1 00:00:15,396 --> 00:00:33,406 Speaker 1: Pushkin. I'm Jacob Goldstein, and this is What's Your Problem? 2 00:00:33,586 --> 00:00:36,946 Speaker 1: My guest today is Sid Jha. Sid is the CEO 3 00:00:36,986 --> 00:00:39,996 Speaker 1: of Arbol, an insurance company that he founded in 2018. 4 00:00:39,996 --> 00:00:45,226 Speaker 1: Sid's problem is this. Can you use new technology, new 5 00:00:45,286 --> 00:00:48,386 Speaker 1: sources of capital, and new ways of thinking about risk 6 00:00:48,826 --> 00:00:52,066 Speaker 1: to make insurance cheaper and more accessible? And let me 7 00:00:52,096 --> 00:00:54,356 Speaker 1: just say right up front here that I love insurance, 8 00:00:54,696 --> 00:00:57,476 Speaker 1: and I'm not afraid to admit it. Insurance is a 9 00:00:57,536 --> 00:00:59,456 Speaker 1: way of thinking about the world, a way of thinking 10 00:00:59,496 --> 00:01:03,446 Speaker 1: about risk. Insurance tells us that in the aggregate, a 11 00:01:03,536 --> 00:01:05,866 Speaker 1: lot of the most important risks that we face are 12 00:01:05,906 --> 00:01:09,246 Speaker 1: in fact not random. They are predictable. They are quantifiable. 13 00:01:09,826 --> 00:01:12,496 Speaker 1: And over the last few centuries, People have built on 14 00:01:12,526 --> 00:01:15,656 Speaker 1: this insight and they have turned insurance into what is 15 00:01:15,696 --> 00:01:19,596 Speaker 1: really a financial technology that's essential to the modern world. 16 00:01:20,196 --> 00:01:23,636 Speaker 1: That is why I love talking about insurance. In our conversation, 17 00:01:23,776 --> 00:01:26,176 Speaker 1: Sid and I talked about the kinds of risks that 18 00:01:26,196 --> 00:01:30,076 Speaker 1: his company covers, everything from bad cotton harvests to hurricanes. 19 00:01:30,636 --> 00:01:33,896 Speaker 1: We talked about climate change and we talked about how Arbol, 20 00:01:33,936 --> 00:01:37,176 Speaker 1: how Sid's startup, is competing with giant companies that have 21 00:01:37,196 --> 00:01:40,926 Speaker 1: been selling insurance for generations. To start, We talked about 22 00:01:41,026 --> 00:01:41,686 Speaker 1: the big picture. 23 00:01:42,506 --> 00:01:46,226 Speaker 2: Insurance is one of the key pieces of infrastructure for 24 00:01:46,246 --> 00:01:48,986 Speaker 2: a society to grow and honestly function. 25 00:01:49,906 --> 00:01:51,766 Speaker 3: Almost every decision you make. 26 00:01:52,106 --> 00:01:57,256 Speaker 2: About how your finances are deployed or how much you 27 00:01:57,356 --> 00:02:02,696 Speaker 2: save versus invest, underlying it, you are assuming that you 28 00:02:02,736 --> 00:02:05,326 Speaker 2: are insured on a lot of things. So imagine a 29 00:02:05,386 --> 00:02:07,586 Speaker 2: world where you don't have health insurance and you don't 30 00:02:07,626 --> 00:02:10,476 Speaker 2: have home insurance. Think of how much money you would 31 00:02:10,556 --> 00:02:12,716 Speaker 2: have to keep aside for self-insurance. 32 00:02:12,916 --> 00:02:15,856 Speaker 1: Well, you couldn't get a mortgage without home insurance, for 33 00:02:15,916 --> 00:02:17,716 Speaker 1: one thing. So first of all, you'd have to save 34 00:02:17,796 --> 00:02:19,476 Speaker 1: up to pay cash for your house, right? 35 00:02:19,716 --> 00:02:20,866 Speaker 3: Absolutely. Absolutely. 36 00:02:21,306 --> 00:02:24,106 Speaker 2: But, you know, that's just one because you're kind of 37 00:02:24,126 --> 00:02:26,466 Speaker 2: required for it. And that's because the bank is required 38 00:02:26,486 --> 00:02:28,846 Speaker 2: for it. So if you think about the banking system 39 00:02:28,906 --> 00:02:33,226 Speaker 2: and lending and insurance are essentially sister activities. A lot 40 00:02:33,266 --> 00:02:37,466 Speaker 2: of lending cannot happen without insurance because then if you 41 00:02:37,506 --> 00:02:41,076 Speaker 2: don't have the right insurance, then the interest rate a 42 00:02:41,116 --> 00:02:44,856 Speaker 2: lender has to charge to satisfy that risk can be 43 00:02:44,956 --> 00:02:47,276 Speaker 2: exorbitantly high. And I lived in India until I was 44 00:02:47,276 --> 00:02:50,636 Speaker 2: 11 and I'm very aware of the economic setup there. 45 00:02:51,396 --> 00:02:54,276 Speaker 2: If you look at countries with low insurance penetration, one 46 00:02:54,296 --> 00:02:56,776 Speaker 2: of the things that becomes very obvious is how high 47 00:02:56,816 --> 00:03:00,546 Speaker 2: the cost of borrowing is or how much capital is 48 00:03:00,706 --> 00:03:07,356 Speaker 2: trapped to essentially cover unexpected outcomes. And this goes from 49 00:03:07,736 --> 00:03:10,216 Speaker 2: families up to big infrastructure projects. 50 00:03:10,356 --> 00:03:13,706 Speaker 1: And like specifically, I mean, you mentioned... being a little 51 00:03:13,766 --> 00:03:17,026 Speaker 1: kid in India, as you mentioned that, are you thinking 52 00:03:17,046 --> 00:03:19,266 Speaker 1: of something in particular? What kinds of things did you 53 00:03:19,326 --> 00:03:21,706 Speaker 1: see that are representative of what you're talking about? 54 00:03:22,166 --> 00:03:24,926 Speaker 2: I mean, there's a plethora of these things. Back then, 55 00:03:24,966 --> 00:03:26,746 Speaker 2: I was there in the 90s, and it was a 56 00:03:26,766 --> 00:03:29,526 Speaker 2: very challenging time in that country. I mean, a lot 57 00:03:29,546 --> 00:03:32,986 Speaker 2: of families had to keep money aside just to cover 58 00:03:33,566 --> 00:03:36,076 Speaker 2: costs of the death of a loved one or unexpected 59 00:03:36,126 --> 00:03:40,936 Speaker 2: medical emergencies. Home insurance was a non-existent concept. My grandparents' 60 00:03:40,956 --> 00:03:44,366 Speaker 2: family lost their house in flooding. There's no insurance to 61 00:03:44,396 --> 00:03:48,326 Speaker 2: rebuild that. It is just restart saving again to rebuild 62 00:03:48,366 --> 00:03:51,046 Speaker 2: this thing. Crops used to get lost all the time. 63 00:03:51,066 --> 00:03:55,786 Speaker 2: There was no crop insurance. And so your whole life 64 00:03:56,206 --> 00:03:58,696 Speaker 2: revolves around the cycle of debt that you have to 65 00:03:58,736 --> 00:04:02,456 Speaker 2: go into every time there is an unexpected event. So 66 00:04:02,486 --> 00:04:04,386 Speaker 2: you're a farmer, you got a rice paddy or a 67 00:04:04,406 --> 00:04:08,386 Speaker 2: sugarcane field, that goes out. That year, you're going to 68 00:04:08,446 --> 00:04:12,346 Speaker 2: go to lenders to borrow money to survive. That interest 69 00:04:12,386 --> 00:04:15,326 Speaker 2: rate can be in the hundreds of percent. 70 00:04:15,866 --> 00:04:17,676 Speaker 3: At best, you might get 50 to 80%. 71 00:04:17,676 --> 00:04:21,436 Speaker 2: That is a cycle of debt you're very unlikely to 72 00:04:21,476 --> 00:04:23,556 Speaker 2: get out of because next year, even if the crop 73 00:04:23,616 --> 00:04:25,036 Speaker 2: is good, you're just barely surviving. 74 00:04:25,596 --> 00:04:28,896 Speaker 1: So we have more than that, but at the time 75 00:04:28,916 --> 00:04:37,636 Speaker 1: you're starting... Arbol, what are the meaningful, practical problems with insurance? 76 00:04:37,696 --> 00:04:39,646 Speaker 1: What do you see as the limits of insurance that 77 00:04:39,666 --> 00:04:40,926 Speaker 1: you think you can improve on? 78 00:04:41,666 --> 00:04:44,646 Speaker 2: When we were starting Arbol, climate was just starting to 79 00:04:44,686 --> 00:04:47,886 Speaker 2: become bigger discussion. So when you have areas with higher 80 00:04:47,926 --> 00:04:51,376 Speaker 2: volatility of outcomes, and that could be hurricane or hail 81 00:04:51,556 --> 00:04:56,156 Speaker 2: or severe convective storms or wildfire, you have a lot 82 00:04:56,336 --> 00:05:01,466 Speaker 2: more of the population living in higher volatility areas, whether 83 00:05:01,486 --> 00:05:04,186 Speaker 2: it's coastlines or the Midwest with the hail zones. 84 00:05:04,586 --> 00:05:08,266 Speaker 3: And you've seen losses really ramp up over the last 85 00:05:08,306 --> 00:05:08,766 Speaker 3: few years. 86 00:05:09,356 --> 00:05:13,956 Speaker 2: So what insurance was designed for was a different pattern 87 00:05:13,976 --> 00:05:16,936 Speaker 2: of population and risk. So when you brought up climate change, 88 00:05:17,376 --> 00:05:19,376 Speaker 2: a lot of this is not necessarily just climate change. 89 00:05:19,416 --> 00:05:21,756 Speaker 2: It's just that populations are living closer to the forests 90 00:05:21,956 --> 00:05:25,146 Speaker 2: or coastlines or in hail zones and many other sort of. 91 00:05:25,086 --> 00:05:26,026 Speaker 3: Vulnerable areas, right? 92 00:05:26,386 --> 00:05:31,106 Speaker 2: So As these shifts are happening, the insurance system I 93 00:05:31,146 --> 00:05:34,886 Speaker 2: see as still very inflexible. You have lots of intermediaries. 94 00:05:34,946 --> 00:05:37,666 Speaker 2: So from customer all the way to the reinsurance capital, 95 00:05:38,066 --> 00:05:41,676 Speaker 2: you have probably at least five, if not eight to 96 00:05:41,676 --> 00:05:45,096 Speaker 2: 10 layers of intermediaries. So one is that the insurance 97 00:05:45,136 --> 00:05:48,696 Speaker 2: you pay for, a large portion of that can be 98 00:05:49,056 --> 00:05:52,256 Speaker 2: just the fees to get the risk from a customer 99 00:05:52,656 --> 00:05:54,996 Speaker 2: origination point all the way to capital. 100 00:05:55,476 --> 00:05:58,756 Speaker 1: So it's like too much intermediation is making it more expensive. 101 00:05:58,756 --> 00:06:01,546 Speaker 3: 30, 40% of your premium can be fees. 102 00:06:02,366 --> 00:06:06,506 Speaker 2: Wow. But it's not like everybody's, you know, making money 103 00:06:06,546 --> 00:06:07,166 Speaker 2: hand over fist. 104 00:06:07,246 --> 00:06:11,346 Speaker 1: There's so many different players between me, the buyer of insurance, 105 00:06:11,446 --> 00:06:14,966 Speaker 1: and the ultimate... person who's actually supplying the capital. And 106 00:06:15,006 --> 00:06:17,326 Speaker 1: it's not like any one person is ripping me off. 107 00:06:17,426 --> 00:06:19,206 Speaker 1: It's just an inefficient system. 108 00:06:19,566 --> 00:06:22,236 Speaker 2: Yeah. You will often have like five, six companies involved 109 00:06:22,266 --> 00:06:24,676 Speaker 2: in an insurance deal. And each of them has to 110 00:06:24,746 --> 00:06:27,656 Speaker 2: obviously make money for their shareholders. And it just becomes 111 00:06:27,696 --> 00:06:32,296 Speaker 2: this very difficult system to streamline. Then on top of that, 112 00:06:32,496 --> 00:06:36,076 Speaker 2: you have a humongous patchwork of regulations, right? So different 113 00:06:36,116 --> 00:06:40,236 Speaker 2: state regulators and everybody has different modalities on how you 114 00:06:40,276 --> 00:06:45,536 Speaker 2: incorporate even new types of models. And then finally, this system, 115 00:06:45,796 --> 00:06:48,286 Speaker 2: the way it is, then makes it very difficult for 116 00:06:48,726 --> 00:06:51,766 Speaker 2: different capital, new capital pools to enter the market. But 117 00:06:52,446 --> 00:06:55,666 Speaker 2: that compounds the problem because new capital wouldn't make it 118 00:06:55,686 --> 00:06:58,846 Speaker 2: more affordable. So you have a bunch of compounding issues 119 00:06:59,506 --> 00:07:02,776 Speaker 2: all circling around this difficult to model, difficult to write 120 00:07:03,276 --> 00:07:06,676 Speaker 2: sort of areas which are, you know, the climate risk 121 00:07:06,696 --> 00:07:08,616 Speaker 2: has increased a lot and the property values in a 122 00:07:08,636 --> 00:07:10,716 Speaker 2: lot of these climate sensitive zones have increased a lot. 123 00:07:11,316 --> 00:07:15,776 Speaker 2: And so that combination is what's making insurance affordability and 124 00:07:15,816 --> 00:07:19,046 Speaker 2: insurance availability more and more challenging. 125 00:07:19,496 --> 00:07:22,606 Speaker 3: That's the baseline, right? But life is not static. 126 00:07:22,886 --> 00:07:25,426 Speaker 2: You have new kinds of risks that are popping up 127 00:07:25,706 --> 00:07:30,126 Speaker 2: like AI data centers that are so enormous in scale 128 00:07:30,746 --> 00:07:35,006 Speaker 2: and have such complex risk structures. And you're seeing the 129 00:07:35,046 --> 00:07:38,346 Speaker 2: insurance industry struggle to meet that demand. 130 00:07:39,206 --> 00:07:39,866 Speaker 3: Oh, interesting. 131 00:07:39,926 --> 00:07:44,606 Speaker 1: Like I'm building a whatever billion dollar AI data center 132 00:07:44,666 --> 00:07:46,486 Speaker 1: and I want to insure it. And the insurers are like, 133 00:07:46,546 --> 00:07:48,266 Speaker 1: what does that even mean? And how do we do that? 134 00:07:49,606 --> 00:07:50,056 Speaker 3: Exactly. 135 00:07:50,116 --> 00:07:52,876 Speaker 2: There's so many types of risks. There's climate risk. There's 136 00:07:53,096 --> 00:07:56,656 Speaker 2: mechanical failure risk. There's actually a ton of revenue risk 137 00:07:56,696 --> 00:08:00,716 Speaker 2: because if, let's say, you have a transformer or something 138 00:08:00,736 --> 00:08:04,076 Speaker 2: that gets damaged when a hurricane hits a data center, well, 139 00:08:04,546 --> 00:08:06,706 Speaker 2: The loss is not just the loss of that transformer, 140 00:08:06,726 --> 00:08:08,566 Speaker 2: it's the fact that you might be sitting in queue 141 00:08:08,586 --> 00:08:10,066 Speaker 2: for the next two years to get a new one. 142 00:08:10,786 --> 00:08:12,566 Speaker 3: And what does that do to your revenue profile? 143 00:08:12,646 --> 00:08:15,326 Speaker 1: There's also political risk, right, at this point. 144 00:08:15,346 --> 00:08:15,976 Speaker 3: There's a lot of that. 145 00:08:15,996 --> 00:08:18,676 Speaker 1: They will suddenly say, you actually can't build the data 146 00:08:18,696 --> 00:08:20,016 Speaker 1: center that we told you you could build. 147 00:08:20,076 --> 00:08:20,456 Speaker 3: Exactly. 148 00:08:21,026 --> 00:08:22,826 Speaker 2: You know, we're going to be putting out some stuff 149 00:08:22,906 --> 00:08:24,966 Speaker 2: on this where we are doing a bunch of work 150 00:08:25,026 --> 00:08:27,916 Speaker 2: on analyzing climate risk. And as new data centers are 151 00:08:27,936 --> 00:08:30,536 Speaker 2: popping up in areas that are, you know, a little 152 00:08:30,596 --> 00:08:32,836 Speaker 2: closer to the coast or in hail zones and many 153 00:08:32,876 --> 00:08:35,956 Speaker 2: other higher risk areas. And, you know, we do a 154 00:08:35,976 --> 00:08:37,366 Speaker 2: lot of climate modeling in-house. 155 00:08:37,426 --> 00:08:38,476 Speaker 3: And so we think we have. 156 00:08:38,406 --> 00:08:41,726 Speaker 2: A good view on how the risk characteristics of these 157 00:08:41,766 --> 00:08:44,066 Speaker 2: data centers is changing as new ones are getting built. 158 00:08:44,566 --> 00:08:47,566 Speaker 2: To me, one big issue is that Most of the 159 00:08:47,586 --> 00:08:50,126 Speaker 2: data center boom has happened in the last couple of 160 00:08:50,166 --> 00:08:55,926 Speaker 2: years where you've not had major hurricanes hit many areas. 161 00:08:56,086 --> 00:08:59,286 Speaker 2: And so I think it's still a new concept for 162 00:08:59,306 --> 00:09:02,386 Speaker 2: a lot of these guys. So you have property on 163 00:09:02,406 --> 00:09:05,636 Speaker 2: the coast, you have crops growing in an area where 164 00:09:05,776 --> 00:09:07,376 Speaker 2: insurance coverage is muted. 165 00:09:07,396 --> 00:09:12,356 Speaker 3: You have wildfire prone areas. All of that is stuff 166 00:09:12,376 --> 00:09:12,776 Speaker 3: we cover. 167 00:09:14,156 --> 00:09:16,916 Speaker 2: We go in areas where the incumbents are scared to 168 00:09:17,336 --> 00:09:21,236 Speaker 2: go into, whether because the risk is difficult to model 169 00:09:21,416 --> 00:09:24,056 Speaker 2: or their capital structure is not built for it. We 170 00:09:24,096 --> 00:09:27,556 Speaker 2: bring technology, we bring novel capital. It's a marriage of 171 00:09:27,696 --> 00:09:32,156 Speaker 2: technology and capital to solve the end problem, which is 172 00:09:32,836 --> 00:09:35,446 Speaker 2: I can't get good coverage. If I get coverage, it's 173 00:09:35,466 --> 00:09:38,846 Speaker 2: too expensive or it's got too many exclusions. And that's 174 00:09:38,866 --> 00:09:39,706 Speaker 2: what we're trying to solve. 175 00:09:41,106 --> 00:09:45,306 Speaker 1: So when you started your company in 2018, you focused 176 00:09:46,186 --> 00:09:49,096 Speaker 1: on this kind of weird type of insurance that hasn't 177 00:09:49,116 --> 00:09:53,776 Speaker 1: been used very much historically. It's called parametric insurance. And 178 00:09:54,116 --> 00:09:55,596 Speaker 1: let me just say what the basic idea is, and 179 00:09:55,636 --> 00:09:59,676 Speaker 1: we can talk about it. Like, say I'm a business. Traditionally, 180 00:09:59,916 --> 00:10:02,466 Speaker 1: I can buy insurance that covers me if something happens 181 00:10:02,666 --> 00:10:05,706 Speaker 1: to my business, if I get robbed or if my 182 00:10:05,786 --> 00:10:10,906 Speaker 1: factory burns down or whatever. But parametric insurance... is different, right? 183 00:10:10,986 --> 00:10:14,576 Speaker 1: It's this type of insurance that pays out based not 184 00:10:14,666 --> 00:10:17,556 Speaker 1: on what happens to my business, but based on some 185 00:10:17,736 --> 00:10:21,976 Speaker 1: objective thing that happens out in the world. So, for example, 186 00:10:22,036 --> 00:10:25,696 Speaker 1: I might buy a parametric insurance That pays off if 187 00:10:25,736 --> 00:10:28,296 Speaker 1: there's a certain amount of rainfall in a certain place 188 00:10:28,356 --> 00:10:30,936 Speaker 1: in a certain period of time, or if the output 189 00:10:30,956 --> 00:10:34,996 Speaker 1: of some particular crop in some region is below some threshold, 190 00:10:35,336 --> 00:10:38,676 Speaker 1: this kind of thing. And so I'm curious, like, who 191 00:10:39,196 --> 00:10:41,436 Speaker 1: is the customer for that? You know, who out in 192 00:10:41,476 --> 00:10:44,636 Speaker 1: the world is not being served by traditional insurance and 193 00:10:44,736 --> 00:10:46,556 Speaker 1: needs parametric insurance? 194 00:10:47,316 --> 00:10:50,676 Speaker 2: So when you start out, right, in a very difficult 195 00:10:50,696 --> 00:10:53,616 Speaker 2: to traverse market like insurance with a ton of large incumbents, 196 00:10:53,926 --> 00:10:58,876 Speaker 2: you have to find the right wedges to build on. 197 00:10:58,886 --> 00:11:02,006 Speaker 1: I mean, you need somebody who's really not being served 198 00:11:02,086 --> 00:11:03,246 Speaker 1: by what's currently available. 199 00:11:03,486 --> 00:11:03,966 Speaker 3: Exactly. 200 00:11:04,106 --> 00:11:07,106 Speaker 2: Our biggest one was actually cotton gins. What was one 201 00:11:07,126 --> 00:11:09,926 Speaker 2: of our early sort of home runs, if you will. 202 00:11:10,566 --> 00:11:12,926 Speaker 2: So a cotton gin sources cotton from a bunch of 203 00:11:12,966 --> 00:11:17,566 Speaker 2: counties sitting in, let's say, Texas. And if that cotton 204 00:11:17,586 --> 00:11:20,746 Speaker 2: crop fails in those counties or is well below average, 205 00:11:21,236 --> 00:11:23,276 Speaker 2: Those gins have trouble. And we actually have a great 206 00:11:23,356 --> 00:11:25,956 Speaker 2: blog post from a few years ago about a gin 207 00:11:25,996 --> 00:11:27,976 Speaker 2: that actually came to us and said, we really helped 208 00:11:27,996 --> 00:11:30,776 Speaker 2: save their business from our payout because the cotton crop 209 00:11:30,796 --> 00:11:31,836 Speaker 2: was terrible back then. 210 00:11:31,876 --> 00:11:34,426 Speaker 1: You say cotton gin, I think of Eli Whitney, but 211 00:11:34,486 --> 00:11:36,866 Speaker 1: today a cotton gin is like a company that is 212 00:11:36,966 --> 00:11:41,166 Speaker 1: spinning cotton into thread or fabric or weaving? 213 00:11:41,766 --> 00:11:44,506 Speaker 2: Yeah, taking the raw cotton and coming up with the 214 00:11:44,546 --> 00:11:47,956 Speaker 2: middle product, if you will. Then other companies will take 215 00:11:48,016 --> 00:11:51,906 Speaker 2: that and So a lot of the cotton gins are 216 00:11:52,046 --> 00:11:53,336 Speaker 2: small to medium-sized businesses. 217 00:11:53,746 --> 00:11:56,016 Speaker 1: And what are they actually making? What are they turning 218 00:11:56,296 --> 00:11:57,436 Speaker 1: the raw cotton into? 219 00:11:57,736 --> 00:11:59,536 Speaker 3: It'll be something like yarn, basically. 220 00:11:59,716 --> 00:12:00,336 Speaker 1: Something like yarn, okay. 221 00:12:00,356 --> 00:12:02,036 Speaker 3: That can then be used by a larger company. 222 00:12:03,516 --> 00:12:06,136 Speaker 2: And they'll be involved in a lot of different related operations, 223 00:12:06,196 --> 00:12:09,686 Speaker 2: like warehousing, transportation of cotton, and things like that, all 224 00:12:09,706 --> 00:12:12,446 Speaker 2: of which depend on a cotton crop. Right. 225 00:12:12,566 --> 00:12:16,826 Speaker 1: And unlike the farmer, that small or mid-sized business can't 226 00:12:16,886 --> 00:12:20,446 Speaker 1: buy like federal crop insurance, even though they are very 227 00:12:20,506 --> 00:12:22,386 Speaker 1: largely dependent on the local crop. 228 00:12:23,436 --> 00:12:23,836 Speaker 3: Exactly. 229 00:12:24,256 --> 00:12:27,356 Speaker 2: So now if we dig deeper into this wedge, and 230 00:12:27,476 --> 00:12:30,496 Speaker 2: it's more to show our thought process, and this was 231 00:12:30,576 --> 00:12:34,136 Speaker 2: where very, very challenging in the beginning. You go after 232 00:12:34,156 --> 00:12:37,416 Speaker 2: the wrong market early and it's lights out, right? So 233 00:12:37,596 --> 00:12:41,606 Speaker 2: here you have a high correlation to revenue with that risk. 234 00:12:42,436 --> 00:12:44,396 Speaker 3: You have official US. 235 00:12:44,176 --> 00:12:48,816 Speaker 2: Government data on county level yields that market trusts, and 236 00:12:49,096 --> 00:12:53,656 Speaker 2: you have capital providers who like that lack of correlation, 237 00:12:53,726 --> 00:12:57,046 Speaker 2: because this is not even correlated to cotton futures. Cotton 238 00:12:57,086 --> 00:12:59,766 Speaker 2: futures trade off of a global balance sheet. This is 239 00:12:59,826 --> 00:13:03,026 Speaker 2: like local West Texas cotton output. 240 00:13:03,986 --> 00:13:05,106 Speaker 3: This is very localized. 241 00:13:06,236 --> 00:13:10,216 Speaker 2: So that's an example of all the three pieces coming 242 00:13:10,276 --> 00:13:14,076 Speaker 2: together us being able to model the risk and having 243 00:13:14,136 --> 00:13:18,696 Speaker 2: it be correlated to revenue and customer knowing that customer 244 00:13:18,736 --> 00:13:22,446 Speaker 2: education being actually a very short ramp here because in agriculture, 245 00:13:22,506 --> 00:13:26,286 Speaker 2: they really understand that risk of the business and capital 246 00:13:26,326 --> 00:13:31,406 Speaker 2: providers being interested in supporting that risk. And then around 2022, 247 00:13:31,406 --> 00:13:35,056 Speaker 2: we started to get into the parametric reinsurance market. 248 00:13:35,096 --> 00:13:36,496 Speaker 3: And I would say we were pioneers. 249 00:13:36,536 --> 00:13:39,716 Speaker 2: We did the first large scale transactions in it. 250 00:13:40,026 --> 00:13:43,236 Speaker 1: To be clear, reinsurance is the insurance that an insurance 251 00:13:43,266 --> 00:13:43,936 Speaker 1: company buys. 252 00:13:44,056 --> 00:13:44,476 Speaker 3: Absolutely. 253 00:13:44,516 --> 00:13:47,956 Speaker 2: Yes. Yes. Now in parametric reinsurance, your client is not 254 00:13:47,996 --> 00:13:52,116 Speaker 2: an individual with a house. It is an insurance company 255 00:13:52,466 --> 00:13:56,656 Speaker 2: with say a hundred thousand houses they cover. The beauty 256 00:13:56,696 --> 00:13:59,736 Speaker 2: of that is that each one becomes a data point. 257 00:14:00,186 --> 00:14:02,906 Speaker 2: So as a storm tracks through, as a hurricane tracks through, 258 00:14:03,346 --> 00:14:07,146 Speaker 2: each point is paying based on storm track and wind 259 00:14:07,166 --> 00:14:08,866 Speaker 2: speed and a lot of these different factors. 260 00:14:08,886 --> 00:14:11,286 Speaker 1: So they have the law of large numbers on their 261 00:14:11,306 --> 00:14:14,676 Speaker 1: side because they're insuring a hundred thousand houses in the 262 00:14:15,196 --> 00:14:17,136 Speaker 1: hurricane zone or whatever. Exactly. 263 00:14:17,636 --> 00:14:17,836 Speaker 2: Yeah. 264 00:14:18,396 --> 00:14:22,096 Speaker 1: Just to be specific, was it hurricane risk in particular? 265 00:14:22,136 --> 00:14:25,646 Speaker 1: Like you were selling insurance to insurance companies And you 266 00:14:25,686 --> 00:14:29,246 Speaker 1: paid them if there were a certain number of hurricanes 267 00:14:29,326 --> 00:14:31,346 Speaker 1: of a certain strength in a certain amount of time, 268 00:14:31,446 --> 00:14:32,166 Speaker 1: something like that? 269 00:14:32,906 --> 00:14:34,616 Speaker 3: Yeah, but more structured than that. 270 00:14:34,656 --> 00:14:39,196 Speaker 2: So every storm that went through, every single data point, i.e. 271 00:14:39,256 --> 00:14:42,676 Speaker 2: house they covered, would trigger a payout, a small payout, 272 00:14:43,096 --> 00:14:45,276 Speaker 2: based on where the eye of the storm was, the 273 00:14:45,296 --> 00:14:46,776 Speaker 2: wind speed, and a few other factors. 274 00:14:47,056 --> 00:14:50,016 Speaker 1: Oh, I see. So in a way, they're buying reinsurance 275 00:14:50,626 --> 00:14:52,446 Speaker 1: house by house. Yes. 276 00:14:52,986 --> 00:14:53,866 Speaker 3: And it's the portfolio. 277 00:14:53,886 --> 00:14:58,096 Speaker 2: Oh, interesting. structured reinsurance full portfolio and calibrated to their 278 00:14:58,116 --> 00:15:02,496 Speaker 2: loss history. So not some generic like two hurricanes come 279 00:15:02,536 --> 00:15:04,456 Speaker 2: and I pay you this much because that doesn't serve 280 00:15:04,476 --> 00:15:05,216 Speaker 2: the insurer's needs. 281 00:15:05,636 --> 00:15:07,896 Speaker 3: They need it to be specific. So a regulator will 282 00:15:07,956 --> 00:15:10,316 Speaker 3: accept that as a valid reinsurance hedge. 283 00:15:11,116 --> 00:15:13,576 Speaker 1: Oh, interesting. Okay. And that worked. 284 00:15:14,016 --> 00:15:14,316 Speaker 3: Yes. 285 00:15:18,556 --> 00:15:30,776 Speaker 1: We'll be back in just a minute. What role does 286 00:15:30,836 --> 00:15:33,296 Speaker 1: climate change play in your business and in the growth 287 00:15:33,336 --> 00:15:33,896 Speaker 1: of your business? 288 00:15:34,856 --> 00:15:36,496 Speaker 3: Great question and comes up a lot. 289 00:15:36,596 --> 00:15:40,336 Speaker 2: So I always like to say that, like all risks, 290 00:15:40,616 --> 00:15:46,336 Speaker 2: climate change is a very multi-dimensional problem. Every place has 291 00:15:46,456 --> 00:15:50,876 Speaker 2: different sensitivity to climate change and every kind of risk 292 00:15:50,936 --> 00:15:53,326 Speaker 2: has a different level of sensitivity. So if you look at, 293 00:15:53,366 --> 00:15:56,946 Speaker 2: for example, certain kinds of wildfire risk, yes, have gone. 294 00:15:56,846 --> 00:15:58,266 Speaker 3: Up tremendously due to climate change. 295 00:15:59,126 --> 00:16:02,806 Speaker 2: But hurricane risk, if you had to pick a risk 296 00:16:02,866 --> 00:16:06,316 Speaker 2: that you would say, oh, this is clearly an uptrend 297 00:16:06,576 --> 00:16:09,596 Speaker 2: in the number of hurricanes hitting Florida or something, you 298 00:16:09,616 --> 00:16:10,836 Speaker 2: will not see that in the data. 299 00:16:10,956 --> 00:16:12,836 Speaker 3: And you could go back 100 years, right? 300 00:16:12,896 --> 00:16:17,956 Speaker 2: Because there are many complicated counterbalancing factors. The other thing 301 00:16:18,016 --> 00:16:22,696 Speaker 2: that actually is a big counterbalance is that there might 302 00:16:22,756 --> 00:16:25,256 Speaker 2: be a climate change trend in, say, temperature, especially in 303 00:16:25,276 --> 00:16:29,216 Speaker 2: the northern latitudes in some places. But the cycles of 304 00:16:29,276 --> 00:16:32,426 Speaker 2: things like El Nino, La Nina, and NAO, and many 305 00:16:32,586 --> 00:16:35,866 Speaker 2: other climactic cycles that can last from a few months 306 00:16:35,926 --> 00:16:42,046 Speaker 2: to a year, often swamp that considerably. And so insurance 307 00:16:42,106 --> 00:16:45,246 Speaker 2: is written on an annual basis. So if I'm sitting here, overall, 308 00:16:45,426 --> 00:16:50,926 Speaker 2: climate change does increase volatility of outcomes. in different areas. 309 00:16:51,066 --> 00:16:54,386 Speaker 2: And your biggest defense is diversification. You want to be 310 00:16:54,446 --> 00:16:58,196 Speaker 2: writing lots of different lines. And this was actually one 311 00:16:58,216 --> 00:17:01,236 Speaker 2: of the things that is such a big barrier to 312 00:17:01,296 --> 00:17:03,996 Speaker 2: entry when you're starting out. A lot of our cohorts 313 00:17:04,016 --> 00:17:07,416 Speaker 2: who started around that time chose specific lines to write. Like, 314 00:17:07,476 --> 00:17:09,176 Speaker 2: I'm going to be a wildfire specialist. I'm going to 315 00:17:09,196 --> 00:17:13,856 Speaker 2: be a hurricane specialist. We consciously chose to build multiple 316 00:17:13,936 --> 00:17:17,196 Speaker 2: lines all at the same time while being a startup. 317 00:17:17,296 --> 00:17:20,286 Speaker 3: And it was very difficult. But you needed that to. 318 00:17:20,246 --> 00:17:24,586 Speaker 2: Escape over-reliance on any one factor. Because yes, volatility is 319 00:17:24,676 --> 00:17:27,776 Speaker 2: rising in a lot of these risks. 320 00:17:28,676 --> 00:17:31,356 Speaker 1: I like this big picture idea you're mapping out. And 321 00:17:31,576 --> 00:17:34,656 Speaker 1: at this moment when you're coming in and that the 322 00:17:34,696 --> 00:17:37,416 Speaker 1: world is changing faster than the insurance industry is changing 323 00:17:37,476 --> 00:17:39,496 Speaker 1: and it is getting harder to ensure the risks that 324 00:17:39,536 --> 00:17:44,536 Speaker 1: people need to ensure. I mean, if we get to now, so, 325 00:17:44,596 --> 00:17:47,456 Speaker 1: you know, there's the parametric part, like when do you, 326 00:17:48,256 --> 00:17:51,426 Speaker 1: decide that you need to do more than sell parametric insurance? 327 00:17:51,526 --> 00:17:55,346 Speaker 2: So we started selling more traditional insurance about a couple 328 00:17:55,546 --> 00:17:59,966 Speaker 2: years ago. As I mentioned, we were doing parametric reinsurance 329 00:17:59,986 --> 00:18:04,296 Speaker 2: for property carriers. We ended up acquiring a struggling one 330 00:18:05,196 --> 00:18:08,356 Speaker 2: and have completely revamped how it operates in terms of 331 00:18:08,926 --> 00:18:11,396 Speaker 2: better modeling, more efficiency, and many other things. 332 00:18:11,476 --> 00:18:14,316 Speaker 3: And we have learned a lot across that over time. 333 00:18:14,636 --> 00:18:18,416 Speaker 1: And specifically, what kinds of insurance are you selling? 334 00:18:18,756 --> 00:18:22,116 Speaker 3: That was homeowners and commercial in coastal areas. 335 00:18:22,436 --> 00:18:25,116 Speaker 1: And is coastal areas everywhere in the country or some 336 00:18:25,156 --> 00:18:26,016 Speaker 1: particular region? 337 00:18:27,336 --> 00:18:29,056 Speaker 3: East Coast, Gulf, and Hawaii. 338 00:18:29,076 --> 00:18:33,576 Speaker 1: Uh-huh. So it's basically hurricane risk. Is that the big 339 00:18:33,656 --> 00:18:33,956 Speaker 1: one there? 340 00:18:34,136 --> 00:18:35,606 Speaker 3: Yeah. Hurricane risk is a big one. 341 00:18:35,966 --> 00:18:38,956 Speaker 2: But what we learned through this whole process is how 342 00:18:39,016 --> 00:18:43,936 Speaker 2: do you work in technology with very experienced insurance professionals? 343 00:18:44,836 --> 00:18:48,716 Speaker 2: How do you work in AI co-piloting to streamline a 344 00:18:49,656 --> 00:18:53,576 Speaker 2: regulated entity that has, beyond hurricane risk, a ton of 345 00:18:53,656 --> 00:18:58,816 Speaker 2: other operational and modeling needs because you're writing the full stack. 346 00:18:58,876 --> 00:19:01,836 Speaker 2: You're not just writing hurricane coverage. It's not just wind speed. 347 00:19:02,116 --> 00:19:04,946 Speaker 2: You have to model all sorts of different risks that 348 00:19:04,966 --> 00:19:07,556 Speaker 2: the customers may be facing and price their own for that. 349 00:19:08,016 --> 00:19:11,526 Speaker 1: Anything that can happen to your house, more or less. 350 00:19:11,586 --> 00:19:13,206 Speaker 3: Yes. Or your commercial property. 351 00:19:13,926 --> 00:19:18,726 Speaker 1: And so are you better at it than big insurance 352 00:19:18,746 --> 00:19:20,846 Speaker 1: companies that I've heard of? Like, are you just better 353 00:19:20,906 --> 00:19:21,706 Speaker 1: at modeling risk? 354 00:19:22,986 --> 00:19:26,546 Speaker 2: Modeling risk is one, I would say, just one of 355 00:19:26,966 --> 00:19:29,926 Speaker 2: many components that go into it. A lot of the 356 00:19:29,966 --> 00:19:34,026 Speaker 2: big insurance companies have, I would say, somewhat high cost 357 00:19:34,046 --> 00:19:36,726 Speaker 2: structures or brittle cost structures. We have brought that down 358 00:19:36,766 --> 00:19:40,506 Speaker 2: significantly as well. It makes us more nimble. And then finally, also, 359 00:19:41,176 --> 00:19:45,616 Speaker 2: we avail a wide array of capital providers beyond just 360 00:19:45,656 --> 00:19:51,346 Speaker 2: traditional reinsurers. You know, what is better is always remains 361 00:19:51,386 --> 00:19:53,386 Speaker 2: to be seen. When you're managing a risk business, you're 362 00:19:53,406 --> 00:19:56,066 Speaker 2: only as good as your last, you know, risk. 363 00:19:56,626 --> 00:19:59,046 Speaker 1: That's fair. So are you saying you can sell policy? 364 00:19:59,406 --> 00:20:02,946 Speaker 1: cheaper just because you run the business more efficiently and 365 00:20:02,986 --> 00:20:05,746 Speaker 1: have access to a wider capital pool, and therefore you 366 00:20:05,766 --> 00:20:08,136 Speaker 1: can sell policies more cheaply and still be profitable? 367 00:20:08,796 --> 00:20:09,516 Speaker 3: In some cases. 368 00:20:09,636 --> 00:20:14,276 Speaker 2: And in some cases, what's different about us is, especially 369 00:20:14,296 --> 00:20:16,596 Speaker 2: there's a lot of regional carriers in these areas, unlike 370 00:20:16,616 --> 00:20:20,336 Speaker 2: all of those guys, we are also doing in-house climate 371 00:20:20,356 --> 00:20:24,616 Speaker 2: modeling to compare against the outside models that everybody uses. 372 00:20:24,876 --> 00:20:26,866 Speaker 2: So we have our own view of risk, we have 373 00:20:26,926 --> 00:20:29,386 Speaker 2: our outside view of risk, and we can tailor it 374 00:20:29,766 --> 00:20:32,766 Speaker 2: because we have gone through the pain of building our 375 00:20:32,826 --> 00:20:35,976 Speaker 2: own modeling and tweaking all the assumptions and knowing, okay, 376 00:20:36,416 --> 00:20:39,256 Speaker 2: in this case, I trust our model more. Or in 377 00:20:39,316 --> 00:20:43,136 Speaker 2: this case, I see why the outside vendors are pricing 378 00:20:43,176 --> 00:20:45,836 Speaker 2: risk this way. And then a lot of it comes 379 00:20:45,876 --> 00:20:48,926 Speaker 2: down to, remember, as an insurance company, we're not retaining 380 00:20:49,206 --> 00:20:52,146 Speaker 2: a huge amount of risk. We are retaining some, but 381 00:20:52,226 --> 00:20:55,706 Speaker 2: we are reinsuring the rest. So if you optimize all 382 00:20:55,746 --> 00:21:00,266 Speaker 2: three of these components, you can do better than your competition. 383 00:21:01,506 --> 00:21:04,126 Speaker 1: As I was thinking about your parametric insurance business, I 384 00:21:04,166 --> 00:21:08,486 Speaker 1: was thinking about prediction markets. And it feels like there 385 00:21:08,526 --> 00:21:10,736 Speaker 1: is some overlap there. Is there some overlap there? 386 00:21:11,576 --> 00:21:11,936 Speaker 2: There is. 387 00:21:12,036 --> 00:21:15,696 Speaker 3: And we are very familiar with these guys. We are 388 00:21:15,716 --> 00:21:18,116 Speaker 3: very aware. There is an overlap. 389 00:21:18,476 --> 00:21:21,416 Speaker 2: I would say that the issue, especially with the kinds 390 00:21:21,456 --> 00:21:24,036 Speaker 2: of risks that we do parametric on, is that because 391 00:21:24,076 --> 00:21:29,126 Speaker 2: we work with institutions at larger scales, whereas prediction markets 392 00:21:29,366 --> 00:21:29,986 Speaker 2: are much. 393 00:21:29,906 --> 00:21:32,606 Speaker 3: More on the retail and smaller ticket size. 394 00:21:33,166 --> 00:21:36,406 Speaker 2: Yeah. That problem I mentioned in the early days where 395 00:21:37,066 --> 00:21:40,646 Speaker 2: we spent inordinate amounts of time trying to build distribution. 396 00:21:41,306 --> 00:21:44,486 Speaker 2: Prediction markets have had a tough time building distribution in 397 00:21:45,046 --> 00:21:49,546 Speaker 2: these kinds of risks because it takes a lot of 398 00:21:49,626 --> 00:21:54,726 Speaker 2: effort to find actual hedgers. Prediction markets have not really 399 00:21:54,806 --> 00:21:59,176 Speaker 2: moved beyond betting. Even if you look at their weather markets, 400 00:21:59,216 --> 00:22:01,716 Speaker 2: and this is something we've had the discussions with them about, 401 00:22:02,196 --> 00:22:03,996 Speaker 2: is if you look at their weather markets, the bulk 402 00:22:04,036 --> 00:22:05,876 Speaker 2: of it is, oh, I'm going to bet on tomorrow's 403 00:22:06,056 --> 00:22:10,576 Speaker 2: high temperature in New York. That's great from a betting standpoint, 404 00:22:10,596 --> 00:22:14,896 Speaker 2: but what does that solve for anybody, right? The contracts 405 00:22:14,936 --> 00:22:18,176 Speaker 2: are not designed for hedging, and they're not designed for 406 00:22:18,236 --> 00:22:21,336 Speaker 2: institutions to take advantage of, and obviously there's many institutions 407 00:22:21,356 --> 00:22:23,796 Speaker 2: who also won't really interact with them because of the 408 00:22:23,856 --> 00:22:31,726 Speaker 2: regulatory uncertainty. Yes. It is hard to overstate how much 409 00:22:32,606 --> 00:22:35,806 Speaker 2: regulatory constraint exists in these markets. When you're dealing with 410 00:22:35,846 --> 00:22:39,256 Speaker 2: a utility, there is a huge amount of infrastructure that 411 00:22:39,296 --> 00:22:41,856 Speaker 2: goes into things like letters of credit and many other 412 00:22:42,196 --> 00:22:45,336 Speaker 2: things to make sure that they feel comfortable that you'll 413 00:22:45,356 --> 00:22:49,056 Speaker 2: be around to make that payout. Is a utility going 414 00:22:49,096 --> 00:22:50,676 Speaker 2: to trust any of these markets yet? 415 00:22:51,176 --> 00:22:52,426 Speaker 3: I don't think so. 416 00:22:52,816 --> 00:22:56,426 Speaker 1: Going back to your company, when you think about the 417 00:22:56,486 --> 00:23:01,876 Speaker 1: kind of medium-term future, what are your main worries? Like 418 00:23:01,916 --> 00:23:04,796 Speaker 1: if things go wrong, what do you think might go wrong? 419 00:23:06,436 --> 00:23:10,626 Speaker 2: Well, when you manage a risk business, you always have 420 00:23:10,686 --> 00:23:12,026 Speaker 2: that aspect of going wrong. 421 00:23:12,066 --> 00:23:13,486 Speaker 3: Now we reinsure a lot of things. 422 00:23:13,546 --> 00:23:17,966 Speaker 2: We are also in other markets, we're just arranging the risk, 423 00:23:18,046 --> 00:23:23,416 Speaker 2: not taking the risk. But you still need capital markets 424 00:23:23,456 --> 00:23:28,776 Speaker 2: to be around to supply the risk capacity, whether that's reinsurers, 425 00:23:28,856 --> 00:23:32,316 Speaker 2: alternative capital, or a mix of both. So there's always 426 00:23:32,356 --> 00:23:37,356 Speaker 2: the worry that some outside event happens that suddenly tightens 427 00:23:37,416 --> 00:23:41,336 Speaker 2: liquidity incredibly, and that causes some difficulty in arranging these 428 00:23:41,436 --> 00:23:41,956 Speaker 2: risk pools. 429 00:23:42,196 --> 00:23:45,036 Speaker 1: Like a financial crisis. You're talking about a financial crisis. 430 00:23:45,336 --> 00:23:49,516 Speaker 3: Exactly, exactly. Shifting regulatory risks are always a worry. 431 00:23:50,076 --> 00:23:53,536 Speaker 2: So one of the things that actually led us to 432 00:23:54,856 --> 00:23:59,036 Speaker 2: get into that coastal insurance business was the law changes 433 00:23:59,076 --> 00:24:02,586 Speaker 2: that were enacted in Florida. The way things were set 434 00:24:02,766 --> 00:24:07,626 Speaker 2: up prior was that litigation costs were going out of control. 435 00:24:07,646 --> 00:24:11,146 Speaker 2: And there's a whole discussion we can have separately, but 436 00:24:11,186 --> 00:24:15,266 Speaker 2: basically the laws made it very difficult for insurers to 437 00:24:15,936 --> 00:24:18,876 Speaker 2: essentially deal with the litigation risk that was spiking out 438 00:24:18,916 --> 00:24:21,786 Speaker 2: of control. So Florida was you know, compared to its 439 00:24:21,846 --> 00:24:25,846 Speaker 2: percentage of home values insured was much, much, much higher 440 00:24:26,006 --> 00:24:29,886 Speaker 2: on the litigation percentage. Then reforms were made and those 441 00:24:29,926 --> 00:24:33,006 Speaker 2: reforms have been excellent and they have brought balance back 442 00:24:33,026 --> 00:24:35,966 Speaker 2: and actually brought a lot more carriers re-entering the market. 443 00:24:36,006 --> 00:24:38,126 Speaker 2: So sometimes as you, you know, we're going back again, 444 00:24:38,226 --> 00:24:40,826 Speaker 2: back to climate risk, climate risk is one component. There 445 00:24:40,896 --> 00:24:44,616 Speaker 2: are a lot of other social risks that insurers have 446 00:24:44,696 --> 00:24:48,466 Speaker 2: to bear with And as laws kind of change around this, 447 00:24:48,506 --> 00:24:53,526 Speaker 2: it can change your risk of litigation or just operational costs. 448 00:24:53,946 --> 00:24:56,886 Speaker 1: Give me the happy story briefly. I'm going to tell you, 449 00:24:56,946 --> 00:24:58,846 Speaker 1: I want to save a few minutes for a lightning round. 450 00:24:58,886 --> 00:25:00,926 Speaker 1: So give me the like one minute happy story. 451 00:25:01,346 --> 00:25:05,526 Speaker 2: The happy story is that we are, especially with the 452 00:25:05,606 --> 00:25:08,956 Speaker 2: advent of AI, we are able to scale across more 453 00:25:09,016 --> 00:25:13,896 Speaker 2: geographies and more types of risks faster and faster, launching 454 00:25:13,956 --> 00:25:15,796 Speaker 2: more products much more rapidly. 455 00:25:16,696 --> 00:25:19,916 Speaker 3: And that to me, if you can. 456 00:25:19,836 --> 00:25:25,256 Speaker 2: Incorporate the AI story with the capital story, you now 457 00:25:25,316 --> 00:25:28,076 Speaker 2: have a machine that can really start to address these 458 00:25:28,156 --> 00:25:30,556 Speaker 2: gaps in a much more scalable way. 459 00:25:31,156 --> 00:25:32,896 Speaker 3: That to me is very, very exciting. 460 00:25:37,356 --> 00:25:39,156 Speaker 1: We'll be back in a minute with the lightning round. 461 00:25:49,886 --> 00:25:53,506 Speaker 1: Let's finish with the lightning round. You mentioned in another 462 00:25:53,586 --> 00:25:57,516 Speaker 1: interview that you spent part of your early childhood in 463 00:25:57,556 --> 00:26:00,236 Speaker 1: the jungles of southern India where your dad worked for 464 00:26:00,256 --> 00:26:04,316 Speaker 1: the Forest Service. And I'm curious, what is a memory 465 00:26:04,356 --> 00:26:07,546 Speaker 1: you have from there? What is something that sticks in 466 00:26:07,566 --> 00:26:07,866 Speaker 1: your mind? 467 00:26:09,426 --> 00:26:11,846 Speaker 2: So one memory I had was one of the things 468 00:26:11,886 --> 00:26:15,406 Speaker 2: he was in charge of was managing a snake park. 469 00:26:15,966 --> 00:26:18,216 Speaker 1: And so A snake park? 470 00:26:18,236 --> 00:26:18,906 Speaker 3: Every weekend or two. 471 00:26:18,916 --> 00:26:19,536 Speaker 1: A snake park? 472 00:26:19,556 --> 00:26:21,296 Speaker 3: Yeah, a snake park. And I'll get to why. 473 00:26:21,336 --> 00:26:24,896 Speaker 2: So it's basically a park where they would have giant 474 00:26:25,096 --> 00:26:29,956 Speaker 2: pits of venomous snakes. And I remember this very starkly 475 00:26:29,976 --> 00:26:33,316 Speaker 2: as a kid. And basically, the snake venom is used 476 00:26:33,336 --> 00:26:36,536 Speaker 2: to create anti-venom. And India used to, at least, have 477 00:26:36,616 --> 00:26:38,536 Speaker 2: one of the highest rates of snake venom deaths in 478 00:26:38,556 --> 00:26:40,996 Speaker 2: the world. And producing anti-venom was a big deal. 479 00:26:41,296 --> 00:26:41,976 Speaker 3: So I used to go. 480 00:26:41,896 --> 00:26:44,836 Speaker 2: There every weekend or every other weekend. I have many 481 00:26:44,876 --> 00:26:47,996 Speaker 2: memories of seeing these. crazy snakes all over. 482 00:26:48,156 --> 00:26:49,556 Speaker 1: How do you feel about snakes now? 483 00:26:51,526 --> 00:26:57,966 Speaker 2: I have a combination of fascination and fear that, you know, 484 00:26:58,566 --> 00:27:02,666 Speaker 2: I find them fascinating animals. And if you actually look 485 00:27:02,726 --> 00:27:05,996 Speaker 2: at the structure of snake venom, it is a, they 486 00:27:06,036 --> 00:27:08,956 Speaker 2: are some of the most interesting chemicals that nature has 487 00:27:09,016 --> 00:27:12,076 Speaker 2: ever produced. And it's a huge variety of them. So 488 00:27:12,816 --> 00:27:15,236 Speaker 2: I grew up reading a lot about snakes and stuff, 489 00:27:15,296 --> 00:27:18,996 Speaker 2: as you might imagine. So anyway, they're fascinating animals, but 490 00:27:19,036 --> 00:27:20,506 Speaker 2: also quite frightening. 491 00:27:21,146 --> 00:27:23,906 Speaker 1: What is one thing that running Arbol has taught you 492 00:27:23,986 --> 00:27:24,686 Speaker 1: about risk? 493 00:27:25,786 --> 00:27:29,106 Speaker 2: So before I started Arbol, I was in liquid markets. 494 00:27:29,466 --> 00:27:34,066 Speaker 2: I buy futures contracts on oil. Okay, today things changed. 495 00:27:34,746 --> 00:27:35,506 Speaker 2: I can sell out of it. 496 00:27:37,146 --> 00:27:37,426 Speaker 3: Arbol. 497 00:27:38,396 --> 00:27:42,716 Speaker 2: has been a masterclass on managing risk on so many 498 00:27:42,856 --> 00:27:46,976 Speaker 2: different levels. One is that the actual risk are both right. 499 00:27:47,176 --> 00:27:50,046 Speaker 2: Like when you write an insurance contract or when you 500 00:27:50,086 --> 00:27:52,686 Speaker 2: do a weather derivative, even if you're doing it on 501 00:27:52,726 --> 00:27:56,486 Speaker 2: behalf of someone else, that risk is very unlikely to 502 00:27:56,526 --> 00:27:59,526 Speaker 2: be transferable. Once in a while, it can be, but 503 00:27:59,606 --> 00:28:00,806 Speaker 2: you are stuck with that thing. 504 00:28:01,396 --> 00:28:03,536 Speaker 1: It's not a product that people are trading all day, 505 00:28:03,596 --> 00:28:04,936 Speaker 1: every day, like commodities. 506 00:28:04,976 --> 00:28:09,046 Speaker 2: No. Like we go into this hurricane season with, okay, well, 507 00:28:09,186 --> 00:28:11,366 Speaker 2: if there's a hurricane, we are going to take losses. 508 00:28:11,746 --> 00:28:14,536 Speaker 2: There is no getting out of it pretty much, right? 509 00:28:14,836 --> 00:28:19,036 Speaker 2: So that kind of risk, it makes you a lot 510 00:28:19,156 --> 00:28:21,916 Speaker 2: sharper with how you model risk, how you price risk 511 00:28:21,956 --> 00:28:24,796 Speaker 2: and how you structure risk and how much you offload 512 00:28:24,836 --> 00:28:28,236 Speaker 2: at the beginning of the season so that you're very 513 00:28:28,276 --> 00:28:31,996 Speaker 2: well covered. And it's a very much greater scale of 514 00:28:32,036 --> 00:28:34,836 Speaker 2: challenge than to me in liquid markets where you could 515 00:28:34,856 --> 00:28:38,596 Speaker 2: get out of risk. And then the biggest thing is 516 00:28:39,096 --> 00:28:42,626 Speaker 2: when you are working inside of a larger institution, a 517 00:28:42,806 --> 00:28:45,226 Speaker 2: lot of things you don't realize are taken care of 518 00:28:45,326 --> 00:28:50,986 Speaker 2: by other divisions. Here, you have obviously the regular wildfire, hurricane, 519 00:28:51,046 --> 00:28:54,606 Speaker 2: crop risk sitting on your head. But then you have 520 00:28:54,996 --> 00:28:59,236 Speaker 2: legal risk, regulatory risk. You have business strategy risk, like 521 00:28:59,336 --> 00:29:02,216 Speaker 2: where do you go next? Which products do you invest? 522 00:29:03,016 --> 00:29:05,346 Speaker 2: You have only a limited amount of capital invested. to 523 00:29:06,246 --> 00:29:10,246 Speaker 2: spend on building different divisions or different areas. Do you 524 00:29:10,286 --> 00:29:12,766 Speaker 2: go into emerging markets? Do you go into Europe? Do 525 00:29:12,786 --> 00:29:16,556 Speaker 2: you go deeper in the US? These are decisions and 526 00:29:16,616 --> 00:29:20,076 Speaker 2: risks that you have to handle day in and day out, 527 00:29:20,116 --> 00:29:23,156 Speaker 2: whereas in a larger firm, a lot of things are 528 00:29:23,236 --> 00:29:24,556 Speaker 2: outsourced to other divisions. 529 00:29:26,136 --> 00:29:29,036 Speaker 1: Last one. What's one thing you learned working for Ken 530 00:29:29,076 --> 00:29:33,206 Speaker 1: Griffin at Citadel, the hedge fund? And is he scary? 531 00:29:33,256 --> 00:29:34,786 Speaker 1: That's really what I want to know. Is he a 532 00:29:34,806 --> 00:29:35,326 Speaker 1: scary guy? 533 00:29:35,406 --> 00:29:36,266 Speaker 3: No comment. 534 00:29:36,286 --> 00:29:45,606 Speaker 2: I actually emailed him recently saying I learned a ton 535 00:29:45,826 --> 00:29:48,706 Speaker 2: working at Citadel. I learned a lot about process, I 536 00:29:48,726 --> 00:29:54,336 Speaker 2: would say. that obsession with a clean process and continuous 537 00:29:54,396 --> 00:29:59,636 Speaker 2: refinement of inefficiencies and blind spots that build up in 538 00:29:59,676 --> 00:30:03,496 Speaker 2: a company. And he's a master at kind of being 539 00:30:03,776 --> 00:30:11,576 Speaker 2: on top of details that are just Unbelievable. For a 540 00:30:11,596 --> 00:30:14,756 Speaker 2: man who manages his scale of businesses, the amount of 541 00:30:14,796 --> 00:30:21,406 Speaker 2: detail he can navigate within each mini area of the 542 00:30:21,446 --> 00:30:24,886 Speaker 2: company is very impressive. And I learned a lot on 543 00:30:24,926 --> 00:30:29,306 Speaker 2: that aspect, like just how many process failures you can 544 00:30:29,346 --> 00:30:33,236 Speaker 2: avoid by just being so utterly focused on all these details. 545 00:30:34,196 --> 00:30:36,496 Speaker 1: I know you got to go. Thank you for your time. 546 00:30:37,556 --> 00:30:40,476 Speaker 3: I appreciate it. This was fun. 547 00:30:47,896 --> 00:30:51,886 Speaker 1: Sid Jha is the founder and CEO of Arbol. One 548 00:30:51,926 --> 00:30:54,426 Speaker 1: quick note, we're going to be taking a break for 549 00:30:54,446 --> 00:30:56,486 Speaker 1: a couple of weeks after this show, but we'll be 550 00:30:56,546 --> 00:31:00,886 Speaker 1: back soon. Today's show was produced by Gabriel Hunter Chang, 551 00:31:01,326 --> 00:31:04,726 Speaker 1: engineered by Sarah Bruguier, and our showrunner and editor is 552 00:31:04,786 --> 00:31:06,586 Speaker 1: Jake Harper. I'm Jacob Goldstein.