1 00:00:15,396 --> 00:00:33,146 Speaker 1: Pushkin. I'm Jacob Goldstein, and this is What's Your Problem? 2 00:00:33,286 --> 00:00:36,846 Speaker 1: My guest today is Saif Khawaja. He's the co-founder and 3 00:00:36,866 --> 00:00:40,346 Speaker 1: CEO of a company called Shinkai. Saif's problem is this. 4 00:00:40,926 --> 00:00:43,886 Speaker 1: Can we find a way for industrial fishermen to kill 5 00:00:44,016 --> 00:00:46,516 Speaker 1: fish and that is more humane than the way the 6 00:00:46,556 --> 00:00:50,296 Speaker 1: fish are currently killed, and is still economical, still works 7 00:00:50,356 --> 00:00:53,566 Speaker 1: in the market. Saif didn't start out as a fish guy. 8 00:00:53,956 --> 00:00:57,146 Speaker 1: He was a college student studying engineering, and he happened 9 00:00:57,166 --> 00:01:00,366 Speaker 1: to read an essay by the philosopher Peter Singer. The 10 00:01:00,466 --> 00:01:04,486 Speaker 1: essay is called If Fish Could Scream. And in the essay, 11 00:01:04,626 --> 00:01:07,716 Speaker 1: Singer argues that the way fish are typically killed now 12 00:01:08,176 --> 00:01:12,676 Speaker 1: is slow and cruel. In particular, it's common for fish 13 00:01:12,736 --> 00:01:15,726 Speaker 1: to be left to slowly suffocate after being pulled out 14 00:01:15,746 --> 00:01:19,046 Speaker 1: of the water. After Safe Red Singer's essay, he started 15 00:01:19,106 --> 00:01:22,126 Speaker 1: looking more deeply into the subject, and he found a 16 00:01:22,186 --> 00:01:26,456 Speaker 1: traditional technique used by Japanese fishermen that involves putting a 17 00:01:26,556 --> 00:01:29,716 Speaker 1: spike through the fish's brain to quickly kill the fish. 18 00:01:30,496 --> 00:01:34,456 Speaker 1: This is called ikejime, and many people believe that besides 19 00:01:34,516 --> 00:01:37,586 Speaker 1: killing the fish more quickly, this technique also makes the 20 00:01:37,616 --> 00:01:42,746 Speaker 1: fish meat last longer and taste better. Saif started his company, Shinkai, 21 00:01:43,106 --> 00:01:46,246 Speaker 1: to try and figure out how to turn Ikejime into 22 00:01:46,406 --> 00:01:50,326 Speaker 1: an industrial process, to use technology to build a machine 23 00:01:50,666 --> 00:01:52,856 Speaker 1: that can be used on fishing boats to kill fish 24 00:01:52,936 --> 00:01:57,176 Speaker 1: quickly rather than let them suffocate slowly. It's early still, 25 00:01:57,316 --> 00:02:00,296 Speaker 1: but the signs are promising. The company has built a 26 00:02:00,296 --> 00:02:04,086 Speaker 1: machine that's currently on roughly 20 fishing boats, and they're 27 00:02:04,116 --> 00:02:07,026 Speaker 1: on track to sell several million pounds of fish this 28 00:02:07,086 --> 00:02:10,396 Speaker 1: year to restaurants and grocery stores around the United States. 29 00:02:11,096 --> 00:02:14,256 Speaker 1: To start, I asked Saif about that essay, If Fish 30 00:02:14,296 --> 00:02:18,216 Speaker 1: Could Scream, that inspired him to start the company. What 31 00:02:18,256 --> 00:02:21,016 Speaker 1: was it like for you to read this essay? What 32 00:02:21,056 --> 00:02:23,576 Speaker 1: happened in your mind, in your heart, when you read 33 00:02:23,616 --> 00:02:24,076 Speaker 1: this essay? 34 00:02:24,536 --> 00:02:28,796 Speaker 2: Yeah, it's interesting. So to talk through my emotions a 35 00:02:28,796 --> 00:02:30,556 Speaker 2: little bit, I'll take you back to a story when 36 00:02:30,606 --> 00:02:32,706 Speaker 2: I was a young kid. And, you know, I didn't 37 00:02:32,746 --> 00:02:33,966 Speaker 2: grow up on a farm, but I had a lot 38 00:02:34,006 --> 00:02:37,086 Speaker 2: of family members that had farms, and grew up in 39 00:02:37,106 --> 00:02:39,166 Speaker 2: the Middle East, grew up Muslim. One time when I 40 00:02:39,206 --> 00:02:42,046 Speaker 2: was a child, I noticed two goats by the side 41 00:02:42,066 --> 00:02:44,826 Speaker 2: of our home and decided to become friends with them 42 00:02:44,866 --> 00:02:47,786 Speaker 2: because I love animals. And so for about a week, 43 00:02:47,926 --> 00:02:50,676 Speaker 2: I was feeding them lettuce and petting them, and I 44 00:02:50,716 --> 00:02:53,476 Speaker 2: gave them names. And then at the end of the week, 45 00:02:54,056 --> 00:02:56,256 Speaker 2: they were gone, and I was having lamb for dinner. 46 00:02:56,276 --> 00:02:59,256 Speaker 2: And I didn't really realize until I asked where the 47 00:02:59,516 --> 00:03:02,966 Speaker 2: lamb went, and, hey, here's a lamb or a goat. But, 48 00:03:03,246 --> 00:03:05,266 Speaker 2: you know, once I heard, I was crying and I 49 00:03:05,286 --> 00:03:07,686 Speaker 2: was sad and I felt bad. I built this emotional 50 00:03:07,726 --> 00:03:10,776 Speaker 2: relationship with the animals. So when I read that essay, 51 00:03:10,806 --> 00:03:13,516 Speaker 2: it kind of gave me some emotions like that where, 52 00:03:14,016 --> 00:03:16,656 Speaker 2: you know, I think I was jarred a little bit 53 00:03:16,696 --> 00:03:19,176 Speaker 2: by the, like, lack of empathy, the fact that I'd 54 00:03:19,196 --> 00:03:21,896 Speaker 2: created an emotional relationship with an animal that other people 55 00:03:21,986 --> 00:03:24,046 Speaker 2: didn't have that same emotional connection with. 56 00:03:24,606 --> 00:03:27,006 Speaker 1: That's how it is growing up, right? When you grow up, you... 57 00:03:27,686 --> 00:03:30,566 Speaker 1: When you're a little kid, you understand things like, oh, 58 00:03:30,686 --> 00:03:33,096 Speaker 1: I like that animal, and we just killed it. And 59 00:03:33,136 --> 00:03:36,316 Speaker 1: it probably didn't want to die. But then you're sort of, 60 00:03:37,076 --> 00:03:38,576 Speaker 1: you live in the world, and there's a set of 61 00:03:38,656 --> 00:03:41,296 Speaker 1: norms in the world, and you kind of take the 62 00:03:41,336 --> 00:03:44,456 Speaker 1: norms for the most part, right? And you, if everybody 63 00:03:44,476 --> 00:03:46,776 Speaker 1: around you eats meat, you probably eat meat and get 64 00:03:46,876 --> 00:03:48,866 Speaker 1: over the fact that you didn't want your friend the 65 00:03:48,906 --> 00:03:52,946 Speaker 1: goat to die. So you read this essay, and you 66 00:03:52,966 --> 00:03:55,846 Speaker 1: just start thinking about this issue of fish suffering, right? 67 00:03:57,146 --> 00:04:00,616 Speaker 1: It's not the killing. It's the fact that it's the 68 00:04:00,776 --> 00:04:04,696 Speaker 1: asphyxiation that is a slow, terrible process. And if we 69 00:04:04,736 --> 00:04:07,376 Speaker 1: could kill the fish without that, that would be better 70 00:04:07,436 --> 00:04:09,476 Speaker 1: for the fish, just at a minimum. 71 00:04:09,516 --> 00:04:11,606 Speaker 2: Well, the fish are going to be killed either way, right? 72 00:04:11,716 --> 00:04:14,226 Speaker 1: They're going to die, but we can agree that how 73 00:04:14,266 --> 00:04:17,766 Speaker 1: the animal dies matters, right? And so you're sort of 74 00:04:17,806 --> 00:04:19,606 Speaker 1: down this rabbit hole, and you find this, it's a 75 00:04:19,666 --> 00:04:25,246 Speaker 1: kind of manual, very artisanal, traditional Japanese method, right? This 76 00:04:25,306 --> 00:04:28,546 Speaker 1: is not what they're doing in industrial fisheries in Japan, right? 77 00:04:28,566 --> 00:04:31,586 Speaker 1: This is like a guy with a metal spike putting 78 00:04:31,626 --> 00:04:33,126 Speaker 1: it through one fish's head, correct? 79 00:04:33,426 --> 00:04:36,116 Speaker 2: Well, there are some examples of high-volume fisheries where they 80 00:04:36,126 --> 00:04:38,916 Speaker 2: will do this, but for farms. But for the most part, no. 81 00:04:39,036 --> 00:04:41,676 Speaker 2: I agree with you. I just want to be, for 82 00:04:41,696 --> 00:04:45,796 Speaker 2: the listeners out there, there are some counterexamples, but as 83 00:04:45,836 --> 00:04:47,076 Speaker 2: a whole, it's simpler. 84 00:04:47,576 --> 00:04:49,836 Speaker 1: So there are some people who have tried to automate 85 00:04:49,896 --> 00:04:50,996 Speaker 1: this before you. 86 00:04:51,796 --> 00:04:54,276 Speaker 2: People have tried since the 1970s. No one has done 87 00:04:54,316 --> 00:04:59,516 Speaker 2: it successfully, necessarily. But in the industrial cases, it's mostly, 88 00:04:59,936 --> 00:05:02,156 Speaker 2: there's like a team of six to eight people I 89 00:05:02,156 --> 00:05:03,896 Speaker 2: have an industrial fishery sitting there doing this by hand. 90 00:05:05,076 --> 00:05:08,816 Speaker 1: Okay. So where do you get the idea, I'm going 91 00:05:08,836 --> 00:05:11,976 Speaker 1: to build a machine to kill fish more humanely? 92 00:05:12,836 --> 00:05:14,516 Speaker 2: You know, for this part, I wish I had something 93 00:05:14,556 --> 00:05:19,486 Speaker 2: more romantic than thinking, why not solve the problem directly? Yeah. Again, 94 00:05:19,506 --> 00:05:22,546 Speaker 2: which is an engineering response and fix the root cause, 95 00:05:22,606 --> 00:05:25,346 Speaker 2: which is build something that can actually do that process. 96 00:05:26,046 --> 00:05:29,616 Speaker 1: Yeah. it is this interesting middle ground, right? Because one 97 00:05:29,676 --> 00:05:34,196 Speaker 1: response to reading that Peter Singer essay is to stop 98 00:05:34,236 --> 00:05:36,576 Speaker 1: eating fish, say, or to say, I'm going to try 99 00:05:36,596 --> 00:05:39,616 Speaker 1: and get other people to stop eating fish. But there's 100 00:05:39,656 --> 00:05:44,216 Speaker 1: a sort of narrow, a middle ground where you're landing, 101 00:05:44,236 --> 00:05:46,676 Speaker 1: which is, it's okay to kill fish to eat them, 102 00:05:47,076 --> 00:05:51,586 Speaker 1: but we shouldn't make them suffer gratuitously. Like, did you 103 00:05:51,646 --> 00:05:53,526 Speaker 1: immediately go to that middle ground? 104 00:05:54,046 --> 00:05:59,536 Speaker 2: I did. I do believe that some genetics are designed to, 105 00:05:59,816 --> 00:06:01,416 Speaker 2: they've co-evolved with eating meat. 106 00:06:01,936 --> 00:06:06,276 Speaker 1: So, okay. Now you're on the path of building the machine. 107 00:06:06,296 --> 00:06:07,636 Speaker 2: Yeah. 108 00:06:08,416 --> 00:06:12,276 Speaker 1: Tell me about the nail and the plastic fish. Yeah. 109 00:06:13,716 --> 00:06:21,966 Speaker 2: So, the idea was I should show conceptually the idea 110 00:06:22,046 --> 00:06:24,786 Speaker 2: that this could be automated in some form. And so, 111 00:06:25,916 --> 00:06:29,286 Speaker 2: Bought a plastic fish, had a 3D printer in my 112 00:06:29,306 --> 00:06:32,026 Speaker 2: dorm room, taped a nail to the end of it, 113 00:06:32,706 --> 00:06:36,146 Speaker 2: and using some open source computer vision tools, not, again, 114 00:06:36,166 --> 00:06:38,126 Speaker 2: super technical, but enough to be able to show it, 115 00:06:38,806 --> 00:06:41,536 Speaker 2: sent this video to Y Combinator of this plastic fish. 116 00:06:42,456 --> 00:06:46,416 Speaker 2: And then I sent a cold email to the general partner, 117 00:06:46,476 --> 00:06:50,886 Speaker 2: Jared Friedman, and interviewed and got in and took the 118 00:06:50,926 --> 00:06:53,056 Speaker 2: company full time in early 2022. 119 00:06:53,056 --> 00:06:54,416 Speaker 1: Got into Y Combinator. 120 00:06:54,656 --> 00:06:57,056 Speaker 2: Yeah, that was the impetus for halving the capital to 121 00:06:57,096 --> 00:06:58,056 Speaker 2: really take it full-time. 122 00:06:59,136 --> 00:07:01,916 Speaker 1: So tell me how it works. There's a boat right now. 123 00:07:01,936 --> 00:07:06,186 Speaker 1: There's boats out in the water doing this. Probably at 124 00:07:06,226 --> 00:07:10,126 Speaker 1: this minute, certainly today. Just tell me what are those boats. 125 00:07:10,206 --> 00:07:12,526 Speaker 1: Tell me what's happening. Like, wherever you want it to be, 126 00:07:12,566 --> 00:07:14,346 Speaker 1: whatever kind of fish. But give me something specific. 127 00:07:14,426 --> 00:07:16,996 Speaker 2: Yeah, let's throw out, like, our bread and butter is 128 00:07:17,156 --> 00:07:20,786 Speaker 2: a trap boat that's catching black cod. And so traps, 129 00:07:21,086 --> 00:07:23,106 Speaker 2: think of them as an analog to the lobster pots 130 00:07:23,526 --> 00:07:27,226 Speaker 2: where they're either long chain of nets or cages that 131 00:07:27,246 --> 00:07:29,466 Speaker 2: the fish will swim into. And then they'll pull them up, 132 00:07:29,886 --> 00:07:33,296 Speaker 2: you know, each net by net. And so the fish 133 00:07:33,316 --> 00:07:34,956 Speaker 2: are then brought onto the deck of the boat. There's 134 00:07:34,976 --> 00:07:37,516 Speaker 2: normally like a pool of water where they're sitting. And 135 00:07:37,556 --> 00:07:41,396 Speaker 2: then they are put into the machine today by hand, 136 00:07:41,416 --> 00:07:43,576 Speaker 2: one by one. And so each fish will go in. Okay. 137 00:07:44,136 --> 00:07:46,676 Speaker 2: We have a computer vision layer that, you know, from 138 00:07:46,716 --> 00:07:49,176 Speaker 2: a camera that's running constantly that will scan the fish. Okay. 139 00:07:49,706 --> 00:07:52,186 Speaker 2: figure out the species and then, okay, if it's this species, 140 00:07:52,236 --> 00:07:54,816 Speaker 2: then this is a cutting path for piercing the brain, 141 00:07:54,916 --> 00:07:57,396 Speaker 2: for cutting the gills. And then we have mechanical systems 142 00:07:57,416 --> 00:08:01,316 Speaker 2: that will do those while holding fish down in, you know, 143 00:08:01,336 --> 00:08:03,416 Speaker 2: our own method that we developed. The fish will come 144 00:08:03,436 --> 00:08:06,056 Speaker 2: straight out and they go out into a half ice, 145 00:08:06,096 --> 00:08:08,606 Speaker 2: half water slurry. And so the heart, which is still 146 00:08:08,686 --> 00:08:10,646 Speaker 2: active after brain death, will pump the blood out of 147 00:08:10,666 --> 00:08:12,986 Speaker 2: the meat. And then the fish are then moved into 148 00:08:13,006 --> 00:08:15,406 Speaker 2: the hold once the bleeding process is complete. 149 00:08:16,586 --> 00:08:17,786 Speaker 1: What's the machine look like? 150 00:08:18,986 --> 00:08:21,716 Speaker 2: So we say it's like a refrigerator. In practical reality, 151 00:08:21,736 --> 00:08:24,176 Speaker 2: it's basically this metal box, about two and a half 152 00:08:24,216 --> 00:08:28,356 Speaker 2: by three feet for the bigger one, and then about 60% 153 00:08:28,356 --> 00:08:32,116 Speaker 2: smaller for the ones that are going in salmon boats. Okay. 154 00:08:32,366 --> 00:08:35,646 Speaker 2: And it is, for all intents and purposes, a black box. 155 00:08:35,826 --> 00:08:37,786 Speaker 2: We put fish in, flop it around, it comes out limp. 156 00:08:38,466 --> 00:08:42,746 Speaker 2: The doors are on because we need the computer vision 157 00:08:43,266 --> 00:08:45,296 Speaker 2: to be consistent, and so we have an internal lighting system, 158 00:08:45,336 --> 00:08:46,336 Speaker 2: and so that blocks out the sun. 159 00:08:46,356 --> 00:08:46,436 Speaker 1: Okay. 160 00:08:46,856 --> 00:08:49,276 Speaker 2: Because if you have different lighting systems, it makes the 161 00:08:49,316 --> 00:08:50,796 Speaker 2: AI less reliable. 162 00:08:51,196 --> 00:08:52,206 Speaker 1: Makes sense. Makes sense. 163 00:08:52,826 --> 00:08:55,086 Speaker 2: And then, yeah, the fish comes straight out. So you 164 00:08:55,106 --> 00:08:59,406 Speaker 2: can hear the process happening, of course. But outside that, 165 00:08:59,746 --> 00:09:03,126 Speaker 2: the producers and the harvesters don't see what's happening. 166 00:09:03,706 --> 00:09:06,086 Speaker 1: Does this system change the number of fish that a 167 00:09:06,146 --> 00:09:08,426 Speaker 1: boat can catch and kill in a day? 168 00:09:08,766 --> 00:09:11,356 Speaker 2: So the way that fishing is regulated is through a 169 00:09:11,376 --> 00:09:14,376 Speaker 2: quota system for the most part. And so fishermen have 170 00:09:14,416 --> 00:09:15,596 Speaker 2: a fixed amount of fish that can come out of 171 00:09:15,616 --> 00:09:18,806 Speaker 2: the water. And the idea here for the reason we 172 00:09:18,826 --> 00:09:20,376 Speaker 2: do this model is that the fishermen get paid a 173 00:09:20,406 --> 00:09:23,476 Speaker 2: little bit more, and so they can draw more value 174 00:09:23,536 --> 00:09:25,896 Speaker 2: from the quota that they're allocated. So the amount of 175 00:09:25,916 --> 00:09:28,916 Speaker 2: fish that they fish does not change. That is also 176 00:09:28,956 --> 00:09:30,856 Speaker 2: why we do not work with every single vessel, because 177 00:09:30,896 --> 00:09:33,626 Speaker 2: there are some vessels, like these massive commodity boats, that 178 00:09:34,266 --> 00:09:36,146 Speaker 2: in theory, if we were to work with them, they'd 179 00:09:36,166 --> 00:09:38,326 Speaker 2: have to slow down operations, and we haven't built systems 180 00:09:38,366 --> 00:09:40,426 Speaker 2: to account for the massive volume. 181 00:09:41,176 --> 00:09:44,696 Speaker 1: So I want to talk about a little bit about 182 00:09:44,776 --> 00:09:49,236 Speaker 1: the sort of development. And in particular, there's this paper 183 00:09:49,256 --> 00:09:55,416 Speaker 1: that was published earlier this year. One of your engineers, 184 00:09:55,456 --> 00:09:59,146 Speaker 1: I guess, was a co-author, right, of studying this device 185 00:09:59,186 --> 00:10:04,106 Speaker 1: you built to automate killing fish in this more human 186 00:10:04,146 --> 00:10:08,686 Speaker 1: way and comparing it to the sort of manual Ikejime and, like, asphyxiation, 187 00:10:08,706 --> 00:10:09,086 Speaker 1: the tradition. 188 00:10:09,396 --> 00:10:09,656 Speaker 2: Right? 189 00:10:10,836 --> 00:10:14,736 Speaker 1: And let me say thank you for doing that paper. Like, 190 00:10:14,796 --> 00:10:17,286 Speaker 1: it's helpful. You know, people make all these claims, and 191 00:10:17,306 --> 00:10:18,926 Speaker 1: then when there's, like, a paper, it's like, oh, great. 192 00:10:19,046 --> 00:10:21,686 Speaker 1: It's like we can really talk about, at least at 193 00:10:21,706 --> 00:10:23,186 Speaker 1: the time you were doing this research, what worked and 194 00:10:23,206 --> 00:10:26,446 Speaker 1: what didn't. And, I mean, one of the clear things 195 00:10:26,506 --> 00:10:29,606 Speaker 1: that they said in the paper was that I'll just 196 00:10:29,666 --> 00:10:32,326 Speaker 1: read it. Ikejime had said, should be preceded by a 197 00:10:32,406 --> 00:10:36,846 Speaker 1: stunning step ensuring immediate loss of sensibility. Because the fish 198 00:10:36,866 --> 00:10:40,086 Speaker 1: didn't always just die as fast as you wanted them to, essentially. Like, 199 00:10:40,926 --> 00:10:43,706 Speaker 1: do you do that now? Did you add the stunning step? 200 00:10:45,166 --> 00:10:49,046 Speaker 2: So, we did not. And this was a point of 201 00:10:49,066 --> 00:10:51,616 Speaker 2: disagreement that we had with some of the philosophies around 202 00:10:51,626 --> 00:10:54,546 Speaker 2: how the research came out versus that. First of all, 203 00:10:54,576 --> 00:10:58,236 Speaker 2: that paper was from early 2023 and from the studies 204 00:10:58,246 --> 00:11:00,296 Speaker 2: that just didn't get published till later. And we've made 205 00:11:00,316 --> 00:11:02,636 Speaker 2: a lot of improvements into the machine and process and 206 00:11:03,336 --> 00:11:05,756 Speaker 2: particularly the speed, right? That's the most important part. And 207 00:11:05,776 --> 00:11:08,036 Speaker 2: so back then, you know, we were doing a fish like, 208 00:11:09,236 --> 00:11:13,276 Speaker 2: I don't know, once every, don't quote me, hard quote 209 00:11:13,296 --> 00:11:14,876 Speaker 2: me on this, but I'd say it's like around once 210 00:11:14,896 --> 00:11:17,436 Speaker 2: every 20 to 30, maybe even 40 seconds per fish. 211 00:11:17,836 --> 00:11:18,796 Speaker 2: That's too long, right? Yeah. 212 00:11:19,376 --> 00:11:21,296 Speaker 1: Because most of you haul up a bunch of fish 213 00:11:21,336 --> 00:11:23,116 Speaker 1: and they're all just sitting there waiting to get killed 214 00:11:23,156 --> 00:11:26,226 Speaker 1: in the machine. So they're essentially asphyxiating in the traditional way. 215 00:11:26,346 --> 00:11:28,406 Speaker 2: Exactly. And so I think what was clear is that 216 00:11:28,766 --> 00:11:32,066 Speaker 2: there was a potential for the process, but the speed 217 00:11:32,086 --> 00:11:34,286 Speaker 2: needed to be improved for it to really be an 218 00:11:34,306 --> 00:11:38,466 Speaker 2: industrial process that was like humane driven, right? Yeah. And 219 00:11:38,526 --> 00:11:42,026 Speaker 2: the other part to it is sometimes stunning can actually 220 00:11:42,066 --> 00:11:45,246 Speaker 2: be less humane for them because you have to dial 221 00:11:45,266 --> 00:11:47,966 Speaker 2: in a specific voltage for the fish. And for wild fish, 222 00:11:47,986 --> 00:11:50,646 Speaker 2: it's basically impossible to do that because you're catching fish that, 223 00:11:50,866 --> 00:11:53,316 Speaker 2: you know, say a black cod, the smallest you're getting 224 00:11:53,356 --> 00:11:55,756 Speaker 2: is like two pounds that you can pull out the water, 225 00:11:56,056 --> 00:11:58,356 Speaker 2: and the largest is like 15, right? And so that 226 00:11:58,376 --> 00:12:02,476 Speaker 2: breadth means a very broad voltage range that you need 227 00:12:03,436 --> 00:12:07,126 Speaker 2: to be flopping between for the stunner. And so that 228 00:12:07,146 --> 00:12:09,166 Speaker 2: alone means that, you know, if you go too high, 229 00:12:09,206 --> 00:12:10,886 Speaker 2: you're going to damage the meat in a way that 230 00:12:10,906 --> 00:12:13,086 Speaker 2: makes this process kind of redundant. If you go too low, 231 00:12:13,126 --> 00:12:16,106 Speaker 2: you're basically tasing the fish, but it's still conscious and 232 00:12:16,126 --> 00:12:18,866 Speaker 2: going to continue to flop around. And so what we'd 233 00:12:18,886 --> 00:12:21,076 Speaker 2: rather do is just not add an extra part of 234 00:12:21,096 --> 00:12:23,976 Speaker 2: the process, but improve the speed for the fish and 235 00:12:24,016 --> 00:12:26,296 Speaker 2: how fast we can do it. And so now it's 236 00:12:26,316 --> 00:12:28,036 Speaker 2: a couple of seconds to be able to process each 237 00:12:28,056 --> 00:12:31,796 Speaker 2: individual one. So again, this is just our perspective and 238 00:12:32,076 --> 00:12:34,576 Speaker 2: our view on what the best way is to handle 239 00:12:34,596 --> 00:12:39,136 Speaker 2: the fish. And so the data isn't necessarily reflective of 240 00:12:39,556 --> 00:12:41,076 Speaker 2: all the conversations that were happening in the background. 241 00:12:42,096 --> 00:12:44,296 Speaker 1: Sure. No, I mean, like I said, I think it's 242 00:12:44,336 --> 00:12:46,596 Speaker 1: great that you published this paper or that this paper 243 00:12:46,636 --> 00:12:52,896 Speaker 1: was published. I mean, the findings weren't that compelling, right? Like, 244 00:12:53,086 --> 00:12:55,666 Speaker 1: it seemed like for the larger fish, it didn't work 245 00:12:56,666 --> 00:12:59,846 Speaker 1: particularly reliably. And I know it was a prototype. I'm 246 00:12:59,886 --> 00:13:02,206 Speaker 1: just trying to figure out what to make of it, right? Like, 247 00:13:03,346 --> 00:13:05,546 Speaker 1: are you going to do another study? Like, how do 248 00:13:05,586 --> 00:13:06,996 Speaker 1: I know that it works better now? 249 00:13:08,236 --> 00:13:10,716 Speaker 2: So we've done internal studies and external studies now. We 250 00:13:10,736 --> 00:13:13,426 Speaker 2: just haven't published them yet because the publishing cycle takes 251 00:13:13,446 --> 00:13:15,346 Speaker 2: a very long time. So I'm happy to send you 252 00:13:15,366 --> 00:13:18,566 Speaker 2: data on the latest on what it compares to versus 253 00:13:18,606 --> 00:13:19,346 Speaker 2: handy Kijime. 254 00:13:19,746 --> 00:13:21,706 Speaker 1: So let me ask, how well does it work now? 255 00:13:22,286 --> 00:13:25,016 Speaker 2: So it depends on the criteria of what you're looking at. 256 00:13:25,356 --> 00:13:27,836 Speaker 2: And so we don't have the capacity to look at 257 00:13:27,896 --> 00:13:31,776 Speaker 2: every single fish and put a little EEG thing on 258 00:13:31,796 --> 00:13:35,766 Speaker 2: their brain. But in terms of being able to get 259 00:13:36,496 --> 00:13:38,596 Speaker 2: implied loss of consciousness, which is the fish going limp, 260 00:13:39,036 --> 00:13:41,976 Speaker 2: which is destroying the brain or things like that. You know, 261 00:13:41,996 --> 00:13:45,616 Speaker 2: we're hitting, again, not having the most, you know, like 262 00:13:45,696 --> 00:13:48,256 Speaker 2: up-to-date information with the team from today, but I'd say 263 00:13:48,296 --> 00:13:51,776 Speaker 2: about 90% is like the number that we're hitting. And 264 00:13:51,856 --> 00:13:53,916 Speaker 2: that's with a, you know, five to six second cycle 265 00:13:53,956 --> 00:13:54,696 Speaker 2: time for each machine. 266 00:13:55,556 --> 00:13:58,196 Speaker 1: And that happens like in the ocean, in the real world? 267 00:13:58,576 --> 00:14:01,156 Speaker 2: Yeah, that's in, you know, all the machines are deployed 268 00:14:01,276 --> 00:14:02,996 Speaker 2: on vessels out in the ocean. 269 00:14:03,276 --> 00:14:03,456 Speaker 1: Yeah. 270 00:14:05,496 --> 00:14:05,576 Speaker 2: Um. 271 00:14:06,736 --> 00:14:10,736 Speaker 1: So we've been talking about the, let's call it the 272 00:14:10,776 --> 00:14:12,876 Speaker 1: ethical side or the suffering of the fish. That is 273 00:14:12,896 --> 00:14:16,176 Speaker 1: what we have been talking about. I know you talk 274 00:14:16,196 --> 00:14:19,426 Speaker 1: a lot about the quality of the fish, you know, 275 00:14:19,466 --> 00:14:23,366 Speaker 1: setting aside, do the fish suffer less? Is the fish better? 276 00:14:23,506 --> 00:14:25,026 Speaker 1: I know you make the case that the fish is 277 00:14:25,066 --> 00:14:27,186 Speaker 1: better and there are fancy chefs who agree with you. 278 00:14:28,366 --> 00:14:29,666 Speaker 1: Tell me about that side. 279 00:14:29,686 --> 00:14:32,256 Speaker 2: Yeah, there are a couple of things we can open 280 00:14:32,296 --> 00:14:35,886 Speaker 2: up into here. Quality by itself doesn't mean anything. Much, right? 281 00:14:36,106 --> 00:14:38,166 Speaker 2: Is it quality in terms of shelf life? Is it 282 00:14:38,186 --> 00:14:41,786 Speaker 2: about texture? Is it about flavor? Is it about nutrient density? Right? 283 00:14:41,846 --> 00:14:42,986 Speaker 2: It's a very abstract term. 284 00:14:43,586 --> 00:14:48,046 Speaker 1: I mean, I've seen you mention shelf life before. That's 285 00:14:48,116 --> 00:14:51,316 Speaker 1: another one where, like, has anyone studied that? I looked 286 00:14:51,356 --> 00:14:54,256 Speaker 1: for evidence about shelf life. Is that out there and 287 00:14:54,276 --> 00:14:55,636 Speaker 1: I just didn't see it? I didn't see it. 288 00:14:56,016 --> 00:15:00,316 Speaker 2: Yeah. We do internal rigor mortis studies that we share 289 00:15:00,576 --> 00:15:03,426 Speaker 2: with customers. We haven't released it yet, but that's part 290 00:15:03,446 --> 00:15:05,366 Speaker 2: of the reason we've been able to get Many large 291 00:15:05,386 --> 00:15:06,906 Speaker 2: retailers as they believe the data. 292 00:15:08,546 --> 00:15:12,246 Speaker 1: But when you say rigor mortis, like that's time to 293 00:15:12,306 --> 00:15:13,266 Speaker 1: rigor mortis from death? 294 00:15:13,866 --> 00:15:16,116 Speaker 2: Yes. And that's very well understood to be a proxy 295 00:15:16,146 --> 00:15:18,816 Speaker 2: for the stress that the animal goes through before it 296 00:15:18,976 --> 00:15:22,276 Speaker 2: passes away, as well as in turn a proxy for 297 00:15:22,296 --> 00:15:28,276 Speaker 2: the shelf life and the texture after the rigor mortis 298 00:15:28,466 --> 00:15:29,226 Speaker 2: peak has been reached. 299 00:15:30,646 --> 00:15:33,526 Speaker 1: I'm actually more interested in the sort of outcome measures 300 00:15:33,606 --> 00:15:36,716 Speaker 1: that a chef or a person cooking fish at home 301 00:15:36,736 --> 00:15:37,296 Speaker 1: would care about? 302 00:15:37,336 --> 00:15:39,316 Speaker 2: Well, so chefs are going to be looking at rigor mortis. 303 00:15:39,486 --> 00:15:41,526 Speaker 2: They just don't call it that. Animals, you know, and 304 00:15:41,546 --> 00:15:44,006 Speaker 2: actually meat in general, goes through something that I like 305 00:15:44,026 --> 00:15:47,506 Speaker 2: to draw akin to ripening, in that basically, you know, 306 00:15:47,526 --> 00:15:50,746 Speaker 2: people will hold fish up, and when it has entered rigor, 307 00:15:50,846 --> 00:15:52,606 Speaker 2: it's going to stay basically very limp. And so if 308 00:15:52,626 --> 00:15:54,616 Speaker 2: you hold it in the middle of your palm, it's 309 00:15:54,626 --> 00:15:57,796 Speaker 2: going to be bending on both sides. Chefs and, you know, 310 00:15:57,856 --> 00:16:00,816 Speaker 2: buyers and retail will associate that with freshness. And so 311 00:16:01,156 --> 00:16:03,276 Speaker 2: then when the fish goes into rigor... Even though it's 312 00:16:03,296 --> 00:16:04,876 Speaker 2: sitting in your palm, it should be limp over. It's 313 00:16:04,996 --> 00:16:09,606 Speaker 2: actually completely parallel to the ground. And so it's very rigid. Basically, 314 00:16:09,786 --> 00:16:12,606 Speaker 2: once that starts, when that process happens, the body has 315 00:16:12,666 --> 00:16:15,386 Speaker 2: realized that the animal has passed away. And so after that, 316 00:16:15,406 --> 00:16:18,066 Speaker 2: the deterioration, the kind of enzyme breakdown of the animal, 317 00:16:18,086 --> 00:16:21,096 Speaker 2: the biological breakdown of the animal begins. So it's not 318 00:16:21,146 --> 00:16:22,976 Speaker 2: really a proxy for whether the fish is bad or not. 319 00:16:23,016 --> 00:16:24,976 Speaker 2: It's kind of like the start of the time. And 320 00:16:25,016 --> 00:16:27,076 Speaker 2: so we both delay that time as well as delay, 321 00:16:27,576 --> 00:16:31,246 Speaker 2: in turn, the biological breakdown time. So a suffocated fish 322 00:16:31,546 --> 00:16:33,986 Speaker 2: will go into peak rigor a couple of hours after 323 00:16:34,006 --> 00:16:37,446 Speaker 2: passing away, called two to four hours. Hour process is 324 00:16:37,486 --> 00:16:40,066 Speaker 2: like a 35 to 38 hour time to rigor. 325 00:16:40,486 --> 00:16:43,386 Speaker 1: Compared to two to four for a standard commercial fishery. 326 00:16:43,546 --> 00:16:43,886 Speaker 2: Exactly. 327 00:16:44,366 --> 00:16:46,596 Speaker 1: So that's a big difference. The difference between four hours 328 00:16:46,646 --> 00:16:47,996 Speaker 1: and 35 hours is quite large. 329 00:16:48,126 --> 00:16:48,456 Speaker 2: Exactly. 330 00:16:49,266 --> 00:16:52,116 Speaker 1: What does that mean for people eating that fish? 331 00:16:53,076 --> 00:16:59,376 Speaker 2: So we suggest to retailers that they get anywhere from 332 00:16:59,436 --> 00:17:01,956 Speaker 2: twice to thrice the use of shelf lives. How much 333 00:17:01,976 --> 00:17:04,196 Speaker 2: they pass on to the consumer is not necessarily under 334 00:17:04,236 --> 00:17:06,156 Speaker 2: control and how much we know about, but we know 335 00:17:06,176 --> 00:17:08,696 Speaker 2: that that possibility is there for the consumers to have 336 00:17:09,236 --> 00:17:12,056 Speaker 2: better shelf life. It is also a proxy for texture. 337 00:17:12,216 --> 00:17:14,436 Speaker 2: And so generally, if you're buying a fish that's processed 338 00:17:14,456 --> 00:17:17,556 Speaker 2: this way and it's given to you early enough, you 339 00:17:17,886 --> 00:17:22,966 Speaker 2: will have better texture that's more rigid. There's some, obviously 340 00:17:22,986 --> 00:17:25,606 Speaker 2: this is subjective, but there's some improvement in flavor as well. 341 00:17:26,066 --> 00:17:27,346 Speaker 2: And so these are some of the things that when 342 00:17:27,366 --> 00:17:30,096 Speaker 2: you think about a fish that's been processed in our 343 00:17:30,116 --> 00:17:32,536 Speaker 2: method that you can look for benefits. 344 00:17:35,926 --> 00:17:51,176 Speaker 1: We'll be back in just a minute. Let's talk about 345 00:17:51,216 --> 00:17:57,496 Speaker 1: where you are now. Um, How many of your machines 346 00:17:57,996 --> 00:18:02,986 Speaker 1: are out in boats today or this week or this month? 347 00:18:03,996 --> 00:18:07,266 Speaker 2: Yeah, I'd probably say about 20 vessels, you know, with 348 00:18:07,286 --> 00:18:11,546 Speaker 2: a couple of them having two. And then we're expecting 349 00:18:11,566 --> 00:18:15,576 Speaker 2: to get to 25 peak this year with other robots 350 00:18:15,596 --> 00:18:18,756 Speaker 2: being built because we need spares and test units and so. 351 00:18:18,516 --> 00:18:21,616 Speaker 1: On and so on. And where are they and what 352 00:18:21,656 --> 00:18:26,176 Speaker 1: kind of fish are they? Humanely killed, hopefully humanely killed. 353 00:18:26,456 --> 00:18:30,536 Speaker 2: So they're deployed across the country from Alaska down to California. 354 00:18:31,036 --> 00:18:34,876 Speaker 2: Most of them being the Pacific Northwest or Alaska. And then, um, 355 00:18:35,636 --> 00:18:37,516 Speaker 2: Texas up to Boston, but mostly in the Gulf. 356 00:18:37,696 --> 00:18:38,476 Speaker 1: And what kind of fish? 357 00:18:39,216 --> 00:18:41,236 Speaker 2: Black cod is our bread and butter. Um, we do 358 00:18:41,276 --> 00:18:42,936 Speaker 2: mostly black cod. We're going to do, you know, a 359 00:18:43,736 --> 00:18:45,656 Speaker 2: couple of million pounds of fish total this year. Most 360 00:18:45,696 --> 00:18:48,616 Speaker 2: of that will be black cod. We also do harvest 361 00:18:48,836 --> 00:18:52,336 Speaker 2: some sockeye salmon. We just announced that recently. We have 362 00:18:52,996 --> 00:18:58,396 Speaker 2: Pacific cod, rockfish, vermilion rockfish, red snapper, a couple other 363 00:18:58,436 --> 00:19:01,036 Speaker 2: bycatch species in the Gulf, and black sea bass in 364 00:19:01,056 --> 00:19:01,476 Speaker 2: the Northeast. 365 00:19:02,416 --> 00:19:03,966 Speaker 1: And what's the business model? 366 00:19:05,096 --> 00:19:08,506 Speaker 2: So we give the machines to fishermen for free. It's 367 00:19:08,526 --> 00:19:11,826 Speaker 2: structured as a lease, but they pay $ 0 per the payments. 368 00:19:12,586 --> 00:19:14,466 Speaker 2: And then we buy the fish. We have right of 369 00:19:14,486 --> 00:19:17,756 Speaker 2: first refusal on all the fish that they process with it. 370 00:19:18,306 --> 00:19:21,386 Speaker 2: And then we sell that fish under our brand. you know, 371 00:19:21,406 --> 00:19:23,806 Speaker 2: the parent company being Shinkie, the technology company that develops 372 00:19:23,826 --> 00:19:27,506 Speaker 2: all the IP, that all employees are underneath. We have 373 00:19:27,546 --> 00:19:29,766 Speaker 2: a subsidiary called the Ceremony Fish, and we call the 374 00:19:29,806 --> 00:19:33,206 Speaker 2: fish Ceremony Grade, as a nod to, like, Ceremony Grade Matcha. 375 00:19:33,906 --> 00:19:35,486 Speaker 2: And the kind of idea there is... I. 376 00:19:37,106 --> 00:19:39,546 Speaker 1: Should say Ceremony has an S at the beginning and 377 00:19:39,586 --> 00:19:40,216 Speaker 1: an I at the end. 378 00:19:40,306 --> 00:19:40,946 Speaker 2: Yes, exactly. 379 00:19:40,966 --> 00:19:43,846 Speaker 1: Yes, yes, yes. Kind of vaguely Japanese, yeah. 380 00:19:44,006 --> 00:19:46,416 Speaker 2: I think that's the way that you anglicize how the 381 00:19:46,456 --> 00:19:49,496 Speaker 2: Japanese say Ceremony in Japanese, but so that's kind of 382 00:19:49,516 --> 00:19:52,456 Speaker 2: the way we chose to spell that way. And we 383 00:19:52,536 --> 00:19:56,706 Speaker 2: came up with ceremony grade as this kind of marker 384 00:19:56,726 --> 00:19:59,196 Speaker 2: for quality and, you know, something that we believe we 385 00:19:59,236 --> 00:20:01,876 Speaker 2: can stand for because, you know, when you go and 386 00:20:01,896 --> 00:20:05,276 Speaker 2: buy fish at the grocery store, you know, for the 387 00:20:05,316 --> 00:20:07,756 Speaker 2: most part, you'll be told it's for that species, it's 388 00:20:08,056 --> 00:20:12,666 Speaker 2: wild or it's farmed, never necessarily that the exact geography 389 00:20:12,706 --> 00:20:14,746 Speaker 2: that has been caught in or the type of gear 390 00:20:14,966 --> 00:20:18,066 Speaker 2: or so on. And, When you look at cattle or poultry, 391 00:20:18,186 --> 00:20:22,466 Speaker 2: like in beef, you have grass-fed, USD choice, USD prime. 392 00:20:22,626 --> 00:20:27,206 Speaker 2: For chicken, you have pasture-raised or similar, right? And there's 393 00:20:27,246 --> 00:20:30,656 Speaker 2: not really an analog to process for fish, only for genetics. 394 00:20:30,876 --> 00:20:34,836 Speaker 2: And so that was where we saw that there was 395 00:20:34,856 --> 00:20:39,996 Speaker 2: an opportunity. That's alternative from the sustainability certification that exists, ASC, MSC, 396 00:20:40,016 --> 00:20:41,756 Speaker 2: which all do a great job but are really focused 397 00:20:41,816 --> 00:20:42,536 Speaker 2: on sustainability. 398 00:20:43,916 --> 00:20:48,426 Speaker 1: So... I am your customer, I think, depending on how 399 00:20:48,466 --> 00:20:50,956 Speaker 1: much more it costs than regular fish. Like, I buy 400 00:20:51,256 --> 00:20:56,156 Speaker 1: pasture-raised chicken. Great. And I don't even know if it 401 00:20:56,176 --> 00:20:59,856 Speaker 1: tastes better. It doesn't taste different to me. But I land, 402 00:20:59,876 --> 00:21:01,816 Speaker 1: I think, more or less where you land on the 403 00:21:02,016 --> 00:21:05,986 Speaker 1: moral question of, like, the killing is not the fundamental 404 00:21:06,006 --> 00:21:10,006 Speaker 1: problem to me. It's the, like, raising the animals in hell, essentially. 405 00:21:10,046 --> 00:21:10,226 Speaker 2: Right. 406 00:21:10,346 --> 00:21:11,286 Speaker 1: It seems quite bad. 407 00:21:11,306 --> 00:21:11,446 Speaker 2: So.... 408 00:21:15,266 --> 00:21:18,806 Speaker 1: I mean, as a potential customer, like the obvious questions 409 00:21:18,826 --> 00:21:20,796 Speaker 1: to me are, one, how much more expensive is it? 410 00:21:22,456 --> 00:21:24,876 Speaker 2: So I don't want to deflect the question, but it's 411 00:21:24,976 --> 00:21:27,466 Speaker 2: very dependent on SKU. I'll give you some numbers as 412 00:21:27,486 --> 00:21:27,986 Speaker 2: an example. 413 00:21:28,606 --> 00:21:29,006 Speaker 1: Salmon. 414 00:21:29,186 --> 00:21:32,786 Speaker 2: So our salmon is probably 15% more than commodity. 415 00:21:33,406 --> 00:21:36,356 Speaker 1: Oh. That's not so much more. I feel like pasteurized chicken, 416 00:21:36,376 --> 00:21:39,516 Speaker 1: the gap is bigger than that. Of course, the absolute 417 00:21:39,596 --> 00:21:42,966 Speaker 1: numbers are higher for the fish, so 15%. But 15% 418 00:21:42,966 --> 00:21:45,906 Speaker 1: more is not that much more. Do you make money 419 00:21:46,426 --> 00:21:48,986 Speaker 1: at that level? Did the unit economics work. 420 00:21:48,846 --> 00:21:52,066 Speaker 2: For you there? Yeah. So we've gone from economy to scale. 421 00:21:52,176 --> 00:21:54,696 Speaker 2: We have our own infrastructure. We vertically integrated more. We 422 00:21:54,716 --> 00:21:57,116 Speaker 2: can actually offer a price that is not quite at 423 00:21:57,156 --> 00:22:00,956 Speaker 2: par with commodity, but for the process and rolling into it, 424 00:22:01,256 --> 00:22:03,656 Speaker 2: and the brand and the quality that we stand for 425 00:22:03,676 --> 00:22:05,996 Speaker 2: in handling. All those things do need a little bit 426 00:22:06,036 --> 00:22:09,416 Speaker 2: more of a premium to cover. But is that a 427 00:22:09,416 --> 00:22:11,076 Speaker 2: scale where we can do millions of pounds and still 428 00:22:11,116 --> 00:22:12,056 Speaker 2: find homes for the fish? 429 00:22:12,706 --> 00:22:17,706 Speaker 1: Honestly, that's a lower premium than I expected. Do you 430 00:22:17,746 --> 00:22:19,006 Speaker 1: find people are willing to pay it? 431 00:22:20,546 --> 00:22:22,026 Speaker 2: I do find that people want to pay it. I 432 00:22:22,046 --> 00:22:26,866 Speaker 2: think retailers need a lot of evidence before they expand programs. 433 00:22:27,006 --> 00:22:28,566 Speaker 2: So we start at a small scale. Most of our 434 00:22:28,586 --> 00:22:32,136 Speaker 2: revenue today is probably 85 to 95%, depending on the week, 435 00:22:32,736 --> 00:22:36,656 Speaker 2: is food service. So we wholesale to distributors who will 436 00:22:36,676 --> 00:22:37,706 Speaker 2: then sell to local restaurants. 437 00:22:38,236 --> 00:22:40,106 Speaker 1: Is it like fancy restaurants? 438 00:22:41,126 --> 00:22:43,566 Speaker 2: Initially, yes. Although now, you know, it's kind of like 439 00:22:44,066 --> 00:22:47,146 Speaker 2: white tablecloth, but not super expensive, you know? So we're 440 00:22:47,186 --> 00:22:50,286 Speaker 2: not focused on the Michelin three star, you know, that 441 00:22:50,306 --> 00:22:53,156 Speaker 2: was very much at a time, you know, 2024, 2025, 442 00:22:53,156 --> 00:22:56,456 Speaker 2: where we were focused. But today, you know, we're hoping 443 00:22:56,496 --> 00:22:58,846 Speaker 2: for the kind of You know, there's this difference I 444 00:22:59,326 --> 00:23:02,536 Speaker 2: call for between premium and luxury, where luxury you're paying 445 00:23:02,596 --> 00:23:05,976 Speaker 2: for more of an experience and an emotion than necessarily 446 00:23:05,996 --> 00:23:09,296 Speaker 2: for utility, versus premium you pay more, but you get 447 00:23:09,336 --> 00:23:12,636 Speaker 2: more utility per dollar. And so we were a luxury product, 448 00:23:12,736 --> 00:23:15,686 Speaker 2: whether you're selling, you know, fish quadruple the price of 449 00:23:15,706 --> 00:23:18,206 Speaker 2: the commodity. But today with a small markup, you get 450 00:23:18,266 --> 00:23:20,386 Speaker 2: more utility per dollar because you're more likely to use 451 00:23:20,426 --> 00:23:22,606 Speaker 2: your fish because of the shelf life or the quality 452 00:23:22,646 --> 00:23:25,186 Speaker 2: or so on. And so in that way, you know, we... 453 00:23:26,636 --> 00:23:29,216 Speaker 2: do charge a premium, but again, the utility is better. 454 00:23:31,086 --> 00:23:35,286 Speaker 1: So the one other thing that I feel like would 455 00:23:35,326 --> 00:23:40,266 Speaker 1: be useful for me is some kind of third-party certification. Like, 456 00:23:41,246 --> 00:23:44,296 Speaker 1: I more or less believe you, but like, you know, 457 00:23:44,316 --> 00:23:48,256 Speaker 1: when I buy whatever, vitamins, like I always buy the 458 00:23:48,336 --> 00:23:53,276 Speaker 1: ones where there's third-party certification because like, Well, a lot 459 00:23:53,316 --> 00:23:55,076 Speaker 1: of times the vitamins aren't what they say they are, 460 00:23:55,296 --> 00:23:57,526 Speaker 1: you know, and the same exists for. 461 00:23:59,006 --> 00:23:59,286 Speaker 2: Meat. 462 00:23:59,926 --> 00:24:02,506 Speaker 1: Is that a possibility for your fish? 463 00:24:04,866 --> 00:24:07,316 Speaker 2: The question I'd ask is who's a third party? And 464 00:24:07,336 --> 00:24:09,676 Speaker 2: so that's part of why we have to design our 465 00:24:09,716 --> 00:24:10,156 Speaker 2: own standard. 466 00:24:10,256 --> 00:24:13,816 Speaker 1: Well, sure. But right now there is no third party, right? 467 00:24:13,896 --> 00:24:19,956 Speaker 1: Like it's... your incentives are not aligned economically. They're just not. 468 00:24:20,156 --> 00:24:22,496 Speaker 1: You know what I mean? Like a third party is 469 00:24:22,676 --> 00:24:24,816 Speaker 1: economically a nice structure. And I know there are problems 470 00:24:24,856 --> 00:24:28,276 Speaker 1: who's paying them and there are always problems. But it 471 00:24:28,336 --> 00:24:31,496 Speaker 1: does seem useful. There's a reason we have them in 472 00:24:31,576 --> 00:24:33,426 Speaker 1: many domains like yours. 473 00:24:35,446 --> 00:24:38,726 Speaker 2: You know, we've definitely explored alternative business models for the 474 00:24:38,786 --> 00:24:40,966 Speaker 2: company where like longer term, there might be an opportunity 475 00:24:40,986 --> 00:24:44,316 Speaker 2: for us to, you know, rent the machines, robotics as 476 00:24:44,336 --> 00:24:46,276 Speaker 2: a service, as many people do, and then we become 477 00:24:46,296 --> 00:24:49,706 Speaker 2: that third party. But, you know, and each individual retailer 478 00:24:49,746 --> 00:24:52,586 Speaker 2: does have their own audits where any clients that we make, 479 00:24:52,646 --> 00:24:54,866 Speaker 2: they have to back up. So, you know, for a 480 00:24:54,886 --> 00:24:58,606 Speaker 2: major nationwide retailer, you know, we would be submitting lots 481 00:24:58,626 --> 00:25:01,166 Speaker 2: of paperwork across where the fish is coming from, what 482 00:25:01,326 --> 00:25:03,756 Speaker 2: traceability data do we have, what's the shelf life, we'll 483 00:25:03,776 --> 00:25:06,076 Speaker 2: give them samples, I'll do the studies, I'll back up 484 00:25:06,116 --> 00:25:08,876 Speaker 2: the claims. And only after all jumping through all those hoops, 485 00:25:09,436 --> 00:25:11,376 Speaker 2: then we get approved to be a vendor and so on. 486 00:25:12,266 --> 00:25:15,246 Speaker 1: Yeah, so in that universe, the retailer is the third party. 487 00:25:15,446 --> 00:25:18,346 Speaker 2: Exactly. But if chefs don't like the fish, they wouldn't 488 00:25:18,366 --> 00:25:19,636 Speaker 2: buy it. That's the other part to it. 489 00:25:20,436 --> 00:25:23,716 Speaker 1: Well, so, I mean, that's separating the, like, how does 490 00:25:23,736 --> 00:25:26,616 Speaker 1: the fish taste versus how much did the fish suffer, right? 491 00:25:26,736 --> 00:25:28,676 Speaker 1: And the thing I'm thinking about is how much did 492 00:25:28,696 --> 00:25:32,656 Speaker 1: the fish suffer? And I understand your claim that those 493 00:25:32,716 --> 00:25:35,616 Speaker 1: two are related, but, like, they could be weakly related, right? 494 00:25:37,876 --> 00:25:41,446 Speaker 1: Are other people doing anything like what you're doing? Is 495 00:25:41,466 --> 00:25:44,606 Speaker 1: there an industry of sort of humanely killed fish? 496 00:25:45,806 --> 00:25:49,146 Speaker 2: There are mechanical companies that focus on industrial automation, but 497 00:25:49,186 --> 00:25:52,766 Speaker 2: it's mostly for factory farming, right? In the context there, 498 00:25:52,786 --> 00:25:57,986 Speaker 2: the fish are like 18, 20 generations of the same genetics. 499 00:25:58,126 --> 00:26:01,126 Speaker 2: And because of that, they're very much bred to size 500 00:26:01,186 --> 00:26:03,506 Speaker 2: and bred to morphology, which means that that geometry you 501 00:26:03,566 --> 00:26:06,376 Speaker 2: can use a mechanical machine to process. But in the 502 00:26:06,396 --> 00:26:08,876 Speaker 2: wild environment, different diets, the same fish can be the 503 00:26:09,156 --> 00:26:12,346 Speaker 2: same weight, but have different shapes and sizes. And so 504 00:26:12,366 --> 00:26:14,366 Speaker 2: because of that, you do need something that can handle 505 00:26:14,386 --> 00:26:16,926 Speaker 2: that variation. And that's why we have a computer vision 506 00:26:16,946 --> 00:26:19,546 Speaker 2: layer that will scan each fish and pick out where 507 00:26:19,586 --> 00:26:21,226 Speaker 2: is the brain, where is the gills and so on. 508 00:26:22,266 --> 00:26:24,886 Speaker 2: There are alternative methods as well, rather than actually like 509 00:26:25,446 --> 00:26:29,436 Speaker 2: euthanizing or exsanguinating the animal. Instead, you can, like we 510 00:26:29,476 --> 00:26:32,336 Speaker 2: mentioned earlier, stun it with electricity. We just found that, 511 00:26:32,836 --> 00:26:36,126 Speaker 2: you know, It doesn't necessarily match the industrial throughput that 512 00:26:36,146 --> 00:26:36,466 Speaker 2: you need. 513 00:26:37,086 --> 00:26:39,006 Speaker 1: So you're saying nobody else is doing it for wild 514 00:26:39,046 --> 00:26:40,906 Speaker 1: fish because it's hard and you figured it out and 515 00:26:40,946 --> 00:26:41,506 Speaker 1: nobody else has. 516 00:26:41,526 --> 00:26:43,976 Speaker 2: I wouldn't say we've a hundred percent figured out. Like 517 00:26:44,016 --> 00:26:50,076 Speaker 2: we can't do like massive commodity boats just yet. Right. 518 00:26:50,116 --> 00:26:51,476 Speaker 2: Because even that volume is really high. 519 00:26:51,836 --> 00:26:52,016 Speaker 1: Right. 520 00:26:52,036 --> 00:26:54,226 Speaker 2: We're going on our first very large vessel this year. 521 00:26:54,236 --> 00:26:58,076 Speaker 2: It's 180 feet. And you know, We're expecting success and 522 00:26:58,096 --> 00:27:00,076 Speaker 2: we've done a couple of trials, but it's our first 523 00:27:00,096 --> 00:27:01,736 Speaker 2: time going on for a full season where the boat's 524 00:27:02,096 --> 00:27:04,826 Speaker 2: not coming back for a couple of months. 525 00:27:05,086 --> 00:27:07,146 Speaker 1: So you're worried that the machine is going to break? 526 00:27:07,206 --> 00:27:09,046 Speaker 1: You're going to have to send an engineer out on 527 00:27:09,086 --> 00:27:10,736 Speaker 1: a helicopter or something to fix it? 528 00:27:11,986 --> 00:27:13,746 Speaker 2: Because it's the first time we are sending an engineer 529 00:27:13,806 --> 00:27:16,126 Speaker 2: out there, but it's one of those opportunities that if 530 00:27:16,166 --> 00:27:18,596 Speaker 2: we win that, then that's a great precedent to set 531 00:27:18,796 --> 00:27:22,096 Speaker 2: for trying larger and larger boats as well. And so 532 00:27:22,856 --> 00:27:24,316 Speaker 2: I'm not saying that I'm going to be the first 533 00:27:24,356 --> 00:27:26,736 Speaker 2: person to say, We have conquered this problem and no 534 00:27:26,776 --> 00:27:29,676 Speaker 2: one else has solved it. It's all like an industry effort. 535 00:27:30,096 --> 00:27:34,096 Speaker 2: At the same time, we have for the specific use 536 00:27:34,126 --> 00:27:37,626 Speaker 2: case of the gear types, the fishermen, the species, we 537 00:27:37,666 --> 00:27:39,886 Speaker 2: believe that we have a product that can handle the 538 00:27:39,906 --> 00:27:43,626 Speaker 2: fish really effectively, can generate the quality shelf life benefits 539 00:27:43,646 --> 00:27:47,866 Speaker 2: that we claim under the theoretical underpinnings of which come 540 00:27:47,946 --> 00:27:52,276 Speaker 2: from this inspiration from this artisanal process in Japan. 541 00:27:53,136 --> 00:27:54,456 Speaker 1: How much fish are you going to sell this year? 542 00:27:55,906 --> 00:27:58,246 Speaker 2: I can't give you a hard number, so I can 543 00:27:58,266 --> 00:28:02,286 Speaker 2: give you an indicative amount, which is a couple million pounds. 544 00:28:02,306 --> 00:28:04,216 Speaker 1: I don't know if that's a lot or a little. 545 00:28:05,026 --> 00:28:07,716 Speaker 1: How many pounds of fish does America buy in a year? 546 00:28:08,516 --> 00:28:15,836 Speaker 2: I think the data I can recall is about 9 547 00:28:15,836 --> 00:28:18,416 Speaker 2: billion pounds of fish a year. For us to call 548 00:28:18,476 --> 00:28:24,636 Speaker 2: it a couple million pounds is a great proof of concept, 549 00:28:24,656 --> 00:28:26,336 Speaker 2: but there's a long way to go for us to 550 00:28:26,376 --> 00:28:27,856 Speaker 2: be a major player, you know? 551 00:28:28,446 --> 00:28:34,306 Speaker 1: Yeah, yeah. Yeah, yeah. What do you think might go wrong? Like, 552 00:28:34,346 --> 00:28:35,926 Speaker 1: I'm sure there's a lot of things to be worried about. 553 00:28:35,966 --> 00:28:38,466 Speaker 1: It seems quite complicated what you're trying to do on many, 554 00:28:38,506 --> 00:28:41,526 Speaker 1: many dimensions. Like, what's at the top of the list 555 00:28:41,606 --> 00:28:43,026 Speaker 1: of things you're worried about right now? 556 00:28:43,726 --> 00:28:45,786 Speaker 2: Well, we have a hard business to build, which is 557 00:28:45,916 --> 00:28:49,796 Speaker 2: we're building an industrial robotics company in a very demanding 558 00:28:49,816 --> 00:28:54,766 Speaker 2: environment while also building a consumer brand, and a perishable 559 00:28:54,786 --> 00:28:56,946 Speaker 2: logistics supply chain. So, like, in each one of those, 560 00:28:56,966 --> 00:29:00,586 Speaker 2: you probably have many risks. So, you know, I'll be 561 00:29:00,606 --> 00:29:03,276 Speaker 2: the first to say it's hard, but, you know, solve 562 00:29:03,296 --> 00:29:04,516 Speaker 2: hard problems and get big rewards, right? 563 00:29:04,736 --> 00:29:07,636 Speaker 1: That's the opposite of play stupid games, win stupid prizes. 564 00:29:07,696 --> 00:29:08,076 Speaker 2: Exactly. 565 00:29:08,236 --> 00:29:10,136 Speaker 1: Play hard, interesting games. Yeah. 566 00:29:10,416 --> 00:29:13,876 Speaker 2: Yeah, so I'd say, first of all, making the machines 567 00:29:14,236 --> 00:29:16,986 Speaker 2: work for an increasingly larger number of vessel types and 568 00:29:17,046 --> 00:29:21,486 Speaker 2: operator types so that fishermen feel not just... excited but 569 00:29:21,706 --> 00:29:24,766 Speaker 2: confident that this is a program that they could stick 570 00:29:24,816 --> 00:29:27,656 Speaker 2: with longer term. That's kind of the main engineering focus 571 00:29:27,676 --> 00:29:29,976 Speaker 2: right now. And then, you know, on the perishable logistics side, 572 00:29:30,556 --> 00:29:32,336 Speaker 2: you know, that's very much an economies of scale business. 573 00:29:32,776 --> 00:29:36,716 Speaker 1: Didn't you just buy a big fish processing plant factory 574 00:29:36,796 --> 00:29:39,276 Speaker 1: in Washington? Is that part of this story that you're 575 00:29:39,296 --> 00:29:39,986 Speaker 1: talking about right now? 576 00:29:39,996 --> 00:29:42,506 Speaker 2: Exactly, yeah. So we feasibly could have bought one four 577 00:29:42,526 --> 00:29:44,886 Speaker 2: or five times the size and try to fill it out. 578 00:29:45,166 --> 00:29:47,866 Speaker 2: But we decided to find one that's on the smaller end. 579 00:29:47,906 --> 00:29:50,936 Speaker 2: In fact, a company that's up because they really hit 580 00:29:50,956 --> 00:29:53,536 Speaker 2: a large scale they were selling this plant because this 581 00:29:53,596 --> 00:29:56,176 Speaker 2: is what they this was their very first plant and 582 00:29:56,196 --> 00:29:57,676 Speaker 2: so we're kind of following in their footsteps in that 583 00:29:57,696 --> 00:30:02,296 Speaker 2: way so that's 16,000 square feet it's in Tacoma which 584 00:30:02,356 --> 00:30:06,566 Speaker 2: is just outside of Seattle the great benefit of Having 585 00:30:06,586 --> 00:30:08,866 Speaker 2: that vertical integration is we have a little bit more 586 00:30:08,926 --> 00:30:12,506 Speaker 2: control versus partnering with third parties with how the fish 587 00:30:12,546 --> 00:30:14,996 Speaker 2: are packed. And eventually, you know, we just bought a 588 00:30:15,016 --> 00:30:17,016 Speaker 2: retail packing machine. So we're hoping to get these nice 589 00:30:17,056 --> 00:30:19,136 Speaker 2: little trays for consumers to be able to buy the 590 00:30:19,396 --> 00:30:21,236 Speaker 2: frozen versions of the fish. 591 00:30:22,096 --> 00:30:26,516 Speaker 1: So what's the what's the like happy story of where 592 00:30:26,556 --> 00:30:29,536 Speaker 1: you are in five years? Like if it if it works, 593 00:30:30,756 --> 00:30:31,656 Speaker 1: what will it look like? 594 00:30:32,556 --> 00:30:35,906 Speaker 2: Put it simply, middle-class Americans can go into their grocery 595 00:30:35,926 --> 00:30:38,986 Speaker 2: store and buy fish that tastes better and lasts longer 596 00:30:39,006 --> 00:30:43,566 Speaker 2: but are handled more humanely for the same price. I 597 00:30:43,586 --> 00:30:45,166 Speaker 2: don't think you need to complicate it more than that. 598 00:30:45,466 --> 00:30:49,666 Speaker 2: It's ultimately a game of we want to use technology 599 00:30:49,766 --> 00:30:53,146 Speaker 2: to bring down the marginal cost of doing this process 600 00:30:53,686 --> 00:30:58,916 Speaker 2: so that it can be applied through basically every single fish. 601 00:30:59,416 --> 00:31:01,536 Speaker 2: The same way that cattle and poultry have legal handling 602 00:31:02,366 --> 00:31:05,246 Speaker 2: you know, to be killed in a similar method. Yeah. 603 00:31:06,606 --> 00:31:09,186 Speaker 1: Do you imagine, I mean, I feel like if you 604 00:31:09,206 --> 00:31:11,366 Speaker 1: get there, other people will be trying to do what 605 00:31:11,406 --> 00:31:14,126 Speaker 1: you're doing. And I'm sure you have some intellectual property, but. 606 00:31:15,506 --> 00:31:15,886 Speaker 2: I don't know. 607 00:31:16,166 --> 00:31:19,706 Speaker 1: I don't know how much, and people are, whatever, things happen, right? Like, 608 00:31:20,186 --> 00:31:22,806 Speaker 1: for you, is that a good world or a bad world? If, like, 609 00:31:22,896 --> 00:31:26,576 Speaker 1: lots of people are out there developing their own systems 610 00:31:26,636 --> 00:31:29,136 Speaker 1: that say they do, in fact, kill fish in a 611 00:31:29,256 --> 00:31:31,996 Speaker 1: more humane way than they're killed today. Mm-hmm. Does that 612 00:31:32,016 --> 00:31:33,016 Speaker 1: make you happy or sad? 613 00:31:33,036 --> 00:31:36,616 Speaker 2: It makes me happy. I think we, obviously, we have 614 00:31:37,056 --> 00:31:40,896 Speaker 2: economic outcome that we're trying to underwrite for investors. And so, ideally, 615 00:31:41,156 --> 00:31:43,276 Speaker 2: you know, what we end up doing is just move 616 00:31:43,316 --> 00:31:45,336 Speaker 2: very quickly and partner with a lot of big companies. 617 00:31:45,716 --> 00:31:48,386 Speaker 2: And all of them are working with our technology. And 618 00:31:49,186 --> 00:31:52,186 Speaker 2: we maybe adjust the business model, you know. But we 619 00:31:52,486 --> 00:31:55,646 Speaker 2: originally actually tried to sell the technology. We didn't really 620 00:31:55,706 --> 00:31:58,226 Speaker 2: have that as, like, we didn't get the response we wanted. 621 00:31:58,556 --> 00:32:00,756 Speaker 1: You wanted to sell the robots, sell the machines. 622 00:32:00,796 --> 00:32:01,156 Speaker 2: Exactly. 623 00:32:01,236 --> 00:32:04,866 Speaker 1: That was your first model. Seems way easier to build 624 00:32:04,906 --> 00:32:08,586 Speaker 1: that business, right? Like, buy my machine is way easier than, like, 625 00:32:08,866 --> 00:32:11,506 Speaker 1: we're going to create a fish brand and buy a 626 00:32:11,546 --> 00:32:14,746 Speaker 1: fish processing plant and sell it to restaurants. Like, that 627 00:32:14,766 --> 00:32:16,126 Speaker 1: seems like a way harder business. 628 00:32:16,266 --> 00:32:18,516 Speaker 2: Yeah, but it just came out of the point of 629 00:32:18,546 --> 00:32:23,296 Speaker 2: view that unless we did it ourselves, people would just 630 00:32:23,336 --> 00:32:26,216 Speaker 2: not understand the value for what we were creating. And 631 00:32:26,256 --> 00:32:27,496 Speaker 2: so we had to put our money where our mouth was, 632 00:32:27,676 --> 00:32:30,156 Speaker 2: which is, doing this vertical integration to be able to 633 00:32:30,616 --> 00:32:33,516 Speaker 2: actually go to these retailers, earn the credibility, and show 634 00:32:33,556 --> 00:32:36,236 Speaker 2: them what we could do. And now we're starting to 635 00:32:36,256 --> 00:32:38,656 Speaker 2: get some traction. And so the hope is over the 636 00:32:38,676 --> 00:32:42,126 Speaker 2: next one to two years, these big processors, these big 637 00:32:42,166 --> 00:32:46,546 Speaker 2: dock buyers, the big fishing unions will come to us 638 00:32:46,606 --> 00:32:50,846 Speaker 2: and potentially partner with us. And that's a little bit 639 00:32:50,866 --> 00:32:53,126 Speaker 2: of a roundabout way to get to the end goal, 640 00:32:53,266 --> 00:32:55,226 Speaker 2: but as long as we get to the end goal. 641 00:32:59,346 --> 00:33:01,386 Speaker 1: We'll be back in a minute with the lightning round. 642 00:33:13,686 --> 00:33:15,326 Speaker 1: The ads are over. Now we're going to do the 643 00:33:15,346 --> 00:33:18,576 Speaker 1: lightning round. Is it true that you and your grandfather 644 00:33:18,616 --> 00:33:19,676 Speaker 1: have matching tattoos? 645 00:33:20,236 --> 00:33:24,506 Speaker 2: So same theme tattoo. They're both anchors. Um, I just 646 00:33:24,526 --> 00:33:26,766 Speaker 2: have a different style of anchor. Mine is an American 647 00:33:27,106 --> 00:33:29,586 Speaker 2: traditional sailor tattoo, it's a Sailor Jerry one. He just 648 00:33:29,606 --> 00:33:31,846 Speaker 2: has like a gray, or had, now that he's passed, 649 00:33:32,046 --> 00:33:34,206 Speaker 2: but he has a gray, like a normal line tattoo. 650 00:33:35,446 --> 00:33:39,376 Speaker 1: Tell me about your grandfather having a tattoo. It's unusual 651 00:33:39,436 --> 00:33:40,796 Speaker 1: for a grandfather, in my experience. 652 00:33:41,436 --> 00:33:44,956 Speaker 2: Yeah, it's a funny story. So he loves sailing, loves fishing. 653 00:33:45,556 --> 00:33:49,716 Speaker 2: The story goes, you know, he went to this carnival 654 00:33:50,396 --> 00:33:54,186 Speaker 2: where they were doing tattoos, and, you know, His dad 655 00:33:54,206 --> 00:33:55,766 Speaker 2: had told him not to get one, and he just 656 00:33:55,806 --> 00:33:57,706 Speaker 2: decided to walk in and get one. And then, you know, 657 00:33:57,726 --> 00:33:58,886 Speaker 2: it was just like a spur of the moment when 658 00:33:58,906 --> 00:34:00,966 Speaker 2: he was 17, and it kind of just marked his 659 00:34:01,006 --> 00:34:03,716 Speaker 2: passion for that. My reason for why I got it, 660 00:34:03,966 --> 00:34:07,166 Speaker 2: in contrast, was a little more intentional, which is, you know, 661 00:34:07,186 --> 00:34:09,956 Speaker 2: I moved to the U.S. when I was 18, and, 662 00:34:10,006 --> 00:34:12,936 Speaker 2: you know, me having grown up in the Middle East 663 00:34:12,996 --> 00:34:15,236 Speaker 2: and kind of adjusting to culture, I wanted to have 664 00:34:15,456 --> 00:34:17,556 Speaker 2: some mark to commemorate the fact that I was, like, 665 00:34:17,956 --> 00:34:19,836 Speaker 2: embracing a new culture and taking my own spin on it. 666 00:34:20,256 --> 00:34:22,956 Speaker 2: And so... The one that I have is the Sailor 667 00:34:22,976 --> 00:34:25,736 Speaker 2: Jerry one, which, you know, Sailor Jerry, he was in 668 00:34:25,756 --> 00:34:28,516 Speaker 2: the Navy, stayed in Japan, took inspiration from the art 669 00:34:28,536 --> 00:34:31,456 Speaker 2: style and mixed it with traditional American tattoos to come 670 00:34:31,496 --> 00:34:33,876 Speaker 2: up with this new version for each of them. So, 671 00:34:34,396 --> 00:34:36,246 Speaker 2: funnily enough, I guess now saying it out loud, it 672 00:34:36,256 --> 00:34:37,766 Speaker 2: kind of has a bit of a nod to Shinkei. 673 00:34:39,166 --> 00:34:40,986 Speaker 1: Give me one piece of fisherman wisdom. 674 00:34:42,206 --> 00:34:43,366 Speaker 2: Don't bring a banana on a boat. 675 00:34:46,426 --> 00:34:47,006 Speaker 1: Tell me more. 676 00:34:47,146 --> 00:34:51,436 Speaker 2: Yeah, it's an old sailor. story for bad luck and 677 00:34:51,836 --> 00:34:54,816 Speaker 2: I think it comes from the fact that bananas carry 678 00:34:55,356 --> 00:34:58,056 Speaker 2: fruit flies with a higher percentage and so people would 679 00:34:58,096 --> 00:35:01,336 Speaker 2: bring bananas and the flies would like lay larvae eggs 680 00:35:01,436 --> 00:35:03,656 Speaker 2: and they would basically eat all the food and supplies 681 00:35:04,036 --> 00:35:06,076 Speaker 2: and so if you run out of them for a 682 00:35:06,076 --> 00:35:07,936 Speaker 2: certain amount and they go rotten you have to turn around. 683 00:35:09,796 --> 00:35:12,086 Speaker 1: It's a bad luck I thought it was I thought 684 00:35:12,106 --> 00:35:14,946 Speaker 1: it was slipping on the peel when you said that it's. 685 00:35:14,866 --> 00:35:18,026 Speaker 2: Probably there too exactly I guess in contrast, you should 686 00:35:18,046 --> 00:35:20,606 Speaker 2: definitely bring oranges so you can stave off scurvy if 687 00:35:20,626 --> 00:35:23,346 Speaker 2: you're going to go for a long trip. 688 00:35:23,386 --> 00:35:29,086 Speaker 1: Good fruit tips from fishermen. Tell me about your houseplant game. 689 00:35:30,146 --> 00:35:32,526 Speaker 2: Wow, you did your research. 690 00:35:32,546 --> 00:35:33,006 Speaker 1: That's great. 691 00:35:34,426 --> 00:35:36,886 Speaker 2: Yeah. So I actually have a bunch of aloe vera. 692 00:35:36,906 --> 00:35:39,646 Speaker 2: That's kind of inspiration for my mom that I use 693 00:35:39,886 --> 00:35:42,726 Speaker 2: for skin. And I also make into tea once in 694 00:35:42,746 --> 00:35:45,286 Speaker 2: a while. I have a snake plant that I keep 695 00:35:45,306 --> 00:35:48,896 Speaker 2: in my room. I've never quite cracked the code for 696 00:35:48,916 --> 00:35:53,356 Speaker 2: why American AC feels stuffy. And so having a bunch 697 00:35:53,376 --> 00:35:56,256 Speaker 2: of snake plants helps my room stay clean. Outside that, 698 00:35:56,416 --> 00:35:57,956 Speaker 2: I just love to be with the plants. Um, and 699 00:35:58,016 --> 00:36:00,736 Speaker 2: I love other people having, you know, whenever I go 700 00:36:00,756 --> 00:36:03,016 Speaker 2: into someone's home, feeling a little bit more Zen, it 701 00:36:03,056 --> 00:36:06,326 Speaker 2: feels just that extra bit of integrated into nature. 702 00:36:07,926 --> 00:36:09,566 Speaker 1: Do you think plants are sentient? 703 00:36:11,586 --> 00:36:16,066 Speaker 2: I actually do. I love you asking these questions. Um, Yeah, 704 00:36:16,296 --> 00:36:18,576 Speaker 2: I don't know if it's the same density of sentience 705 00:36:19,156 --> 00:36:22,716 Speaker 2: that I would call, you know, an animal would have 706 00:36:22,736 --> 00:36:25,676 Speaker 2: or something similar. You know, they exhibit behavior, right? You 707 00:36:25,716 --> 00:36:28,376 Speaker 2: shine light, you know, and they'll grow towards the light. 708 00:36:29,236 --> 00:36:33,646 Speaker 2: They have been able to communicate in long distances. And so, again, 709 00:36:33,726 --> 00:36:36,846 Speaker 2: it's hard to kind of prescribe, you know, is that 710 00:36:36,886 --> 00:36:41,106 Speaker 2: sentience or not? Yeah, it's hard to describe what that 711 00:36:41,166 --> 00:36:42,086 Speaker 2: really means. Yeah. 712 00:36:43,076 --> 00:36:46,276 Speaker 1: They definitely have preferences. They definitely have preferences. 713 00:36:46,656 --> 00:36:47,676 Speaker 2: Yeah. 714 00:36:48,056 --> 00:36:50,736 Speaker 1: If you weren't doing the job that you're doing, if 715 00:36:50,756 --> 00:36:54,786 Speaker 1: you weren't in fish, what would you be doing? 716 00:36:55,386 --> 00:36:57,246 Speaker 2: You know, if someone just asked me this question, I'll 717 00:36:57,286 --> 00:37:01,386 Speaker 2: tell you the answer. Probably run a dairy farm in 718 00:37:01,406 --> 00:37:04,096 Speaker 2: New Zealand. Peace and quiet. 719 00:37:04,156 --> 00:37:06,296 Speaker 1: Is that true or is that just like your escape 720 00:37:06,416 --> 00:37:08,476 Speaker 1: fantasy when your job seems really hard? 721 00:37:08,986 --> 00:37:10,376 Speaker 2: I mean, there's many other things I would do. I 722 00:37:10,416 --> 00:37:13,916 Speaker 2: have so many hobbies, but that's probably the one that 723 00:37:14,136 --> 00:37:16,936 Speaker 2: came to mind most recently. 724 00:37:18,256 --> 00:37:20,516 Speaker 1: What was the hardest thing about working as a deckhand? 725 00:37:21,896 --> 00:37:25,056 Speaker 2: I think the feeling that you can go out for 726 00:37:25,136 --> 00:37:29,126 Speaker 2: hours and not have anything to show for it while 727 00:37:29,186 --> 00:37:32,626 Speaker 2: working very hard and trying again and again. It's difficult 728 00:37:32,666 --> 00:37:35,206 Speaker 2: to predict how good the catch is going to be, 729 00:37:35,506 --> 00:37:37,466 Speaker 2: and that's part of the excitement and also part of 730 00:37:37,506 --> 00:37:40,946 Speaker 2: the disappointment when things do or don't go well. 731 00:37:42,126 --> 00:37:44,466 Speaker 1: Thank you for your time. I really appreciate it. For sure. 732 00:37:45,046 --> 00:37:45,306 Speaker 2: Thank you. 733 00:37:52,666 --> 00:37:57,366 Speaker 1: Fave Khawaja is the co-founder and CEO of Shinkai. Please 734 00:37:57,506 --> 00:38:01,316 Speaker 1: email us at problem at pushkin.fm. Tell us what kinds 735 00:38:01,356 --> 00:38:02,996 Speaker 1: of shows we should do more of or less of 736 00:38:03,026 --> 00:38:05,036 Speaker 1: or particular people you think would be good on the show. 737 00:38:05,576 --> 00:38:07,796 Speaker 1: You can also find me on X and on LinkedIn 738 00:38:07,816 --> 00:38:11,156 Speaker 1: at Our show is produced by Gabriel Hunter Chang and 739 00:38:11,256 --> 00:38:14,316 Speaker 1: Trina Menino. Our editor is Lydia Jean Cott, and our 740 00:38:14,416 --> 00:38:17,636 Speaker 1: engineer is Sarah Bruguier. I'm Jacob Goldstein, and we'll be 741 00:38:17,676 --> 00:38:19,826 Speaker 1: back next week with another episode of What's Your Problem?