Hiten Patel: Thank you for joining us today. I'm delighted to have on the show Junta Nakai, who is the head of financial services at Databricks. Welcome, Junta.
Junta Nakai: Thanks for having me. Happy to be here.
Hiten: So a great place to start would be for you to give us an intro to your role and the company, and what you're doing now.
Junta: Sure. So Databricks is a data and analytics company. It's currently the second largest enterprise software company in the world, unicorn after OpenAI. And we just recently did around 43 billion dollars a few months ago. And what we provide is a cloud-based analytics environment for many of the world's largest corporations. So, we have about 10,000 customers around the world, and they use our platform to be able to get the most out of their data. So anywhere from visualizing the data, moving the data, curating the data, all the way to doing gen AI and the more advanced things around artificial intelligence that people are excited about in a single platform, end to end. And that's the value proposition of Databricks, which is just helping customers get the most out of their data. And within that context, I head up financial services. So financial services is now our single largest business within Databricks.
We have well over 1500 customers around the world, from retail banks to asset managers, to FinTechs, to insurance companies. And similar to almost all the other customers that we have, they're trying to leverage arguably the most important asset that they have today, which is their data. And there's a number of use cases that are driven by that. But I spend most of my time talking to customers around the world, financial services customers around the world, about their data and AI initiatives and some of the things they're trying to do to transform their business to thrive in the future. So that's a quick background about Databricks and what I do for Databricks today.
Hiten: Amazing. And let's, before we dive into Databricks, just let's take us on a little bit of a background talk. I remember when we first met, you just left Goldman, you were working for an early-stage company that was doing exciting things. Just for the listeners, tell us a little bit about the backstory before you got here.
Junta: Sure. So, I graduated college and the first job I had was at Goldman Sachs in the equities division. And I was there for about 14 years, and I was on the trading floor and maybe 12 years into my career I kind of had this epiphany and I said, I don't know if this job is going to exist for that much longer. And the way I thought about it simply is I was kind of looking around the floor and back in 2004 when I started in the equities division, there were hundreds and hundreds of traders, trading equities, just stocks back and forth. And when I left, there was a handful, maybe you could count them on two hands left. So you went from hundreds and hundreds to literally single digits. And it turns out that matching buyers and sellers is a job suited for algorithms. So you have this biggest explosion of electronic trading and all this stuff that was happening.
Junta: And within that context, I was kind of thinking about my career and I had this epiphany of, Hey, what's the point of a career? Is it to maximize my short-term cashflow or is it to maximize the NPV [Net Present Value] of the entirety of my career? And I thought, hey, it's probably the latter. And then when I thought about, well, what is the most exciting trend that's happening today? It is probably AI. So this was back in 2016 and I took, sorry, 2017, and I took the plunge. I left Goldman, I joined a very small FinTech that was doing really interesting things around AI to automate workflows in capital markets. So if you think about how bonds are traded and it's very manual and trying to figure out what are ways we could automate that, and I was there for about a year and that company got acquired by a larger FinTech company. And in early 2019 I joined Databricks. Databricks at the time was a very small company. I think it just had become a unicorn. And the reason that I made that switch is again, you kind of think about the future. If AI is going to be the most important, maybe technology of our lifetimes, I want it to be part of a group of people that are trying to be disruptors rather than sit around and be disrupted. And that was the big plunge that I made into tech. And that was a mid-career switch. It was quite difficult.
Hiten: Well, I mean, it's such an orthodox start to the career, right? Played safe, graduate program, mainstream career that pays well and it's, look, I think it sounds like you saw it sooner, right? I mean, it's very mainstream now. Everyone's talking about it. They had Davos the other week, everyone's obsessing over AI, LLM [Large Language Models]. It's gone crazy mainstream. But it's amazing the influence and impact you can have by seeing and understanding these things sooner, right? The timing that you make, the move. I do remember you sitting down, I can't remember, I think it was about 2019 when we chatted after you just started Databricks, and I'm going to make you do this again. I was fascinated by the description, but I must admit I didn't think I've fully comprehended the role it plays and what it does, and given how much influence it's now having out in the world, it'd be great just to kind of spell out in layman's terms, why has this company been able to add so much value and play such an important role? What is it bringing that some of the end clients, particularly in financial services, can't do themselves?
Junta: So let me take it a little high level. So I think at this point, most people would agree that cloud is the future, right? Public cloud, moving to the cloud is the future. I think most people would agree that AI is the future. I think most people would agree that data is growing tremendously. And I think most people would agree that open source and open formats are the way innovative technology or foundational tech stacks are being created today. So you may have heard of the Apache Foundation, Linux Foundation, these are kind of foundations that manage a lot of big open-source projects that are out there in software. And Databricks is uniquely positioned because arguably it's the only company that I can think of around the world that ticks all four of these boxes. It's cloud native, it’s multi-cloud, it's open source, open format. At its core, it's AI enabling AI and it enables customers to handle tremendous amounts of data.
So what does that mean in practice? Well, if you think about a typical financial service company or a typical company, they have lots of tech debt, lots of legacy technologies. So imagine they might have a database, they might have a data warehouse, they might have a data lake. There's all these different types of databases out there. They have tools that help them move data around from one place to another that's called the ETL tool. They have an orchestration tool that helps them manage all the movement of data. They have a governance tool that helps them understand where the data is coming from, where it's going. They have a visualization tool like Power BI or Tableau or some of the things that you may have heard of. They have an AI tool, I mean just to leverage data effectively. Historically, companies had to stitch together lots of different technologies together.
Junta: And that the complexity, the cost, the redundancy, just the time it takes to get data anywhere was extremely prohibitive for many, many years. And where Databricks came around is Databricks came around and said, Hey, what if you could do that all from one place? So instead of stitching together eight or nine or ten different tools, what if we just gave you one interface where you could do everything from low level data engineering all the way to the most advanced generative AI use cases from a single place, from a single pane of glass. And that's the real value proposition that Databricks provided. And as I mentioned today, we have about 10,000 customers. So not just in financial services like Capital One and JP Morgan, all these other companies, we have tons of companies across all sectors. So a good example might be Rolls Royce. People are very focused on the carbon footprint, especially in Europe.
So there's, I think this movement to take more trains and less planes, et cetera, et cetera. And what Rolls Royce has done is they realized that sustainability is a data problem at its core. So if sustainability or ESG is a data problem, sustainability is an AI problem. And what they do today is a single engine, a Rolls Royce engine on a plane generates gigabytes of data per flight. And what they do is they take all that data, stream it into Databricks, they run all these models and AI models to optimize the engine, optimize the engine performance. So they might say, Hey, maybe we slow down here or maybe accelerate here, maybe change out. I mean there's all these predictive intelligence that goes on. And by doing so, they've saved millions and millions of tons of CO2 per annum just by optimizing the engine. So here's just a practical use case of once you have data in a single place, you just find new ways to monetize and new ways to use that data to advance particular goals that you might have, whether it be transformation of your business or reduce carbon footprint. So at the high level, those are the things that Databricks does and really just democratizes access to data and AI to a wide array of customers around the world.
Hiten: Very, very powerful. Very powerful image you paint there. And do you need to be a specialist to be able to use the tool? Do you need to be a data engineer or is this being pitched? There's a lot of solutions out there trying to be low code, no code, trying to be something that a wider set of participants can use. What's your kind of approach and philosophy to how you engage with the user?
Junta: So historically, the users have been fairly technical. They've been data engineers and data scientists because Databricks was an environment where coders came and coded an R, and Python, and Scala and all the different languages. Today that has shifted quite dramatically. So today we have BI, so visualization capabilities within Databricks, we have to your point LLM capabilities where you can now query against your data in natural language. So the addressable universe has expanded tremendously. So now fast forward to, let's say three years ago to today, we have a lot more business users that are using Databricks that are getting the full power of the data at their disposal because now they could query against their data and do things in natural language or I wouldn't even say low code, it's just no code, it's just language. And it is really, really complicated to actually do that.
And we spent a tremendous amount of time and effort over the past few years to invest in those kind of capabilities to enable less technical people to use it. And that's incredibly important because if you think about transforming a business using data, you basically have to do two things ahead of that. So transformation is wonderful, but it's kind of the end goal. The two big steps you have to do before that is you have to modernize your technology. Kind of like what I said, instead of having 10 different tools and data siloed everywhere, you put it into a single place so you could access it. The second thing you do is you democratize it, right? It is really powerful when a trader or investment banker or a wealth manager has access to powerful data sets at his or her disposal. So once you modernize your tech stack, that enables you to democratize data and analytics across your organization, and that ultimately helps you transform. So that democratization layer is super important. That's one area we spent a lot of time focusing on.
Hiten: Just hearing you describe that. I guess back in the day when we used to build more simple models as consultants, we used to say poor data in, you get poor output out. I guess thinking about that premise in the situations you described, what preconditions do you need in your clients for Databricks to be successful and to have the kind of impact that you describe?
Junta: Yeah, so really the only precondition that we have is a customer is on the cloud. That's it, really. So Databricks is unique in that it is the only software company that I am aware of where all three public cloud vendors are investors in. So GCP, Azure and AWS are all investors in Databricks. Why do they like us so much? Well, we drive tons of data gravity and consumption to the cloud. If people are using Databricks on Azure, we're driving tremendous amount of data to move to Azure and also a tremendous amount of compute to be done on Azure. So the real precondition is the company has to have a bet that the cloud is the future. And I think at this point the vast majority of companies already gotten there. So it's not that much of an issue. Five years ago, when I joined Databricks, I mean that wasn't necessarily the case. Some companies are reluctant to use that, but that's really the only precondition.
Hiten: So take me on that journey. I guess when we first started and you told me you were going to be building financial services for these guys, tell me how has the pitch changed when you used to have to walk in five or six years ago on pitch to generic bank or however you want to describe it, what were the challenges? What was the level of engagement back then and what is it like now when you guys walk in and speak with them?
Junta: Yeah, so five years ago we spoke to only the leading-edge financial service companies. Really the only ones that are very forward-looking that had started to embrace the cloud and they maybe more specifically had a particular new business unit or a new idea that they wanted to make cloud native. So our areas of success, I'll give you an example, would be at HSBC. HSBC, one of the first areas that we got into was a new business in Hong Kong called Pay Me, which is their payments platform. It's kind of like the Venmo over in Hong Kong. And if you think about just the behemoth that HSBC is, we have the most traction five years ago on new business units, new initiatives because those are typically born on the cloud. So those are the type of opportunities that we capitalized on five years ago. And another way to say that is to maybe it was much more focused on a specific business use case.
Junta: Fast forward to today, the conversations that we're having is much, much more strategic. Now, it's CEO level, C-level conversations because it's about, again, using the most important asset that they have, which is data effectively and being able to monetize that data. So now we've gone from specific business unit in a particular region to really having strategic transformation conversations with the largest financial companies in the world. That's the big evolution. And the way to think about this is actually super simple. So let's take JP Morgan as an example. I'm just using this as a hypothetical example. So JP Morgan spends about 15 billion dollars a year on IT. This is probably one of the largest IT budgets around the world, and Citibank and Wells Fargo, if you look at the other two biggest banks, they also spend 10 to 12 billion dollars a year. So collectively they spend 40 billion dollars a year.
Junta: Let's say the top three banks in America spend 40 billion dollars a year on IT. If that was all towards innovation, that would dwarf like Apple's R&D budget. But of course, that money doesn't go to innovation. And based on a lot of the conversations that I've had with customers and also Wall Street analysts, they reckon maybe 10 or 20%, maybe 30, but let's just say 20% of that budget goes to R&D today, right? Just 20% of the budget now. Meaning if you're JP Morgan, let's say, and you had a 15-billion-dollar IT budget, hypothetically, let's say only 2 billion or 3 billion is going to R&D, the remainder, the 12 billion is going to just keeping the lights on, maintaining the complexity of the legacy that I showed before. Now if you think about what's happened over the past few years, where AI has become literally a CEO level priority at every single company, you cannot be a winner in the AI age if you're spending 80% of your budget managing tech debt, you can't.
So this is what the transformation has happened is that now it's becoming really C-level executives that are having conversations with us about how are we going to reallocate this tech spend away from legacy towards R&D. So this is an old adage of running the bank versus changing the bank. And that's become a really boon for us because now we're getting high level enterprise-wide tech decisions that are being made to prepare companies for the future. So that's why the deals that we do, the businesses, the customers that we have are just getting bigger and bigger for that particular reason because again, it's become a CEO level priority, and they have to get the tech stack right before they could do anything else. Like I said, modernize, democratize, transform, and those are the three steps that companies have to do.
Hiten: So it sounds like suddenly by the issue being elevated up the seniority, higher priority to the value that you can bring is suddenly easier to see and people are kind of sponsoring and doing more transformational things. So it sounds like you're well and truly swimming with the tide at your back, Junta, in this space.
Junta: No, it wasn't always the case, but I feel like I always say we've been pushing a rock uphill for years, and now we're probably closer to the apex where the rock can start rolling down on its own a little bit because there's momentum and awareness about cloud and AI and importance of data and all the things that we've been talking about for years and years.
Hiten: Fast forward the tape for me there. You've led me on nicely there. I just think about the amount of change that's happened in say five years, 2019 to now has been quite mind bending in this space and just the prominence of the influence Databricks is having on multiple industries, and that change has also been transformational. Going another five years forward, your little crystal ball and how do you see things playing out?
Junta: Yeah, well, I think that the next decade is probably going to be the most prosperous decade in the history of humanity. And the reason I think this is because we've been having a productivity crisis for decades. If you look at productivity in the developed world, it really hasn't changed much since the nineties where maybe the initial influence of the internet has started to fade away. We've had kind of a productivity crisis. And if you think about what drives economic growth, right? Productivity is one of the big key factors around the world and how gen AI is going to be used initially is to not generate more revenues, is to help people be more productive. It's kind of like a person augmented with AI is going to be better with a person not augmented with AI. And there's been a lot of case studies. I recently read one in your industry in consulting that Harvard Business Review published, and basically the finding was that mediocre consultants have the most to gain from gen AI.
So while everybody's productivity goes up, the best consultant’s productivity goes up a little bit, but the underperforming consultants, their productivity goes up the most, right? So what we're going to have is this massive productivity boom that we haven't had in three decades, and that's going to have significant impact on the global economy, which I think is going to add hopefully trillions and trillions of dollars to GDP, let's say by the end of the decade. Now, what that means specifically for financial services is that it represents a tremendous amount of risk and a tremendous amount of opportunities. And what I think is going to happen is what I call basically the Teslafication of financial services, which is you look at Tesla today, and Tesla is worth more than pretty much every other automotive company combined because the disruptor who can leverage AI and data into their product, treat their product as almost like a softer product, gains the disproportionate share of the value.
Junta: And I think that's going to happen in financial services. I think we're going to have a few that either come out of nowhere and become humongous, or the incumbents that really help to lead that shift are going to gain disproportionately to everybody else. And to an extent we're already starting to see that in some markets, and a market I like to look at is maybe Brazil. Brazil has Itaú and has these big behemoths and banks that have been around for forever, but they also have, FinTechs like Nubank. Nubank is a challenger bank in Brazil. I don't know how many people have heard about it. It's been probably around a decade, but it's an incredible success story. And it became Latin America's largest IPO back in the day a few years ago. It surpassed Itaú by market cap. So you have a challenger bank that came out of nowhere and became the largest financial institution in Latin America.
Junta: I think those things are probably going to happen more. So you'll have this kind of a very clear delineation of winners in losers in this environment going forward in the next few years. So I think that's probably what's going to happen. From a product perspective, I think for people that deal with banks and insurance companies, I think these companies are going to become far more people centric versus product centric.
Hiten: Interesting.
Junta: So banks today are pretty product centric. It's like, here's a 30-year mortgage for you, here's a home equity loan for you, and here's the interest rate. It is kind of like they create products. I think the banks or the financial service institutions of the future are going to create highly personalized experiences and offerings to individuals. So they're going to be much more customer centric rather than a product centric going forward. And I think that's going to be a big shift that's going to happen
Hiten: Enabled by the data footprint that's there and the customization that's enabled by some of the productivity that you've referred to.
Junta: Absolutely. I mean, think about it. Think about the UK, right? UK has so many transients, right? So it is a great destination for talent from all over the world to work in, or same with the United States. Why should you, Hiten, if you move to New York, why should you not be able to get a bank account? That makes no sense. But today, if you're a new immigrant in America, it's incredibly hard to get a bank account, that makes no sense. The data is there. So here's just an example of becoming much more customer centric rather than product centric.
Hiten: Yeah. Just one thing I wanted to invite your views on. There's a lot of debates out there right now around things like data management, data governance, content licensing. There's suddenly this view that now data is suddenly an asset, not just a thing. I guess your views on that, how does this play out? And in particular for end customers and regular folks on the street, I think clearly there's been a bunch of people who've known and understood and thought about the space like yourself for the last few years, but as companies like Databricks come so large, so prominent, are there things that every person or everyone on the street needs to be aware of or understand? Any thoughts on that point?
Junta: I think that the average person should hope that whichever company that's handling their data, they're using Databricks in the backend. And the reason I say this is this - data governance is the most important thing that a regulated institution thinks about first. So if you're a bank or healthcare company, you can't have that data being used for malicious purposes or leaking or feeding models that it shouldn't be. And the foundation of that is exactly what I said before. When you have 10 different tools trying to manage in 10 different data silos, it becomes literally impossible to govern and understand that data. And by governance in simple terms, it means where's the data coming from? Where is it going? How's it being transformed? How is it being used? And who has access to that data? These are the foundational things that every company has to get right, or else data is going to be used for purposes that shouldn't be used for.
Junta: When you consolidate all those technologies together in a single place, it becomes far easier to understand the entire lineage of the data, where it's coming from, how it's being used, rather than having 10 different disparate services that don't talk to each other. Another big thing is that AI has been a big boon to data governance. Now, you could use AI to ensure that the quality of data is good, people are using it for the right purposes, et cetera. So AI is actually being used on the governance angle to make sure all this stuff. Now, this is critically important because there's going to be problems down the road if low quality data is being used or unauthorized data is being used to feed specific models. And you're already seeing this in some of the big lawsuits that are happening around the world about how the data is being used and all this stuff.
But AI is the easiest part of AI. The hardest part of AI is everything that goes before that, and the most foundational piece of that is governance. Now, think about this also from a bank. There's always this conflict at a bank or asset manager between democratizing data and securing the data. Compliance wants to shut everything down, and the business wants to democratize. They give access to that data. Those two have always been at odds with each other. If you have a robust governance framework, you could have both. You could securely shut down the data and make sure that only people that need to have access to that data have access to that data while also giving the power of the data to specific individuals and business units that need that data to drive their business forward. Those two things can only happen if you have a very robust governance framework. So it’s going to become, it already is probably the most important thing that companies think about. The great news for, I think the layman on the street is that the technology is catching up across the board to make that easier to do. So hopefully that will make more secure people more comfortable with companies using their data in the future.
Hiten: That's very helpful. That's very helpful. Thank you for sharing. I can't help but feel, it's like my fictionalized version of what you paint. It's like some kind of data police and people managing their data, they manage their cash in their wallet. Now you walk around the street, you don't actually really have that many assets on you, but this idea that you're leaving a big trailing data footprint and is it where you want it to be and is it bit used for things that are of adding value to you or that you're even aware of is probably a potential mindset shift that the next generation might need to be wrestling with.
Hiten: Going to switch gears slightly, Junta, I'd love to get your perspectives just on navigating the career. I think you described earlier on the show a great ability to kind of think about your career in a longer term frame. Think about the trends. I think clearly you've made a fantastic transition, a great time into Databricks, but can you talk about a challenging situation that you've faced in either of your chapters? What was one of those biggest challenges? What was the biggest learnings that you took away from it, something that you think the audience would benefit from hearing about?
Junta: Yeah, so actually the thing that I learned the most throughout my career, this is actually counterintuitive, which is do not take advice from somebody.
And this is what I mean by that. I'm being a little facetious here, but what I mean by that is very simple. If you ask successful people for advice, the advice they're going to give you is going to be inevitably colored by a very specific set of circumstances that worked for them. So for example, when I was thinking of leaving Goldman, when I was thinking of this existential crisis and thinking like, Hey, I should probably move into tech, I would do the same thing just anyway. I would go to the partners, I'd go to all these people, I was like, Hey, give me some life advice. And they would say, oh, just put your head down and work harder. Just keep doing what you're doing because you know what? It is not malicious, it's just that worked for them. So again, it's inevitably colored by a set of circumstances that can't be replicated.
So the way that I think I've learned the most is that it's okay to seek advice, but you should seek advice from a very diverse and broad array of people and only take the bits and pieces that resonate with you and tailor that to something that's productive to you. So in that particular example, if I just put my head down and kept working fine, I might've been fine for years and been very happy with my career. Yeah, sure, that could have happened, but I would've never made that kind of transition that I did if that was the North Star that I was guiding towards. So that's one thing I really learned is that you have to create your own POV based on all the diversity of the feedback that you get, and then really figure out what's right for you. And in that element, I think the very practical advice is, as I told you, don't listen to advice, but practical one, it is better to be a year early than a day late.
Junta: That's it. So you kind of mentioned, Hiten, is that nowadays there's a lot of people who are like, "Hey, maybe I want to transition from finance to tech", or "maybe I want to do more AI", and it's order of magnitude harder to do that today than seven years ago. So it was just, to me, it's always better to be a year early than a day late and things might not plan out for a while, but if you truly believe that the secular tailwinds are in your favor, then I think that's actually a very good thing to just keep in your mind going forward.
Hiten: Oh, that's very, very powerfully said. I think you can, it's obvious when you're swimming with a tide and then when everyone else is then trying to pile into that and it kind of crowds things out, right? It's kind a little bit like the big boom in the financial services that was happening in the 90s and 00s. I came out of university like 2007, and clearly you're at the tail end of that. So all of that success and successful advice had been driven by people. There was the wind in the back and their sails. And I think you say sometimes you got to, yeah, call it early, but no, it's great. It's great to hear you frame it that way. Talk to me a little bit about what you do outside of work. Any hobbies or interests, particularly anything that kind of informs or supports some of the stuff you do day to day in your professional seat? )
Junta: So two things I like to do. One is I write a lot. So I write a lot of articles for multiple publications, and that really helps me crystallize some of the ideas that I have and also test it out with people. So the feedback and the comments that you get really help you crystallize a thought process. So writing is something I'd like to do, and I do a lot of. The second thing that I do, which is I own a sake brewery in Brooklyn, so I'm a part owner of the largest craft sake brewery in the country in America. And I love that because it introduces me to people that I would never otherwise interact with in the food and beverage industry. I mean is really big, but it's just a totally different cast of characters than finance or technology, and it's really mind opening and lovely to be able to interact with those people. And it is also nice to have a passion to do something that's kind of really, really different from what you do on a day-to-day job and think about it and learn about it. And I've learned so much about the food and beverage industry over the past few years just by being associated with it. Nobody drinks beer anymore, and that trend has been going, everybody drinks tequila.
Hiten: Yeah, yeah.
Junta: All these kind trends that you see, I saw it a little bit earlier probably than everybody else. I kind of see it firsthand talking to a bunch of people in that industry. So super interesting.
Hiten: That's awesome. That's awesome. And is sake going to go down the route of tequila where you need these celebrity brand ambassadors? Are you're lining up some big A or B-listers to champion your brand?
Junta: I hope so. Unfortunately, I don't think we have the money to get The Rock to. I mean, Sake is actually in a sense, very similar to tequila is, I don't know about Europe, but in the United States sake is typically kind of like a low-end beverage that you drink in college at a Japanese restaurant, you drink too much of it. And in the similar vein, maybe 20 years ago, tequila was the same. Tequila was this crappy drink that came with a lime and salt and you shot it at a bar. And fast forward to today, it's the fastest growing liquor category by far. And it is high end, it's gone through this massive premiumization. And I think that the same thing is happening with sake is that people are realizing, hey, sake is not this crappy thing I drank out of a cup, wooden cup in college. I mean, just like anything else, there's tremendous range to that. And high-quality sake is delicious and relatively affordable compared to some of the high-end wines out there. So I think that it'll probably go through the same awareness boom that the tequila has gone through
Hiten: Hopefully. Well, I look forward to trying out your latest top of the range, and so next time I'm over in the US. So no, it sounds like an awesome outside hustle. Just like to wrap up really by inviting guests to throw or share the spotlight. So we like guests to call out an individual or a company that's impressing you most right now and is deserving of the attention of listeners to go look up and pay attention to.
Junta: Yeah, I don't know if I have a specific company or product since I used to be working in sales and trading. I look at macroeconomics quite a bit, and I think a country that's having its moment that actually has tremendous implications to the rest of the world is Japan actually. So for those younger listeners, they probably don't remember, but at a point the Japanese stock market was the biggest market in the world, and it hit its peak in 1989, and we're still ways away from that peak, actually not that far. But if you look at Japan today, it's within 10% of all-time highs and the market is at 34-year highs today. And why I think that's incredibly important, because Japan is kind of the canary in the coal mine of demographics. So Japan had arguably the worst demographics where today I think 600,000 people net die a year.
So population shrinks about half a million people. I mean, that's the size of Miami disappearing every year. So in spite of that, they've started to figure out how to make the economy work in that kind of tremendous demographic headwind. And why I think that's really important for the rest of the world is if you look at Western Europe, the demographics are pretty bad, especially in Italy. People don't realize, yes, Japan has terrible demographics, but it has the best demographics in East Asia. Surprisingly, China, Korea and Taiwan have worse demographics than Japan. So this demographic headwind is going to strike many, many more countries over the next decade. So figuring out how companies without, in Japan, they went through three decades of stagnation. So looking at a country like Japan and trying to figure out like, hey, how can we break out of that mould and not go through three decades of stagnation that Japan went through? And specifically to financial services, I actually think has a lot of lessons there too. Back in 1989, at the peak of the Nikkei, the four largest companies in the world, by the way, four largest companies in the world, top 4. 1, 2, 3, 4 were Japanese banks in 1989.
Hiten: Wow.
Junta: The fifth one was ExxonMobil, by the way. So fast forward to today, obviously none of those are anywhere near the top four. So banks are going to have to, I mean, there's bubbles and all this stuff, but I think financial service institutions typically have a hard time navigating economic decline and demographic headwinds. So there's a lot of lessons, I think, to be learned from looking at the stagnation over the past three decades and how hopefully the country is managing to get out of that stagnation. So there's a lot of lessons learned there, and I think the market's on fire over there, but it, it's really exciting time for the Japanese equity market. I think it's worth a lot of people paying attention to.
Hiten: Amazing, amazing. That's very fascinating. I must admit, I find that quite intriguing and a lot of what you described has passed me by. So definitely keen to dig in and look into that. Junta, thank you for being so generous with your time. Thank you for taking us on such a creative agenda of Databricks through to sake breweries. I think it's a testament to you that you can bring all those things together in such a passionate, energetic way, and I've always enjoyed our conversations. So thank you for coming on and sharing your views with us today.
Junta: Thank you so much for having me. This is great.
