubble up to the top of marketing speak the way glitzy new features do that may or may not >>be secure. >>The, uh so then moving on, how has container orchestration changed? How your customers approach compliance assessment and reporting. >>Yeah, This has been an interesting experience and observation as we've worked with some of our customers in these areas. Eso I'll call out three areas. One is the integration of assessment tooling into the overall development process. The second is assessment frequency and then the third is how results are being reported, which includes what data is needed to go into the reporting. There are very likely others that could be addressed. But those are three things that I have noticed personally and working with customers. >>What do you mean exactly? By integration of assessment tooling. >>Yeah. So our customers all generally have some form of a development pipeline and process eso with various third party and open source tools that can be inserted at various phases of the pipeline to do things like status static source would analysis or host scanning or image scanning and other activities. What's not very well established in some cases is how everything fits within the overall pipeline framework. Eso fit too many customers, ends up having a conversation with us about what commands need should be run with what permissions? Where in the environment should things run? How does code get there that does this scanning? Where does the day to go? Once the out once the scan is done and how will I consume it? Thies Real things where we can help our customers understand? Um, you know what? Integration? What? Integration of assessment. Tooling really means. >>It is fascinating to hear this on, baby. We can come back to it at the end. But what I'm picking out of this Ah, this the way you speak about this and this conversation is this kind of re emergence of these Japanese innovations in product productivity in in factory floor productivity. Um, like, just in time delivery and the, you know, the Toyota Miracle and, uh, and that kind of stuff. Fundamentally, it's someone Yesterday, Anders Wahlgren from cloud bees, of course. The C I. C D expert told me, um, that one of the things he likes to tell his, uh consult ease and customers is to put a GoPro on the head of your code and figure out where it's going and how it's spending its time, which is very reminiscent of these 19 fifties time and motion studies, isn't it that that that people, you know pioneered accelerating the factory floor in the industrial America of the mid century? The idea that we should be coming back around to this and doing it at light speed with code now is quite fascinating. >>Yeah, it's funny how many of those same principles are really transferrable from 50 60 70 years ago to today. Yeah, quite fascinating. >>So getting back to what you were just talking about integrating, assessment, tooling, it sounds like that's very challenging. And you mentioned assessment frequency and and reporting. What is it about those areas that that's required? Adaptation >>Eso eso assessment frequency? Um, you know, in legacy environments, if we think about what those look like not too long ago, uh, compliance assessment used to be relatively infrequent activity in the form of some kind of an audit, whether it be a friendly peer review or intercompany audit. Formal third party assessments, whatever. In many cases, these were big, lengthy reviews full of interview questions, Um, it's requests for information, periods of data collection and then the actual review itself. One of the big drawbacks to this lengthy engagement is an infrequent engagement is that vulnerabilities would sometimes go unnoticed or unmitigated until these reviews at it. But in this era of container orchestration, with the decomposition of everything in the software supply chain and with clearer visibility of the various inputs to the build life cycle, our customers can now focus on what tooling and processes can be assembled together in the form of a pipeline that allows constant inspection of a continuous flow of code from start to finish. And they're asking how our product can integrate into their pipeline into their Q A frameworks to help simplify this continuous assessment framework. Eso that's that kind of addresses the frequency, uh, challenge now regarding reporting, our customers have had to reevaluate how results are being reported and the data that's needed in the reporting. The root of this change is in the fact that security has multiple stakeholder groups and I'll just focus on two of them. One is development, and their primary focus, if you think about it, is really about finding and fixing defects. That's all they're focused on, really, is there is there pushing code? The other group, though, is the Security Project Management Office, or PMO. This group is interested in what security controls are at risk due to those defects. So the data that you need for these two stakeholder groups is very different. But because it's also related, it requires a different approach to how the data is expressed, formatted and ultimately integrated with sometimes different data sources to be able to appease both use cases. >>Mhm. So how does Miranda's help improve the rate of compliance assessment? Aziz? Well, as this question of the need for differential data presentation, >>right, So we've developed on exposed a P I S that helped report the compliance status of our product as it's implemented in our customers on environment. So through these AP eyes, we express the data and industry standard formats using plastic out Oscar is a relatively new project out of the mist organization. It's really all about standardizing a set of standards instead of formats that expresses control information. So in this way our customers can get machine and human readable information related to compliance, and that data can then be massaged into other tools or downstream processes that our customers might have. And what I mean by downstream processes is if you're a development team and you have the inspection tools, the process is to gather findings defects related to your code. A downstream process might be the ticketing system with the era that might log a formal defect or that finding. But it all starts with having a common, standard way of expressing thes scan output. And the findings such that both development teams and and the security PMO groups can both benefit from the data. So essentially we've been following this philosophy of transparency, insecurity. What we mean by that is security isn't or should not be a black box of information on Lee, accessible and consumable by security professionals. Assessment is happening proactively in our product, and it's happening automatically. We're bringing security out of obscurity by exposing the aspects of our product that ultimately have a bearing on your compliance status and then making that information available to you in very user friendly ways. >>It's fascinating. Uh uh. I have been excited about Oscar's since, uh, since first hearing about it, Um, it seems extraordinarily important to have what is, in effect, a ah query capability. Um, that that let's that that lets different people for different reasons formalize and ask questions of a system that is constantly in flux, very, very powerful. So regarding security, what do you see is the basic requirements for container infrastructure and tools for use in production by the industries that you are working with, >>right? So obviously, you know, the tools and infrastructure is going to vary widely across customers. But Thio generalize it. I would refer back to the concept I mentioned earlier of a secure software supply chain. There are several guiding principles behind us that are worth mentioning. The first is toe have a strategy for ensuring code quality. What this means is being able to do static source code analysis, static source code analysis tools are largely language specific, so there may be a few different tools that you'll need to have to be able to manage that, um, second point is to have a framework for doing regular testing or even slightly more formal security assessments. There are plenty of tools that can help get a company started doing this. Some of these tools are scanning engines like open ESCAP that's also a product of n'est open. ESCAP can use CS benchmarks as inputs, and these tools do a very good job of summarizing and visualizing output, um, along the same family or idea of CS benchmarks. There's many, many benchmarks that are published. And if you look at your own container environment, um, there are very likely to be many benchmarks that can form the core platform, the building blocks of your container environment. There's benchmarks for being too, for kubernetes, for Dr and and it's always growing. In fact, Mirante is, uh, editing the benchmark for container D, so that will be a formal CSCE benchmark coming up very shortly. Um, next item would be defining security policies that line with your organization's requirements. There are a lot of things that come out of box that comes standard that comes default in various products, including ours, but we also give you through our product. The ability to write your own policies that align with your own organization's requirements, uh, minimizing your tax surface. It's another key area. What that means is only deploying what's necessary. Pretty common sense. But sometimes it's overlooked. What this means is really enabling required ports and services and nothing more. Um, and it's related to this concept of least privilege, which is the next thing I would suggest focusing on these privileges related to minimizing your tax service. It's, uh, it's about only allowing permissions to those people or groups that excuse me that are absolutely necessary. Um, within the container environment, you'll likely have heard this deny all approach. This denial approach is recommended here, which means deny everything first and then explicitly allow only what you need. Eso. That's a very common, uh uh, common thing that sometimes overlooked in some of our customer environments. Andi, finally, the idea of defense and death, which is about minimizing your plast radius by implementing multiple layers of defense that also are in line with your own risk management strategy. Eso following these basic principles, adapting them to your own use cases and requirements, uh, in our experience with our customers, they could go a long way and having a secure software supply chain. >>Thank you very much, Brian. That was pretty eye opening. Um, and I had the privilege of listening to it from the perspective of someone who has been working behind the scenes on the launch pad 2020 event. So I'd like to use that privilege to recommend that our listeners, if you're interested in this stuff certainly if you work within one of these regulated industries in a development role, um, that you may want to check out, which will be easy for you to do today, since everything is available once it's been presented. Matt Bentley's live presentation on secure Supply Chain, where he demonstrates one possible example of a secure supply chain that permits image. Signing him, Scanning on content Trust. Um, you may want to check out the session that I conducted with Andres Falcon at Cloud Bees who talks about thes um, these industrial efficiency factory floor time and motion models for for assessing where software is in order to understand what policies can and should be applied to it. Um, and you will probably want to frequent the tutorial sessions in that track, uh, to see about how Dr Enterprise Container Cloud implements many of these concentric security policies. Um, in order to provide, you know, as you say, defense in depth. There's a lot going on in there, and, uh, and it's ah, fascinating Thio to see it all expressed. Brian. Thanks again. This has been really, really educational. >>My pleasure. Thank you. >>Have a good afternoon. >>Thank you too. Bye.

>>C G technology is focused on data where we have long believed that data is in our DNA. We help maximize humanity's potential by delivering world class, precision engineered data solutions developed through sustainable and profitable partnerships. Included in our offerings are hard disk drives. As I'm sure many of you know, ah, hard drive consists of a slider also known as a drive head or transducer attached to a head gimbal assembly. I had stack assembly made up of multiple head gimbal assemblies and a drive enclosure with one or more platters, or just that the head stacked assembles into. And while the concept hasn't changed, hard drive technology has progressed well beyond the initial five megabytes, 500 quarter inch drives that Seagate first produced. And, I think 1983. We have just announced in 18 terabytes 3.5 inch drive with nine flatters on a single head stack assembly with dual head stack assemblies this calendar year, the complexity of these drives further than need to incorporate Edge analytics at operation sites, so G Edward stemming established the concept of continual improvement and everything that we do, especially in product development and operations and at the end of World War Two, he embarked on a mission with support from the US government to help Japan recover from its four time losses. He established the concept of continual improvement and statistical process control to the leaders of prominent organizations within Japan. And because of this, he was honored by the Japanese emperor with the second order of the sacred treasure for his teachings, the only non Japanese to receive this honor in hundreds of years. Japan's quality control is now world famous, as many of you may know, and based on my own experience and product development, it is clear that they made a major impact on Japan's recovery after the war at Sea Gate. The work that we've been doing and adopting new technologies has been our mantra at continual improvement. As part of this effort, we embarked on the adoption of new technologies in our global operations, which includes establishing machine learning and artificial intelligence at the edge and in doing so, continue to adopt our technical capabilities within data science and data engineering. >>So I'm a principal engineer and member of the Operations and Technology Advanced Analytics Group. We are a service organization for those organizations who need to make sense of the data that they have and in doing so, perhaps introduce a different way to create an analyzed new data. Making sense of the data that organizations have is a key aspect of the work that data scientist and engineers do. So I'm a project manager for an initiative adopting artificial intelligence methodologies for C Gate manufacturing, which is the reason why I'm talking to you today. I thought I'd start by first talking about what we do at Sea Gate and follow that with a brief on artificial intelligence and its role in manufacturing. And I'd like them to discuss how AI and machine Learning is being used at Sea Gate in developing Edge analytics, where Dr Enterprise and Cooper Netease automates deployment, scaling and management of container raised applications. So finally, I like to discuss where we are headed with this initiative and where Mirant is has a major role in case some of you are not conversant in machine learning, artificial intelligence and difference outside some definitions. To cite one source, machine learning is the scientific study of algorithms and statistical bottles without computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference Instead, thus, being seen as a subset of narrow artificial intelligence were analytics and decision making take place. The intent of machine learning is to use basic algorithms to perform different functions, such as classify images to type classified emails into spam and not spam, and predict weather. The idea and this is where the concept of narrow artificial intelligence comes in, is to make decisions of a preset type basically let a machine learn from itself. These types of machine learning includes supervised learning, unsupervised learning and reinforcement learning and in supervised learning. The system learns from previous examples that are provided, such as images of dogs that are labeled by type in unsupervised learning. The algorithms are left to themselves to find answers. For example, a Siris of images of dogs can be used to group them into categories by association that's color, length of coat, length of snout and so on. So in the last slide, I mentioned narrow a I a few times, and to explain it is common to describe in terms of two categories general and narrow or weak. So Many of us were first exposed to General Ai in popular science fiction movies like 2000 and One, A Space Odyssey and Terminator General Ai is a I that can successfully perform any intellectual task that a human can. And if you ask you Lawn Musk or Stephen Hawking, this is how they view the future with General Ai. If we're not careful on how it is implemented, so most of us hope that is more like this is friendly and helpful. Um, like Wally. The reality is that machines today are not only capable of weak or narrow, a I AI that is focused on a narrow, specific task like understanding, speech or finding objects and images. Alexa and Google Home are becoming very popular, and they can be found in many homes. Their narrow task is to recognize human speech and answer limited questions or perform simple tasks like raising the temperature in your home or ordering a pizza as long as you have already defined the order. Narrow. AI is also very useful for recognizing objects in images and even counting people as they go in and out of stores. As you can see in this example, so artificial intelligence supplies, machine learning analytics inference and other techniques which can be used to solve actual problems. The two examples here particle detection, an image anomaly detection have the potential to adopt edge analytics during the manufacturing process. Ah, common problem in clean rooms is spikes in particle count from particle detectors. With this application, we can provide context to particle events by monitoring the area around the machine and detecting when foreign objects like gloves enter areas where they should not. Image Anomaly detection historically has been accomplished at sea gate by operators in clean rooms, viewing each image one at a time for anomalies, creating models of various anomalies through machine learning. Methodologies can be used to run comparative analyses in a production environment where outliers can be detected through influence in an automated real Time analytics scenario. So anomaly detection is also frequently used in machine learning to find patterns or unusual events in our data. How do you know what you don't know? It's really what you ask, and the first step in anomaly detection is to use an algorithm to find patterns or relationships in your data. In this case, we're looking at hundreds of variables and finding relationships between them. We can then look at a subset of variables and determine how they are behaving in relation to each other. We use this baseline to define normal behavior and generate a model of it. In this case, we're building a model with three variables. We can then run this model against new data. Observations that do not fit in the model are defined as anomalies, and anomalies can be good or bad. It takes a subject matter expert to determine how to classify the anomalies on classify classification could be scrapped or okay to use. For example, the subject matter expert is assisting the machine to learn the rules. We then update the model with the classifications anomalies and start running again, and we can see that there are few that generate these models. Now. Secret factories generate hundreds of thousands of images every day. Many of these require human toe, look at them and make a decision. This is dull and steak prone work that is ideal for artificial intelligence. The initiative that I am project managing is intended to offer a solution that matches the continual increased complexity of the products we manufacture and that minimizes the need for manual inspection. The Edge Rx Smart manufacturing reference architecture er, is the initiative both how meat and I are working on and sorry to say that Hamid isn't here today. But as I said, you may have guessed. Our goal is to introduce early defect detection in every stage of our manufacturing process through a machine learning and real time analytics through inference. And in doing so, we will improve overall product quality, enjoy higher yields with lesser defects and produce higher Ma Jin's. Because this was entirely new. We established partnerships with H B within video and with Docker and Amaranthus two years ago to develop the capability that we now have as we deploy edge Rx to our operation sites in four continents from a hardware. Since H P. E. And in video has been an able partner in helping us develop an architecture that we have standardized on and on the software stack side doctor has been instrumental in helping us manage a very complex project with a steep learning curve for all concerned. To further clarify efforts to enable more a i N M l in factories. Theobald active was to determine an economical edge Compute that would access the latest AI NML technology using a standardized platform across all factories. This objective included providing an upgrade path that scales while minimizing disruption to existing factory systems and burden on factory information systems. Resource is the two parts to the compute solution are shown in the diagram, and the gateway device connects to see gates, existing factory information systems, architecture ER and does inference calculations. The second part is a training device for creating and updating models. All factories will need the Gateway device and the Compute Cluster on site, and to this day it remains to be seen if the training devices needed in other locations. But we do know that one devices capable of supporting multiple factories simultaneously there are also options for training on cloud based Resource is the stream storing appliance consists of a kubernetes cluster with GPU and CPU worker notes, as well as master notes and docker trusted registries. The GPU nodes are hardware based using H B E l 4000 edge lines, the balance our virtual machines and for machine learning. We've standardized on both the H B E. Apollo 6500 and the NVIDIA G X one, each with eight in video V 100 GP use. And, incidentally, the same technology enables augmented and virtual reality. Hardware is only one part of the equation. Our software stack consists of Docker Enterprise and Cooper Netease. As I mentioned previously, we've deployed these clusters at all of our operations sites with specific use. Case is planned for each site. Moran Tous has had a major impact on our ability to develop this capability by offering a stable platform in universal control plane that provides us, with the necessary metrics to determine the health of the Kubernetes cluster and the use of Dr Trusted Registry to maintain a secure repository for containers. And they have been an exceptional partner in our efforts to deploy clusters at multiple sites. At this point in our deployment efforts, we are on prem, but we are exploring cloud service options that include Miranda's next generation Docker enterprise offering that includes stack light in conjunction with multi cluster management. And to me, the concept of federation of multi cluster management is a requirement in our case because of the global nature of our business where our operation sites are on four continents. So Stack Light provides the hook of each cluster that banks multi cluster management and effective solution. Open source has been a major part of Project Athena, and there has been a debate about using Dr CE versus Dr Enterprise. And that decision was actually easy, given the advantages that Dr Enterprise would offer, especially during a nearly phase of development. Cooper Netease was a natural addition to the software stack and has been widely accepted. But we have also been a work to adopt such open source as rabbit and to messaging tensorflow and tensor rt, to name three good lab for developments and a number of others. As you see here, is well, and most of our programming programming has been in python. The results of our efforts so far have been excellent. We are seeing a six month return on investment from just one of seven clusters where the hardware and software cost approached close to $1 million. The performance on this cluster is now over three million images processed per day for their adoption has been growing, but the biggest challenge we've seen has been handling a steep learning curve. Installing and maintaining complex Cooper needs clusters in data centers that are not used to managing the unique aspect of clusters like this. And because of this, we have been considering adopting a control plane in the cloud with Kubernetes as the service supported by Miranda's. Even without considering, Kubernetes is a service. The concept of federation or multi cluster management has to be on her road map, especially considering the global nature of our company. Thank you.