Hello, and welcome to theCUBE pod episode 53. I'm John Furrier with Dave Vellante this week. We're extracting the signal from the noise. We break down the top stories we're tracking, events we're going to, and of course, the hottest news in technology and in the enterprise, which is mainly cloud AI and everything around open source and data. So much going on, funding announcements to big news going around the internet. Dave, good to see you. Hey John. One of the next year of the pod, we had completed a full year an hour on to year two and a great audience developed, not huge numbers, but like big numbers, but still not huge numbers, not in the top 10 of podcasts, but getting a nice kernel of elite enterprise technologists who are really doing deep tech. So I got to say the results so far from the podcast, we did it every week. I think we missed one week because of travel or holidays, but pretty much a full year. And so this year we up our game on our packaging and get our format tweaked, great, great year. Great year. Yeah, we're getting good feedback. People seem to like it, listen to, and I love it. I get good John time once a week. Well, we had great feedback last night. I did an event in Palo Alto, a networking event put on by Mindshare PR, plugged for Heather Simmons. And we had a great, great cast. We had Shujata Banjirji. She is a VP. She would have been an SVP under VMware, VP of VMware Labs, part of Broadcom. Neema Badey, who's at NVIDIA, cube with cube friend and Ehab Theresey, who's the senior vice president at Dell Technology Core, working on all the systems stuff around AI. And boy was the turnout amazing. Founder of OCP. I'm sorry, the CEO of OCP was open compute project. And just a great conversation. And they're all loving the pod. They're like, they're all like builders and they're all loving the little nugget. So shout out to Shujata. He came on at MWC, he was great guest. He's deep tech. Well, the thing is, is that what's going on is that there's a lot of infrastructure going on that's going to enable the AI applications that was there. Chris, my burger was there from Newstack and Ray Wang was there. Just a ton of great people. And it was just fun to see the networking on the vibe of like dot com kind of vibe or web 2.0, when the magical moments of discovery and entrepreneurs and builders. And it's not just young Gen Z's. There's senior people too on their third four startup, multi-exited entrepreneurs and VCs all at NOLA's and Palo Alto. And like I said, the pod is getting some good traction and the feedback is, you know, get more structured and get some guests. We'll work on that. Chris is at Newstack now. I didn't know that. Or maybe I didn't know that. He's good. He's really good. He's working for like three or four publications, but great to see him. And you were in New York. Yeah. You were in a tour of the NYSE and you had the Amazon event. Yeah, I had, so Matt Wood, who's the Vice President of Amazon's AI, gave us, you know, his overview of their AI strategy.  We got demos. We got robot demos. It was very cool. And then, yeah, I was at NYSE. I spent time with their head of technology. Met with him for about 45 minutes, which was really interesting. Got a tour of the facility. You know, saw what's going on down there. Spent some time, did a little pilot with those guys and their TV set up. So that was good. It was great, actually. Well, I put a little teaser out there on Facebook and LinkedIn. And so it got massive viral buzz. People were like, what's going on with the NYSE? Had our logo all over the floor. People thought we went public. I'm not going to go public. Maybe, yeah, right. But there is going to be a New York cube. And that's just a matter of time. We're looking at final details there, but we'll be partnering with NYSE and others. What was cool was when they flashed the SiliconANGLE logo. If you haven't seen it, go to John or my Twitter. They flashed the logo. SiliconANGLE logos were on every station. You know how they have the trading stations? So they had, it was like a cube. And then all four sides of the cube were three sides of the, whatever, the shape had SiliconANGLE red logo on there. And we were up where they ring the bell. And you could just see out over the show floor or the stock exchange floor. It was pretty cool. Great publicity, too, all the background. CNBC sets down there. So having the cube on the New York Stock Exchange floor will be a great addition. Hope to pull that off and other, and all the VCs down there, too, are reaching out as well. So I think we're going to have a great set up in New York. And there's, you know, this, the media landscape has changed. It's talking to the head of IBM media on this and a bunch of other folks here in the East, when they're on the West Coast. IDC had their big event here. There's a real need for deep tech analysis on business, the impact of business. And, you know, leaders that are in our network, the CEOs that we talk to every day and check in with, love the fact that we talk about the impact of the earnings and the stock price from a technology perspective, not just from sales. And I think that is a need. And I was just on the phone with IBM covering their master's application that they're launching the new features with. And even more and more analytics are coming into the sports side of things. And so the real tech audience wants the deep tech. And I think that's going to be a nice compliment to the New York scene, where we have a lot of financial analysts who read our stuff and do watch our videos come on theCUBE and are in our community. So I think this intersection of financial technology, deep tech. And you saw that at video day, but there are GTC event. You had a deep tech conference. I mean, the hardcore developer conference. You had financial Wall Street-like crowd coming in, the financial analysts coming in, really unpacking, because I think the competitive advantage for the financial side is going to be getting on the right wave and connecting the dots. And the smart money's going deep tech and getting in the weeds and really digging in to the technology shifts. Back in the day, when I started the Wall Street service with Alexa McClellan, who hired her out of Goldman when I was at IDC. So back then, the sell side analysts, remember the sell side analysts, they print the reports that the hedge funds read. And then the hope is that they trade through the firm. So Goldman Sachs will write a report or UBS will write a report. And the hope is that the fidelities of the world will then make trades through the idea generator. But back then, the Wall Street analysts, the sell side analysts, they could actually get paid on investment banking deals. It was the mother of all conflicts of interest. So you could take a company public and then the sell side analyst was incented essentially to write good things about the company. So really, really huge conflict of interest. And so the SEC said, well, that's got to stop. So I can't remember exactly when it was like sometime early 2000s, probably like around Enron, you know. And so what happened was after that, and by the way, the sell side analysts would make bank because they were, you know, they were making it up at the back end. You remember Mary Meeker and she's still around and she was like a rock star analyst and John Levinson from Goldman Sachs and a number of folks that just made a lot of dough with that little, you know, wink, wink. So the SEC blew it away. And then what happened was the sell side analysts, all of a sudden became like spreadsheet jockeys, right? And so almost like they lost their interest in like deep tech. And so it became watered down for a long time. What's happened now is, you know, Wall Street making a lot of dough and they've started to properly fund these sell side analysts and sell side analysts are getting really good. A lot of them kind of left over from those days that have, you know, now have wisdom and a lot of new names that really understand tech. And so they've reduced, they've eliminated that conflict of interest.  And so, but I've know, you saw it at the Broadcom session.  Many of them, not all of them, but many of them are really, really good. So the deep tech. The sell side just for the folks listening, sell side is the folks who should be unbiased opinion of a company based upon proprietary research of the company's security. So basically they're supposed to be independent. Yeah, and the buy side are much more about getting the story right, selling to hedge funds, getting to the alpha, whatever they do.  Yeah, so buy side is making bets. They're like, those are the people who actually trade the stocks, right? So, and so, so the sell side, you know, their incentive is to write good research, do good research so that, you know, they make good calls. And then the buy side is obviously that's, their jobs depend on it, both sides depend on it. But the buy side is the ones who makes the bets. They're the hedge funds. So the sell side, sell side of the folks are the ones that you see on TV. The ones that they see, like on CNBC, they're the analysts, quote, analysts that are supposed to give an independent opinion from on the stock and the company. And that day, as we talked, I've saw some analysts on CNBC, I won't name names in our industry knows who they are. They're basically, oh, Intel's going to tail one. Meanwhile, they just lost $7 billion. So there's a real problem with analysts on the sell side right now. Not being tainted, but there's a challenge.  And if you're getting paid by Intel to say good things about Intel and then a week later, they lose money. You're obviously not a research analyst.  So there's a real problem with analysts. You mentioned IPOs, that was solved by the SEC. But in the industry analyst community, there's a similar problem going on now with this so-called independent analysts on TV should have an unbiased opinion based upon research, not if they're getting paid. It's coming back again in our industry. This has been a big controversy, Dave. And we've been seeing it. Yeah, so you're right on on the sell side.  He goes on TV all the time. He's really, really good. Aaron Rakers, I don't think he goes on TV but he's a really good analyst with Wells. The B of A semiconductor analyst is really, really good. There's a lot of really good ones. And they really study tech and they go deep. So it's been an interesting transition to watch. We used to have two Goldman Sachs analysts inside of IDC. We had like a $2 million deal with Goldman. It was an exclusive. We couldn't sell to any other sell side firms. And then after the 87 stock market crash, they canceled the deal. And so I started the service with Alexa. We hired her out of Goldman and then we crushed it at IDC today as a great. Well, there's also explained there's financial analyst and an industry analyst because I think it's important because I was referring to the industry analyst on TV saying. Yeah, so that's right. It's a similar model though, independent. Well, financial analyst is certified. They have a CF, most of them have a CFA, a certified financial analyst. And they're qualified, they've been certified and their job is to pick stocks basically and make calls. And they're usually really, really good writers. Like I said, they're increasingly technical because they got to be, this is a technical business that we're in, whereas the industry analysts are like Gartner, Forrester, IDC. They tend not to be really in tune with the financials. It's funny. I mean, I love going to financial analyst conferences because I think that, frankly, I think financial analysts are smarter than industry analysts. No offense to my industry analyst colleagues. But in fairness to industry analysts, industry analysts care much more about product and getting deep into the business of the company. I shouldn't say that business. They care more about like go-to-market type stuff and just kind of things that are less financially oriented. And so they tend not to care about income statements and balance sheets, which of course I care a lot about. But anyway, it comes down to transparency. So it's a New York's hot. Tell us about your Amazon. You went down there, I saw Matt Wood on LinkedIn posted some really good commentary. He went down and talked to the media. He talked to the analysts. You were there for an NDA discussion with AWS and Matt Wood, talk about AI. Was that NDA or can you talk about that or? I can't talk about it, but there was a lot of NDA on there, but I can talk about it. And so first thing is it was pouring sideways when I got there, I drive to New York because the weather sometimes holds up. Like Andy Thuray, if like got canceled, couldn't get in. And so he was kind of bummed, but there were a lot of local New York analysts there. There was a couple of people from Gartner. Doug Henshin was there from Constellation. He's a really good analyst and a number of the folks as well. It was probably only like, I don't know, 10 analysts.  He had this seven, it was seven or eight point, called it a journey or steps. He called it steps on AI adoption. It really wasn't like linear steps, but he took us through that, like started with training and then he made a big deal out of security and privacy, which there obviously, AWS has always been focused on security privacy designed in. And of course it was timely. They didn't bring it up. They didn't bring up that report. Did you see this? Just a quick aside, a review of the summer of 2023 Microsoft Exchange online intrusion from the China hackers. Yeah, I can see that. So they didn't bring it up. They should have. Maybe it was inside conversations, but they know they were cool. That does nothing to do with Amazon. That was Microsoft.  Just to clarify, because it's a little non sequitur there. The Cyber Safety Review Board issued a damning report Tuesday on last summer's discovery on Microsoft's breach. That was the survey. And basically revealed a wide conspiracy between the Chinese government and other people. And they got sensitive authentication key and broke into Microsoft's managed accounts to steal massive sensitive information from the US government. And they just basically got hammered. They got taken to the woodshed. And I'm sorry, I didn't mean to go the first. No, no, no, let's stay on it. It's relevant because, okay. So it's unbelievable, John. Well, explain what you held up because I... So in 2023, there was a...  In 2023, there was a hack from Chinese. It was linked to Chinese actors, but you're holding your hand over a board. I know, I'm gonna explain it. I'm gonna explain it. It was linked to Chinese state actors. And then the US government found out about it. It wasn't even Microsoft that found out. They had alert Microsoft. So the US government was like, WTF, we have to look into this. So the Cyber Safety Review Board was asked by the Secretary of Homeland Security to do a deep dive on this. So they published this back in late March. And I'll just give you a couple of poll quotes that I pulled out. By the way, we reported that. I reported on the podcast. Yeah, yeah. And SiliconANGLE wrote it up. Friday at four o'clock or something. They announced it in November. Remember that? They said it was the last possible minute before... They did such a poor job. No, they tried to sneak it on Friday before the weekend. They got eviscerated for that. So look at this poll quote, quote, the board finds that this intrusion was preventable and should never have occurred. The board also concludes that Microsoft's security culture was inadequate and requires an overhaul. And then the second poll quote, throughout this review, the board identified a series of Microsoft operational and strategic decisions that collectively point to a corporate culture that de-prioritized both enterprise security investments and rigorous risk management. And then the third takeaway was they cited best practice cloud examples that were kind of exemplary from Google, Amazon, and Oracle cloud infrastructure. And then they had a bunch of contributions from some other people.  Hang on, we're at risk. And this report just eviscerated them and said they still haven't figured out what happened, why it happened, and they really didn't come clean for a long, long time. And so that should be a real cause for concern. And AWS, to their credit, they didn't really hammer on that. They just said, hey, we're focused on security. We designed it in. I think they did a good job of explaining that. It's funny to me that Charles Fitzy, he's always crappin' on Amazon for like, you see him this week, like searching for a compression algorithm for experience for AI. He's always crappin' on Amazon because they're maybe behind the race in AI. But he doesn't even bring this up. I mean, to me, this is the number one biggest concern is security. Yeah, I mean, this is really Microsoft puttin' their head in the sand. The other, this is so much backstory, but if you zoom out for a minute, okay, and look at Microsoft, they hired Charlie Bell away from Amazon in 2021. It was a big move. He was supposed to be the right hand, the Andy Jassy-era parent. And then they had this whole initiative of this secure future initiative that they were gonna implement, and they did, nothing really happened. So they're getting killed inside the industry by people throwin' a lot of darts at them, saying, hey, you guys, you know, they're supposed to implement this new thing and it just didn't work. Now, the thing that's interesting about this report that came out, again, we reported it, the minute it happened, we were like calling them out immediately. So we were all over it. The customers didn't have all the facts. So nobody knew. They hid the ball, and what was even worse is, they knew about it, okay? So they actually knew about it for long before. So again, the government doesn't trust Microsoft. So they issued a huge thing. Now, remember, we reported back in 2013 that Amazon won the deal to CIA. So this is a huge setback for the cloud of Microsoft. Azure team, you gotta be really lookin' at this and saying, look, this is a major, major, major setback. And if I'm Microsoft, and I'm Amazon, I mean, if I'm Amazon, I'm like, look it, we won the CIA, we have a GovCloud, Microsoft's foray into public sector just isn't working. So that's going to be really, really interesting to see. And to your point about the poll quote, they just don't have the corporate practices according to the government. But I got to tell you, this CSRB, which is the entity that gave that report, they are not happy. And then they, they was very much a threat and they knew, and they knew about it. The other thing that comes up is that OpenAI now has these co-pilots. So I don't know if you remember, but when we had Microsoft Ignite, I was hanging around the Seattle Westin, trying to get some stories from some folks there. And I overheard many security people saying, we are turning off co-pilot because they were launching it with default on. So this is- I reported that too, John. This is going to be a breaking analysis. This is going to be a huge blowback for Microsoft. So, you know, I'm surprised Amazon's not jumping on this because they're getting killed by OpenAI and Microsoft. Well, I know, I want to talk about that in a second too, but said Microsoft's failure to detect compromise of its cryptographic crown jewels on its own, relying instead on a customer. The customer was the government to reach out to identify anomalies. It got, it compromised the email accounts of the Commerce Secretary, Gina Ramondo, U.S. State, United States Ambassador to People's Republic of China, Nicholas Burns, Congressman Don Bacon. Can you imagine the private emails talking about China? I mean, just, it's just really incredible. And then, and then wait for one more thing. However, by the conclusion of this report, this review, Microsoft was still unable to demonstrate to this board that it knew how Stormo558, that's the actors, had obtained a 2016 MSA key, 2016 technology. And they were using, they were doing manually, manual updates to it. They hadn't even automated it. I mean, just really, really bad. No, no best practice. So wow. The report basically slammed them for dragging their heels and- But John- And delaying, because they publicly tried to bury this thing. And the thing that killed them on this one, the experts already knew it was sounding the bell, alarm bells on this. And this is the hackers getting into the government email accounts managed by Microsoft. So, it's unbelievable. So I think this can be, again, back to the setback with open AI giving Microsoft a huge cloud burst in the market share numbers and the vibe of the market. This is going to be a setback on the public sector. And I think Amazon CIA deal in the GovCloud will reap the benefits from this. But John, but here's the thing. If you look at the data, the spending data from our partners at ETR, open AI, open AI is the number one company in the whole survey, 1800 IT decision makers. Open AI is the number one in terms of spending momentum on their platform that percent of customers that are spending more net customers. It's like off the charts. And then Microsoft is like right there with them. Like they are off the charts. And then, but I'll give you some other data points. Anthropic is rocketing. Doesn't have the presence, doesn't have as many, you know, the responses in the survey, not nearly as many as an open AI. Databricks is also rocketing. And the other thing is Google and AWS are actually coming together. You know, I've reported for years on the cloud spending and AWS and Microsoft are by far the leaders in cloud spend in terms of the customer momentum. But in AI, Google and Amazon are coming together while Microsoft and open AI are like running away with it. So my point is that it's despite these reports like this, they're still kicking butt. And it's a real risk. And I think CEOs need to step back and CIOs and say, hang on, we have to do some risk assessment here because this is bad, bad, bad. The Dion Hinchcliffe from Constellation has got a survey going on around business uses. What are the questions? What is the primary LLM that you use today for your work in business? ChatGPT 41%, 41.7%. Cloud, which is the cloud family, that's which is Anthropic 25%. Gemini 16. Mistral, Lama, not registering up even under one. Basically zero on the chart. So that's interesting to see that. So let me give you some others like metrics here. So net score is the net percentage of customers. Basically, who's spending more? But what percent of customers spending more than less?  Open AI is at 80%. So say 80% of customers are spending more and out of a survey of 1800, 555 are saying we're using open AI. Compared to Anthropic, 77%, 76% net score, a huge net score, by the way. Anything over 40% is great, but only 89 responses. Microsoft, 74%, with 611. So you combine Microsoft and open AI, it's over 1,000 out of 1800. Databricks 69%, with 209. And then MetaLama, 60%, very strong, but only 87. So not as popular. AWS, 58%, with 370. And Google, 56%, with 340. And then, so all kind of coming together with this. But the other thing is Meta was ahead of Anthropic and Databricks last quarter. They flipped. Anthropic and Databricks are now ahead in terms of spending momentum. And then there's other guys like Oracle and IBM are in the chart, not as much momentum, but they got a pretty big presence in the marketplace. So the race is on, John. I have a question for you. I want to run something by you, something that I heard from Matt Wood. He kept talking about how model optionality and diversity is an advantage. And you know how a lot of people are saying that LLMs are just gonna get commoditized? You hear it all the time, right? You know what I'm talking about, right? Okay, so I asked him, I said, well, wait a minute, a lot of people think LLMs are gonna get commoditized. Where do you stand on that? And he said, definitively, no, I don't believe that at all. That model diversity, right tool for right job. And we're gonna play off of these other models. We're gonna use models to train other models. And that's gonna create, he didn't use this term, but essentially implied that's gonna create a moat for Amazon. I was like, hmm, and then I was on another call, same day with the head of strategy at a very large company, a tech company. And I asked that individual, what do you think about LLMs? He goes, you know, I've been around a long time, used to be a VC, I do a lot of strategy work. I think they're gonna get commoditized. VC gave it away, he's the wrong answer. Never ask a VC about a trend ever. If you're an entrepreneur listening to this, never ask a VC for advice. Because they're not entrepreneurial. Well, what do you think though? What do you think? Absolutely, they're not gonna get commoditized. Never, no way. So Amazon's assumption you believe is correct. Yeah, look at, the models are just the beginning of the new substrate of how the new architect is rolling. There is so much change going on in this market with AI and the infrastructure layer with data, everything's changing. Databases, Tesla's not even using data, databases to train their thing, he's video. They're using video images. We talked about that last night on the panel. All computer science tools and mechanisms are gonna radically change. All the sacred cows of computer science will evolve to the new normal, which is going to be AI infrastructure. Again, we talked about this last night, our Palo Alto media, too bad it wasn't recorded. LLM and foundation models will be a long tail power law which we publish of you. Look up the long tail of LLM, you'll see it. What's going to happen is API is going to connect the world. I'll continue to do that. LLMs will integrate with each other. They're going to talk to each other, they're going to have mechanisms for interacting, data interacting with each other and that's going to be the new model and that's why commoditization is not going to happen. It's going to be a value market, meaning whatever LLMs have value, other LLMs will seek them out and integrate with them for better value. So the combination of fusing data sets will be the model and absolutely will not be considered because a small model could be very valuable. If the data is valuable, data is only valuable in the eye of the beholder who wants to use it. So what does even that mean? It's going to get commoditized. Now, if you're going to argue anything, you can argue that open AI is going to get commoditized because it's vanilla.  So some areas you're not going to be accurate, hence hallucinations. So what's going to happen? It'll interact with other models. Smaller models will interact with larger models. So the power largest represents the size and scope or domain specific of the model. So now you're hearing specialty models. What does that mean? That means the data is specialized around something and that's what enterprises are going to have the value. So that's where the value is. It's going to be in the data and the workflow. So just like, let's take an example. I went out on detail last night on this. Let's take a semiconductor example. Is ARM a foundry? No. They design the chip and then ship it to Taiwan. So they actually don't even do that. They just have the software framework and then they give it to designers. They give it to designers. They give it to a standard and then ship it to TSM. Okay, so the thing is so tight. It's like they're getting the tape out in like a lap of time. Let me finish. Arms were in Broadcom. Broadcom does the same thing. So they design and then they get someone in a foundry to build it. In AI, what's happening is the people who design the value, the combination of the workflows or data could actually design a winning interaction and then ship it to say a foundry using quotes, meaning some sort of centralized compute farm, training farm, inference farm, reinforced learning farm, neural network, knowledge graphs, there will be someone like a cloud player or a hyperscale or maybe meta who will be processing and you can process it on the hyperscale clouds, on premise or edge, the compute side is going to get really, really robust there. So the value is going to come from designing AI and so people could use AI. So do you build AI in yourself or do you use AI? So that's really going to be the distinction. So Matt Wood is accurate on that. I would say that he's absolutely correct. Like Matt, models will not commoditize because what does it even mean commoditize? Well, it's okay. So that's part, well, what it means is that the value is going to flow up the stack. And that's what a lot of people want to believe but the second part of that question is, okay, if Matt Wood is correct, that models won't get commoditized, let's assume he's correct. And that becomes a key criterion, model optionality. Will other competitors to AWS be able to match that? In other words, last year at Ignite, Microsoft announced, I think some stuff with Cohere and you know, a couple of, I forget who, but, and I remember tweeting out, well, okay, so they're adding optionality. Will they be able to do so in a way that competes effectively with AWS or will AWS be able to build a mode around it? That's a wrong question, the wrong question. It's a question, I'm sorry, it's a question. Here's how I flip it. You can't say it's moving up the stack and being commoditized or abstracted away are two different things, okay? You can have commodity means comes across as no value or a low-switching cost. Well, that's what they're saying. They're saying low-switching costs. Low-switching costs race to zero. Well, depends on how valuable the model is. So again, some, if you're going to be obsolete, let's just say a model like OpenAI or Mestral. If they stop innovating or working on their model, they'll become obsolete, right? So in fact, I introduced a new term last night on the panel called tech obsolescence, not tech debt.  So there's no debt because it's irrelevant. So you can be irrelevant and be commoditized by not keeping up. Yeah, there's a changing landscape, land change, trying to keep trying to catch up and stay ahead and there, some are, say, losing ground. So what's happening is it's moving so fast. You're not being commoditized, you're being beaten. So commoditization is a whole nother conversation. By the way, I'm not defending the commoditization. No, no, no, I'm just trying to explain my thoughts because abstracting away the complexity is not a bad thing. If the models are valuable for the application, which is the top of the stack, that's where the value is. So the question is, the question of moving up the stack is about the application getting value. That's data and infrastructure capabilities, compute, big iron performance, low latency, real time. So as that infrastructure gets tooled, which is being done now, that's the bottleneck right now is infrastructure. So I think the commodity conversation is more about who stays ahead. So it's not being commoditized, it's becoming competitive. Well, and these guys are investing like crazy. I mean, it's like- Now Fitzgerald pointed out on Platform Economics today, on his blog that Anthropic is in bed with Amazon, obviously, but their alternative to open AI. He's reporting that some people are telling him that they're working on a competing version. Dude, that friggin' report came out last November. He's making it sound like he has some inside baseball. That's like old news. Olympus, right, Olympus came out- Explain. So Olympus, I think Reuters broke the story last November, like mid-November, around Thanksgiving, that AWS was working on a new foundation model that was world-class, two trillion parameters, I wanna say, called Olympus, and that their objective was to surpass Anthropic.  Like, well, no, that's just classic AWS. They did the same thing with Graviton, and they're doing the same thing with their silicon chips. They're just barraisers. That's what Amazon does. So it's not either or with Amazon. It's like, oh, snowflake or red ship. Yes. You know, so that is just a misunderstanding of AWS. But having to comment on some of the earlier comments, that abstraction layer, you know, Amazon's never been really that great at those abstraction layers, and that's what Q was supposed to be. And Matt Wood kind of laid that out. You know, Q's supposed to be the easy button. And I think they're trying to apply it, like Q for supply chain, Q for data, connectors to Jira and ServiceNow and Slack. And so it's gonna be interesting to see, John, will they actually build those apps for the people who don't wanna build them themselves, or will they allow their ecosystem to do it? And I think, again, the answer is yes, both. I mean, there's no brainer that, I mean, Amazon has to have their own model at some point. Well, they do, they have Titan, but people say Titan's met, you know, it's like, but they, of course- According to Alex Heath at the Verge, he's reporting that in his post on end of March, March 29th, Amazon has Rowett Prasad aggressively performing goals to beat- Yeah, he's the Alexa guy. He was the head of Alexa who, Jassy said, you're reporting to me, let's fix this. He's running their AGI group. Yeah, which is- Right, which is- It may not be AWS, it might be Amazon, because Amazon uses AI and all their products. I think you're right, John. Not just AWS. Well, you know Jassy better than anybody. I think Jassy said, hey, this is too important. You're gonna report to me. I think that's the guy, the guy you're talking about, used to run Alexa and Jassy, he reports to Jassy now. I mean, I was talking about this last night. I'm gonna get you, I want to get your thoughts on this riff here. I want to riff on something for a second. So last night I was talking to someone who talked about the meetup we were at, all these AI experts in the panel, AI experts in the audience. And one of them was like, hey, so it's really exciting seeing everyone's talking about pitching their companies and a lot of activity, entrepreneurial activity, and a lot of comparisons to the dot-com bubble. You think it's gonna burst? I pulled your line. Yeah, it's gonna burst at some point. When? I don't know, I have no idea, but it'll burst. And I said, it's a real good bubble though. It's a good bubble. I said, not a bad bubble. And then we started getting on to AI and who's gonna be the winners and losers. And I said, if you remember the web, the internet web days, there were web pages and search engines. And you had DNS, which ran, and that was government funded by the Department of Commerce. And it was like a public utility, the domain name system, which is what the URLs were based out of. If you think about the web and AI as an example, the comparison to the worldwide web early days and AI, there's some similarities. You use the web, if you were a business, by building web pages. And you had to do that. You had to handcraft HTML, get a domain name, host it, put content on it, and people could browse it and use the website and do stuff, browse for information, and then ultimately buy a product or do something, navigate to some content. And that was an easy way to use the internet. Then you had search engines like AltaVista and then Google came along, which were harder to do. So with AI, it's a similar thing. We're seeing people are using AI, like RAG and retrieval augmentation generation, using AI for chatbots, co-pilots, agents, all changing the game on the interface side.  And that's the distinction. Are you building AI? Which means you got to hire specialty talent. They all probably make it 300 grand a year and you're getting poached by every other company. You got to have real intellectual property in algorithms and hardcore deep tech. That's hard. So you're going to have suppliers of AI to users who are going to use AI. So if you think about it, that's going to be very interesting dynamic, David. What do you think about that? Because in a way, we have AI for theCUBE, but we're not building machine learning algorithms and we're using AI tools. So we're like the webpage. So what the webpage was was, you took advantage of the technology. So there's users and buyers and people are trying to figure out where they go and it's okay to be a user because there's benefits there. And so data and workflows that you could use and get value.  But like one of the things that I learned this week, and it's so true, you'll appreciate this. I think it was, yeah, it was Matt Wood. I think it was Matt Wood. He said, you know, it's not the greatest analogy of the world, but it's like Swiss cheese. What do you mean? Well, the data corpus is like Swiss cheese when you would build AI and you build a rag, for example. When there's data there, it's actually pretty good, but the problem is when there's no data there, it's like this big hole. And so what the rag does is the AI will start grabbing from different pieces and make stuff up. And so what you have to do is figure out either how to fill those holes or how not to go into those holes. And so that's the kind of thing that AWS does and they do it really well. The other thing that I learned, he said that a lot of times these models, they're really good at the beginning and then they have a U-shape quality curve and they get kind of crappy and then over time they get maybe better. He said, we're flattening that curve and that's their role. They're like hardcore technologists secure it, make it better, give me tools so that I can apply it to my own corpus of data like we've been talking about on the power law of Gen AI. Power law of Gen AI is completely playing out. You see it everywhere. So I got to ask you something, John. It's kind of related to what you're talking about before you meet up. I want to get this out there. I've been meaning to put this out in the Kube pod. I've been holding it since March. I have a friend who's like- You've been holding it in? Yeah, but I just haven't got around to it. I have a friend who's like deep inside AI, like three letter agencies and like a handful of AI experts in the world and he's one of them. Like maybe there's a hundred that are like this guy. So, and he's really down on full self-driving has been forever.  It's like, so he's always been saying that. No, of course I don't really know. I'm like, I'm excited about full self-driving, but okay. So back in March, when Tesla, I was reading about Tesla scrapping its old full self-driving code, rewritten stack and becoming a learning system. This person's that their thing is, it's got to be a learning system. Why do we not let kids drive until 16 years old?  Not going to happen. So I send this note by a text and say, I don't really understand, you know, if this is going to work. He said, this is what I was explaining to you when the last time we talked and what they needed to do. The way they were doing it was amateurish. It was just a bandaid. And then he said, I've been pushing the rope with them, meaning Tesla and others to get them to do this for quite a while, but I'd like to see a new sensor architecture. But so far they know better. And so I'm going to put this out there. And if I don't really understand it, but I'm hoping that either you do or somebody in the community does, he said their sensor mapping and spatial reconstruction are human perception based. Instead of machine perception based, that's wrong and will give inherent bias to any data analysis. So not truly 100% adaptive or heuristic, not to sound heady, but building a machine perception system is beyond most people's understanding, much less capability. So I said, meaning Tesla's ingesting and analyzing human driver video to train the AI? My understanding, this is a step up from hard coding, a series of if this then do that in C++, but you're saying this is still adequate. So look, for example, talking down to me, when using sensor data to reconstruct, say a room, the sensor data has anomalies that are somewhat sensor specific, typical sensor systems change in quotes the data to fit a human perception of what it should look like. For example, straight walls, square corners, but any time you change data, you lose fidelity. So if you're creating an adaptive heuristic learning system, I've been preaching, it is better to let the learning happen with unprocessed data, not with data change to fit the human perception. He's like, again, talking down to me, you getting this or do we need a whiteboard? And I said, I thought this new Tesla approach in just actual video. You should have said get a whiteboard. Wait, wait, I've got it. Next time I'm in town. And I said, but I thought this new Tesla approach in just actual video and has the AI to quote, figure it out. Are you saying the Tesla sensors are providing associated metadata that has changed to fit the human perception? And he just sent back a smiley face, whiteboard needed son. So anyway, I caught, I didn't catch it, right? I obviously didn't grok what he was saying, but I get it out there. But I'm hoping that you or somebody in the community who's got more technical depth can explain, but I'm gonna go do the whiteboard session. It's hard to grok specifically the way you read that, but I would say that what I, the countries we had last night with some of the folks in the room who were under NDA with Tesla is how they're getting the data is not conventional data. It's not like your classic database. So they are feeding video into it to train the video and that there's a lot of AI agent technology being built. And this came up a lot. Agent Cockroff was there actually. So I am again, he and I were talking about how agents are being used in infrastructure. So the whole AI ops field is changing. Back to Tesla and as an example to other environments is a complete changeover is happening with AI in the sense that with agents, with AI agents, the ability, not just chatbots, it's like agents that can understand data, repeated processes, identify patterns, and look at these kinds of things. And then take action and generate a response based upon something else is so new and compelling. But the challenge is that before generative AI, code was written to fill the gap for those things. Meaning like we had to build mechanisms in software to handle, to manage things to be in real time and do things with generative, with AI agents. You can actually make the agents work on behalf of things that were built for the wrong reasons. So those other things that were built free generative can be disabled because generative agent, AI agent will take care of it. If the agent's good. So if you build the right agents, how to manage data ingestion, looking at certain processes that are repeating themselves.  I want to kind of, I can, after a while, you pretty much know what you're going to see. You get formats and you can come in, you can train some an agent to fix those. You can look at things like microservices and cloud, for instance, and say, we don't need some of those things we had to build before because we now have agents who can do those things and generate things on the fly. And keep things in smaller controlled areas. And it's better management. So you'll see things that were built for reasons prior to generate AI that will have to go away because we don't need them anymore because an agent will handle it. So that's the big conversation that we've been having when some of these tech circles is, what are those mechanisms that are no longer needed for the generate AI generation? And I think that's just like to go to the web example. I don't need to go to the library anymore. I can just go to a web page. So the library is irrelevant to me. So there's going to be things that will go away because they don't, we're not needed anymore. Or not convenient. So I buy that. What I would say to that is, in order for that to happen, the system of agency, if you will, taking action, you've got to have a proper data foundation. Sounds like bromide, but you do. So I think behind that vision that you just laid out has to be some kind of fabric and a knowledge wrath that has access to coherent and unified metadata. That's not like hidden to use an Amazon example because I know Amazon better, but they've got metadata that's stuck in glue. They have other metadata that's stuck in data zone and data zones that they understand this. But to be able to surface that in real time and be able to take action on it. This is why we always use Uber for the enterprise because Uber basically had to write its own software to do this, to make all these different disparate data elements coherent so that you can take trusted action. Because which data do you trust? Which data are people actually have access to? Some people have access to that data. Well, it's kind of the same data over here, but it's in a different format. Which one do I use? So that's a really hard problem. But I think the vision that you're laying out is the future data platform. When we talk about the six data platform, it has that element as part of it. It's a system of agency. So it's a great vision. And I think it's actually going to happen within the next five years. We'll see. I mean, we'll track all this. The great thing about it is, I was saying to the folks last night at the panel and other people in the industry this week that it's a great time to be covering technology. And that's why I'm excited about the New York expansion for theCUBE is because there's such a thirst and appetite for technology, deep tech impact. Companies need help right now to move the needle on their business. They need real research. They need real advisory. They need real consultative approach. That's why Ascension is doing so well. So we're in the picks and shovel stages of AI, but problematically the developer community is so going crazy with demand. It's the infrastructure that's slowing everything down. And there's a huge debate about this. And I'm definitely hardcore on this because there's no doubt in my mind from what I see, the problem with AI right now is the infrastructure is just not there yet to fully turn the, move the needle in a big way. The activity on the developer side is booming. The solutions are coming online. You're seeing things hit the activity of building stuff with AI, using AI is off the charts. The RAG stuff and the retrieval stuff, co-pilots and chatbots is proof points that people are using their data and their knowledge of their workflows to write software and use AI to scale it. At lower costs, maybe what took 100 people now does 10 what took 10 now takes one. So like the human involvement to manage it is changing. So that activity is off the charts and scaling fast. The underlying infrastructure is the problem. The GPUs, how much is going to cost? What do I stand up? I mean, the edge is not existed. There's nothing on the edge right now. So again, this is a huge opportunity for the Dell's, the Eula package of the world, Amazon. I even think Meta will be a major supplier in this. So, Google. IBM too, IBM's the other one I'm excited about. I haven't been excited about IBM stock in 10 years. IBM is perfectly positioned.  In this case, IBM got a big rebound. They've been doing Watson for over a decade. Now it's prime time. They got all that DNA and muscle in consulting. They got business transformation and they got this Switzerland position where because they don't have a cloud and they have kind of everything else, they can actually be the monster connector of all the other stuff. So I won't call them an integrator, but they can just be a great solution provider to everyone and bring technology to the table. So they're going to sell Watson X to, they're going to provide Watson X to a lot of difficulty.  So cloud native goes mainstream with Watson X. It's a great combo. So they got to play at the Adobe conference last week, the Adobe summit and, and, but I guess my point being, I think the AI trade is going to expand beyond, I think it already is beyond just the picks and shovels.  And I mean, I think anybody with an install base and a brain and some resources is going to do really well with, with AI. Yeah. Well, speaking of AI, I heard that Amazon is doing great on the compute side. Obviously they're getting their teeth kicked in by open AI and the market perception.  Azure, obviously the security breach that you pointed out is a really black eye for them. But on the compute side and Google on the compute side, I'm hearing people talking about this big time that they can't get the compute that they need. So I'm telling you right now, there's a cold chill going through the industry right now from developers and people trying to scale up AI. I need horsepower, I need compute. And that's why I think there's going to be a surge for servers again, clustered systems, AI systems. So again, I was talking about this with Dell and HPE again, all past the month that you're going to see a massive surge in server sales. Why? Cause people are building a new AI clusters, spine and leaf built into mega systems. It's all going to come together. So a whole nother data center architecture wave is coming back. Not because cloud's irrelevant. It's that cloud operations now supports data centers for AI horsepower. So the cloud guys have to get their act together and to provide the compute and GPUs. Otherwise an NVIDIA will step up to the table, a core we will step up to the table or GROC has the inference engine will step up to the table. So you're going to see a lot of action Dave on this. And next week at Google Cloud, we're going to unpack all this. Let me ask you this. So Matt Woods said, it was really a strong statement. He's like, look, we were the first to ship H 100s. We have 400 instances, EC2 instances. We will be the first to ship Blackwell. So we're really making that point strongly. But you remember we attended that luncheon with VAS that was hosted by VAS that GTC with the Genesis cloud.  So I reached out to VAS and said, can you give me a list of those guys? Cause I don't have them at the top of my head. So Genesis cloud was the one that we met, but there's core 42 core, we've lambda, Neville. And I'm sure there's others that are raising tons of money. What do you think about those alternative GPU clouds? I think that's huge. I talked about this year and a half ago. We call that tier two clouds. Remember we called it the cloud power loss. You think they're all going to make it though? There's a lot of them out there. Well, I'll tell you why the demand is so high. So first of all, I think there's a was we're in a secure and a security, a scarcity crunch with GPU. So one, having a managed service with some sort of elastic front end to it, whether it's pay as you go, managing costs is going to do well for any company's got that because right now the demand for GPUs are high. And until the horsepower comes on premise where people can build their own clusters, you're going to see managed services being used to the extent that someone can't get compute or GPU from Amazon, Azure or Google, they're going to go to Corweave or if they got a better price, they're going to go there at their price sensitive and not locked into the cloud, AKA using the stacks of services say at AWS. So that's that's that. I think Matt Wood and Amazon are going to absolutely get it right in the history. If we look back on history, I think the bet that Amazon's making is we will never yield on our ability to deliver infrastructure as a service. Everything that's come out of reinvent in the past for years has been leading up to this moment where Chick Custom Silicon is leading the way and they have the horsepower to do it both on training and inference. They might look like they're fumbling with them at the LLM level with bedrock, but I think they just set in the table and know that no matter what anyone does, the game will be determined in the later innings. And I think that's going to be a play from Amazon. Google is trying to catch up to stay in the game, but if they can't get compute, that's going to hurt Google. So I think Google's doing really well right now. They just got to get their act together on some of these SLAs from what I'm hearing in the field. So, and they're doing good. So there again, Gemini, I think we'll be embedded in. People are trash in Gemini, but there's uptake. So it wasn't a launch that the press freaked out on over some bad data, which is understandable, but they could have done a better job not defending Google. I'm just saying that you don't judge them on that. You judge them on how fast they can stand up the models and who's using it and can they deliver compute? And that's going to be Google. So clearly, Dave, this is going to be the end game. The later innings will determine the winner in this game. I have another source that I can't, it's anonymous source, but I met with this individual in New York City. We had drinks at the bar.  And I was asking about Microsoft and OpenAI. He goes, I won't go near it. He goes, I go, wow, we use it. It's a better product actually. He goes, I won't touch it. I won't touch what? And OpenAI. He goes, nope, nope, don't trust it. I'm like, really? He goes, yeah, I can't put my stuff into, I can't use it. It's banned in my company. We're big bedrock believers. I said, what about Google? Google shakes his head. He goes, they got the best tech. He goes, they just don't know how to sell. And he goes, they don't know how to service me, but their tech's unbelievable. So he's big. He said Amazon shop number one and Google for AI stuff. And so that's kind of interesting. Well, I mean, that is, that's a problem for Korean, George Korean. They have had challenges building all the infrastructure for their business operations. I mean, Google's sometimes their own worst enemy from what I can see, they got a great tech, but their discipline has always been big back end, not a lot of front end, you know, classic Google search kind of philosophy. But they've been trying over the years. It's just, it's not an overnight success.  It's hard. It's not easy. I mean, if anyone can do it, someone from an Oracle background like Korean, but, you know, think about that, the task he had. I mean, when Diane Green was there, she got the product stuff together, but it was just too much product up leveling and too much business operations. Amazon's a well-oiled machine compared to Google. Microsoft, I'm probably sure it has the best operational go to market stuff because they've got DNA in the enterprise for years and decades. So I would say Microsoft's probably at the best machinery from the customer standpoint. Amazon number two, Google number three. And if you put Oracle in the mix, I put them at one or two because Oracle's a well-oiled machine when it comes to sales at marketing. I'm so disappointed with Microsoft.  But then, again, just reading this report, I'm just freaked me out. We got to talk about Intel. Can we? Yeah. I mean, like I said earlier, I was really kind of, you know, seeing people on TV just a week ago, oh my God, Intel's tailwind. They're going to be great. Oh my God. After that foundry, after the foundry, like, Bruhaha. So the stat is they, you know, to their credit, they're going to split out the foundry business from, you know, the design business. And they said they had a last year a $5.2 billion operating loss or two years ago. And last year was $7 billion loss on $19 billion in revenue. And they're saying break-even is three years out. And they want to have 40% gross margins by 2030.  Lawyer texted me, John, because you know where he stands on arm and Intel. He said Qualcomm X Elite Win Arm Chip seems to have found a lot of favor with PC makers, boasting 75 trillion operations per second, double exclamation point in red. IDC's PC forecast is plus 2%. Therefore, x86 volume has to be declining. The only way volume to get Intel to volume is to take very large arm orders away from Samsung and TSMC. And you got TSMC obviously, the gold standard. Samsung just upped its investment in that Taylor, Texas plant to 44 billion. And, you know, you look at Intel, the vast majority of its foundry business, if not all of its foundry business is internal captive. And it's like, so unless they can take huge orders away from Samsung and TSMC, which is a very low probability, they can't get to volume, which means they can't get the cost, they can't get the yields. And so again, I asked the question, if de-risking the supply chain for the United States for its critical infrastructure is the objective. Would it perhaps be better to invest in TSM and Samsung, who are way further along? Samsung, you know, says it's gonna be profitable now.  I just read, would it be better just to invest in them and say, you know, look, Intel, go design. Who needs Intel for foundry? I mean, this is a bit, this is an important question that I want to unpack. It's like taboo. But is the important criterion, John, that it's manufactured in the United States or does it have to be a US company? Like Toyota has 140,000 employees in the United States. So that's, I think, a question that has to be asked. That's a tough question.  Whether it's Intel, Foundry or TSMC, it has to be someone in America. I think America should have some foundry. But the government bailing out Intel is not the answer. Intel's hemorrhaging, right? So again, the question is twofold. Foundries are needed. You got to build chips. That's a net. I do think that's a national security challenge. I do agree with that. Don't like the bailout. I don't think Intel might have been a good call there, but Intel's already like seeing massive financial hemorrhaging on this. And Bernstein's piling on too. They're already down 13%, one of the biggest shareholders. So Intel's licking their wounds. So the bailout thing bothers me with the government. I think they could have maybe looked at a different scenario. That thing's just a subsidy. Subsidies don't work. And I'm not a subsidy fan of subsidy. You got to have a turnaround plan with real meat on the bone. So that's one. Number two, you don't know what you don't know. For example, the market on semiconductors, for the people who know about semiconductors, and this is something that you got to be really careful of.  You got to get it right. Not just say, you know, got no business being on TV saying Intel's going to be a great company. And then they just crash and burn. You shouldn't even be invited back. But if you took up the financials of the semis, the AI and allocation optimization is going to be critical because you have a supply chain problem. This is the key business problem. You got to deliver the chips and the foundries are going to be backlogged. So you can design all the chips you want. So there's a need for foundry. The question is it takes too much time to ramp them up. So this is a tough call. I don't know what to say. I mean Qualcomm, Broadcom, they're all building chips. They all got to have supply constraints. I mean, if you get the allocation right, you're going to have a good numbers because you'd be just shift.  And so I don't know, Dave, it's just like... I mean, history shows that number one in the market and a big market does really well. Number two does okay. Number three essentially fails. I mean, we're seeing that in cloud and the cloud's big enough and Google's got enough other business that it can sort of hide its deficient cloud business compared to the other two. But this is the classic case. You see it in so many markets. And if Intel can't become like a viable number two surpassing Samsung, but Samsung has to keep up with Apple. And it's got light years of experience in building advanced chips. And so not light years, but I mean, it's well ahead. So I think realistically, at least Pat is realistic. Like I'm not going to catch TSM. I can maybe be number two, but if he doesn't get to number two, it's going to be just a pouring money down a sinkhole. And that's really, I've said it before, Intel could go bankrupt.  And I think you got to ask the question, is that the right use of taxpayer money? Also, they'll make like take Qualcomm for instance, Qualcomm has many different like platforms of chips. So the allocation of the resource to the different chips matter too, right? So it's just, it's just, it's a tough game. We'll see. I mean, we in talking to the Broadcom folks and Intel folks over the years on Qualcomm, the semi guys know the business. I mean, they're in the hardware game. Everything's about turn, next cycle, next front, next improvement, moving the units out there and being ready for the next generation. So we'll see. And again, Intel, we'll get to keep an eye on those guys, continue to keep an eye on it. The good news, Dave, we got it. We got everything right and we're not even getting access to Intel. So, you know, Intel is not really embracing the industry, engaging the industry analysts on this because I think they're just trying to feed the market PR. So it's interesting to see Intel's analyst relations strategy around this. I mean, I don't know.  And I mean, we're doing another, we're doing a breaking analysis this month to unpack Nvidia. Obviously, we'll talk about Intel, ARM, alternative XPUs. He's somewhat down on the chiplets, as you know. I've been testing that, but I think there's a real market for it. So I'm going to push him on that. And I think he thinks that the edge opens up a lot of opportunities for specialized processors and XPUs. So that's going to be fun. Well, anyway, let's, we're getting down to the end here. I want to put a quick note out there. We're going to be at Google Next next week. Google Next is their cloud event. What's interesting, Dave, about this, is that Google Next is, they just had their event and they moved it up this year to make sure it's in Vegas this year, not San Francisco. Again, one of the companies that are moving, moving the event out of San Francisco because of all the issues there. But we have a great lineup. We got, we have a ton of leaders in the ecosystem. McKinsey, Accenture, Automation Anywhere, Quantify, Elastic, Deloitte, Google execs coming on, top, top execs coming on, PWC. We have ARM coming in, first an SVP of infrastructure coming in from ARM. So I'm going to talk about semi-conductor, got ARM coming on. We're going to have ACL coming on.  And we're going to have J.O.T. Banzi, who's the CEO founder of Harness, who's also a VC. We got the chief product that was from Box coming on, Diego Dugakin, and we got the CTO, the field CTO of Databricks, Gabe Monroy, developer experience VP at Google Cloud, Bobby Allen's coming on. We're going to have a whole day dedicated to analysts. We're going to have five segments dedicated to analyst angles. Oh, nice. Analyst angles plural with an S. CUBE collective folks. So the CUBE collective are open, United artists of analysts, United analysts model is coming together nicely. We're going to have actually three dedicated segments to non-Cube research analysts. And of course we'll have our CUBE research analyst angle on there too. So you're going to see a lot of great sponsored content coming out from some of the best leaders. We're going to extract their stories and share them, but we're going to have great CUBE editorial and reporting and analysts coming on. So Google next should be great. And of course this month we get SAS and then next month the Red Hat Summit, Bumi World. I got two kids graduating from college, Horace A, Alter-Rig, Delta Tech World, IBM thing, Medica World, Memorial Day, Dave it's going to be summer and it's going to be like, what the hell just happened? I know. Hey, just a quick, I want a quick hit rubric filed for IPO and they're going on the NYSE. And it doesn't look great on paper, but I think it's actually fine. They have $6.3 billion valuation, they're losing money, but it's because they're transitioning from a perpetual model to an ARR model. They're ARR business, subscription business, growing like crazy. And so I actually think they're going to do really well. And I think we're going to be interested to see if Databricks goes, if Arctic Wolf goes, and then maybe eventually Cohesity, which by the way, speaking of Arctic Wolf, we're going to have RSA is coming up as well. And by the way, we've got official word now that at RSA, the Monday we're having an event. We're hosting an event with the New York Stock Exchange and Intel Capital and some two other companies with us we're going to be hosting an event, open policy, Intel Capital, and waiting here for a few others. The Cube will be headlining a party, invite only. It's going to be at the Lamar in San Francisco, the Monday happy hour from five to seven. If you're going to RSA conference and you're going to be there, send me and Dave a note or one of us a note and we will send you a link to sign up for a special VIP happy hour.  So great to have these happy hours, Dave. This is going to be, usually we go to the happy hours. Now we're having our own. That's going to be awesome, John. Yeah. All right. Well, let's sit for over an hour and have a good rap here. Again, Google next is going to be great next week. It's going to be, I'm expecting to see massive amounts of AI announcements, but we'll be squinting through those analysis. We're going to ask some questions about the infrastructure. We're going to have Mr. Lohimer back on. You know, he's awesome. He's former VMware Dave. And we're going to have all the infrastructure guys coming on as well. So we're going to, we're going to get them. We're going to get the stories. So see you next time. Thanks for listening. That's Q pod 53. Thanks for listening.


This lecture is on data recording and signal transmission. As you know in machinery condition monitoring, it is very important that we get the data out of this machine and then record it, so that we can do these signal analysis on it. Well, so what is the format of recording is what we are going to discuss in this class, the methods to record certain signal and then how do you do the analysis. You all recall that in computer rated data acquisition, we notice the importance of sampling frequency and then you know the number of data points. This plays an important role in the selection of the data recording media or format. Well, before we come to this idea, I would like to tell you something about the traditional techniques of data recording. Well, you almost have visited certain plants could be a power plant, could be a steel plant, so they have a control room in which I am talking about things in the industry about you know 3, 4 decades ago and then all they had were lot of analog meters essentially RMS or peak meters and data logging was done by plant personnel. This was the format of data recording may be couple of decades ago, but now what they were doing there is as a function of time, they would note down the parameters like the current drawn by a motor, may be the temperature of the bearing, may be the supply voltage and this time mind you or may be in every 30 minutes in hours. This was being done by a human being and if there was an and this is this should be the log sheets, they would essentially form the log sheets and then and this were available in the plant room and then if there was an abnormality at certain conditions, people would go back to a certain date and look into the log sheet of the plant and then try to come to a conclusion like what went wrong and so on or suddenly if in the plant operator notices that the temperature is suddenly rising is increasing, then they would certain alarm condition and then people would take precautionary measurements as to how to reduce this kind of abnormal behavior of the machine or how do you control them. But there were lot of problems with this, this was a completely manual mode, there are problems in which human errors could creep in because of wrong recording, wrong writing, wrong reading, this was one possible. Another was it was tiring and cumbersome because you know visually you have to read all the meters in the control room and in the control rooms could be panels of meters and so on and this was a very cumbersome and tedious process and then there are lot of chances by which errors could creep in and there are been instances where people have reported that because of a wrong reading recorded by person the interpretation was wrong and then the analysis of the prognosis was not correct. Well, another method was there were other than writing, so the first one was human intervention with physical data logging. But closely with this is using a either a printer or a strip chart recorder these are the analog and wherein there will be just a voltage signal coming out because of the parameter and then with time basically there will be a paper roll which will be moving at a certain speed v meters per second and then there will be pens on this and they will be writing or plotting the parameters and this would be on the roll and this is essentially what is a strip chart recorder and then people used to have such large rolls of data wherein we can look into the problem and then find out get basically a time history of the signal by looking at the strip chart recorders and some of these data logging meters used to have you know trip set tripping levels or alarm levels set preset that means when the alarm level increased or the signal level increased beyond the set level it would trigger an alarm. And this was what was the traditional method of I would say data recording, but as you can see the problem with this kind of data recording is if I was to less down one was lot of scope of human error it is manual thus TDS time consuming it is not real time it is difficult to difficult to obtain past data because then one has to physically pull out the log sheets and that would be cumbersome and then because of large amount of writings archiving or storage issues present. Now, because of this disadvantages since the sixties people have started to use other means of recording and that was the machine mode and first would be a tape recorder like the spool or the cassette of course cassette came much later and the spool type tape recorder. Now, we will look into this different types of tape recorders and then what are the present format of tape recorder and how we do the digital recording in such tape recorders. So, if I was to look into the different formats of data recording available today one could be the analog recording or the digital recording. What happens in the analog recording we used to have the direct recording method wherein on a cassette or a tape recorder or a spool type tape recorder the data was stored because of the hysteresis in the ferritic because the signal would be converted basically essentially we have a voltage signal which is a time varying or an AC voltage this could be my voltage signal which has to be recorded. Now, because of this varying voltage there will be an magnetic flux created and the signal is recorded through the head of the tape recorder. So, we have a magnetic head and then the tape is made to go at a particular speed and the take up spool and the Holmes spool and they go at a particular speed and some of them could be 6.77 inch per second and this is a value or this is a industry format then it was in the inch per second speed linear speed and this dependent on the rpm you know we used to have the old LPZ in a 45 rpm or 233 and one third rpm etcetera. So, these are the particular speeds at which these things used to run of course, in the I am not talking about the LP spools because in LP spools what happens the data once stored cannot be erased or changed. But, we are not talking about the LP spools in this case we are talking about in head wherein because of the magnetism in the introduced in the ferritic tape the data could be stored and then of course, we also have the possibility of having an erase head if the erase head is there you could demagnetize it and then the tape the information stored in the tape could be removed. But, the advantages or disadvantages of this kind of systems are this had a very poor dynamic range by dynamic range I define as the highest recorded signal level to the lowest recorded signal level and they were also having a frequency limit like an upper limit or lower limit and traditionally these magnetic tapes had a very low upper limit could be as low as 10 hertz and may be as high as may be 12 kilo hertz so on. And, this later on improved with the type of tapes you know there were metal tapes, chromium tapes etcetera wherein this could be go up to may be 70 kilo hertz. But, traditionally this direct recording tape recorders were used mostly for recording audio signals and as you know the audio signals are in the range of 20 hertz to 20 kilo hertz and these recorders did a good job in recording such audio signals or for that matter any signals which had which were in this frequency range. So, when you are talking about machinery condition monitoring we could be recording signals from noise or acoustics signals vibration signals and may be temperature signals or certain process parameters like pressure etcetera. And, our requirement is that with each device we have to either reproduce the original signal. But, the problem was this direct recording was data storage because once we store it in a magnetic media there is a every possibility possible chance that in a magnetic environment they may deteriorate the physical tape may get corrupted because of moisture high temperature etcetera. You must have seen if in at home if you store your old and if you ever have your old school tapes or cassette tapes stored elsewhere you will see if you do not care about the moisture around you know there will be lot of fungi formation in those tapes and then there will be you will lose the signal quality etcetera. And, these are the problems and of course, from a signal processing point of view the major problem is it cannot record the low frequency signal. But, you know in these early 70s or late 60s this pool type direct recording tape recorders were quite popular because that was the only technology available then and they were doing fairly a good job with recording audio signals particularly in a music recording etcetera. Of course, if you want to then have a permanent recording in the media people use this the record record player in the record the LP records the EP records etcetera wherein permanently the song tracks could be cast and kept in those vinyl discs. But, we are not talking about you know we obviously cannot store data because data has to be recovered for an analysis and we do not want to record it permanently in a recording media. So, that the provision of erasing was not possible in the LP records of the EP records. But, in the direct recording by having the series head we could have always reduced erase the data recorded. Then, they coming back to the next format of recording that is the FM recording that is the frequency modulated recording. The FM recordings are very popular and convenient because they can measure as low as DC and then can go as high as may be 12 kilo hertz etcetera. So, low frequency low frequency recording are possible by the FM recording of course, the recording media could again be a spool or a cassette tape and so on. But, the advantages the low frequency recording could be done and it has a relatively high dynamic range. So, this was popular from the may be from the 70s to the may be may be mid 80s people used to use this FM recording for recording the signals this is from mid or mid 70s to 80s people were doing the FM recording. But, then this later on from mid 80s we have come up with this format of pulse code modulation recording which were essentially an high frequency recording used in the in the video recording. People use the same pulse code modulation technique which was used in video recording for recording audio signals and people then use them in data recorders or digital audio tape recorders and these are quite popular till very recently may be may be till about late or may be outside till the year 2000 they were very popular digital data recorders and the advantage of this data recorders are they are DC to about 20 kilohertz range high dynamic range compact and so on. If you see here I have just made a comparison in this slide on the data recording methods by the direct recording the frequency modulator recording the digital recording the lower frequency limit in direct recording about 20 hertz upper limit is 20 kilohertz noise floors 10 millivolts that means you cannot record any signal as than 10 millivolts dynamic range is very poor 40 decibels and time compensation is not available. Whereas, in frequency modulation recording I can go as low as DC or 0.1 hertz to about 12 kilohertz of course this has improved lately again noise floor is 10 millivolts but, then we can have a very very high dynamic range of 80 decibels and time compensation is possible. The digital audio recording or the data format which we use can have a low frequency limit of DC and then the high limit high frequency limit is 20 kilohertz noise floor is 0.1 millivolts and 100 decibels. But, the advantages of the digital recording over the other recording is that the noise floor being very very low we can have signals from devices like thermocouples as you know thermocouples give a very very low millivolts signal as opposed to may be an axial armature may be 10 millivolts and if I was to for a good compare this are basically low frequency signals and these are relatively high frequency signals. Now, having a signal less than or around 0.1 millivolts it would be very difficult to measure either by FM or direct recording but, you have a digital audio tape you could record such low signals in low frequency as well as high values and at high frequencies because they have a high dynamic range and then low noise floor. But, all these three types of tape recorders are available in the market I mean of course, you know it will be very difficult for you to find a digital sorry a direct recorder or an FM tape recorder with spool of the cassette nowadays. But, even finding a direct recorder could be difficult at times because you know nowadays people are using the DVD recorders or totally recording in a flash car or a compact media or even writing it straight to the hard disk or the device. But, the advantages of these recorders are they are portable low power consumption some of these recorders can be even powered from the 12 volt DC adapter given in your car. So, the mobility of such recorders is excellent because why do we have this data recorders in the first place? We have this data recorders because we cannot possibly go to the plant may be the plant is a coal mine a very dusty environment. We cannot go and sit next to a crusher or a grinding mill and do the vibration analysis has to find out the remaining useful life of this crusher or the grinding mill rather have the transducers mounted around the grinding mill or the crusher and then record the signal on a tape recorder and then this tape recorder obviously has to be portable has to be lightweight could be powered by any source and then once we have recorded it in a particular media we can bring this tape recorder back to our laboratory wherein we can do the signal analysis and try to find out the condition of this machine. It is not that the tape recorder is fixed or tape recorder is always there in the lab no this is not the objective of our recording here or recording in condition based maintenance or monitoring is to be have a big steel plant for that matter next to you sitting in your analysis office. Obviously, I cannot bring a steel plant to my office, but if I have recorded the signals in a tape recorder which reproduces the signals as they were recorded or as they were measured right at the steel plant my job is done and with that objective actually we are recording this signals and then we will see what are the advantages of using a portable recorder with high dynamic range where the data can be stored for long long of time. So, right in the beginning of this class I told you about the importance of two factors one is the sampling frequency and other is the number of data points. If you will recall because I need to do signal analysis that means if I have a signal like this some signal this has to be sampled at a fixed interval this has to be fixed at the sampling interval and the total time of this data total time is t is equal to n times delta t this t is equal to n times delta t where n is the number of data points. In the previous diagram all those crosses I had done they were actually the locations of the sampled digital data. So, in terms of the storage memory requirement because this digital data has to be stored in some memory location in the digital recording. Now, if every data requires 16 bits bits or 2 bytes of memory space suppose I am sampling at f s is equal to may be 100 kilohertz that means delta t is equal to 1 by 100 kilohertz. So, 10 to the power minus 5 seconds is my sampling interval and if I have to find out the total time is equal to n times delta t n is equal to the total time by delta t. Now, if I want to record 10 minutes of data at a sampling frequency of 100 kilohertz my number of data which has to be stored is 10 minutes 10 into 60 seconds into 1 by delta t which is 1 by 10 to the power minus 5 that is 600 into 10 to the power 5 data points. Now, if every data point requires 2 bytes I require the memory required to store 10 minutes of data sampled at 100 kilohertz in a 16 bit recorder would be 2 into 600 into 10 to the power 5 bytes. I would know 1 kilo byte is equal to 1024 bytes if I approximate it as you know may be for the sake of discussions may be 10 to the power 3 bytes the memory required 2 into 600 into 10 to the power 5 that is equal to about 1220 into 10 to the power 6 approximately 120 megabytes m b. Though I have used an approximate that 1 kilo byte is 1000 byte that is not quite right, but just for the sake of this discussion actually 1024 bytes. So, approximately they to summarize to store 10 minutes of data sample at 100 kilohertz in a 16 bit digital recorder would require approximately 100 120 megabyte of digital memory. Now, you can now multiply now if you want to instead of 10 minutes you want to make it 100 minutes another extra 0 coming here. If you want to sample at a still higher frequencies you know here the because n is equal to t by t by delta t or t times f s. If you sampling frequency is increased n would increase if your time is increased n would increased. So, depending on that you can do a calculation and then try to understand what is the amount of memory required, but in digital recording the most limiting factor is the sampling frequency. In digital recorders depending on our frequency of interest or depending on the frequency of interest we can have this sampling frequency decided from the Shannon sampling theorem. Now, suppose we are talking about a mechanical signals basically in the audio range from 0 to 20 kilohertz may be we sample at sampling frequency f s is equal to 44.1 kilohertz and this is a set sampling frequency and then we can record as much as you want provided we have the memory space, but nowadays there are very very high sampling devices which can sample at about 100 mega samples per second and this is the present state of the art is we can have a to d systems a to d card which can sample at about 100 mega samples per second and then this high samplings are actually used for studying ultrasonics because ultrasonics occur at frequency below or greater than 20 kilohertz or you want to study about acoustic emission which occur from anywhere from 2 to 5 megahertz. So, if I was to record any ultrasonic signals or capture an acoustic emission phenomena I have to be sampling at very very high frequencies could be you know 100 megahertz and so on and these a to d cards are very costly very difficult to find at times. Now, the less nowadays systems are coming in where such sampling devices are there and then we eventually we have to store this data n data recorder data points n data points and from the previous example you just saw that by playing around with the number of data points sorry by playing around with the sampling frequency and the time data time which you require to record we can increase the number of data points. Now, so how do such digital data recorder replace the conventional you know you recall the analog data recording people are doing by hand how does it replace how does it replace analog recording. So, in this data logger which we will call I can decide frequency of storage may be continuously and imagine I have a signal which is low frequency content and another which has high frequency content like the case of the thermocouple and the actual number. So, depending on my interest low frequency content could be sample at a low frequency high frequency content could be sample at a higher frequency the in the same data logger the number of input channels is also a very important characteristics. For example, you know there are 16 channel recorders even there are 128 channel recorders and so on that depends on the interest of course, we all the previous examples we have done with one channel if simultaneously I would not record 16 channels of data or 128 channels of data I need to also record them and multiply the number of data points by the small n and small n is the number of channels. And this data points has to be stored in a memory which is RAM memory may be which could be erased and then if you want to have a permanent record you can write to CD DVD or even put it in your hard disk or may be also in a memory card or a device. Nowadays you know you get cameras you know flash cards with you know 32 GB or 64 GB storage capacity. And these are traditionally you know compared to the data recording facilities we had in the 1960s where in a typical spool or a cassette tape would at most have you know 60 minutes or 90 or 120 minutes of data could be stored. This was the duration of the spool cassette type 60, 90, 120 obviously we cannot do continuous data recording over months in such recording because of the limitation of the storage space. But nowadays with such compact memory cards with capacity of 32 or 64 GB you know 64 GB would last you quite a may be you know just showing example in the data recorder how this can be stored. So, come back here this is a typical data channel recorder which we have in our laboratory this is a this is where the digital cassette tape goes in. And if you will see here there are 8 channels of input and 8 output channels and then you can record about 120 minutes of data in each tape at a maximum frequency of 20 kilo hertz in each of the 8 channels. So, the number of channels times the frequency bandwidth is a constant in this kind of data recorders. So, it is 8 channels at 20 kilo hertz and then you could be if you want to make more channels if you want to make 16 channels it has to be sampled at 10 kilo hertz and so on. So, this option can be there are 32 channels at 5 kilo hertz, but the limitations of such data recorders are I obviously cannot record for months together recorder is not possible and then I obviously cannot record at very very high frequencies may be in mega hertz. And then of course, again this has to be stored in data tapes which could be contaminated. So, of recently quite you know in the last may be 5 6 years people are using this digital data recorders like you see one which we have in our laboratory here. You can have 16 channels of record and then you can give in signals and for such a recorder we can have very very high sampling frequencies and then we can record lot of data points. Just give an example for this digital data recording with about 2 giga point of memory space there is a provision of changing the sampling rate 100 mega samples per second or 10 mega samples per second with single channel I can be recording at 20 seconds of data 100 mega samples you know mega hertz per second. So, very very high sampling rate whereas you know even you know we were happy with 5 kilo samples per second. Even if you for a mechanical signal if you sample at 100 kilo samples per second with single channel you can record 5 hours of data and if you are running at very very slow phenomena like monitoring temperatures 200 samples per second that itself is very very high while monitoring temperatures you could be recording with single device for about days without worrying about your memory space about the reproducibility of such signals and so on. A very important point comes in this recorders is the how do you determine the frequency response of the data recorder you could also try this with the digital recorder a simple way to do this is suppose we have a data recorder all you have to do is record a random noise signal at the input and then play back the signal output signal and then simultaneously plot this as a function of frequency play back signal by the input signal. And you will see a frequency because if the play back has to be of the same level as the random noise signal and if you have recorded a random noise of a particular band width you will see and in a dB scale this would be 0 dB this value because in log of 1 is equal to 0. So, if play back is of the same level as the input signal it will be 1. So, it will be 0 decibels. So, this is the useful frequency limit of the signal. Research it actually does this, but it is a good idea to check it out if you have an FFT analyzer you can also see the if you have an FFT analyzer you can also see the phase response. So, the phase of the transfer function between the play back and the input and this phase angle should also not vary and this usually this phase angles typically are the of the order of 2 to 3 degrees. So, then you can see the frequency limit of the signal. So, the upper frequency limit is decided by doing such a small test using a random noise in a data recorder and you could use that same technique in a digital recorder as well and there should not be any crosstalk between between the channels. Now, once we have recorded this signal we also need to transfer the signals and usually the transmission rate of the signal or signal transmission rate depends on the number of channels multiplied by the acquisition rate and the resolution. So, the data transfer rate even if it is given in bits per second and acquisition rate in samples per second resolution in bits per second. So, the data rate the data transfer rate transfer or transmission rate is the number of channels multiplied by the acquisition rate multiplied by the resolution and acquisition rate it is the unit is samples per second and resolution is bits per sample and the units of data transmission rate is bits per sample sorry bits per second. And usually when we use wireless for data transmission rate we have about the maximum level of 10 mbps or you know 1 gb gb. So, we can decide what would be the sampling rate and this is the limitation of the present day data transmission by signal by wireless transmission as to the maximum data transfer rate available to us and which is about 10 mbps or 1 gbps and then this depends on the number of channels of data you are transferring the acquisition rate and resolution. Typically the resolutions nowadays people use is about 16 samples per second or 24 samples per second and the sampling rate for a low frequency signal it is easier to transfer by wireless, but when we are talking about high frequency signals you know you want to sample or send and 10 channels of vibration data over the wireless the data transmission rate of the present wireless technology is limiting these levels of data transfer. And so that would conclude our discussions on the signal recording in the analog digital domain and the signal transfer by in the wireless. Thank you.