In this insightful edition of the Mixture of Experts series, Don Muir, Chief Executive Officer and co-founder of F2 AI, joins John Furrier, co-founder and co-CEO of SiliconANGLE Media, to explore innovations in artificial intelligence within financial markets. They convene at theCUBE's New York Stock Exchange studio to discuss the emerging landscape of AI-driven financial analysis and its potential to revolutionize financial markets.
Muir, an accomplished entrepreneur with a background in private equity, shares their journey from founding Arc to pioneering with F2 AI. This venture originated from Muir's vision to enhance financial data analysis using AI, initially incubated within Arc’s successful debt capital markets business. The conversation, led by Furrier of theCUBE, navigates through the innovative technology that F2 AI introduces to private credit funds and financial institutions.
Key insights from the discussion focus on the transformative role of AI in automating financial processes. Muir explains that F2 AI uses advanced AI techniques to eliminate manual tasks, thereby empowering professionals rather than replacing them. The discussion highlights the importance of adopting AI-native strategies in today's rapidly evolving market, with analysts emphasizing how AI-driven methodologies can provide a significant competitive edge.
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Joran Dirk Greef, TigerBeetle
Join us for an insightful episode featuring Raj Verma, CEO of SingleStore, as he shares his expertise in the rapidly-evolving landscape of data infrastructure and Artificial Intelligence. Hosted by theCUBE's John Furrier, this conversation takes place at the prestigious New York Stock Exchange, spotlighting SingleStore's strategic position and the innovative partnerships shaping the future of AI infrastructure. Verma's insights reveal the dynamic intersection of Wall Street and Silicon Valley.
In this episode, Verma discusses the transformative role of databases in AI development and the critical importance of modernizing data estates to capitalize on new AI capabilities. According to Verma, integrating data effectively can significantly enhance AI's operational efficiency, emphasizing the need for organizations to harness their own data. theCUBE analysts explore the future of enterprise technology, echoing Verma's predictions for AI-driven disruption across various industries. Don't miss out on the key takeaways from this engaging discussion. Learn more about SingleStore here: [SingleStore](https://singlestore.com). #AI #Cybersecurity #DataInfrastructure #SingleStore #NYSE
Stay connected with the latest in tech innovation by following the full series with theCUBE at NYSE Wired.
00:00 - Intro
00:06 - Launching into New Ventures: A Market and Partnership Overview
04:31 - AI Evolution: Infrastructure Trends and Applications Across Markets
08:57 - Modernizing Data Estates for the Future of AI and Agents
11:58 - Challenges with AI Hallucinations and Data Reliability
16:11 - Advancements in Data Technologies and Enterprise AI Integration
19:32 - Shifts in Enterprise Data Usage for AI
23:30 - The Future of System Software and Applications
31:24 - Disruption in Professional Services and SaaS Models
35:15 - Navigating the Future: AI, Innovation, and Strategic Roadmaps
In this Mixture of Experts segment from the NYSE, Joran Dirk Greef, founder of TigerBeetle, joins theCUBE’s John Furrier to discuss the critical evolution of Online Transaction Processing (OLTP) and the emergence of databases as a fourth pillar of critical infrastructure. Greef explains the concept of "modern legacy," noting that while general-purpose databases have served the industry for decades, they struggle to meet the demands of a world where transaction volumes have surged 100,000x due to cloud, energy and gaming sectors. He details how TigerBeetle was...Read more
exploreKeep Exploring
What is TigerBeetle and how does it change the efficiency of financial transactions compared to legacy systems?add
What is a Tiger Beetle and what characteristics make it an intriguing creature?add
What is the significance of databases and data movement in the context of large-scale AI systems?add
What are the key differences between SQL and OLTP in relation to data management and transaction processing?add
What are the current customers and use cases for your service or product?add
What are the future plans for expanding the ecosystem and enhancing transaction capabilities?add
>> Hello, I'm John Furrier with theCUBE host here at our New York Stock Exchange CUBE Studio. Of course, we have our Palo Alto Studio, I think Wall Street and Silicon Valley. This is part of our mixture of experts. We interview the leaders who are experts in their field to unpack the core issues in technology. Got a great entrepreneur here, creator and database, database data systems, distributed computing, Joran Dirk Greef, creator, founder of TigerBeetle, innovative company, doing great things in from South Africa, where a little trivia. EC-II team is there, a lot of smart people. Joran, great to see you again.
Joran Dirk Greef
>> John, so great to be with you.>> People don't know this, but we were flatmates as you said, or roommates as we say in America at our Mali trip with Bill Tai and the Acti global community, the kite surfing community. All the pros were there. You're good. I'm learning. Brian Bauman is very good. NYC Wired community is thriving and growing. Thanks for being part of it. Thanks for coming in.
Joran Dirk Greef
>> No, thanks. It's such a pleasure.>> I got to know you in Maui and we had a long chat about some of the things you're working on. TigerBeetle, very well funded. You got some great investors. You build some great technology in an area that everyone loves, databases and you're doing some very innovative things. So first, explain TigerBeetle, what you guys are doing, and then let's get into how data's changing the game for AI agents, transactions.
Joran Dirk Greef
>> Yeah, John, so OLTP, it's the OG of databases. 40 years ago, the field got started. There's been a lot of analytics lately, but with TigerBeetle, we went back to the beginning to five years ago, designed the world's fastest financial transactions database, TigerBeetle, a thousand times faster than modern legacy systems. And so that's what we're doing with TigerBeetle. How can we power the world's transactions a thousand times more efficiently? Because of where the world is going, the world needs that kind of->> Explain TigerBeetle, the name, where that came from.
Joran Dirk Greef
>> So a TigerBeetlebeetle is a real creature. It's the world's fastest insect, actually the world's fastest creature scaled for body length, really the fastest thing on earth. Beautiful, metallic chrome green blue colors, can detect echolocation even. It's a fascinating creature. And so it also thrives in tough environments around the world. So we were looking for a name for the fastest transactions database, but it's important not to be fast and big. You want to be fast and small, efficient. And so this thing is so small, so efficient, so fast. Also beautiful, thrives in tough environments. TigerBeetle.>> We are in an era now where you got to think small to be big as they say, because seeing a lot of, I won't say, disaggregation, but the way these large-scale systems are being built in the AI, if look at NVIDIA large-scale systems, there's kind of been a re-architecture of how memory and all these data flows. So data movement, databases are now core. The database is like a new element. I think one of the things we chatted about in Maui, which I found fascinating was I was like, oh yeah, the holy trinity is storage computing. Well, don't forget databases and OLTB, online transaction processing has been known for very, I won't say niche, but very important things. Banking transactions, a lot of large scale fast systems that power real time. Now that is going into everything that we know. Explain this concept, this fourth pillar of database. So compute, networking, storage, which we all know is key to AI Systems, But the database isn't your yesterday database. It's got to be-
Joran Dirk Greef
>> So I think we said there's three hard problems in computer science, naming things, off by one errors and what was the other one? Caching. But so I think databases are really the hardest thing. State. State is the hardest thing. Most software, most computer science is stateless. So we have tons of machines everywhere in the cloud, but those machines come and go. They're ephemeral, they're transient. But where does our information reside? Where does the record, our clay tablets of the New York Stock Exchange, all these trades today, where are the clay tablets stored? And these are digital databases today, no longer clay tablets, but they're just as durable. They have to be. But it's that state, the library of the world's information, the world's transactions. Where do we store that? That's databases. That problem is becoming harder and harder because the volumes are increasing and surging because the world's just moving fast and faster.>> And we were also talking about a term, you mentioned it earlier, modern legacy.
Joran Dirk Greef
>> Modern legacy. I think you coined that term.>> I know.
Joran Dirk Greef
>> Midnight in Maui.>> We were having a few beers, it was fun, but I brought it up as kind of a tongue-in-cheek term because modern legacy, it's not obvious, but there is a modern legacy landscape and we riffed on this for a while. Let's talk about that because I think there's an era of, hey, I bought an Oracle database or I bought this database, I got Postgres, I have this over here. You have a diversity of databases in these environments. It's not a one database to rule the world, but modern a few years ago might not be modern-
Joran Dirk Greef
>> Today.... >> today. So explain what your view of modern legacy is.
Joran Dirk Greef
>> So this is something I've just been seeing and databases are like cathedrals. If you've read Ken Follett Pillars of the Earth, journey of a craft person, they want to build a magnificent cathedral. It costs millions of dollars to build a databases. And these things get built every few decades. So most databases people use today, even in the cloud, they're at least 15 years old. Even all the hyperscaler databases, they are 10, 15 years old. The most popular database is Postgres, MySQL, SQLite, 30 years old, Oracle, 50 years old, king of the mountain. So everything is at least multiple decades old. However, what is modern legacy? Well, it feels like 10 years is still modern, but actually 10, 15 years ago since then, the world in the last 10 years, transaction volumes have surged a 100,000X sectors like cloud, energy, gaming. We have new kinds of businesses and trillion-dollar companies that just didn't exist a few years ago. And so the databases of 10, 15 years ago were not designed for three to four order magnitude increase in scale. They're not specified to this kind of scale. And so with TigerBeetle, we have this opportunity to say, well, where are the volumes? Where's the scale of the world going? Not even, let's ignore the autonomous transactions from AI. Let's just think of the people. People that are now ride-sharing or streaming or everything is a thousand times more transactional. So how do we design a transactions' database? Not 50 years old, let's design it not for a thousand transactions a second, but half a million transactions a second. It's a new way of just thinking and it's just something we have to do because technology has to advance.>> And the modern legacy is a great description. You mentioned Oracle, the king of the hill. If you look at it, we've been covering Oracle for 16 years in theCUBE. Of course we've been following that closely. They've gone from the database that rules the world to a database that never goes away because it's like the clay tablets as you said. But look what they're doing now. They are now in the mainstream cloud game. I just saw a picture of the new CEO of Oracle with Sam Altman. They've moved from the database position into critical infrastructure. Infrastructure, not database.
Joran Dirk Greef
>> Infrastructure. Yes.>> So database is now that-
Joran Dirk Greef
>> Data centers.>> Data centers, hey, data centers, database.
Joran Dirk Greef
>> Networks.>> We have a series called AI factories. NVIDIA's a big part of that. They're leading the way. You can't have an AI factory without a data factory.
Joran Dirk Greef
>> Yes.>> So talk about the role of this transaction because the other thing that you're seeing is more volume, more kinds of micro transactions.
Joran Dirk Greef
>> Yes.>> And this is not new.
Joran Dirk Greef
>> Not new.>> I mean there's been micro payments around for years, so there's always been kind of micro things.
Joran Dirk Greef
>> Yes.>> How does that apply to infrastructure? Where do you see that trend intersecting with TigerBeetle? Because if the volumes are going up, check, we see that. Two, what kinds of transactions fall into the new OLTP? Is it agents? Talking to agents. Is it little gestures?
Joran Dirk Greef
>> Yes.>> Explain the new OLTP landscape. We know what the old looks like.
Joran Dirk Greef
>> Yeah. So we use a media analogy. So let's imagine the world of business is a television screen. The New York Stock Exchange is a television screen and we've got black and white color and we've got big pixels. We can do old grainy movies. And that's business 10 years ago. In the last 10 years, that picture has changed. And now we've gone to retina display. The pixels are so small you can't see them with the human eye and they're multicolor. So it's rich color and that is what the business world wants. We want higher resolution in our business. We want to know what our stock levels not each hour or at the end of the day. We want to know what it is at this nanosecond. We want real time resolution in nanosecond time. Old infrastructure is really much, pretty much by the hour. And so you've got, it's the same TV dimensions, but the number of transactions are more pixels and they're smaller. And that gives you this real time resolution into your business. So let me give us some examples. How does this apply in real life? So if you booked an Uber trip today, maybe you booked five, I don't think I didn't book a rental car coming here. A rental car, you go somewhere, you book it once for three weeks. Now I do five trips a day. So much smaller in value, many more of them. Real time is better for me as the consumer, supply and demand meeting more frequently. And so look at content, it's the same. We used to buy an album than you buy a song 99 cents, I can't even remember that world.>> IPod.
Joran Dirk Greef
>> Yeah. Who has an iPod anymore and 99 cents song no longer. And now you just stream by the second and who knows how the billing is done.>> I love the picture example because I interviewed the head of technology at the NHL here at the NYC and he was showing me some 8K footage, 8K.
Joran Dirk Greef
>> 8K.>> Unbelievable.
Joran Dirk Greef
>> What does that look like?>> It's like you're at the game and they're going to do that. But the pixels is a good representation because at any given time they can be activated and turned on. Pictures are always changing. That's the data in your business. So you got to have the pixels first. Small little pieces, can't see it. That's little transactions is happening for a big picture.
Joran Dirk Greef
>> And you need a lot of database power to power that display.>> What's the secret sauce? Take us through. I mean, I love the market. I think you're in a good spot. Talk about the secret sauce.
Joran Dirk Greef
>> What makes TigerBeetleBeetle work?>> Yeah, what is the yield, the IP or talk about the algorithms. What's the secret sauce that makes it all work?
Joran Dirk Greef
>> Yeah, so I think the key is that 40 years ago, even 50 years ago with SQL, that SQL is a fantastic query language for getting data out of a database. It's a great for reading data and it's a general purpose database interface. So SQL, general purpose, great for getting data out. OLTP is very different actually. The problem is how do you get your business transactions in? How do you do business? The analytics comes later, the SQL comes later. So the big problem is not how do we do queries in the data lake? It's like we've got this big empty dam, how do we just get the water in it? And that is transaction processing TigerBeetle. So we realized the world doesn't need another string database. We need a database that's good with numbers, like a database that can count and do it very fast, half a million a second. So the secret source of TigerBeetleBeetle is it's pretty much just anatomy. Humans, we're pretty good at swimming. Dolphins are specialized, so they make it look. So you could take a general purpose database like a SQL database, any one of those, you could make it 10% faster like a human. We are going to train harder. But a dolphin is going to come along and you just cannot compete with a dolphin.>> It's elegant against fast.
Joran Dirk Greef
>> It's a different category. One is general purpose, walks on land, swims on water. The other one is I specialize for transaction processing.>> That's TigerBeetle.
Joran Dirk Greef
>> That's the whole, you cut the database open. The anatomy is different.>> Talk about the small aspect. You mentioned the TigerBeetleBeetle name small the fastest by mass size. Talk about this phenomenon around how you can break things into small pieces or database pieces or pixels if you guys use the analogy. Why is that important? Why is that trend relevant? Yeah, small databases in the aggregate better.
Joran Dirk Greef
>> It's relevant because, let's take an example from energy. So the world has moved from coal. We no longer price energy once a month kilowatt-hour. We don't price that once a month. We now want to price that every 30 minutes. And the reason is because the sun is the cloud, cloud coverage is coming in or it's raining or it's sunrise, sunset. So the price of clean energy now changes. And if you were to price this once a month, it's very inefficient. So you need to price energy in real time if you want to take advantage of clean energy. Now suddenly your smart meter at home can be switching energy back and forth into the grid. And the faster you can transact, the more->> So more real time.
Joran Dirk Greef
>> More real time.>> What's the alternatives today? Because you mentioned it costs millions of dollars to build a database. Build and they will come was the old model, as they say in the movie Field of Dreams. What is the alternative? I want to say, hey, I want to price energy in real time. Without TigerBeetle, what do I do? What's my design room look like? What's the engineers around the table talking about? What's the scope? Scope that, and then talk about how TigerBeetleBeetle compares.
Joran Dirk Greef
>> Yeah, so this is something that took me a while to understand not only why TigerBeetleBeetle is specialized and fast for transaction processing, but why a general purpose database actually has a theoretical physical limit in terms of Amdahl's law. So if you were to take a general purpose database, even a million dollar horizontally scaled cluster, you can take one of these. There's the promise that you can just buy more machines and scale infinity. But actually in terms of Amdahl's law, you can't for transaction processing contention, SQL Rolex across the network just don't scale. Even if you throw a million bucks of hardware at it just doesn't scale. And so this was something I realized that a general purpose database actually cannot handle today's load. And I came across this working on a central bank switch by the Gates Foundation using a general purpose database, couldn't make it go faster. So what do we do? What you tend to see is huge investment outside the database. In the application, companies are now spending millions on reconciliation. They run 200 machines of Postgres. They could be running six of TigerBeetle. So there's just a ton of cost and complexity outside the database, building a database.>> But so more complexity to make it go faster.
Joran Dirk Greef
>> And it still doesn't go faster. So what they end up doing is the product experience starts to suffer. So all companies take on risk. So for example, a lot of FinTech, they're offering real-time FinTech products. Under the hood, they're still running an old-fashioned daily batch rails and that spread, they're taking that hit. But if you have a real-time transaction system, settlement risk goes away, FX risk goes away. Your business is now... Your exposure is far less. So there's multiple ways. Either your users are going to see it or your business is going to feel it in terms of risk.>> Joran, talk about the business. How old are you guys? When was it formed? Employees, funding, customers momentum. Give us a quick overview.
Joran Dirk Greef
>> Great. So we are only a three-year-old company. We already work with some of them->> Three-year old?
Joran Dirk Greef
>> Yeah. So we celebrated three years this month, September. Thank you. And so three years old, we already have some of the largest wealth management companies, brokerages around the world, Europe, South America, North America, Africa, all over Asia.>> Based in South Africa.
Joran Dirk Greef
>> We're actually based in the US. So US company, half our team in the US, half in Europe.>> West Coast, East Coast.
Joran Dirk Greef
>> Remote. A bit of both. Keep it fresh.>> Remote.
Joran Dirk Greef
>> East side, west side. Yeah, so we are global remote and yeah, it's a great->> You got some great backers too. Who are the financiers again?
Joran Dirk Greef
>> Yeah, so Spark Capital led our series A. Amplify Partners and Coil led our series C. 30 million in funding. Small team of 16, multiple former CTOs. Some of the best engineers that we can find. Some of the nicest people.>> Amplify Spark, very well known for investing in technical entrepreneurs.
Joran Dirk Greef
>> Deeply technical investors.>> And that's the thesis that they have, which is a good one. I got to say one of the things that's impressive about this is that even if you're only three years old, you got the right tailwind with the trend. The trend is your friend, one. People are struggling with the database. Use cases because this comes down to, okay, where's the rubber meeting the road? You mentioned some of the big deployments. What's the environment where you're thriving in right now? Where's your landing zone right now for customers and what are the use cases?
Joran Dirk Greef
>> Yeah, so some of our customers, they're billion dollar unicorns, they're about to IPOs. They want mission-critical transaction processing. TigerBeetleBeetle because of the speed it has to be far safer than any existing general purpose database. We have so much more safety techniques, NASA coding style. So we have customers that want that because they're about to IPO, they want nothing to go wrong, stay out of the headlines. Others are just dealing with massive scale. So we had one customer come to us, they said, look, TigerBeetle, we've got a hundred million transaction workload, we have to migrate this. We only have 45 days to do it because we want to take advantage of new market opening up to us. We have to launch by 1st of January. Our team was working around the clock to help them make it happen. And they went from 26 transactions a second with the general purpose limit with a SQL database. 26 a second, they saturated their central bank limit. They said to us, you don't know what a big day this is for us, how much business we could move because we can go faster. So going faster->> They'll be buying you a beer for sure. I noticed you guys had a dinner event here in New York City, Dave Vellante that I attended. You're attracting a lot of talent to this mission. Also, other fellow entrepreneurs, ecosystem partners. What is the strategy for one, hiring? Who are you looking for? I'm sure people would want to probably join this. What kind of problems you're solving? So what's the makeup of the person you're trying to hire and talk about your partnership strategy. Is there an ecosystem play here or take us through how you're thinking about expanding and your thoughts on that?
Joran Dirk Greef
>> Our vision is we want to lead the world's transactions, power the world's transactions for the next 30 years. We've had a great innings of 30 years the past with general purpose databases. What does the next 30 years look like? The future, how do we design for that? So engineers are attracted by this long-term view of like let's do quality, quality scale.>> Let's scale speed.
Joran Dirk Greef
>> Scale and speed. And the most extreme engineering we could do tiger style and really tiger style is extreme, NASA coding. There's no waste in the data. We're so efficient, streamlined. So engineers, I think the thing that we look for is within 10 seconds they come up to you and their eyes light up.>> They know the problem you solved.
Joran Dirk Greef
>> And my eyes light up and I think this person is just be so nice to be around and they're highly talented and the co-founder of Rust works with TigerBeetle, the creative Rust analyzer.>> Successful companies have that shiny new toy factor and that can be a pejorative, it could be negative in the hype cycle, but when you actually have a better product, it's like, oh my God, I need that. And I noticed that the people that you're attracting in the conversation first I was enamored by it as well, are people that know the infrastructure gain.
Joran Dirk Greef
>> That's why I'm kind of honing in on this fourth pillar of database, not as a software piece, although it's great.>> It's software and hardware.
Joran Dirk Greef
>> It's software and hardware together as infrastructure. So the people that you're attracting are people who worked at Palantir, Facebook, large-scale systems, these are nerds, these are large-scale.>> We love nerds. Because that's the problem. They lived it.
Joran Dirk Greef
>> Yeah, we think orders of magnitude. So we think we could do something that's 2X, We wouldn't do that. We'd do 10X, 100X, 1000X and we literally just designed for orders of magnitude. It's just standard engineering.>> What's the sales strategy? Do you have a sales force? Is it all-
Joran Dirk Greef
>> Oh, we do. Yeah, we do. So our sales strategy->> Just show the product and then just say again, their eyes popped out of their head.
Joran Dirk Greef
>> Yeah, we say that engineering is marketing. So we've put so much into TigerBeetle, incredible... It's just our joy that we pour out our craft. Then we tell the story of that and that goes out, 200,000, 300,000 people watch our talks. And so there's hardly an engineer anywhere that doesn't know if TigerBeetleBeetle and then they reach out. When the timing is right for them, they knock on the door and we say, let us help you. And we turn them around.>> You showed me a few demos, I'm not sure that it made it mainstream, but you have a crafty strategy of luring in the nerds with kind like a game to show them the value proposition.
Joran Dirk Greef
>> Yes.>> What's the idea behind that? What's the purpose of that to say, hey, here's an example of what it would look like if you ran this way?
Joran Dirk Greef
>> So databases, I see it as this invisible world. It's like the movie Inception where architects in an invisible world create these amazing things in machines and do that turn and buildings that go upside down, but no one can see it and our children can't see it.>> The invisible technology that makes things happen that no one's like-
Joran Dirk Greef
>> And we're so excited about, but no one can see.>> You feel like mechanics under the hood.
Joran Dirk Greef
>> And I think the best things in life are invisible. Stories, music. And so that's why I love technology because invisible and then there's this feeling, how can we show this to people? How can we put a game engine on and they can actually see it and run a cluster of TigerBeetles and hit them with a hammer and see them recover. Or how can we build a 3D racetrack and race databases on and see that 1000X?>> With real numbers by the way. Not like fake. The smashing of the TigerBeetle-
Joran Dirk Greef
>> It's running real code.... >> is simulating an outage, which is common. So you basically took database problems and turned it into a kind of visual game.
Joran Dirk Greef
>> And then we plug autonomy into that. So instead of AI, we do autonomous testing, we run a thousand machines and they simulate autonomously all these kinds of events. So we do 2,000 years of testing.>> I wanted to bring that up because one, it's clever, I liked it. But also it points to what I see in your world that'll be a great future headroom, probably fast, faster than when we all think is when agents come in, you're going to see them talking to each other with data. Those are transactions.
Joran Dirk Greef
>> Absolutely.>> And they're going to be in the zillion. There's going to be millions and millions of transactions no one sees. And state is huge because state is where the clay tablets are doing their thing.
Joran Dirk Greef
>> Record those.>> You record those transactions, not family. You want to have delegation, you want to have some of these computer science concepts working, which is hard.
Joran Dirk Greef
>> It's hard.>> Explain why the agents and what's going to be the future transactions that you think will be a big part of your growth.
Joran Dirk Greef
>> Yes. And I think if Tiger Beatle is a thousand times faster than 30-year-old technology, that isn't hard because the world has advanced in 30 years. There's just so much advance in hardware, software, research. You can make a database that much faster. You should.>> Yeah, you did.
Joran Dirk Greef
>> Yes. Yeah, we did.>> But where does it go? The volumes aren't stopping.
Joran Dirk Greef
>> And I think that's the thing. TigerBeetle's already behind. So when you think of autonomous transactions, these things are now so fast and so fine-grained because... And on top of that, you can't use very general-purpose primitives like SQL anymore because of hallucinations. So you need far higher quality primitives to give the LLMs. So you need to give them debit credits that you can guarantee correctness because now you have an audit trail. Oh, did one LLM mess up? We've got the audit trail. Okay, we can do correcting entry, but we've got the whole... So this is where debit credit, which TigerBeetleBeetle brings out of the box is so important. Better primitives for machines to reason with ans go with.>> Yeah. And I think your movie example is good too because what you have on business is you want to light up the data at any given time. The scenes change, it's real time. It's different, it's general, but we can see it. It nothing's on the TV. All right, so where do you guys go from here? You're going to head back to South Africa, you got a distributed team all over the world?
Joran Dirk Greef
>> Yes. Yeah. Six of the team are in town. I'm popping back to Cape Town, just hop across the pond. I love New York City and we are taking a few professors from around the world, Copenhagen, Germany, Netherlands, some of the top professors in databases. They're coming out to Cape Town in two weeks time. We're taking them up Table Mountain to a little bit of a workshop up top there. I love databases so much. I went to Table Mountain.>> I mean, when I got my degree in the eighties, one of my CS degrees was databases and operating system. But the database one was, at that time, wasn't very popular. A database person. Now it's the hottest field.
Joran Dirk Greef
>> It is.>> It is a core pillar.
Joran Dirk Greef
>> All the techniques of computer science into this little thing that we built.>> Well, congratulations and thanks for coming on, our mixture of experts as part of theCUBE and the NYC Wired program and community. Thanks for participating and congratulations.
Joran Dirk Greef
>> Such a huge pleasure, John. Thank you for everything you do.>> Thank you. All right. That would get the entrepreneurs, the creators, the technology that will make things faster, simpler, and if they do their job, they'll be invisible, but they're making things work. The transactions are happening. The state of the data. This is the key secret sauce to make generate and the new AI enabled applications, AI native applications successful. You got to have the horsepower. We're seeing all the spending on data centers, all that will be powering data centers with databases as infrastructure. Of course, theCUBE's got its own data. We're streaming to you. We're doing our part. I'm John Furrier with theCUBE. Thanks for watching.
