Chet, thank you so much for coming on and talking to us today. You are at the forefront of an industry that is exploding right now. With artificial intelligence, large language models and everything that you're doing along those lines inside of DataStax, I wonder if you could start with how'd you get to where you are, your background and all that type of stuff, and then we'll get into all this AI stuff, which is just blowing everybody's mind?
First of all, thank you very much for having me, Clint. You run a great show. You've done some great podcasts, so it's an honor to be here. Thank you very much for having me on. I'll go fairly quickly because I am about 600 years old.
Really quickly, very early, started born and brought up in Calcutta, India, and was an avid reader, very early days and read this book by a guy by the name of Mike Moritz. Most people in the tech industry will know who he is. He used to be a Times reporter and then he went on to run Sequoia Capital, which is probably the most premier VC firm in the world. And the book he wrote was called The Little Kingdom.
It was about two Steves who started a small company called Apple that changed the world. And I read the book in one sitting and I was like, "I've got to go and hang out with these guys."
That's what started my journey in getting to know computers, learning classes, applying for colleges, coming to the US in ‘85 and working really hard and then Steve left Apple, he started doing this company called NeXT, and I really wanted to go and hang out with him.
And so, at very early in my career in ‘87, I started working for NeXT as a campus consultant and then actually was one of the few grads, new grads that were hired at NeXT, which I think single-mindedly, the single most important thing in my career was my first job out of college.
Because if you go back and you think about kids, they say 85% of their personality is cooked or 80% is cooked in the first four years of your life. I think the same thing applies to your professional career.
Just watching the highest concentration of smart people work on a regular basis to change the world actually defined me in ways I didn't imagine. And so, it was NeXT, I did my own gig, sold it to a services company, was part of an IPO team at active software, primarily in the distributed computing space, infrastructure side. As I said, we do things that people don't like doing, which is cleaning septic tanks on the middleware side.
Had a great run at a company called BA, joined a company called Apogee, grew it, took it public and then Google acquired us and that was phenomenal. I loved working at Google.
My biggest thing was I've had this pull tugging that I really wanted to go back and build a billion-dollar business because I think there's something about durability that shows up with a billion-dollar business.
And here I am at DataStax three and a half years later, having a lot of fun, work with great people, and building products that change people's lives.
Yeah, I have to ask. You caught Steve at an interesting time. He'd just been ousted from Apple, starting NeXT to basically compete with Apple and build this new computer and stuff. What was he like then? I mean, seriously, that is a part of his journey and story that probably doesn't get talked too much, just what his day-to- day was like at NeXT.
Let me start by saying I was the guy that got coffee for the guy that made coffee. I want to be very clear where I was on the pecking order. The guy who opened the door, I was below that person when I think about career ladders, but it didn't matter.
I had an office 30 yards away from Steve. I got a chance to see him every day and work. There are many things that people have talked about and how closely they work with Steve. I spent a bunch of time in his orbit, so I know a lot of people that work very closely with him.
Two or three things from my vantage point. One was just a phenomenal, phenomenal deep marketeer and coming down with the exact emotion to solve for. Great person that was very, very clear on what people needed, not what people wanted.
The second thing was great concentration of people, awesome people. NeXT hired some of the best in the industry and you can still see his ramifications all over the industry every time.
Those are the two things that he did extremely well. He knew what people needed and he just got great people, inspired them to do things. Now, some of the techniques he used were actually pretty shitty. Burnout and this and that, and streaming and all that stuff.
At least at NeXT for me, I saw a little mellower version of that because he had already gone through the Apple piece, but let there be no confusion. Apple bought NeXT, but the NeXT leadership team is what reinvented Apple.
There was a point in Apple where the only person on the leadership team at Apple that was not from NeXT was a CFO. Literally everybody else was the NeXT person. If it wasn't for that team coming over, we wouldn't have gotten the new Macs, we wouldn't have gotten the iPhone, we wouldn't have gotten Apple TV and everything else that's come along the way.
Phenomenal innovation culture and that doesn't happen. You can keep inspiring people, but if you don't have great people to be inspired, it's not very meaningful.
What do you think that is? Because when Walter Isaacson's book came out, which I'm sure you read about Steve in his life, there were some things in there. It was like, "Hey, his leadership style was pretty rough. He would yell, he would do these, all these different types of things."
And it seemed like for a bit maybe a year or two after that book came out, everyone started mimicking that as a style as though they were him. It wasn't authentically them to be mad or screaming about, "but Steve did it so we have to do it and let's also put on the black turtleneck or whatever it is."
And I wonder what you learned from him both good and bad from a leadership perspective and how to be a leader and how to lead a company.
It's a great question. Don't compromise on people. Just don't. And I didn't learn this from him. Because we're in the software industry and yes, there's a science to it, there's a computer science to it, but there's a lot of art to it as well. Don't compromise on people was the first thing I learned at NeXT, and that has been consistent in everything I've done.
The second thing was you have to really inspire them to believe that they're capable of far more than they think they are. And that becomes really, really, really, really important. It becomes really important to go and do that, because otherwise you will get something that everybody else can do. How do you take great people and really inspire them?
Those are the two massive things I took away from my NeXT experience. The parts that I chose not to take away were that I don't need to be like anybody. Leadership is the best version of myself. And if you're true to yourself, some people are very enthusiastic, they like shaking hands, kissing babies. I like to do a little bit of that, but mostly spend time thinking about the product, think about the pain that the practitioner has.
How does the buying cycle look? Because I love building product businesses. And so, one is people, the second one is to inspire, but you have to believe before you inspire. And then the other part is on a leadership scene, just be yourself. Because the best version of yourself is the only thing you have. You're not going to become somebody else.
Yeah, I think authenticity gets left out sometimes where we try to mimic the great leaders, but it just doesn't match who we are as people and it comes across as inauthentic.
And Clint, there's so many great leaders. I mean, let's just talk about a few of them. Steve, awesome. Bill Gates, awesome, very different kind of a leader. Jeff Bezos, very different leader. Elon Musk, very different leader. Sun Pacha, very different. Andy Jazzy very different.
And they all have figured out their version of what they bring to the companies that they lead. And my take to anybody is that leadership does not start with people, it starts with you. You need to lead yourself first and be intellectually honest on how you're leading yourself, and then take it to the two people that you work with, six people, 1000 people, 100,000 people and 300,000 people. But it starts with genuinely showing up with a version of yourself.
Yeah. Yeah, I think that's beautiful, man. Tell me about DataStax. How’s it going? How has AI and this boom and the release of ChatGPT, particularly ChatGPT- 4 changed the whole company or has it at all? Have you been doing this for a bit?
Our goal is simple. We want to deliver real-time AI for everyone at three x to scale and halve. That's the one-liner. And we think it's all about the data and also the model, but we think it's about the data.
There is no AI without data and everything needs to be real time. Because imagine you go to ChatGPT and put in a request at the prompt and you get a response back in two days. Useless. You want a response right away.
And that's what AI needs to be. And AI needs to be in this. It needs to be in everything you do in your real-time world because you don't live a batch life. You don't live, "Let's come back in two days" life. You live in the moment and you're making decisions in the moment.
Our take is we have a lot of data. Netflix uses Cassandra, Uber uses Cassandra, Federal Express uses Cassandra, everybody uses this absolutely scalable database and we have some technology that we've put on top to deliver all of it so that people can deliver real-time AI apps and make that happen all with the concept called predictive AI.
Now let's talk about the connection to generative AI, which is ChatGPT. ChatGPT is awesome. You can ask some really good questions, talk about your recipe, talk about your diet, talk about anything else you want, but generally it does not have specific context.
And guess what all our data does? It knows when Chad ordered, when what meal was ordered, it's not available. DoorDash might know that. Verizon might know that, whatever else might know that. How do you take the context that you get from predictive AI, from realtime AI and actually merge it with generative AI like ChatGPT so that you can actually provide more contextual information or contextual suggestions in a ChatGPT form? But it has to be more contextual.
And so it's like, let me give you a very simple example. I'm attending a wedding, I have to give a speech. The speech gets exponentially better if I can actually feed it parameters about who I'm talking about. I can say, "ChatGPT, I'm giving a speech on my niece, give me something and it gives me something. It has it nicely formatted, thank you to everybody. Talk about the girl, talk about the boy, give them some advice, be funny, all that."
But it has no context on the boy or the girl, the bride or the groom. And so, how do I feed it enough context so that it can actually give me something that is really, really useful? And that's what we are in the process of doing. Because large language models are awesome, it gives me a structure of the speech if I may, but what we are providing is a context so you can make that happen.
I mean, is this bigger than the internet? I mean this seems like unbelievable what's happening right here and you're right at the forefront of this.
I think that's the right answer.
Listen, I've been in the industry long enough that I've actually had people who associated with the browser in the early days of the browser and saw how that went, and I've had a chance to watch it indirectly benefit from it because I was at companies that we took public and things like that but was not on the field.
And so, I went back to those people and been really, really thoughtful. And there's not a single person that says that this is not bigger. This is going to be far more disruptive. Now, what is interesting about this is it's going to disrupt almost every industry quicker than ever imagined. If you think about how long it took Amazon to disrupt Walmart, a good 15 years right before it got there. It started working on it a long time ago.
The amount of time for disruption is about half or a third, so it's going to move so amazingly quick, it's not even funny. But there's a flip side of it, at least in my lifetime I haven't seen it, which is I think the regulations around some of this, or I should say the speed breakers or the jumps, the circuit breakers, whatever it might be, are going to come in much quicker in this tech wave than they ever have in the past.
And so, you're going to see this massive innovation go really quickly, but you are also going to see regulators figuring this out quickly as well because this is beyond employment and stuff like that. This can be massively disruptive, especially if generative AI slowly but steadily, because it won't happen in one day, becomes general AI.
And you've got to be really careful about how we do that as an industry, more importantly as a society, like a global society. This transcends countries. I mean, this is about us as humankind, if I may.
Yeah. It's like we got all the data from all of human history now and we got to learn all that and these things are going to be way smarter than us. I mean, Elon says it's more dangerous than nuclear weapons.
I think, listen, just to be clear, nuclear, if you think about nuclear, the nuclear engineering, all of it, they've done phenomenal for the power industry. They've actually helped a lot put to a good use but also very dangerous atomic bombs, things like that, we screwed up. Other people are screwing up. It's always a threat.
I think that regulation, if I may, is going to come in much quicker into the generative AI space, which I think is a good thing. And like I say, it'll start with people in the industry trying to self-regulate themselves. Countries will start do it, but I think us as humankind will probably come in to do this as well.
I don't think we need to slow down innovation, for the record. I think innovation needs to continue happening. We just need to speed up governance and regulation around it. And given the state of our policies and how quickly the lawmakers have to adjust to this new world, that is going to be a challenge.
And I think just because we don't have our shit together on the regulation side, doesn't mean that we should slow down the innovation because the innovation just go as hard as it can because it has a cycle of its own. We just need to let it go.
Actually, shit we don't have together on the regulation side. It's a very kind way of putting that.
That is very, very nice.
I imagine in every country in the world, and I actually think that we have a stuff together sometimes far more than most people do, because you can always look at how things are not going well. But then you look at some of the banking crisis that we recently went through. We'd actually had our stuff together. We reacted well, we moved quickly. No one got hurt. Yes, we took a dip, people screwed up.
We are learning from it, but the government actually worked. It just did. I mean, it just did. Itmdoesn't matter. Left, right, center doesn't matter. But I think I just keep imagining people from OpenAI, from Google, from DataStax, whoever else might be going for a hearing to Congress and I cannot imagine how that goes. And I think just my mind just doesn't accept how that goes.
But I think we'll get it together. I have a lot of faith and we've been through a lot. This world has been through many industrial revolutions. And we so many revolutions I should say, and the industrial revolution, we've gone through the internet. I think this will be another one that will conquer.
From a DataStax specific standpoint, with this whole world changing so quickly and rapidly and so many massive new announcements and innovations from all the big players and even new players and all this type of stuff happening every day, how do you focus? This is where DataStax is going to play in this space because it seems like you could really start being pulled in all sorts of directions.
We already are. There's not a single day that doesn't go by where we don't get pulled into a direction that we didn't think of. And guess what? We need to pay attention to them and very quickly objectively decide whether we want to pursue it or not. And so, our true north is really simple and people ask me, "Chet, where do you think DataStax will be five years from now?"
And I always respond this, "We'll continue to build products that practitioners love that change the trajectory of the enterprises they work for." That's our sweet spot. We want to build products for practitioners and those practitioners will do something with it that'll change the trajectory of the enterprises they work for.
And it is somewhat, it is very much an outside in point of view. But as well, we want to build great products for practitioners and we want enterprises to pay us. We have that common thread etween the two because the practitioners work for the enterprises and they're solving business problems.
I'll step out of DataStax for a second because I think this is relevant. What are the places that we can conceive of? And I'll be short because this might become a six-hour podcast. What are the things that we can conceive of? We know large language models are here to stay and we think there'll be two or three or four players at the most.
They're not going to be that many more. And you can look at that across every industry. There'll be three players, maybe two, maybe four, so that's one. But are they going to be enough? And the answer is no. The people will be able to access large language models beyond ChatGPT, right through APIs and people are going to build generative AI applications, which are going to look very different than anything they've built before.
Those apps are going to be in specific business areas. It could be in customer success, it could be in support, it could be in banking, it could be in the supply chain, could be all of those.
As people land up doing that, what they will realize very quickly is it is extremely disruptive. Now, where is it disruptive? Let's talk about disruption because Clay Christensen did a great job with his book, but I think he missed a piece on the Innovator's Dilemma.
My experience tells me disruptions always happen when there's a technology disruption and when there's a business disruption. That's how large companies get made. Google did not just happen because they had great search technology. They actually did a business disruption on how to monetize it. Same thing with Amazon, same thing with AWS. And so, that's where large, large companies are built on technology and business disruptions.
When people go off and start building apps, if I may, with unique data sets on top of large language models, what they'll realize is that they will have a technical stack that they're building on that they've never used before, which is greatness, but they will also realize that the P&L looks different, how they price it looks different, and the business disruptions will have to come along with it.
You will not be able to charge people for this new generative AI app like Salesforce does today per seat. It's going to be a very different tech model as well as a very different business model. And I think the quicker people think through that, and that doesn't happen by sitting in the cathedral. That actually happens by going to the bazaar and experimenting really quickly that that is where DataStax's opportunity lies.
Our goal is to actually give you a combination of those two where you can create those business models. Because we're an infrastructure provider where you can create those business models as well as give you a tech stack so you can build all the possible apps.
I wonder, what do you see as some of those business models? Could you give us an example, whichever, whatever you're seeing, how are people completely transforming that?
Let me give you a simple one. Again, early days. Just to be clear, but let's talk about jasper.ai. just go to jasper.ai, you click on it, you will see that you can write blogs in minutes. Put in a few parameters, I could take jasper.ai, point it to the datastax.com blogs, and say, "Read every blog that Chet has ever written and then here are the things I want to write about today."
And in about five minutes I'll have a blog that will be about 70% complete and I can go and edit it. That's disruption. How many marketing people would I need to do that and how much time did I have to use to do that?
One of the conversations I was having with a really awesome thought leader, CEO of another company and his take was, "Chet, I think what you and I do for a living, we can go from doing it over a week in 10 hours." I was like, "I think probably not in 10 hours, but you're not wrong. It'll go from doing something in 50 hours a week down to 20."
The only reason I say that is because a large portion of what I do is judgment, is judgment for today and for tomorrow based on history. And that's hard for GPT-4 to come along and help with it.
I think some people get nervous here around how many jobs this is going to displace and what this means for the future of employment. I mean, there's all this talk about universal basic income now, which Sam Altman's really gotten behind OpenAI. What do you think of that? I mean, does it create more jobs? Because like you said, we've been through a lot of these and all of these revolutions have actually created more jobs, not less. Does that happen here?
Absolutely believe that. But I do think it's a shift. Sam may be right. I don't know if the answer is universal based pay and all that stuff, just because I think it's a shift.
If you just look at the marketing function of a tech company, you may end up paying the same amount of money or you may reduce your budget by about 40% and reduce your headcount by 30% to make that happen. And those folks will absolutely go on to do bigger and better things, but they have to be accepting that there's a change in their career. And I think that's where it starts.
But I absolutely believe over time, I think we will normalize, there will be a shift. We'll see unemployment increase for a short period of time because not all industries will move as quickly as a tech company sitting in the valley. I mean, they just won't.
You need a timeframe where this works its way through the US and the world economy, and that's not a two-year period, that's a 5, 7, 10 year period. But when that happens, remember the people who have the people who've lost their jobs will actually recover and find a way to do but better and bigger things.
And so, I think over a decade, I think this is a net positive. I don't think it's a net negative, I think it's a net positive. I'm not sure whether the government needs to step in and do things like universal income and things like that, but it might be required.
What do you think as far as from a software development perspective, it's crazy how much software development can be done on AI now. You don't really need to know how to code.
I'll get geeky on the financial side for a second. Think about a P&L of a software company and you can apply it to cloud, we can apply it to cloud, let's do it as a cloud company. Today, you get for every dollar you get, your gross margins are in the 80% range, so you have 80 cents. It's great. You have 20 cents for the cost of goods sold, things like that. The rest of it, or the rest of the 80 cents, if you want to make 10 or 20 cents off it, you're spending about 50 plus percent on sales and marketing if it's a high growth company, but not too much more. You're spending about 30%, 25 to 30% on R&D and the rest is G&A.
What happens to that 50% envelope called sales and marketing? It reduces in your P&L because you are leveraging all the things that the technology can do for you instead of people. You still need people in marketing, in sales and to go off and make that happen, but you're reducing the cost significantly.
The same thing is true for engineering. You can use ChatGPT to write some code for you, generic as it might be, not make this database serverless. That's a hard thing to do. It's a computer science problem, takes 10 people nine months to go and solve that problem and takes you another 12 months to actually make it hardened so businesses can use it.
You may be able to shrink that cycle a little bit for testing and things like that or some generic things outside the innovation you have, but it's going to reduce that 30% on your P&L down to 20 or 22%.
My point is once that happens, you will have a lot more room to either give back money to shareholders, which I think is a phenomenal idea or actually give some back to them, but also continue to innovate so that you can actually continue to serve your customers, the people who use your product and people who buy your product.
That's super interesting. I wonder also from a DataStax perspective and not to keep bringing up a competitor and OpenAI, but I do think it's interesting that they started with a consumer first product, whereas your yours is, as I understand it, a business enterprise product. And it seems like they're slowly, if not very quickly transitioning into enterprise. I mean, what's the strategy there? How do you think of that? Should you launch a consumer first thing to generate some buzz?
First of all, I think OpenAI and DataStax will be completely complementary. The OpenAI is going to be about large language models. It's a sweet spot. They may give sandboxes for enterprises to play in, but those sandboxes will be specific. You can actually see it. Now they've actually come up with a new program even for consumers, which is, "Hey, if you don't want us to see your history and make it part of our process, we are happy to do that."
They'll do that with enterprises because enterprises will be very concerned that if there are 1000 people from FedEx using ChatGPT, the intelligence doesn't become part of ChatGPT. They will make sure that it's well governed and they are clean on making sure that happened. I think the partnership with Microsoft will help them do that.And obviously Google's going to be a force and Google's going to do that as well.
But I think that's about it. I think they will be the base layer. They are not going to be able to bring this Chet, the context specific information about Chet that is sitting in proprietary data databases that Netflix has, that AT&T has, that FedEx has, that Starbucks has. That data is those enterprises. And that combining that predictive AI with the generated AI that OpenAI does is where the magic will be.
ChatGPT is awesome, but the real magic for enterprises is to actually combine the two, and that's what we will bring. And so, I think of OpenAI as completely complementary. We'll have a plugin, we'll be able to leverage everything they do and go from there.
And by the way, we'll do the same thing with Google. Because there'll be multiple players, we talk about OpenAI being the only player. Actually having spent time at Google, I think Google's done a lot of really, really good stuff that they've not bought to market, and OpenAI being out in market gives them an opportunity to actually make a big deal about what they already have and I think they'll be extremely successful in this space.
This is a away from the AI topic for a second, but I noticed you're in an office right now. I wonder how you're thinking about remote work, hybrid work, everybody in office. That's another big topic in the industry. How are you handling that?
DataStax has always been a distributed company. And so, my first day at DataStax three and half ago years ago, I used the word remote, literally my first all hands, and I've never used the word remote ever since because it's a distributed company. We were formed from an open source project. Open source projects by design, leverage their best talent anywhere they're available. They're loosely coupled. And so, we've always been a distributed company and we always will be.
There are a few changes we've made along the way. The leadership team is in the Bay Area, and so we can actually do more things, especially since we were transforming the company into going from a database company to a data company, to an AI company. That required the leadership team to be very close.
Distributed companies like DataStax will continue to be the way they are. We've not cracked the code. We are continuously figuring out how to behave during COVID, how to behave during inflation, and we are always figuring out the best way to get people to actually collaborate.
You can do most of the collaboration online, but you do have to get meetings together. We had our database team visiting last week. It was phenomenal. They were here for three days. I think what we accomplished in the three days couldn't have been accomplished over a three-week period. We shrink it down.
I personally, I do not believe deeply in the word hybrid work. And let me qualify that because different people mean different hybrids in different ways. I do not believe that the human mind does really well with, "I will go to work when I want to go to work." I think it needs to be structured.
At DataStax as an example, we've said, "Your place of work is home, please come to the office, which is a great office for you to come and collaborate." We have one here, we have one London, Singapore, Sydney just come and collaborate and we bring everybody together.
I think people going in, say, three days a week is really good, but I think as a structure it should be the same three days every week, and there's predictability in their schedules in making it happen.
Now, I don't know, what I don't know is whether the three days a week will hold. Because I think it might go to five days a week or might go to four days a week. And the reason is because at DataStax where it's south of 600 people or you're south of 10,000 people, there's a threshold there where you can actually make it work.
People are mission oriented. The moment you start getting to be large, you want to make sure you're getting the right amount of productivity from people. And if you have 100,000 people, 10% productivity is massive. Do you stay with three days a week? Do you go to four days a week? Do you go back to five days a week? I think people have to make that decision, but the most important thing for me is predictability.
How do you maintain culture? How do you think about culture?
A lot. It's not a one and done. I think the goal to start with, keep it simple. And I've seen what Jeff has done and he has the leadership principles. There are many of them and he's created a very successful franchise.
I generally think what we've done is we've had four values and we have some execution principles that we've attached to it. It's probably about 12 that we actually do. We drive that every day. People talk about it, we're talking about it in the all hands. We are reminding people that you're an owner.
And so, I think it needs to be part of consciousness if I may not to get metaphysical on this, but it needs to be part of everything you do without it being the only focus. You're talking about a project, you talk about how you need to make sure that it has DNI included in it, things like that. That's the first thing.
The second thing is you have to make sure you live it. I just don't believe culture is having the four values and the 12 execution principles on a wall. You have to live and die with it. What that means for me as the leader for this company is actually making sure that I am thinking about those principles as I'm making decisions on behalf of the company. And I think once you put yourself in that situation, it becomes really easy to inspire people to use them to make decisions.
And so, culture is a massive, massive thing and it is alive. I talk about how DataStax is a combination of all the minds that work at DataStax, and it is an entity. You need to make sure you focus on the entity beyond just the people that actually work here. And so, we spend a lot of time thinking about it and doing things.
I'll give you an example. We actually have a coaches' summit that we did in London last month. And we are doing another one next month for folks in the US and we'll do one in Sydney and Singapore as well.
To have a common understanding of what is expected from coaches. That doesn't mean 90% of people that'll be in those things have actually been coaches in the past have been around it, but there's got to be a consistent language and a way that people can actually behave with each other. Having that consistency is really, really important.
Now, let me ask you, how important is it to build in Silicon Valley these days?
Let me start by saying I have a biased point of view on this. I live here, I work here, I've done this. If I look at my, except for four years or five years out of the last 32, I've actually spent all of them here. I'm a big fan. I think if you're doing something innovative, I think access to everything in the tech industry is a lot easier here.
I say this with a grain of salt, but SVB was just as important to creating the the valley as Stanford was. They were a really important part of the fabric in the valley. Everybody got loans from them, they bank there, things like that. And somebody will come in and fill that void.
But you think about money, the VCs, you go to Sand Hill Road up and down, how much money do they manage? Somewhere like a trillion bucks or a billion bucks.
There's a lot going on and you can go and have access to talent, partnership discussions. There's a lot that happens here that is hard to replicate outside. Now, once the company becomes of size, then you actually have an option. And the reason I say series A, series B, series C is because you are finding PMF, you're looking for product market fit, and that's when you want access to talent, you want access to people, you want access to mentors and things like that.
The Valley is great for that. And my personal belief is with all this generative AI stuff going on, I think what you're going to find is a lot of people that left are going to come back. I think there's going to be a little bit of a rush back because they look at this as something like we talked about, which is bigger than the browser, and they will want to be here to make it happen.
It does seem like this wave is still centered right there, but I we try to pretend like Miami or some of these other places are doing that.
And there's some good stuff happening there. Austin has some good things happening and I think New York has some good things happening. Seattle obviously has done fairly well.
Let's talk about New York. New York is the financial capital of this country and it always will be. I mean, Charles Schwab's based in San Francisco. There are lots of firms based in San Francisco. Fidelity is based in Boston. It doesn't replace New York. New York is still the center, and I think the value will continue to be the center.
Yeah, I think that's right. Well, I could talk you forever, but I want to be respectful of your time. And so, I asked this question at the end of every interview because at CEO.com we believe the chances you give are just as important as the chances you take. And I wonder, who has given you a chance in your career that you could talk about or shine some light on for us?
The list is extremely long. I work hard at my craft, try to get better at it every day. But as I say, you can be the best sailor in the world, if the wind doesn't show up, it doesn't matter. You're dead in the water. And so, I've had a lot of help from a lot of people, but I'm going to give you a unconventional answer because it'd be inappropriate for me to mention one person, not the other person.
I'll mention one person. It's been a little easier for me than most people, I think, because my role model has always been my father. And so, true north has actually been a little easier to figure out because whenever I think I'm in a situation where I have to think about something, my father's still alive. But more importantly, I can think about what would my father think about this and what would he say.
And if it wasn't for me being born to my parents, I don't think I would've had this opportunity to do any of this. And if I had to mention a second one, I would say it's definitely my partner and my soulmate. And so, we've been together for a long time and that makes a massive difference.
Having people in your corner is unbelievable.
Yeah. The people I was talking about, the people that I've been around for a long time, I've actually met with them this morning, Monday. And I'm really grateful for them and they're aware of it, but nothing would work if it wasn't for those two individuals.
Yeah, that's beautiful. Chet, thank you so much for coming on. Seriously, what you're building is incredible. I think you're going to be one of the biggest companies in the world here over the next three to five years. It's beautiful. Hopefully we can have you on again, and thank you for taking the time to share your wisdom with us.
Thank you, Clint. This was a blast, man, and look forward to talking to you again.
Likewise. Thank you.