The Intersection of AI and Water: Insights from Varuna CEO Seyi Fabode

Seyi Fabode, a visionary co-founder of Varuna, a trailblazing startup revolutionizing water utility operations with data, unveils his profound insights into the US water utility landscape, the riveting history of water crises, and the urgent call for cutting-edge innovations. In this eye-opening discussion, Fabode illuminates the pathway to transforming water utilities through data-driven intelligence and a reimagined infrastructure.

  • 🚰 Diving into the Depths: Seyi exposes the stark reality of the water utilities' infrastructure – decaying and starved for investment. He passionately argues that tackling this mammoth issue is paramount to averting a water crisis.
  • 🎩 Time-Traveling Through Water Crises: Fabode captivates with tales of the US's water crises history, including a scandalous episode involving Aaron Burr. This journey through time reveals how decisions made in the throes of crises still ripple through today’s water utility infrastructure.
  • 🤖 Varuna - The Game Changer: Seyi outlines Varuna’s ambitious quest to become the powerhouse behind the water systems of the future. Imagine dynamic, smart water systems, underpinned by data, ensuring a steady flow of water wherever demand surges. Varuna’s vision extends to micro storage and minimizing infrastructure meltdowns.
  • ⚡ Energy and Water – A Symbiotic Revolution: Seyi and Ravi paint a thrilling parallel between distributed energy systems and what can be achieved for water. They talk about an interconnected network of water micro-storage facilities, all functioning seamlessly.
  • 🔊 The Clarion Call for Water Advocates: Seyi’s voice booms with the urgency of transforming every water professional into an influential marketer and advocate. With water being the lifeblood of society, he highlights the imperative need for recognition and a paradigm shift in how we converse about water.

Meet Seyi

Seyi Fabode is the CEO and co-founder of Varuna, a technology company that provides a risk management platform for water utilities. With close to two decades of experience in utilities, Seyi leverages his background as an operations engineer to solve water-related challenges. Seyi's passion for solving complex problems led him to start Varuna four years ago, where he applies AI to manage water systems and prioritize risk. Seyi is a newsletter writer and avid reader, and he draws inspiration from his parents' influence in his life.

Transcript


00:00
Ravi Kurani
Hello, everybody. My name is Ravi Kurani and I am the host of Liquid Assets, where we talk about the business of water. The intersection of policy, business and governance on kind of how water looks across the board. Today we have Shea from Varuna Tech. Hi.


00:15
Seyi Fabode
My name is Seyi Fabode, CEO and co founder at Varuna. And at Varuna, we believe if people know better, they'll do better. And we're trying to make sure the people who work in water, the operators in water, get the information to do better to ensure clean water for you.


00:36
Ravi Kurani
And I. Yeah, awesome. And since you kind of jumped off on Varuna, can you give us a little bit of a background of what is Varuna? How does it work? What have you built?


00:45
Seyi Fabode
Yeah, so the simple one liner we use for Varuna is that we track and provide recommendations on how water systems can address water risk. And that risk could be in the form of contamination, asset failure, just systemic conditions like climate change conditions, essentially flooding. Things that could lead to the water system not being able to deliver water to their customers. We help them track and provide recommendations on how they address that.


01:22
Ravi Kurani
That's awesome. And I was cruising through your website earlier today. Can you tactically for the audience, tell us how a deployment is done?


01:30
Seyi Fabode
Right.


01:30
Ravi Kurani
Like, who is your customer? How does Varuna tech work? These risks that you talk about, how do you prevent those?


01:36
Seyi Fabode
Yeah. So an example customer would be City of New York or Amarillo, Texas, or even some private companies being one of those aqua. It's a private company that's one of our customers. And what they do with us is they define where they want to track. Essentially, I'll use Amarillo. Amarillo says, we want to make sure in the north quadrant of the city we stop having these outage issues that we've been having. As a first step, when we show up, you tell us where the problem is. What we then do is we gather external data that is not in the hands of the utility itself. We capture external data like source, water availability, things related to the infrastructure that is outside of the remedy of the water utility itself. We also capture things, likelihood of flooding, watershed information, things that we think of as systemic that impacts the ability of the water system to deliver clean water, but is not in their hands to address.


03:03
Seyi Fabode
Sorry. To manage those things. That's the second bucket. And the third bucket is the internal data we get from them. In the case of the internal data, we're capturing real time data from sensors. We capture or plug into data from other tools they might be using to pull out the data, ingest all of it, analyze it, and find, for example, correlation. Are you constantly having chlorine decay in this part of the city? Because there's stagnation the companies that had 4000 employees in a campus up north who are using water, they're no longer there. And so the storage tank just has water in it. So stuff like that we help them track and we tell them what to do to address those sort of issues.


03:59
Ravi Kurani
Wow, that's super. I'm just even thinking about what you're saying. There's a plethora of data inputs that you can capture, right?


04:07
Seyi Fabode
Absolutely. So we've narrowed that down to about 20 something vectors that we've consistently seen to be more signal than noise in what becomes an outage situation. Yeah.


04:28
Ravi Kurani
And if you're looking at the pareto of those parameters, which ones are important? Right. Like, you guys clearly went through an exercise to figure out the signal versus noise. How did that walk us through that process? What did that look like?


04:42
Seyi Fabode
Yeah, so that is actually one of the more it's kind of local, but it's also not so on the west coast of the US. Source water availability shows up more than it does on the east coast as a high signal for possible outages. In Hawaii, for example, they have great aquifers with source water. Not a problem. What they're seeing, though, is increased demand, and consequently, are their assets capable of keeping up with the increased demand. So the signal is very much dependent on where you are, but it always ends up that it's one of the 26 or 27 that we narrowed it down to. And so we help we provide them a risk prioritized list of things they should do. And that prioritization depends on where you are in the country.


06:00
Ravi Kurani
Wow, really cool. When you're prioritizing those risks, is that like a back and forth with the client, like, let's say with Amaria or with Hawaii? Or do you kind of come in knowing what those issues are before you approach them?


06:15
Seyi Fabode
Yeah. So the amazing thing, Ravi, that everyone would, we would when we started this company, people are like, man, the water industry is not data rich. And we forget that every single city, town in this country puts out a plan for what their water priorities will be at some frequency. And in those plans, they essentially say what problem they're trying to solve with the money they're about to spend. So we do a fair bit of diagnostics before we show up. And to determine who we target, we also try to use the diagnostics and we're saying, oh, look, Monroe, Louisiana. This keeps coming up in either the capital improvement plan or the monthly water board meetings that is recorded and placed online. This is what we're going to focus on for them, even as we focus on everything. But we will go in with this as the highest risk we hear them talking about all the time, and consequently the one we'll help them address in the midst of all the signals.


07:38
Ravi Kurani
Yeah. From your comment you just made a second ago about the water industry not being data rich, there is hidden data sources from these plans that they do. And that sounds like an amazing sales process. Right. First of all, just from like a business standpoint, how do you it's tough reverse engineer those plans. I mean, do you scan them all and do you use Chat GPT or something to kind of pull out the information? What does that look like?


08:07
Seyi Fabode
Interestingly. We've now started testing with Chat GPT, which I'm trying to build the models using our own models and just the firepower that Chat GPT has. But there was a time when we just had folk scanning PBS yeah. Reading through the lines. We did Google for startups the accelerator a few years ago, and they suggest you should have a goal coming in that Google technology can help you solve. I kid you not ravi. The biggest request we had from them was document passing AI. How can we take this away from the manual data extraction into document AI, using through some AI? This was about three, two and a half years ago.


09:08
Ravi Kurani
It was during COVID Yeah, that's really interesting. I do want to revisit the kind of basically the Google accelerator you did as well. I was reading your post on the keyword, but before we jump there, actually, I'd love to kind of go back to the beginning and kind of understand the story of Shea. How did you come up with this idea? Where are you from? I was thinking about this the other day. There's these really interesting drivers between the ages of nine and 18, right. The way that you grow up, what your dad did, what your mom did, some of these things are so interesting to see and I'd love to hear that. What that looks like from your side.


09:46
Seyi Fabode
Yeah, it's such a fantastic question. And I've had to dig into this a little bit because the background here is that I've spent close to two decades in utilities, but mainly on the power utility side as an operations engineer. And then I had a previous startup that was recommending energy efficiency and electricity retail providers to residential and small business customers. Then I did some work at a fund for a bit that was focused on clean energy. And then I'll share one more thing. I've always had a newsletter. I've always had a newsletter of some way, shape or form. And the newsletter, the through line in them was I'd be pulling different random information articles, books, into one concise episode of my newsletter. And then I started Varuna four years ago when I placed Varuna in the context of everything else that's gone on for me, I tied right back to what you just said.


11:07
Seyi Fabode
My dad was a forensic accountant and my mom was a teacher. And what they always sort of there was just this very keen on their part, keen desire for me to get really good at taking a lot of information, understanding it, and pulling out.


11:35
Ravi Kurani
What.


11:36
Seyi Fabode
Have you learned from all of this information? And it's kind of wild. Varuna is the same thing. My previous company was the same thing. The newsletters were the same thing. My mom would give me novels to read, all sorts of novels and then ask me to summarize the novel for her. And that was just a thing. Same thing with every business I've worked on.


12:08
Ravi Kurani
Wow. And that's kind of what Varuna is doing, right? You're taking the novels of Water, right? You're reading Water and you're resummrizing them.


12:19
Seyi Fabode
Back up in terms of it for them. Yeah, it's kind of wild. And then even the name Varuna, the Vedic deity for the elements and the Sanskrit word for enlightenment. We're bringing enlightenment to water. But I hadn't made the connection to just those childhood experiences until a few months ago. Actually, I was having a conversation with, I think, a coach or something.


12:53
Ravi Kurani
Yeah, really cool. That's awesome. And so now, kind of as you arrived to Varuna, can you give us the kind of beginning stories of how that worked? I think would be really interesting.


13:05
Seyi Fabode
Right?


13:05
Ravi Kurani
I mean, as startup founders, everybody thinks there's this through line, right? They think that, hey, there's like a very even arc that goes from A to B to C to D. But as we know, the startup story never works that way. Right. Let's kind of dig a little deep and figure out kind of what were the difficulties that you had in starting Maruna. Where did you kind of trip up? Be really interesting to kind of hear those sides of it too.


13:26
Seyi Fabode
Yeah, no, it's a really good one. Really good question. Again. So I mentioned I was working at a fund I sold. My previous startup did some work at a fund and then did some consulting, some personal consulting. And I was working with the World Bank and a few large power utilities. And the work kept coming back to this realization that we're moving Power forward and Power borrows from telecoms. Telecoms is always ahead of the game. Power was borrowing from telecoms at a cycle that was about six, seven years. And Water further lags Power by another six or seven years in terms of adoption and stuff. And so while I was working with those utilities, some of the work was around what is the future of the utility industry? And some of the work was around how do we build fully integrated utilities that cover the things we can't live without anymore?


14:42
Seyi Fabode
Water power and ability to communicate. And everything was covered for telecoms. But Water was just this barren. Nothing was happening as far as we could tell. And then Flint happened around that period. That was a trigger. Started toying around with an idea for a sensor, put a sensor together with a friend here in Austin and was carrying this mold and the center around, just literally travel to Chicago, try to meet with some people like, hey, we can do this for you. We can help you avoid Flint, was the message. And the thing I would constantly hear was, we're not Flint. What's your problem? Why are you here? Go to Flint. If you can solve the problem, go to Flint. And unfortunately for us, that was sort of a precursor to how difficult it would be to sell in this space. It's amazingly tough because if you show up at a water system and you say, we can help you address your water problem, what you've inherently said is, you have water problem.


16:09
Seyi Fabode
And what they don't want to hear is that they have water problems. Exactly. And so we worked on finally, over the first couple of years, did the grant work of just truck rolls with a few technicians and started to move away from the sensor as the value prop to the information that comes from the sensor or any sensor you have as the value. They wanted to know better so they could do better. And we've had a ton of missteps, we had a ton of issues with hardware during COVID and we continued to figure it out, but there's been no straight through line.


17:04
Ravi Kurani
Yeah, I want to kind of double click on this psychological understanding that you came about, right? Like, you tried a sales strategy in which you went to these folks saying, hey, we'll help you solve Flint, we'll help you solve your water problems. But they don't want to talk about those problems because inherently they don't have them. Right. Basically when you finally decided, like, hey, the flip here is not going to be of how can I use sensors to solve your particular issue, but take your existing sensors or potentially add sensors to gather information and build that up, how did you make that change? Why did you end up arriving at this information play and not somewhere else?


17:46
Seyi Fabode
Yeah, no, it's another really good question. Despite how tough it was, we did get a decent number of customers as we would because one thing were showing them by showing up was we're willing to listen to them and do the work which people tend to appreciate in any business. And so we got some sensors in some systems, and I'll give a quick example. The sensor would show every day at a certain time of the day, chlorine would drop, temperature would rise. And it was this very diurnal problem that we could suggest why, but we lacked the context around that sensor to tell us why and inform us enough to give them a proper recommendation about what task they should embark on or what tactic they should take to address what was obviously a problem. And so I distinctly remember going on truck rolls with some of the technicians who had installed sensors and they're like, oh, I don't check that anymore because I now know why it was happening.


19:18
Seyi Fabode
And so I tried to just go address why it was happening. And it became this, oh, we're just behind the we're not even as up to date as you are, even though we're the ones coming to you with real time information. And so one of the things they very quickly told us on some of these conversations and truck rules was, look, I need to understand not just the points. And even if you have in one city, we had six points, six sensors in one city, like, I don't need to understand just those points. I need to know what is going on with my system such that whatever happens at that point, I can address it without causing more problems because I understand the system. So that's how we landed at what we now have as a risk focused dashboard for them. Really got it.


20:23
Ravi Kurani
Really cool. Let's quickly I know we skipped past this. Let's go ahead and jump to the Google side of things because I was reading that article on the keyword, and I think even for a lot of the potential audience being maybe in policy and business and even technology, I think it'd be interesting to understand what did that process look like? I know Varuna is obviously an AI company as well, right? What is that intersection of AI and water against the Google context?


20:50
Seyi Fabode
Yeah. So some of this is a bit of revisionist history because I've read a few more things that better inform that experience we had. So with the Google for startups experience, our key focus during that experience was to optimize how we got to insights for our customers. So if we're pulling data from your PDFs, historic PDFs, or your Excel spreadsheets, which some of them sent us, or from sensors, there's a need to make it such that all of that work doesn't take us as much time as it would take you, the end use customer, to do it yourself. So our focus then was optimization of the workflow for us to make the experience for the user. The end user seamless and fantastic. Something Google does well with just the search box on the homepage. The second part of that was actually the conversation we had.


22:16
Seyi Fabode
How do we determine what is signal from noise in all this data? And one of the Google has this golden, I think it's called the golden ratio. It's not actually for water. It's more for their data centers. How do they manage their data centers in a way that across their optimizing for where you're pulling the information from what is surfaced to you when you make a search request. And they published some of this documentation for how that's done without revealing any proprietary stuff. And so we just were like, how do you think about this? I've read the stuff. This is what we're trying to do. It applies because we're trying to narrow down a bunch of information into really valuable pieces of insight and got to chat with some phenomenal people at Google who helped inform some of that. So it was a fantastic experience for us.


23:33
Seyi Fabode
Some of the lessons we learned, some of the things we learned, we actually haven't even been able to apply because you need some serious money for some of the work.


23:42
Ravi Kurani
Yeah. Which I do want to actually visit for the folks out there, too, on the fundraising side of things. But one thing I actually want touch on that you said a little while ago was telecom leads the electrical side of things, and then electrical obviously leads the water by a delta of seven to six to seven years each. If you're tactically looking at telecom today right, with I mean, you're seeing 5G come about. You're seeing just even like the birth of mobile. If you re explain today's environment of what telecom looks like from what you saw previously, where are we at today, and where do we have to get to? Right? Because we're looking at a 14 year gap. So I'd love to understand kind of what that delta looks like from your side.


24:27
Seyi Fabode
Yeah, man, it's such a great set of questions. I'll frame it this way. There's a bit of a lull, and telecoms is stalled a little bit in that regard.


24:44
Ravi Kurani
Okay.


24:44
Seyi Fabode
Which doesn't mean there's no innovation happening. It's that the leap of innovation and happening. Where are we with telecoms? We all have mobile phones.


24:57
Ravi Kurani
And.


25:00
Seyi Fabode
My experience with for the most part, my experience with Verizon or at and T or whoever it is very much a reflection of my engagement with the device that and the granularity of my experience. So if I were to do something that the government now needs, all my data from Verizon, they have it both the voice part and when was I on the phone, where was I when I used the phone? They have all that information. With water, we're still billing based on usage with no consideration for, oh, this person is actually boiling and using and watering and whatever it is. Pick how you want to slice and dice. My experience with water, I think the industry, what we can borrow on the waterside from telecoms right now is endpoint my personal experience, endpoint solutions from the usage to the filtration to the billing to the form.


26:39
Seyi Fabode
We shouldn't be using the same water filtered and cleaned from the wastewater treatment plant, from water treatment plant, to water our lawns as we get from our.


26:53
Ravi Kurani
Taps, stuff like that, entirely.


26:56
Seyi Fabode
We shouldn't yeah. So things like that at the end point to fundamentally change the experience of the end user, I think, is the innovation we can borrow. But we're a long way.


27:14
Ravi Kurani
When we talk about that. Right. I think if you look at water as a distribution, between the distribution engine, you have the source water. But a very large user of water are not only the end users, the guys in the homes. Right. But it's also the agriculture. It's the industry.


27:30
Seyi Fabode
Exactly.


27:31
Ravi Kurani
And that's just a little bit of a difference against the telecom side, though, right? Because from a telecom standpoint, given your example, and maybe it is the same, but do you see any difference there in the fact that a large part of industry is actually people that use the water, or is there a distinction that needs to be made? There is kind of the question I'm trying to ask.


27:50
Seyi Fabode
Yes, there's a distinction that needs to be made which should translate into how that large user experiences water differently from the endpoint user. And I'll use a small example, a role I had when I was in UK. So I worked at a power station for a few years and were generating the electricity, and then right after the power station, for a short stint there, I was a product manager for a company that was doing reverse auctions for people like Walmart, buying power from folk like the power station I was working at. The Walmart buyer and user of electricity was a whole different experience from the end use individual, and consequently, the system treated them that way. There was a very clear delineation between the bulk purchaser user and I think those sort of right now, apart from on the end, on the other side of the billing, if I open my tap in my home and the farmer runs a pipe to their tap and opens it on their farm and runs it for five days, I'm just making random that is still water coming out of a tap.


29:32
Seyi Fabode
For an end user, the only difference will be the billing, how much they paid versus how much I pay based on how much I use. I think there's some nuance we can get there, where maybe it's what we would normally call gray water that is used to.


29:55
Ravi Kurani
Flush the toilet.


29:58
Seyi Fabode
Gardening and stuff. And even at the farmer level, maybe they get into, oh, the water system treats the water up until a certain point when it's good enough to be used to water your farm. Anyway, we're not even close yet.


30:18
Ravi Kurani
Interesting. And when you kind of tie this back to Varuna, right, and back to your kind of the shea, synthesizing information and making things available. If you were to fast forward Varuna ten years, 15 years, what does the reality of Varuna look like in the world 20 years from now, 15 years from now, as you look back?


30:41
Seyi Fabode
Yeah, that's another fantastic question, and it actually relates to the question you asked before. I believe strongly that I'll go on a slight tangent which will relate, and I'll come back quickly. So we're in our fourth water crisis in the US. The first one had Aaron Burr. The infamous Aaron Burr. He took money from the New York Senate about $40 million in today's money from the New York Senate to fix New york's water in the 17 whenever he was around, my numbers fill me now. But he took that money, didn't fix the water, and New York had to spend a lot more to get water from the Delaware River, and that's how we landed it. So some decisions were made during the first crisis that set how we still run our water utility during the fourth water crisis today. The second one was around the World Wars.


31:49
Seyi Fabode
We spent a bunch of money, we had a lot of pollution, and we put some infrastructure in place. End of the 60s, early 1970s, same thing. The Cuyoga River fire spent a bunch of money. The Clean Water act came into play. We built some stuff, and we find ourselves in 2023 using the infrastructure that was built over those three prior crises. My fundamental belief is that Varuna will be the engine for designing the future model of the water system different from how it's been built before, because we will have all the data necessary for you to say, you know what? Let's not put a storage tank in the middle of the city this big, where the city puts its name on it. What does micro storage across the city look like? Run from one software system that allows this corner of the city to because the northeast part of the city is where most of the homes are, and demand is high in the mornings.


33:19
Seyi Fabode
How do we move water to those micro storage tanks without bursting pipes? Because they've been designed to withstand the surge in the morning and dynamically adjust where water is to where people are for what need they have, because we've redesigned the whole system. That's where I think we need to get to. And I think Verona will be that tool.


33:48
Ravi Kurani
Wow. That is huge. That's really cool. That's awesome. Jay.


33:53
Seyi Fabode
I dream about it. It's kind of mine. I dream about it. I can see it, but yeah, it's.


34:01
Ravi Kurani
What you said, right? It's Tesla superchargers with batteries in your home that are running exactly. Panels.


34:08
Seyi Fabode
Exactly.


34:09
Ravi Kurani
Load, balance your energy across. Like you said, energy is seven to ten years ahead of where water everything you just described was a distributed energy system. But just for water.


34:19
Seyi Fabode
Absolutely. We're not redefining anything here. We're literally borrowing.


34:26
Ravi Kurani
Yeah. Which sometimes is the best. Right? If you can take an existing idea and remix it, put a different blend of spice and seasonings on it, I mean, works, right?


34:38
Seyi Fabode
Exactly. I totally agree that's one of the books back there is called Still Like an Artist by Austin Cleon, one of my favorite books. It's phenomenal. And I will give him the credit for every innovative thing I've done since I read the book, because I'm like, where else is cool stuff happening? How can it be applied?


35:06
Ravi Kurani
Yeah. When you're in alternate industries, right. When you change the lens of how you can implement something like that in your own it is you still like an artist. Yes, exactly.


35:19
Seyi Fabode
It that's exactly it.


35:21
Ravi Kurani
That's cool. All right, well, we're coming close to the end. I have a few more questions. What's one thing people would ask you? Right. I'm sure you've been on a bunch of these podcasts. What don't you get to talk about? I want to give time for the negative space here to allow you to kind of just talk about what you.


35:37
Seyi Fabode
Want to oh, no, that is another phenomenal one. One of the things that is constantly on my mind is this seeming. And I love that you're working in water as well, Ravi, so you can probably relate to this. It is mind blowing how little attention people pay to this industry. That is every day I walk around wondering, if only you knew how much and how much you can be a participant in fixing the problem. And I'm not saying all of us should work in water, but I do think there's a lot of burned energy on and I don't want to sound like I'm putting anybody's work down. That's not what I mean. I just feel there's so much firepower. And again, I don't want to use terms like that. There's just so much expertise that can be brought to bear in this industry if we only paid a little bit more attention to it.


37:00
Seyi Fabode
Because on the other podcast, we're talking about water with people who care about water. So it's very easy to not realize that what up podcasts aren't Spotify's Top Ten playlist podcast when it could be and should be.


37:21
Ravi Kurani
Yeah, exactly. Which is I think you hit a nail on the head there because one of the things I'm trying to do with liquid assets is if you look at the other water podcasts out there right. A lot of them just talk about policy or they kind of really I think a lot of them are great, but they don't really start to merge that gap between tech and business, which is where people listen to like, the New York Times Daily or the Wall Street Journal or the Bullwark podcast. Right. Like, how do we bring water into that fold? So it does won't be the Spotify Top ten, but maybe it gets one inch higher than it should be.


37:56
Seyi Fabode
Yeah, you never know, it could be the top ten.


38:00
Ravi Kurani
Yeah.


38:02
Seyi Fabode
There's a lot of money in water now, especially with some of the infrastructure funding and then the more personalized and I'll give you props for this as well, with your solution for the market. You're touching people where they care and they'll pay for that.


38:22
Ravi Kurani
Yeah, exactly. And so almost to a certain sense, water has to be a business conversation. It has to be a product conversation.


38:32
Seyi Fabode
Absolutely. And I feel you're nailing it because it's such a bring it into terms that people are already thinking about and this just becomes an additional topic where they're already having. Conversations.


38:53
Ravi Kurani
Yeah, entirely cool. So, last thing here, I just want a quick snippet. What's like a closing line that you can give for the people in water? People looking at water? What's like a one line that Shay would close out with? Wow.


39:11
Seyi Fabode
Yeah, man. You have some really head scratcher questions here, which I truly appreciate. What's a one liner. I think it would really just be. This ties to what I said before. Everyone in water should become a better marketer business person because you are working in an industry where the product is literally life. You should be on the front page of the newspaper for doing the work you do in water, for doing the good work you do. And the reason why that isn't happening is because we don't talk about water and we only talk about water when there's a problem with water. We need to change that and it has to start with the people in the industry.


40:11
Ravi Kurani
Awesome. That was great. Cool, shay, thanks a ton for coming one of the first few opening episodes of Liquid Assets. And for all the listeners out there, where to find us, anywhere you can find your podcasts. And thank you all for listening.


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