From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR this is "Breaking Analysis" with Dave Vellante. The AI heard around the world has put the machine intelligence sector back in the spotlight, but when you squint beyond the press hype the data shows that artificial intelligence is now the number one sector in terms of relative spending velocity. Now, normally market hype leads spending momentum, but the data suggests that spending activity and market penetration seem to be aligning with the hype. Now, while hyperscale cloud players are reaping the rewards, we think this is a rising tide that's going to lift all AI ships, those both plainly in sight and others that may not be so visible. Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. In this "Breaking Analysis" we dig deeper into the AI space with spending data from ETR and one of the best minds in tech generally, in AI specifically, Jeff Jonas, CEO, founder, and chief scientist at Senzing. Jeff, good to see you my friend. Hey, Dave! How are you? I don't know if you remember, it's been a while since we hung out. I've probably seen you, but last time we hung out was in New York City. It was after like a Big Data NYC, we went to Sparks Steak House. And afterwards we had some drinks and so forth, we were recreating the John Castellano murder. (Jeff laughing) I've got that photo, I still have that photo. I'm laying on the ground. Yeah, you were in the chalk line. That was, you were there. I'm flipping through my photos I'm like, "Wait a minute, how did this happen?" >> We got to hang out more, we got to hang out more man. Instigator. Well, thanks for doing this, let's get into it. I'm really excited to be spending some time with you and sharing with our audience. So, let's look at the sector momentum in spending and the pervasiveness. We got the survey of about 1,700 IT decision makers and after dominating the taxonomy during COVID, spending momentum dropped below the magic 40% mark. So, this chart shows in the vertical axis spending velocity, or net score, and it bottomed on October 2022 and it was back on top. And then the horizontal is pervasion or presence in the dataset, basically the N within a sector divided by the total N. And so, you can see that the squiggly line kind of shows where AI was, it sort of was up top, dropped down below the 40% and now it's back. And so, Jeff, the industry surrounding AI and machine learning, obviously growing rapidly, significant hype around it, particularly in the case of generative AI with ChatGPT. But given all this, how do you envision separating genuine progress and applications from inflated promises and the over expectations in the market, where do you see this going? It's going to be huge man. I think that's going to be a rocket, right? I think that little line that we see there's just going to continue to just, is just going to continue to zoom. I think after Watson beat "Jeopardy!" in, however long ago, it captured the world's imagination and tens of billions of dollars went into this, into the field of AI. It's going to be 100 times that big, and the utility of LLMs is going to be, it is phenomenal. It's overestimated, but it's still huge. So, I think it's straight up from here. Why do you think that IBM Watson didn't just run the table on this whole space? I think people over imagined all of its utility. I think that original algorithms, the stack, was really tailored to actually beat the game "Jeopardy!." I remember being in Singapore and somebody was telling me they wanted to use those algorithms to do fluid modeling for tsunamis over the surface of the Singapore landmass. I'm like, "That is the wrong use for that." So, I think those original algorithms had a certain set of utility that are more narrow, and I think the value of LLMs and the range of utility is going to be spectacular, but probably half as big as people actually think. All right, let's get into some of that. So, if we dig into the spending profiles from what we just showed you and then zoom in to AI, so ChatGPT was a catalyst for change in this spending profile that we're showing here. So, let me break it down, the colors. The lime green is new customers for AI platforms, the forest green is, says customers are spending 6% or more relative to last year, the gray is flat spending plus or minus 5%, and the pink spending is down 6% or worse, and the bright red, which is nothing, is churn. So, when you subtract the reds from the greens you get something called net score, which is the percentage of customers that are spending more. And you can see that soaring in the blue line. And that yellow line is that N in AI divided by the N in all sectors, that N to overall 1,700. And it's on a very, very steep rise in terms of, it bottomed in October, and then that was like a month before ChatGPT. So, in terms of, Jeff, in terms of technical advancements, how do you see the development of AI and large language models, generative AI, moving beyond supervised learning and LLMs, what do you think is most promising in terms of areas of research in this regard and potential impacts that they have on society? I think the really big thing that's happening is integrating diverse data. The fact that I can go to ChatGPT and ask for a board agenda and then have it reduce it to a rhyme and then reduce that to a haiku is because it's been trained over such diverse data. When researchers in Africa found termite mounds that have natural climate control, and then they started working with people working on skyscrapers and building energy-efficient skyscrapers, and then they'd start, new innovations break out of that. There's so many new innovations and breakthroughs coming from better connected and integration of diverse data. And we're going to see this in a lot of different texts, graph databases, of course what we've seen, LLMs, vector databases, traditional machine learning, my I'm the Symbol section on AI for entity resolution, integrating diverse data about people. But it's this collection of technologies to integrate diverse data that I think's really, man, it's going to accelerate what's happening in personalized medicine, improvements to climate change. Graph, as you mentioned graph, graph's interesting 'cause you get this expressiveness with the graph database, but the way in which you query it is still kind of old. Do you see that changing where you're going to get the expressiveness of a graph with the query flexibility of like SQL? I think the way humans interface with computers is about to radically change with these LLMs. In my particular case where you've got a entity resolution algorithm that's got JSON in to JSON out, now you can literally do conversational entity resolution. You'll say, "Hey, can you load a record that looks like this?" It'll automatically load it. Why do these two records come together, and you can get the answer in plain English. Imagine, and so you package that type of qualitative human language and you put that on top of graph databases, on top of entity resolution, on top of anything, you're not going to do Jeff Tab Tab, Jonas Tab, June Tab, 22nd. You're going to literally just say it or type it. And so, how we interface with computers is going to be, by the way, I've been thinking about this. You know how sometimes you want to change the behavior of some software, so you're having to look in settings and then look through the tabs.

You're just looking for the one switch, okay? That's going away. We're literally going to be just like, "Can you make it not do that anymore?" And it goes, "Yeah, I got that for you." And you have no idea, >> Oh my God. and you just dug way in to change one setting. It's so true, you take a screenshot and you'd have to follow the map. It's terrible. But to your point, cloud is code and code is now language. Yeah, this is going to happen so, I keep trying to tell people how fast this is going to happen. I want you to imagine you're talking to somebody that you want to work with. You're like, "Hey, are you a Mac or an Apple person?" They're like, "Oh, I'm not really into computers." You're like, "Excuse me?" They're like, "Yeah, I just don't do computers." They go like, "I put in the work," like no emails, nothing, no computers? They're like, "Yep." It's going to be exactly this way in like a year, maybe less. You're going to be like, "Hey, what kind of an AI assistant do you use, use ChatGPT, Bing, Bard, what do you got?" They go, "Oh, I don't use assistants." You're like, "You don't use assistants? You replay the recorded one-hour meeting and then rewrite the meeting notes and summarize it by hand." They're like, "Yeah, yeah, I put in the work." You're going to be like, "Oh." >> I'm old school. Yeah, I'm old school. (Dave laughing) All right, let's move on. The big three cloud players, they're obviously benefiting, but their positions have changed since ChatGPT was announced, which is what you're shown here. And of course, OpenAI has stormed the castle. So, this shows sort of AWS in the upper left, Microsoft upper right, and then Google in the lower left and then OpenAI, sort of pre and post ChatGPT and we put in sort of the key performance indicators. And you can see the positions have switched, like Microsoft basically cut the line on spending momentum on AI with its relationship with OpenAI. And OpenAI now dominates the combination, at least for now. You see AWS, their momentum picked up since they announced Bedrock in the spring, but they went from number one to number three in terms of the spending velocity.

They're all pretty high. You know, Google is Google. So, Jeff, my question is so you look at OpenAI's GPT-3 and now GPT-4, a lot of questions about responsible AI, how it's got potential misuse of powerful AI models, particularly when they become commercially available and they hallucinate. How do you think organizations should be thinking about this tension between broad-based access and potential misuse, especially as these models become more capable? Well, you definitely want to be careful not submitting data that's going to become, live in somebody else's logs and then be used for training, and now you've got some of your proprietary data in there. So, there needs to be careful work on that, and there'll be more remedies for more organizations to have, run their own local models or have their data protected when they're using public models. It's going to be very interesting about what data was used to train what and what entitlements did people have to use that data? Are they using data that they had the rights to use? And I think that's going to be really interesting. There will be some benefits to those that are, have a wider range of data. When I asked ChatGPT about who I am, it confuses me with the "Ironman" movie 'cause I do Ironman Triathlons. I mean it's damn convincing, like it tells me I met with John Favreau, the filmmaker, in Vegas in a think tank. And I'm like, "Did I?

I sit in think tanks and I meet filmmakers, did I meet him?" But I did some more research and called some people to see if these claims about me were true, and it's not. But here's the thing about hallucinations is if you're complaining about these things having hallucinations you're already using 'em the wrong way. They're not designed for truth, like literally they're qualitative not quantitative. My litmus test is if you were to ask the generative AI tomorrow the same question and if you get a different answer, if that's a problem, you're already using it the wrong way. So, qualitative is its best use, not truths. Yeah, at one point ChatGPT said that I started theCUBE with Jason Calacanis, which was kind of funny. I don't even know him. (Jeff laughing) I know him I guess, he doesn't know me. John knows him though. So, he is connected through Furrier so maybe that's how ChatGPT hallucinated. But to your point, you don't use it for that purpose. You use it for ideation, you use it for summarization and a number of other things that it's really good at. And do you think, now over time is there any reason why something like the ChatGPT of today can't go in that direction and be something that is designed to actually maybe ask questions if it doesn't know, or basically hedge more? It's going to evolve obviously. It's generative though. Its bones, in its bones it's generative. I think that a definitive paper on this was written by the WolframAlpha guy. He wrote like a 42-page paper on it, and I really love that paper 'cause it kind of describes how it works. But it basically, it already commits to a bunch of words, on Thursday they went to the beach to, then it's like swim, sunbath, and it just rolls the dice to pick the next word. And then it goes, "Okay, I committed to sunbath, now what?" That's what generative does, and it's phenomenal. But that's, you can't use that for things that you need the same answer tomorrow. That's not the way to use it. And it doesn't have any source. You can't ask it about where did it get the information. When I ask it about this, all this Jeff Jonas-"Ironman" conflation, it finds quotes that I said in "WIRED" magazine or a blog post that I never said.

It gives you links that aren't true. It's sourceless, you can't get attribution for it. And so, again, I sound like I'm whining, but it's phenomenal. It's going to do all kinds of things. But if you need to get the, if you are wishing to get the same answer tomorrow and you need it explainable then it's not going to be the LLM type of technology. It'll be something else. I mean, and just to what we're doing, we have an AI we've been working on now for a long time that does entity resolution, figures out who's who, realtime self-learning. We get the same answer tomorrow, and it's explainable. So, I don't know, it's going to get an inserted. Things like this, WolframAlpha gets inserted into ChatGPT so it can do math. I think Entity Engine, Senzing, or whatever, will be used to call 'em antipsychotics into these LLMs.

So, we'll see things maybe integrated with them and then these LLMs will put flowery words around it. But don't underestimate what else is coming. I'm just saying LLMs themselves that lose attribution, they don't know really the source, are only going to do so many things themselves. That spells huge opportunity to me, especially in the enterprise where you do need things that are explainable and with all the other enterprise features and characteristics that we know and love. So, it's not just about the big three clouds and OpenAI, as we're showing here, there are many others. You got players like Databricks that have been visible. They just made that big acquisition and sort of turned a weakness into a strength a couple weeks ago at their big conference. You got guys like Anthropic, Hugging Face is not shown here but they're prominent. You got guys like DataRobot, so a lot of these AI specialists. Of course IBM and Oracle driving into their own products. IBM of course with Watson. But a lot of folks that are pushing AI into their platforms, they don't necessarily show up in the spending data. So, Jeff, with several companies and organizations working on similar AI technologies how do you think firms can differentiate their AI products or solutions from others in the market? What's going to be that unique value prop that firms should offer that's going to set them apart? Man, that is going to be tough. I'm speculating right now that there are companies out there that are getting term sheets, but they're going to be out of business before they get their first round. Like that's how fast it's moving, you know? Warren Buffet calls it the moat. It's going to be hard to have moats in a lot of cases. You got to build things that everybody else doesn't have. Folks that think you can take three public, kind of publicly available things, wrap 'em together and call it unique. It's not unique. So, if you want to have something unique you have to have, you better have real work. You have to have something that's really different, and that's hard for others to replicate. So, that is the big question. And I'll tell you the VCs, as an LP, I am an LP in four funds, you got to be really careful what you fund 'cause things, they just, how are they going to be different?

There's a lot of mystery around that, especially, you can add an LLM. Anybody can add an LLM to anything right now, so then you better have something different that's not the LLM. And by the way, lastly, you better add an LLM onto your widget as soon as you can or you're not going to be very interesting. Because if a company has money to spend they're going to spend it on things that have AI and LLMs related to them versus something else 'cause they've got to report it up to the leadership who's going to report it up to the board because the board's asking the leadership, "How are we investing in this?" So, you got to get in the wave. So, is data going to be that differentiator or is that going to be, you were talking before about IP leakage essentially. But do you feel like that corpus of data is ultimately going to determine and define the moat? I think it's going to make a big difference. When I ask Bing about who I am, because they have, maybe it's because they have LinkedIn, but its quality of understanding is so much better. I do think he who has the most knowledge and can harness it is going to have the best advantage. And then how is that going to be liberated, that data, so that others can build those into their systems? And what's the pedigree of that data? It's going to be a very interesting world coming. It's exciting. So, it sure is. So, Furrier and I talk about this a lot on our CUBE pod is, you try to compare with other waves, they're never identical and past is not prologue, but you think about the internet it benefited a lot of incumbents. And yeah, there was a lot of disruption too, but everybody was able to take advantage of it. How do you see this, this wave relatively? I think this wave's going to be 10 times bigger than the Watson wave. I mean, sorry, 100 times bigger, maybe 1,000 times bigger. There are so many exciting ways to integrate LLMs into systems to create better experiences for organizations using technology. Yeah, that's it man. This could be, it's going to show up everywhere by the way, and you don't even have to do it yourself. I mean, it's going to be in your office. I mean, it's going to be in the apps you're using at the office. It's going to be in these tools you use. It's just being integrated left and right, and the world's going to become more conversational. I think, a weird thing is like cursive, they don't teach cursive anymore in school. And you have to wonder when things become really natural language oriented if, at what point do keyboards go away? Yeah, typing with our thumbs is probably not going to be the norm. (Jeff laughing) You kind of referenced this earlier that jump on board, you got to apply LLMs like now, yesterday, months ago. So, how should leaders think about investing in AI? What would your advice be? Well, what would be my first advice on that? Well, there's a lot of, a lot of people are labeling things AI, and it's a bit of a stretch. So, one thing you want to do is probably find that it's legitimate, you want to make sure that it's going to work within your compliance and your, like having repeatable answers. You want to use it in ways that are going to be responsible. You're going to have to be able to answer to the decisions that you're making. I think if it gets used, if LLMs get used qualitatively, to package up and put the words around the messaging to the customer or around the advice, it's not the actual core of the advice. I think it's probably a bit safer. And I think a lot of organizations are going to get their AI through partners that are specializing in AI. I mean, I'm trying to outsource entropy.

The second law of thermodynamics, the world's trying to break into small pieces, spread out and cool off. And you have to be careful where you spend your energy. We spend our energy, the food we eat in organizations, to hold things together. And why do organizations, no one's out there, got an army of people doing AI on spell check and grammar check or you plug in an API. That's the kind of thing I'm trying to do for entity resolution, who's who. And I think organizations are going to find lots of technology components that have AIs built in them that they can integrate into the workflows that they have or the products that they're building for the market. Yeah, you mentioned entropy, you think about security and randomness of data you probably, I presume, you could use AI to sort of measure essentially the degree of randomness in data to identify potential malware as just one of about 8 billion examples. But do you think, I mean the security guys seem to want to build this in themselves. I don't know, maybe bolt it on in a lot of cases. Well, if you can buy something and plug it in, like most organizations aren't building their own credit card settlement. You'd use Stripe. Most organizations aren't building their own mobile comms, they would use a Twilio. So, I definitely think you try to have some, outsource the entropy and have somebody else's specialist slug it in. Hey, this thing about anomalies though in big data, things that are rare, like one in a million, happen a million times a day. So, rare doesn't always mean interesting. And by the way, entropy might be winning. I don't know, as I think about it (laughs) these LLMs, they're creating more data, and the more data, I blogged about my thing with the Ironman and then LLMs are going to end up reading that and then they're going to insert it into their knowledge base. We're going to need a clear demarcation line between data that was generated by humans and actual sources versus data just purely generated.

That data will be what, February something, 2023. A clear line in the sand, what data existed before that? You better know man. Second law of thermodynamics, doesn't it say entropy increases over time? You might be right. >> Yeah, yeah, that's what I mean, it's winning. (Dave and Jeff laughing) I used to think in this entity business you're trying to figure out who's who to, like hey, they're all the same people. Decades ago I'm like, "Well, you won't need to do this for very long because every, there'll just be common keys. Everyone shares the key." There are now more keys than ever. You have more handles about you, entropy's winning. The universes have taken, it's having its way with us. I'm going to have to retitle this "Breaking Analysis" entropy is winning. All right, last question, if you had to start a company today, and this is your chance to plug Senzing, but I would say other than the one that does entity resolution, although you can work that into your answer, how would you go about thinking about what to start? Well, I would first say that you want to build a capital efficient company. This idea of just throwing more money on and going with vanity metrics, how many people you have, how much money you've raised, and growth at all cost, I think is just basically out the window. I just don't think that's the future. I think if you're going to start a company you got to figure out unit economics, and you have to be able to make money along the way and early. And I see some companies that are really good product people, marketing people, and they figure out how to market anything. And you see some companies have really good engineering technology but they don't know how to market it. And it's really the pairing of the two. If you can't figure, you can have an A tech and not a great go-to market and then you're just going to be wha-wha. So, anyway, capital efficiency, and you probably have to be as good as marketing as you are in your tech. Well, to your point, I mean this term zombie-corns has come about. People were all celebrating, "We're a unicorn, we're a unicorn." And it's like then they can't get their next round and there's somebody, three kids who just got laid off from whatever company are disrupting them. And it's all getting compressed so quickly, it's amazing. It is, we're in a golden age. This is so exciting. Jeff Jonas, we got to hang out more. Love having you on theCUBE. You're an amazing guest. Thanks so much for spending some time with us. It's always fun. All right, I want to also thank Alex Myerson who's on production, manages the podcast, Ken Shifman too. Kristen Martin and Cheryl Knight, they help get the word out on social media and in our newsletters. And Rob Hof is our editor-in-chief over at siliconangle.com, does some great editing. Remember, all these episodes are available as podcasts wherever you listen, just search "Breaking Analysis" podcast. I publish each week on wikibon.com and siliconangle.com. David.vellante@siliconangle.com if you want to get in touch with me or @dvellante for DM, talk to me on LinkedIn on our post. And check out etr.ai, they got some great survey data focused on the enterprise-tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat instrumental music)