 From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. MIT professor and economist, Eric Brynjolfsson said recently that he'd be disappointed if AI didn't lift the current anemic 1.2% productivity growth rate to 3% or even 4% or even higher. This would be a good thing for business and government as it could potentially help with the labor shortage, drive earnings growth and increase tax revenues which would ostensibly help address current debt levels. This is one of the promised impacts of AI. Now, while the hype surrounding gen AI has narrowly propped up certain sectors of the market like for instance, AI startups and of course the magnificent seven, the macro effects have not been felt thus far as adoption remains largely experimental. Hello and welcome to this week's Cube Insights powered by ETR. In this Breaking Analysis and ahead of SuperCloud 4, ETRs Eric Bradley and Darren Brabham join me to share the latest trends on AI adoption, how gen AI is being used, some of the deployment models and the AI leaderboard based on spending momentum and presence in the market from ETR surveys. Gentlemen, thanks for taking some time. Good to see you. Thank you so much for having us, Dave. All right, you bet. Okay, first let's look at the enterprise IT market broadly and the impact AI spending is having on other sectors. This graphic shows the dozens of sectors tracked in the ETR technology spending intention survey, TESIS, T-S-I-S. I think there's almost 30 sectors. It shows net score or spending momentum on the vertical axis and presence or sector pervasion on the horizontal plane. The red dotted line at 40% indicates a highly elevated spend velocity within a sector and note that squiggly line on the ML AI sector. So as we exit the isolation economy, AI momentum decelerated along with overall IT spending. But look at what happened the month after chat GPT was announced. ML AI has now taken over as the number one sector in terms of spend momentum. Now the rub is, as we said in our research note, lower for longer, for the past two years, overall enterprise tech spending expectations have decelerated from a high of 7.5% growth expectations to the current 2.9% level in the macro drill down surveys that ETR does. The issue being that CFOs and CEOs are generally not allocating discretionary spending for CIOs to go pursue AI. Rather, the trend appears to be AI initiatives are stealing budget from other sectors, many of which are being de-emphasized as AI experiments are funded. So with that as intro, Eric, Darren, what's your take on the macro spending environment and the impact of AI? I think Eric, you're going to take this one. Yeah, I'll jump on this one first. There's a bunch of data points here I want to add to that. One, yes, we are growing our sectors. We actually just added FinTech. So we're continuing our coverage growth and we'll continue to do so as we need to. But as it comes to spending, yes, 3% doesn't sound that exciting, but we were coming from an area where it was 0.8% at one point last year. And 3% is a huge growth from there. And the other thing I really want to point out that it's important is when we were capturing those really bad low numbers at under 1%, it was the world's largest organizations, the Fortune 100, Fortune 500, Global 2000, that had the worst spend. Right now, we're seeing them back up at the mean average. So when we're seeing the largest organizations in the world actually increase their spend, I do think we're coming out of this. I do think that's a good thing. So that's point one. Point two, when we're talking about the sector net score for MLAI, this is at historic levels. We've been doing this for 12 years tracking things. And right now MLAI has a sector net score of 52%. That's up from 38% just 12 months ago. And by comparison, the overall average of our survey is only 20%. So MLAI is at three times higher than the average in the survey. Only container orchestration comes close by breaking 40. It's really just in rarefied air. It's all alone in that realm. And then quickly, your comment about regarding AI stealing budget I thought was really bad, pertinent. So we did a recent drill down and it showed that 67% of those Global 2000 respondents stated that they're not using net new dollars for their generative AI evaluation. They're actually taking it from other areas and only 33% are actually introducing new money to gen AI. So a really valid point that you brought up there. Yeah, thank you for that. You think we like data folks? All right, let's take a look at the latest ETR data on AI adoption. This chart here shows the results from the October survey. And in this view, IT decision makers, we call them ITDMs, were asked if the customer is evaluating gen AI and LLMs for business use cases. And if so, which ones? And you can see the steep decline from the April 23 survey for those organizations who say they're not evaluating gen AI at the time we were like, wait, why aren't you? For real, but look at how that's come down. The gray to yellow bar is going from 52% to 26% today. And you can see the steep uptick in the use cases from April to October. By the way, the percentage of customers in the Global 2000 that are evaluating gen AI is much, much higher than the industry average with only 14% of the Global 2000 respondents saying that they're not evaluating gen AI. But guys, when we look at the data in terms of what's actually happening in production, we see in this chart two significant takeaways. And I'm sure you guys can add some context here. Most of the action remains in the blue bars in the evaluation stage. And the second point is the use cases are really those most typically associated with like chat GPT like work. You know, very productivity focus and pretty straightforward. Now that's not necessarily a bad thing, but what does this data tell you? Yeah, I mean, so I think, especially in this macro environment, organizations are going to be as cautious as they can, right? They're getting whipped up in the excitement and the buzz of generative AI at rates we've never seen before around a technology, frankly, but tight budgets and uncertain business environments. They really mean organizations are start dipping their toes in the waters that feel the most familiar. So, you know, first off on the previous chart, it's remarkable how fast organizations are evaluating generative AI for business use cases. You know, previously it went from six months ago. It was half organizations were not evaluating and now only a quarter are not evaluating. So that's pretty remarkable pace. But second, you know, relevant to the chart you just showed is, you know, we see pretty safe or tried and true use cases that people are starting with. Business has been testing and refining AI chatbots for customer service for several years now. So it's a natural place to start when a company tries to see if generative AI can do something better or quicker or more nuanced than building the logic flows of a typical chatbot. And so that's why it's the biggest chart, the biggest bar among the use cases here in full production. Text and data summarization, which is kind of mostly how people know the popular chat GPT's abilities. That's a close second in production. So no surprise there either. And then cogeneration documentations next. And again, this builds on years of baby steps in the programming platforms out there that have been trying to build in automation and shortcuts and various flags and help that goes in there. So all that's coming through. But notably though, like you said, the pattern we're seeing here is that a little less than half the levels of production compared to those evaluating for each of these business use cases here. But still a remarkably fast pace and kind of exciting to see where it goes. You know, all three of us have been around for to see several waves. It does feel to your point, like the acceleration of adoption for gen AI is somewhat in line with the hype. I mean, nothing's ever really in line with the hype. If you guys know what I mean by that, but there definitely appears to be a lot of leaning in, maybe earlier on than say other cycles where, you know, we were amazed by the web browser. There were a lot of tire kickers in the cloud, but this one really seems to be much more broad-based. Do you guys agree with that? Yeah, very much so. Actually, you kind of jump in the gun. I was going to add about that in the end, but I will state that, you know, we've been doing this for 12 years. And I watched the cloud go from on-prem to cloud transition. We tracked the data. It moved slowly, really slowly. This is not moving slowly. This is at an astronomical pace. And I'll actually leave a little bit left for later to kind of talk about maybe some of the numbers there. But I completely agree with you. We'll see. There's still a lot left to go as we're going to get into. But yes, I agree with that comment that this is certainly living up to the hype more so than other hype cycles have in the past and certainly moving faster than trends. We've tracked data-wise previously. And of course, there's still a lot of uncertainty out there. So at this point, I'd like to introduce the power law for Gen AI that the CUBE research team put together a while back, and we've shared this before, but the graphic takes the power law framework, that concept, and applies it to Gen AI. The vertical axis we use to represent size of the LLM and the X axis speaks to the model specificity, the domain specificity. So the orange line takes an example from the historical music industry where four music labels, Universal Warner, Sony and EMI had nearly 90% of the market and hence that hard right angle with a very long tail. And then if you adapt this to Gen AI, we project that the large cloud companies, of course, along with NVIDIA and Open AI, they're capturing a lot of the narrative today where consumer adoption, as you can see in that upper right-hand box, that insert is driving volume, and that's always what drives industry economics, but there's other third party and open source models that are, we believe, pulling the torso shown by those red arrows up to smooth out the curve. And then, like many industries, we expect a very long tail of domain specific AI within various industry sectors. And then we take this out to the telco industry in the edge, which we think is where much the AI inference is going to occur on very attractive power per watt platforms like ARM system on chips. So I'm gonna stop there, guys. We're gonna dig into the ETR data to see if there's any evidence that this model is taking shape, but let me get your thoughts on this, if I may. Yeah, and I think you've done, this is a really great graphic your analysts have put together. I think it really captures kind of a rhythm or a curve of innovation or the way things kind of play out as new technologies come into the market. You do have the big players that kind of are dominating a lot of the headspace, but also taking on a lot of it. And then the long tail that goes out there and then niche use cases. So I think you're right on point with this. And I think to pivot a bit, I sort of wonder what's it gonna take to get there for many organizations to start doing this? And I think one of the things that stands out in some of our data, we did ask organizations what's the holdup or what's the thing you're gonna do in your data and analytics pipeline to get ready for generative AI? And I think that speaks volumes to maybe the pace of how organizations are gonna get there and the kinds of tools they're gonna use. The number one answer is improve data quality. That it's a back to basics, right? It's these large language models, they only work as well as the stuff they're drawing from. So if your data is not clean, it's not organized, it's not in a good data warehouse or data lake, it's not findable, it doesn't make sense, then things don't really get off the ground in a very good way. So I think we're gonna see is on the back tail of this hype is gonna come a reckoning with how good is our, how well-kept is our house and are we gonna be able to get back our data quality and our organization to really power some of these use cases. So I think it's sort of a related point and I know Eric, you've been thinking a lot about this in security too. Yeah, that's true, Darren, and I agree with all your points, but first of all, no offense to Stanley Smith Stevens, I'm no expert on power lower models, but I do agree that that's pretty neat chart you guys came up with. But my understanding of the theory is that it's really about exponential relationships of two measures and how they impact each other. And one of the areas where I see that happening that actually contradicts the image there about needing an economic use case for AI is within security, as Darren mentioned. And the reason I say that is because right now the top priority amongst all CIOs is still security. And it should be. We're living through cyber warfare on a daily basis. It's happening right now. And bad actors are using Gen AI and we need to do it as well to protect our precious infrastructure, whether it's utilities, energy, healthcare, banking systems. We have no choice. So I do believe that within security you might see one of the first full fledged Gen AI in production and we could go on for ever and ever and ever about what vendors or how that's gonna happen, but I'll save that for this time. But I do think that we might not have to see an automatic economic ROI driver in order for this to happen. I think it's gonna happen inherently anyway. It's interesting, I was at the CrowdStrike Falcon conference earlier this year, last month actually in September. And they were actually showing what is very close to being shipped a large language model to change the SOC analyst experience. So they're one of the first to actually be close to shipping and I think they actually will be shipping this quarter. And so I think you're right. Security is a very narrow but logical use case for Gen AI and LLMs. And you know, pursuant to that previous conversation that we were having in terms of sort of that long tail and there's a lot of discussion in the community about where the AI work is going to take place. Now interestingly, when you ask those still in production what's holding them back from getting there? As you guys know, the number one answer is we're still in the evaluation phase but the two big ones beyond this are data privacy and security concerns and compliance and legal concerns. The point is there are many people who feel that because of these issues and concerns about IP leakage that much of this work will occur on private infrastructure. This is from an earlier drill down study that ETR did. Note the smaller ends here, but it's still instructive. ETR ask customers, do they use public or private infrastructure for Gen AI workloads? And the data is very clear. It's literally a 50-50 mix. And again, when you look at the data in Global 2000 it's much more weighted toward private with the first two bars jumping up to 42% and the third dropping to 18%. Again, small ends. It was only I think 17 in the Global 2000 in this drill down. But it's still an interesting data point. Now when you look at the leaderboard from the ETR data it's dominated by the big three clouds and of course open AI. This is a similar XY graph with net score or spending momentum on the vertical axis and pervasiveness in the data set on the horizontal. Note the position of open AI in the upper right. It's shot out of nowhere and is a dominant force today. I'm sure you guys will comment on that. But then the big three clouds, what's notable here is their respective positions in change in that position. You can see it at the squiggly lines relative to the announcement of chat GBT. Microsoft shot to the momentum lead with its open AI partnership. AWS, which has always been a player in AI with tools like SageMaker, lost ground on the X axis and Google with things like Vertex AI and Bard and other tools gained ground on both the X and the Y axes. And you can also see data bricks in the mix above that 40% line as there have always been an ML leader and anthropic came out of nowhere. Very interesting toward the bottom of the momentum scale you see Oracle and IBM both in the game and with on-prem and hybrid estate. So gentlemen, lots to unpack here, your thoughts on all this data. Yeah, there's a lot to unpack here. I'm going to go off script for a second because I think it's really interesting when you show those vendor trends and a couple of names again, anthropic just jumped out of the gates, right? We're seeing that at 50% open AI when we first started tracking it in the ETS it broke records. The evaluation rates were absolutely through the roof. We transferred it over here to the larger survey. And again, it is by far the highest we've ever captured. Not surprised to see Microsoft AWS and Google all in there. Data bricks also very well positioned, data coo almost at a 50%. So we're talking a little bit about these, you know, data science tools that are helpful. And then to your point about IBM, I do really want to point out that 16% seems low. It's on the bottom right of the chart but that was going from a negative 8% just 12 months ago. So kudos to them. They had the technology early. They kind of stopped branding it. Now it's getting a resurgence in the second life. But go back to what we were originally saying. I think one of the most interesting things that we've found so far in our data is that there's an even 50-50 split between people that are using vendors or embedded AI into things they already have or just going out and doing it on their own. We have not yet seen a clear leadership between hey, I'm gonna let my tools and my services go ahead and provide it for me or I'm gonna build this myself. So I think we're gonna see that play out. It's gonna be pretty interesting as we do. Yeah, I think it's a horse race, right? I mean, I think it's an exciting space for tech vendors to pay attention to because there's really no declared winner yet. And so there's lots of movement to be had and lots of data to gather on where people are headed. You know, no surprise the three big cloud players, Microsoft, AWS, Google are up in the mix. I think, you know, if I were to conjecture why people are investing so heavily, I think generative AI brought this excitement and sort of validated for many maybe top executives that weren't as close to the ground of ML and AI work. Maybe it validated that there really is some power and excitement here. And so maybe there was a go ahead to maybe say, let's invest in what we already have. Let's add more oomph to the platforms we're already on. So perhaps that's part of it. They are just good platforms in general too and have really good ML ops capabilities. So maybe no surprise there. But yeah, what Eric is referencing this blend between using the embedded capabilities within a vendor versus going into a standalone generative AI company, I think is pretty remarkable. A couple of thoughts here. We can riff on this if you guys have opinions. Some data points. I was at the Dell financial analyst meeting a couple of weeks ago in New York City. HPE had its financial analyst meeting yesterday that I sat through. Both companies known well for their server business, their on-prem infrastructure, their partnerships with NVIDIA and GPUs and so forth. Both companies talked a strong AI game. HPE of course has a supercomputer division. So that's another sort of AI affinity oriented space. But both companies, when you talk to the analysts, there's expectations that 2024 is going to be a year of higher average contract values, bigger configurations, more GPUs and higher prices which will drive growth. But there's no real clear evidence. There's some in the ETR data that that product versus private is going to be sort of 50-50 mixed. My sense is a lot of that is some of the large companies doing building LLMs like an open AI in their own data centers as opposed to necessarily doing it in the cloud. When you look at what's happening in financial services or manufacturing or healthcare, still a lot of, like we said before, chat GBT like use cases to drive basic productivity, summarization of text and the like, ideation. As well to your other points about embedding into software, for example, the likes of Databricks and Snowflake, we saw at both their conferences, they made acquisitions in the case of Snowflake, Neva, natural language processing, search company, actually consumer search that they're pivoting toward enterprise. And in the case of Databricks, Mosaic ML, which significant acquisition there, expanding their portfolios, embedding into their software. You see the same thing with Salesforce, ServiceNow, SAP and others. And so a lot of this is going to be embedded. And again, questions remain as to how much of this is actually going to be done on-prem because today a lot of the action really is in the cloud. Yeah, I'll agree with you. Then I'm going to hand it off to Darren. What we're seeing very much so is right now there's still a lot of concern around it. There's not guard rails yet around this technology. So I think that's why we're not just going ahead and going full force with some of the embedded offerings. Instead, they want to sandbox it. Instead, they want to make sure that there's some safety around it. What data is being put in? Where is it being controlled? Where's the governance? Who has access to it? So that all leads it to you doing it yourself as opposed to going elsewhere. And then the other thing in a recent study, we also found a lot of people originally thought this would be a huge boon for the IT consultants. We found out that people aren't using third party. They're doing this themselves. So only 30% of the people we actually surveyed on that said they were using a third party consultant that they're doing this still themselves. And then I'm going to hand it off to Darren because he's been doing a lot of panel work in this area about what the actual use cases are and how people are using it. Yeah, and just to tail in on that, a lot of people are really concerned about the security issues and the privacy issues. And so sometimes the sandboxing is one approach. Maybe another approach is let's go with a vendor we trust that's rolling these capabilities out. So I think it's kind of a different mentality or sentiment, but it definitely see a lot of organizations doing this kind of sandboxing or skunk works or innovation labs or whatever you want to do it to dip their toe in the water. And this idea of embedded capabilities, one of the things we found in it, we did a whole panel on generative AI and really focused on Microsoft Copilot and GitHub Copilot, kind of this product that was widely announced and had a fairly hefty price tag attached to it if you think about it per license. One of the things that came up, people really aren't batting an eye at the cost. They say if it can save us even a few hours of time a month, it's worth it. So there's sort of an appetite to spend on these things that they can really prove their worth. And I think that's sort of the dollar signs that a lot of these big vendors have in their eyes about why they're embedding these capabilities. And so I think we're going to see, it'll be interesting to see where it tips is going to be more of a independent or more embedded. It's kind of exciting to watch. Great, thank you. All right, let's end with some closing remarks. I'm going to set it up here and then ask you to both to comment. I think you've also got some other data that you want to share. You know, the whole focus on ROI, this is interesting based on some of the things you said. I mean, IT budgets aren't growing despite the fact that they are growing from the basically the flat, but still they're really not consistently back to where sort of pandemic levels for sure and even pre-pandemic levels, but they are stabilizing. But anyway, the point I'm making here and one of you guys agree is enterprise are going to have to show value or these AI projects are going to be cut. I think Eric, you were intimating that, you know, maybe that's not the case because people realize this is such a transformative and important area, but I really do feel like people are going to have to find their way and sink their teeth into this or CFOs are going to start, you know, questioning these investments. So productivity numbers at the macro are going to be, you know, key to these fulfilled promises of AI. And I think right now all the action seems to be in the cloud not withstanding. I'm glad you made that point, Eric, about IBM really bouncing off their lows. We are still watching for meaningful on-prem adoption. I do think it's going to happen. The reason I think that is because people are bringing AI to the data and there's a lot of data that's still on-prem. Competitive landscape is evolving, but those sands are shifting. The trends, you know, still favor Microsoft, open AI and these embedded software opportunities that we've been talking about, Snowflake, Databricks Salesforce, SAP, that should drive demand in 2024. And, you know, the promise of the rising tide of AI lifting all boats. Right now it's been relatively narrow and, you know, our forecast start to show in Q4 we're going to start to see that have a meaningful measurable anyway impact. And of course AI inferencing at the edge is something that we're watching. We think it's a wild card and could produce a very disruptive force from an economic standpoint with very low cost, you know, arm systems at the edge that a lot of people initially poo-pooed but you're seeing it creep into the enterprise in the form of certainly AWS, the big cloud guys, of course Apple and its laptops and of course in the mobile. But we think that that's the way these big trends tend to happen, these big shifts as they start in consumer and in volume economics kind of rule. So I'll leave it there and ask, so Darren and Eric for your final thoughts that close us out. Yeah, I'm going to start at a macro level and hand it off to Dr. Brabham. He's a little bit smarter than me to take us on home. So quickly, a couple of points, first about the CFO and the investment side. Listen, at the end of the day it's all about money. Of course your CFO is going to need justification of what you're spending but what our data is telling us right now is that people aren't spending new money. That AI is going to be able to pay for itself. It's going to be able to take share or budget from other places and bring it to it. It's going to create savings whether that's at full-time employee or whether that's just at the amount of hours it takes to do repetitive tasks. So I don't worry about that that much right now. And the horse is already out of the gate. I don't see too many CFOs sticking their neck out saying, no, sorry, you can't use Gen AI to try to keep up with our market. So I'm a little less concerned about that for now. And then the other point about real-time use cases, the edge, real-time streaming data is happening right now. And we're already asking people about what their number one use cases are for Gen AI and they're answering client-facing. They're answering customer support and chat. They're answering call center technology. So they're talking about things that are happening in real-time and that's really where that edge streaming analytics is happening. And I certainly see Gen AI moving out to the edge for that, the faster you can do it, the faster you can kind of utilize and support those transactions, the better. But from a higher level, I wanna step back and state that I've never seen a technology in the 12 years I've been doing this move so incredibly quickly. It was just November of last year, I think that they even came out and said, hey, chat GPT's out there, go check it out. It was March when it started making headlines in the journal and Forbes. We're talking about less than a year and this is blowing up the data. It's everywhere. It's in every use case evaluation. I remember tracking the cloud for years waiting for kind of workloads to really inch higher and higher and higher. People ask us why we still track AT&T and Verizon in our survey. It's because we were doing on-prem back then because cloud wasn't a thing. So I just wanna iterate. I have never before seen anything move so fast from hype to evaluation to actual production. And I just think this is clearly here to stay. Excellent, thank you. Darren, we'll let you bring us home, please. Sure. Yeah, I've got two big thoughts here, I think, that really kind of get me excited about what's happening with generative AI and get me excited to start asking good questions and capture data around it. One is something we're starting to hear trickle in through some of our interviews with our IT decision makers in our ETR Insight series. Here's one quote to kind of kick it off and I'm paraphrasing is, the RPA players better watch their back or they should be on notice, right? So I think what comes with this is generative AI is, in a way could be a leapfrogging over some of these technologies that were designed to provide shortcuts or improve life. So think about RPA or the process automation market. Let's build automated pipelines to help make our tedious work better to get to business outcomes quicker. Well, generative AI, what if there's a way to throw all that data in a bucket or all of our workflows or our business processes and just query it or have generative AI tell us through process mining what's going on? So we're seeing a lot of the, you know, RPA is an example. Some of these players are scrambling, I think, to embrace generative AI and AI capabilities in general to really try to stay ahead of it and stay relevant. And I think the ones that aren't are gonna be really left in the dust because it makes you wonder why you have it. I think the same can be said about the reporting and BI and analytics market. You know, the augmented analytics vendors like ThoughtSpot and Telius, for instance, and then all the other vendors like Power BI have incorporated these technologies, but natural language processing and chat-based queries of the data to produce charts and graphs for you and explain things in natural language. That's been coming for a while and now generative AI could sort of threaten all those capabilities if it can leap-throg it. UX and UI design, marketing, there are so many sectors, I think, where the tools, the technologies that are really there to facilitate those markets really need you to think about how they're gonna embed those capabilities quickly or are they gonna be left in the dust? And so when we talk about generative AI adoption, we're not just talking about the organizations and how they're exploring it for business use cases. I think this is also a very real question for the vendors that supply software technology and what they're gonna do to keep up or embrace and consume it before they get left behind. That's one major point. The second one's a little more brief is anytime there's a technology change like this and there's this appetite to bring in generative AI interfaces or whatever it might be into an organization and have business users do technical things themselves by querying a chat box or using an interface. You really have to get back to questions of governance. How are you gonna govern that work? How are you gonna reuse the work that one person does in the business? So there's not a waste of time for someone else to reinvent the wheel. So reuse, governance, management of that is all gonna become very important. And then data literacy or just business literacy, training your business users to know when they're getting bogus information from a generative AI prompt or what we call hallucinations, right? From the data, how to know that what they're asking is right and what the output looks right or passes a SNF test. So there's a lot of training to happen as well with the organization, lots of people and process concerns anytime you see a transformative technology like this. So I'll leave it at that. I think those are my two big things I'm watching. Guys, I can't thank you enough for the collaboration. You always bring the A game and the best date in the business. And thank you for spending some time with us. Thank you, we really value the partnership. Our pleasure, yeah. And don't forget, SuperCloud 4 is happening on 1024. This is essentially a preview to SuperCloud 4. It's live in our Palo Alto studio. The topic is gen AI and specifically the transformative effects on industries. We've talked a lot about the impacts on the technology industry. Garen just closing talked about several sectors that are ripe for disruption. We talked about this a lot a couple of weeks ago actually at UI path forward six. And we can see there's an example of a company racing quickly to make sure that RPA which is how they got started is not what they're known for now. They're moving beyond that because they have to otherwise things like gen AI are going to disrupt them. But the other piece of SuperCloud 4 is really looking at the industry transformations, what the impacts are going to be in healthcare, financial services, manufacturing and other sectors because that's where the multi trillion economic impact is going to be felt. So SuperCloud.world, go ahead and sign up 1024 live out of our Palo Alto studios. I want to thank Eric Bradley and Darren Brabham for their contributions as always and the partnership. Alex Meyerson is on production and manages the podcast. Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters and Rob Hoef is our editor-in-chief over at siliconangle.com. Remember all these episodes are available as podcasts wherever you listen. All you do is search breaking analysis podcasts. We've got more than 200 episodes online now. I publish each week on wikibon.com and siliconangle.com or you can email me at David Belonte at siliconangle.com or Eric we're already getting inbounds for our annual predictions post. So they're coming in fast and heavy. Darren, you as well last year. So you can DM me at dvolonte or comment on our LinkedIn posts and please check out etr.ai, they keep expanding. I can't wait to get into some of that FinTech data. I can't wait to get into the ETS, the emerging technology survey, best survey data in the enterprise tech business. This is Dave Vellante for the Cube Insights powered by ETR. Thanks for watching everybody. We'll see you next time on breaking analysis.