 From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. A decade of big data investments combined with cloud scale, the rise of much more cost effective processing power and the introduction of advanced tooling is catapulted machine intelligence to the forefront of technology investments. No matter what job you have, your operation will be AI powered within five years and machines may actually even be doing your job. Artificial intelligence is being infused into applications, infrastructure, equipment, and virtually every aspect of our lives. AI is proving to be extremely helpful at things like controlling vehicles, speeding up medical diagnoses, processing language, advancing science, and generally raising the stakes on what it means to apply technology for business advantage. But business value realization has been a challenge for most organizations due to lack of skills, complexity of programming models, immature technology integration, sizable upfront investments, ethical concerns, and lack of business alignment. Mastering AI technology will not be a requirement for success in our view. However, figuring out how and where to apply AI to your business will be crucial. That means understanding the business case, picking the right technology partner, experimenting in bite-sized chunks, and quickly identifying winners to double down on from an investment standpoint. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we update you on the state of AI and what it means for the competition. And to do so, we invite into our studios, Andy Terai of Constellation Research. Andy covers AI deeply. He knows the players. He knows the pitfalls of AI investment. And he's a collaborator, Andy. Great to have you on the program. Thanks for coming into our Cube Studios. Thanks for having me on. You're very welcome. Okay, let's set the table with a premise and a series of assertions we want to test with Andy. I'm going to lay him out, and then Andy, I'd love for you to comment. So first of all, according to McKinsey, AI adoption has more than doubled since 2017, but only 10% of organizations report seeing significant ROI. That's a BCG and MIT study. And part of that challenge of AI is it requires data, it requires good data, data proficiency, which is not trivial, as you know. Firms that can master both data and AI, we believe are going to have a competitive advantage this decade. Hyperscalers, as we show you, dominate AI and ML. We'll show you some data on that. And having said that, there's plenty of room for specialists. They need to partner with the cloud vendors for go-to-market productivity. And finally, organizations increasingly have to put data and AI at the center of their enterprises. And to do that, most are going to rely on vendor, R&D, to leverage AI and ML. In other words, Andy, they're going to buy it and apply it as opposed to build it. What are your thoughts on that setup and that process? Yeah, I see that a lot happening in the field, right? So first of all, the only 10% of realizing a return on investment, that's so true because we talked about this earlier, the most companies are still in the innovation cycle. So they're trying to innovate and see what they can do to apply. A lot of these times, when you look at the solutions what they come up with or the models they create, the experimentation they do, most times they don't even have a good business case to solve, right? So they just experiment and then they figure it out, oh my God, this model is working. Can we do something to solve it? So it's like, you found a hammer and then you're trying to find the nail kind of thing, right? That never works. Because it's cool or whatever it is today, right? So that's why I always advise when they come to me and ask me things like, hey, what's the right way to do it? What is the secret sauce? We talked about this. The first thing I tell them is, find out what is the business case that's having the most amount of problems that can be solved using some of the use cases, right? Not all of them can be solved. Even after we experiment and do the whole thing, I'd spend millions of dollars on that, right? And then later on you making efficient only by saving maybe 50,000 for the company or 100,000 for the company is really even worth the experiment, right? So you got to start with the saying that where's the wastage that's happening? Where's the need? What's the business use case? It doesn't have to be about cost-efficient and saving money in the existing process. It could be a new thing you want to bring in a new revenue stream. But figure out what is a business use case? How much money potentially I can make off of that? The same way that startups go, right? Pretty straightforward. All right, let's take a look at where ML and AI fit relative to the other hot sectors of the ETR dataset. This XY graph shows net score or spending velocity in the vertical axis and presence in the survey, they call it sector perversion for the October survey, the January surveys in the field. The net squiggly line on ML AI represents the progression since the January 21 survey. You can see the downward trajectory and we position ML and AI relative to the other big four hot sectors or big three, including ML AI is four. Containers Cloud and RPA, these have consistently performed above that magic 40% red dotted line for most of the past two years. Anything above 40% we think is highly elevated. And we've just included analytics and big data for context and relevant to jacent-ness if you will. Now note that green arrow moving toward the 40% mark on ML AI. I got a glimpse of the January survey which is in the field. It's got more than a thousand responses already and it's trending up for the current survey. So Andy, what do you make of this downward trajectory over the past seven quarters and the presumed uptick in the coming months? So one of the things you had to keep in mind is when the pandemic happened, it's about survival mode, right? So when somebody is in a survival mode, what happens? The luxury and the innovations get cut. That's what happens and this is exactly what happened in this situation. So as you can see in the last seven quarters which is almost dating back to close to pandemic, everybody was trying to keep their operations alive, especially digital operations. How do I keep the lights on? That's the most important thing for them. So while the number spent on AI ML is less overall, I still think the AI ML to spend to sort of like an employee experience or the IT ops, AI ops, ML ops, as we talked about, some of those areas actually went up. There are companies, we talked about it. Atlassian had a lot of platform issues till the amount of money people are spending on that is exorbitant simply because they are offering a solution that was not available otherwise. So there are companies out there. You can take AI ops or incident management for that matter, right? A lot of companies have a digital insurance. They don't know how to properly manage it. How do you find an insurance solver immediately? That's all using AI ML and some of those areas actually growing unbelievable, the companies in that area. So this is a really good point. If you can, if you bring up that chart again. What Andy is saying is a lot of the companies that in the ETR taxonomy that are doing things with AI might not necessarily show up in a granular fashion. And I think the other point I would make is these are still highly elevated numbers. If you put on like storage and servers, they would be way, way down the list. And look in the pandemic, we had to deal with work from home. We had to rearchitect the network. We had to worry about security. So those are really good points that you made there. Let's unpack this a little bit and look at the ML AI sector and the ETR data and specifically look at the players and get Andy to comment on this. This chart here shows the same XY dimensions and it just notes some of the players that are specifically have services and products that people spend money on that CIOs and IT buyers can comment on. So the table in search shows how the companies are plotted. It's net score and then the ends in the survey. And Andy, the hyperscalers are dominant. As you can see, you see Databricks there showing strong as a specialist. And then you got to pack a six or seven in there and then Oracle and IBM, one of the big whales of yesteryear are in the mix. And to your point, companies like Salesforce that you mentioned to me offline aren't in that mix, but they do a lot in AI. But what are your takeaways from that data? If you could put the slide back on please. I want to make quick comments on a couple of those. So the first one is it's surprising of the hyperscalers, right? As you and I talked about this earlier, AWS is more about the logo blocks. They discuss that, right? Like what? Like a SageMaker. We'll give you all the components what do you need whether it's an MLOps component or whether it's a code whisper that we talked about or our platform or data or whatever you want. They'll give you the blocks and then you'll build things on top of it, right? But Google took a different way. Matter of fact, if we did this numbers few years ago, Google would have been number one because they did a lot of work with their acquisition of DeepMind and other things. They were way ahead of the pack when it comes to AI for the longest time. Now I think Microsoft's move of partnering and taking a huge competitor out with open AI is unbelievable. I mean, you saw that everybody is talking about chat GPI, right? And the open AI tool, chat GPT, rather. Remember as Warren Buffett is saying that when my laundry lady comes and talks to me about stock market, it's heated up. So that's how it's heated up. Everybody is using chat GPT. What that means is, at the end of the day, it's still in beta, keep in mind. It's not fully- Can you play with it a little bit? I have a little bit. I have, but- It's good and it's not good. You know what I mean? Look, so at the end of the day, you take the massive text of all the available texts in the world today, maximum all together, and then you ask a question. It's going to basically search through that and figure it out and answer that back. Yes, it's good. But again, as we discussed, if there is no business use case of what problem you're going to solve, this is building a hype. But then eventually they'll figure out, for example, all your chats, online chats, could be aided by your AI chatbots, which is already there, which is not there at that level. This could help that, right? Or the other thing we talked about is one of the areas where I'm more concerned about is that it is able to produce equivalent of an original text at the level that humans can produce. For example, chat GPT or the equivalent of the large language transformer can help you write stories as of Shakespeare wrote it. Pretty close to it. It'll learn from that. So when it comes down to it, talk about creating messages, articles, blogs, especially during political seasons, not necessarily just in US, but in anywhere for that matter. If people are able to produce at the mission speed and throw it at the consumers and confuse them, the elections can be won, the governments can be toppled. Because to your point about chatbots, chatbots have obviously reduced the number of bodies that you need to support chat, but it haven't solved the problem of serving consumers. Most of the chatbots are of conditioned response, which of the following best describes your problem? The current chatbots, yeah. Hey, did we solve your problem? No, is the answer. So that has some real potential. But if you could bring up that slide again, Ken, I mean, you've got the hyperscalers that are dominant. You talked about Google and Microsoft is ubiquitous. They seem to be dominant in every ETR category. But then you have these other specialists. How do those guys compete? And maybe you could even cite some of the guys that you know. How do they compete with the hyperscalers? What's the key there for like C3AI or some of the others that are on there? So I've spoken with at least two of the CEOs of the smaller companies that you have on the list. One of the things that they're worried about is that if they continue to operate independently without being part of hyperscalers, either the hyperscalers will develop something to compete against them full scale or they'll become irrelevant because at the end of the day, like cloud is dominant. Not many companies are going to do like AI modeling and training and deployment the whole nine yards by independent by themselves. They're going to depend on one of the clouds, right? So if they are already going to be in the cloud by taking them out to come to you, it's going to be extremely difficult issue to solve. So all this companies are going and saying, you know what, we need to be in hyperscalers. For example, you could have looked at data robot recently. They made an announcement to Google and AWS and they are all over the place. So you need to go where the customers are, right? All right, before we go on, I want to share some other data from ETR and why people adopt AI and get your feedback. So the data historically shows that feature breadth and technical capabilities were the main decision points for AI adoption historically. It's what says to me that it's like too much focus on technology. In your view, is that changing? Does it have to change? Will it change? Yes, simple answer is yes. So here's the thing. The data you're speaking from is from previous years. I can guarantee you, if you look at the latest data that's coming in now, those two will be a secondary and tertiary points. The number one would be about ROI and how do I achieve? I've spent a ton of money on all of my experiments. This is the same thing theme I'm seeing across when talking to everybody who's spending money on AI. I spent so much money on it. When can I get it live in production? How much, how can I quickly get it? Because, you know, the board is breathing down their neck. You already spent this much of money. Show me something that's valuable. So the ROI is going to become, take it from me, I'm predicting this for 2023. That's going to become number one. Yeah, and if people focus on it, they'll figure it out. Okay, let's take a look at some of the top players that once some of the names we just looked at and double-click on that and break down their spending profile. So the chart here shows the net score, how net score is calculated. So pay attention to the second set of bars that Databricks, who was pretty prominent on the previous chart, and we've annotated the colors. The lime green is we're bringing the platform in new. The forest green is we're going to spend 6% or more relative to last year. The gray is flat spending. The pinkish is we're going to, our spending is going to be down on AI and ML, 6% or worse. And the red is churn. So you don't want big red. You subtract the reds from the greens and you get net score, which is shown by those blue dots that you see there. So AWS has the highest net score and very little churn. I mean, single, low single digit churn. But notably you see Databricks and DataRobot are next in line with then Microsoft and Google. Also they've got very low churn. Andy, what are your thoughts on this data? So a couple of things that stands out to me. Most of them are in line with my conversation with customers. A couple of them stood out to me on how bad IBM Watson is doing. If you look at the slide here. Yeah, bring that back up if you would. Let's take a look at that. Look at the, IBM Watson is far right. And the red, the bright red is churn. And again, you want low red here. Why do you think that is? Well, so look, IBM has been in the forefront of innovating things for many, many years now, right? And over the course of years, we talked about this. They moved from a product innovation centric company into more of a services company. And all the years they were making is at one point, you know that they were making about majority of that money from services. Now things have changed. Arvind has taken over. He came from research. So he's doing a great job of, you know, trying to win when themselves as a company. But it's going to have a long way to catch up. I mean, IBM Watson, if you think about it, that played what? You know, Joe Putty and Chess years ago, like 15 years ago. It was jaw-dropping when you first saw it. And then they weren't able to commercialize that. Yeah, you're making a good point. When Gershner took over IBM, at the time, John Akers wanted to split the company up. He wanted to have a database company. He wanted to have a storage company. Because that's where the industry trend was. Gershner said no. He came from M.A.M.X., right? He came from America Express. He said no, we're going to have a single throat to choke for the customer work. They bought PWC for relatively short money. I think it was 15 billion, completely transformed. And I would argue saved IBM. But the trade-off was it sort of took them out of product leadership. And so from Gershner to Palmasano to Rometti, it was really a services-led company. And I think Arvind's really bringing it back to a product company with strong consulting. I mean, that is one of the pillars. And so I think they've got a strong story in data and AI. They just got to sort of bring it together and better. Bring that chart up one more time. I want to, their point is Oracle. Oracle sort of has the dominant lock-in for mission-critical database. And they're sort of applying AI there. But to your point, they're really not an AI company in the sense that they're taking unstructured data and doing sort of new things. So that's really about how to make Oracle better, right? Well, you've got to remember, Oracle is about database for the structured data. So in the yesterday's world, they were dominant database. But if you order to start storing videos and text and audio and other things, and then start doing search of vector search and all that, Oracle is not necessarily the database company of choice. And their strongest thing being apps and building AI into the apps, they are kind of surviving in that area. But again, I wouldn't name them as AI company, right? But the other thing that surprised me in that list, what you showed me is, yes, AWS is number one as it should be. Bring that back up if you would can. AWS is number one as it should be. But what actually caught me by surprise is how data robot is holding. I mean, look at that. The either a net new addition and or expansion, data robot seem to be doing equally well, even better than Microsoft and Google, that surprises me. Data robots, and again, this is a function of spending momentum. So remember from the previous chart that Microsoft and Google much, much larger than data robot, data robot more niche. But with spending velocity and has always had strong spending velocity, despite some of the recent challenges, you know, organizational challenges. And then you see these other specialists, H2O.AI, Anaconda, DataIcu, a little bit of red showing there, C3AI. But these again to stress are the sort of specialists other than obviously the hyperscalers. These are the specialists in AI. All right, so we hit the bigger names in the sector. Now let's take a look at the emerging technology companies. And one of the gems of the ETR dataset is the emerging technology survey. It's called ETS. They used to just do it like twice a year. It's now run four times a year. I just discovered it kind of mid of 2022. And it's exclusively focused on private companies that are potential disruptors. They might be M&A candidates. And if they've raised enough money, they could be acquirers of company as well. So Databricks would be an example. They've made a number of investments in companies. Sneak would be another good example. Companies that are private, but they're buyers. They hope to go IPO at some point in time. So this chart here shows the emerging companies in the ML AI sector of the ETR dataset. So the dimensions are similar. They're net sentiment on the Y axis and mind share on the X axis. Basically the ETS study measures awareness on the X axis and intent to do something with evaluate or implement or not on that sort of vertical axis. So it's like net score on the vertical where negatives are subtracted from the positives. And again, mind share is vendor awareness. That's the horizontal axis. Now that inserted table shows net sentiment and the ends in the survey, which informs the position of the dots. And you'll notice we're plotting TensorFlow as well. We know that's not a company, but it's there for reference. As open source tooling is an option for customers and ETR sometimes like to show that as a reference point. Now we've also drawn a line for Databricks to show how relatively dominant they've become in the past 10 ETS surveys and sort of mind share going back to late 2018. And you can see a dozen or so other emerging tech vendors. So Andy, I want you to share your thoughts on these players. Who are the ones to watch? Name some names. We'll bring that data back up as you comment. So Databricks, as you said, remember we talked about how Oracle is not necessarily the database of the choice. So Databricks is kind of trying to solve some of the issue for AIML workloads, right? And the problem is also, there is no one company that could solve all of the problems. For example, if you look at the names in here, some of them are database names, some of them are platform names, some of them are like MLops companies, like data robot domino and others. And some of them are like future based companies like Tecton and stuff. So it's a mix of those sub-sectors. It's a mix of those companies. We'll talk to ETR about that. They'd be interested in new input and how to make this more granular and these sub-sectors. You've got a hugging face in here, which I know. Yeah, which is an NLP, yeah. Okay, so your take. Are these companies going to get acquired? Are they going to go IPO? Are they going to merge? Well, most of them are going to get acquired. My prediction would be most of them will get acquired because look, at the end of the day, hyperscalers need those capabilities, right? So they're going to either create their own. AWS is very good at doing that. They have done a lot of those things. But the other ones, like particularly Azure, they're going to look at it and say, you know what, it's going to take time for me to build this. Why don't I just go and buy you, right? Or even the smaller players like Oracle or IBM Cloud, this will exist. They might even take a look at them, right? So at the end of the day, a lot of these companies are going to get acquired or merge with others. All right, let's wrap with some final thoughts. I want to make some comments, Andy, and then ask you to dig in here. Look, despite the challenge of leveraging AI, and I can't if you could bring up the next chart, we're not repeating, we're not predicting the AI winter of the 1990s. Machine intelligence, it's a superpower that's going to permeate every aspect of the technology industry, AI and data strategies have to be connected. Leveraging first party data is going to increase AI competitiveness and shorten time to value. Andy, I'd love your thoughts on that. I know you've got some thoughts on governance and AI ethics. We talked about chat, GBT, deep fakes, what help us unpack all these trends? So there's so much information packed up there, right? The AI and data strategy, that's very, very, very important. If you don't have a proper data, people don't realize that AI is your AI in the models that you built on. It's predominantly based on the data what you have. It's not, AI cannot predict something that's going to happen without knowing what it is. It need to be trained. It need to understand what is it you're talking about. So 99% of the time, you gotta have a good data for you to train. So this is where I mentioned to you. The problem is, a lot of these companies can afford to collect the real world data because it takes too long, it's too expensive. So a lot of these companies are trying to do the synthetic data way. It has its own set of issues because you can't use all that. What's that synthetic data? Explain that. Synthetic data is basically not a real world data, but it's a created or simulated data equal and based on real data. Looks, feels, smells, tastes like a real data, but it's not exactly real data, right? This is particularly useful in the financial and healthcare industry for the world. So you don't have to, at the end of the day, if you have a real data about your and my medical history data, if you redact it, you can still reverse it. It's fairly easy, right? So by creating a synthetic data, there is no correlation between the real data and the synthetic data. So that's part of AI ethics and privacy and, okay. So the synthetic data, the issue with that is that when you're trying to commingle that with that, you can't create a models based on just on synthetic data because synthetic data, as I said, is artificial data. So basically you're creating artificial models. So you got to blend in properly. That blend is the problem and how much of real data, how much of synthetic data you could use. You got to use judgment between efficiency, cost and the time duration stuff. So that's one. And risk. And the risk involved with that. On the secondary issues, which we talked about is that when you're creating a, you know, okay, you take a business use case, okay, you take about, you know, investment things, you build the whole thing out and you're trying to, you know, put it out into the market. Most companies that I talk to don't have a proper governance in place. They don't have ethics standards in place. They don't worry about the biases in data. They just go on trying to solve a business case because that's what they start. It's a wild west. And then at the end of the day, when they are close to some legal litigation action or something, or something else happens, and that's when the oh shit moments happens, right? And then they come in and say, you know what? How do I fix this? The governance, security, and all of those things, ethics, bias, data bias, debiasing, none of them can be an afterthought. It got to start with the, from the get go. So you got to start at the beginning saying that, you know what? I'm going to do all of those AI programs. But before we get into this, we got to set some framework for doing all these things properly, right? And then the, yeah, so let's go back to the key points. I want to bring up the cloud again. Because you got to get cloud right. Getting that right matters in AI to the points that you were making earlier. You can't just be out on an island. And hyperscalers, they're going to obviously continue to do well. More and more data is going into the cloud. And they have the native tools to your point. In the case of AWS, Microsoft's obviously ubiquitous. Google's got great capabilities here. They've got integrated ecosystem partners that are going to continue to strengthen, you know, through the decade. You know, what are your thoughts here? So a couple of things. One is the last mile ML or last mile AI that nobody's talking about. So that need to be a tender do that. A lot of players in the market that coming up, when I talk about last mile AI, I'm talking about you know, after you're done with the experimentation of the model, how fast and quickly and efficiently can you get it to production? So that's production being that you know. Compressing that time is going to put dollars in your pocket. Exactly, right? So once. If you got it right. If you get it right, of course. So there are a couple of issues with that. Once you figure out that model is working, that's perfect, people don't realize. The moment you decide, that moment when the decision was made, it's like a, as I say, it's like a new car. After you purchase, the value decreases on a minute basis. Same thing with the models. Once the model is created, you'd need to be in production right away because it starts losing its value on a second's minute basis. So issue number one, how fast can I get it over there? So your deployment, your inferencing efficiently at the edge locations, your optimization, your security, all of this is an issue. But you know what is more important than that in the last mile? You keep the model up, you continue to work on again, going back to the car analogy. At one point you get to figure out your car is costing more than to operate. So you got to get a new car, right? And that's the same thing with the models as well. If your model has come reached a stage, it is actually a potential risk for your operation to give you an idea. If Uber has a model, the first time when you get a car from going from point A to B, it costs you $60. If the model decayed, the next time it might give you a $40 rate. I would take it definitely, but it's lost for the company. The business risk associated with operating on a bad model. You should realize it immediately, pull the model out, retrend it, redeploy it. And that's got to be huge in security, model recency and security, and to the extent that you can get real time is big. I mean, you see Palo Alto, CrowdStrike, a lot of other security companies are injecting AI. Again, they won't show up in the ETR, ML, AI, taxonomy per se as a pure play, but ServiceNow is another company that you have mentioned to me offline. AI just getting embedded everywhere. And then I'm glad you brought up the kind of real-time inferencing, because a lot of the modeling, if we can go back to the last point that we're going to make in the, a lot of the AI today is modeling done in the cloud. The other, the last point we wanted to make here, I'd love to get your thoughts on this, is real-time AI inferencing, for instance at the edge, is going to become increasingly important for us. It's going to usher in new economics, new types of silicon, particularly arm-based. We've covered that a lot on breaking analysis, new tooling, new companies, and that could disrupt the sort of cloud model, if new economics emerge, because cloud obviously very centralized, they're trying to decentralize it, but over the course of this decade, we could see some real disruption there. Andy, give us your final thoughts on that. Yes and no. I mean, at the end of the day, cloud is kind of centralized now, but a lot of these companies, including AWS, is kind of trying to decentralize that, by putting their own sub-centers and edge locations. Local zone, outposts. Yeah, exactly. So they're particularly the outpost concept, and it can even become like a micro-center and stuff. It won't go to the localized level of I go to a single IoT level, but again, the cloud extends itself to that level. So if there's an opportunity need for it, the hyperscalers will figure out a way to fit that model. So I wouldn't too much worry about that, about deployment and where to have it and what to do with that, but figure out the right business use case, get the right data, get the ethics and governance place, and make sure that you get it to production and make sure you pull the model out when it's not operating well. Excellent advice. Andy, I got to thank you for coming into the studio today, helping us with this breaking analysis segment. You know, outstanding collaboration and insights and input to today's episode. Hope we can do more. Thank you. Thanks for having me, I appreciate it. You're very welcome. All right, I want to thank Alex Meyerson, who's 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. He does some great editing for us. Thank you all. Remember, all these episodes are available as podcasts. Wherever you listen, all you got to do is search breaking analysis podcast. I publish each week on wikibon.com and siliconangle.com, where you can email me at david.volante at siliconangle.com to get in touch or DM me at dvolante or comment on our LinkedIn posts. Please check out etr.ai for the best survey data in the enterprise tech business. Constellation of Research, Andy publishes there, some awesome information on AI and data. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching everybody and we'll see you next time on breaking analysis.