1 00:00:07,560 --> 00:00:11,039 Speaker 1: Hire one to Welcome to Bloomberg Intelligence Talking Transport Podcast. 2 00:00:11,080 --> 00:00:14,760 Speaker 1: I'm your host, Lee Klascw, Senior freight transportation logistics analysts 3 00:00:14,800 --> 00:00:18,880 Speaker 1: at Bloomberg Intelligence, Bloomberg's in house research arm. Before diving 4 00:00:18,920 --> 00:00:22,279 Speaker 1: in a little public service announcement, your support is instrumental 5 00:00:22,360 --> 00:00:24,800 Speaker 1: to keep bringing great guests on the podcast, like the 6 00:00:24,800 --> 00:00:27,319 Speaker 1: one we have today. If you haven't already, please do 7 00:00:27,400 --> 00:00:30,600 Speaker 1: take a moment to follow, rate and share the Talking 8 00:00:30,640 --> 00:00:34,280 Speaker 1: Transport Podcast. We appreciate your support. I'm very excited to 9 00:00:34,280 --> 00:00:36,960 Speaker 1: have Ryan Peterson, the founder and CEO of Flex Sport. 10 00:00:37,240 --> 00:00:40,680 Speaker 1: Ryan started Flexsport back in twenty thirteen. He holds an 11 00:00:40,720 --> 00:00:43,800 Speaker 1: MBA from Columbia Business School and ANBA from UC Berkeley. 12 00:00:44,120 --> 00:00:46,440 Speaker 1: Welcome to Talking Transports Podcast. 13 00:00:46,040 --> 00:00:48,559 Speaker 2: Ryan, Yeah, it's great to be on here talking transport. 14 00:00:48,640 --> 00:00:50,040 Speaker 2: That's my favorite thing to do sometimes. 15 00:00:50,080 --> 00:00:52,479 Speaker 1: So yeah, sometimes we get to talk about it more 16 00:00:52,520 --> 00:00:53,200 Speaker 1: than other times. 17 00:00:53,240 --> 00:00:54,840 Speaker 2: You know, when there's a. 18 00:00:54,840 --> 00:00:58,200 Speaker 1: Big problem the supply chain, that's when I see you 19 00:00:58,240 --> 00:00:59,240 Speaker 1: on TV all the time. 20 00:00:59,440 --> 00:01:02,880 Speaker 2: It's one of those times right now too. Actually, all 21 00:01:02,920 --> 00:01:04,600 Speaker 2: of a sudden, the ocean freight world has gone on 22 00:01:05,000 --> 00:01:06,360 Speaker 2: turn turned back on fire. 23 00:01:06,520 --> 00:01:08,960 Speaker 1: So well, before we get into the markets, I want 24 00:01:09,000 --> 00:01:11,240 Speaker 1: to for people that are listening in to learn a 25 00:01:11,440 --> 00:01:13,520 Speaker 1: little bit about flex Work, Can you just talk about 26 00:01:13,560 --> 00:01:14,040 Speaker 1: the company. 27 00:01:14,319 --> 00:01:18,039 Speaker 2: Yeah, Flexports a global logistics provider built on a modern 28 00:01:18,120 --> 00:01:20,040 Speaker 2: technology stack. So we make it easy to ship cargo 29 00:01:20,120 --> 00:01:23,040 Speaker 2: anywhere in the world and gets you visibility control like 30 00:01:23,160 --> 00:01:25,119 Speaker 2: where where's the cargo, when is it going to arrive? 31 00:01:26,240 --> 00:01:29,280 Speaker 2: Ship any quantity of goods in any mode of transport, 32 00:01:29,360 --> 00:01:33,760 Speaker 2: so air, ocean, trucking, rail, clear through customs, get it delivered. 33 00:01:34,560 --> 00:01:39,520 Speaker 2: The fundamental problem that we solve is goods need to 34 00:01:39,560 --> 00:01:42,560 Speaker 2: move all around the world, and any given asset owner 35 00:01:42,920 --> 00:01:45,120 Speaker 2: doesn't can't move those goods door to door. You know, 36 00:01:45,160 --> 00:01:47,640 Speaker 2: you think about a ship, it goes from port to port, 37 00:01:47,680 --> 00:01:49,560 Speaker 2: but the container has to go from door to door. 38 00:01:49,680 --> 00:01:52,240 Speaker 2: And I ultimately really care about the products inside that 39 00:01:52,280 --> 00:01:54,960 Speaker 2: container moving from the factory all the way to the 40 00:01:54,960 --> 00:01:58,200 Speaker 2: consumer's doors or into the into the retail store network. 41 00:01:58,720 --> 00:02:01,000 Speaker 2: And so you need this coordinating layer that sits on 42 00:02:01,080 --> 00:02:04,840 Speaker 2: top of all the asset owners. We call this industry 43 00:02:04,880 --> 00:02:08,280 Speaker 2: freight forwarding, right. I often joke it should be called 44 00:02:08,280 --> 00:02:11,000 Speaker 2: freight email forwarding because there's a lot of coordination that 45 00:02:11,040 --> 00:02:14,799 Speaker 2: happens of like Excel attachments and PDFs and just email 46 00:02:14,880 --> 00:02:18,040 Speaker 2: unstructured messages. So bringing software to bearrow that problem so 47 00:02:18,040 --> 00:02:20,040 Speaker 2: that you're connecting all the different parties in the chain, 48 00:02:20,639 --> 00:02:24,400 Speaker 2: finding the best price, best best routing, and getting people, 49 00:02:24,560 --> 00:02:27,760 Speaker 2: you know, good visibility, communication, compliance. It's a hard problem 50 00:02:27,840 --> 00:02:28,320 Speaker 2: to solve. 51 00:02:28,520 --> 00:02:32,040 Speaker 1: And you guys help with all modes of transportation ocean, air, road, rail. 52 00:02:32,880 --> 00:02:35,040 Speaker 2: Yeah, and so we've grown to were I think we're 53 00:02:35,080 --> 00:02:38,560 Speaker 2: the third largest freight provider for ocean freight in the 54 00:02:38,600 --> 00:02:44,720 Speaker 2: United States, similar in air the top five right and 55 00:02:44,840 --> 00:02:47,800 Speaker 2: top ten globally for both. For ocean, I think we're 56 00:02:47,800 --> 00:02:49,440 Speaker 2: a little bit smaller on their for it. If you 57 00:02:49,440 --> 00:02:52,200 Speaker 2: were to look at our you know, all of the 58 00:02:52,200 --> 00:02:55,800 Speaker 2: freight forwarders in the world, there's we once tried to 59 00:02:55,880 --> 00:02:58,440 Speaker 2: estimate this. It was like twenty thousand different companies. It's 60 00:02:58,480 --> 00:02:59,799 Speaker 2: sort of you have a lot of mom and pop 61 00:02:59,840 --> 00:03:04,359 Speaker 2: slat small business doing this. But if you look at 62 00:03:04,360 --> 00:03:07,040 Speaker 2: the list of the top one hundred, let's say, by volume, 63 00:03:07,440 --> 00:03:10,000 Speaker 2: there's only one that was founded after nineteen ninety four, 64 00:03:10,040 --> 00:03:12,560 Speaker 2: when Netscape was invented, when a web browser came out, 65 00:03:12,560 --> 00:03:15,040 Speaker 2: and that's Flexport. There's a lot of great companies in 66 00:03:15,040 --> 00:03:17,840 Speaker 2: this industry a lot with capabilities and knowledge that you know, 67 00:03:17,880 --> 00:03:20,079 Speaker 2: we admire and look up to. But from a tech standpoint, 68 00:03:20,080 --> 00:03:23,200 Speaker 2: it's just hard to kind of reinvent an old legacy 69 00:03:23,240 --> 00:03:26,640 Speaker 2: bureau business and have it become a built on a 70 00:03:26,639 --> 00:03:29,359 Speaker 2: modern tech stack. So that's that's been our big advantage 71 00:03:29,560 --> 00:03:31,720 Speaker 2: being found. We're lucky, we're just founded in more recent 72 00:03:31,760 --> 00:03:33,840 Speaker 2: period when we can use the cloud, you can use AI, 73 00:03:33,919 --> 00:03:36,600 Speaker 2: you can use all them modern tools and build our 74 00:03:36,640 --> 00:03:39,040 Speaker 2: own software to make it easy for everybody to do this. 75 00:03:39,480 --> 00:03:41,800 Speaker 1: Yeah, and you mentioned it's a very fragmented market, and 76 00:03:41,840 --> 00:03:45,640 Speaker 1: you know, the freight forwarders that don't embrace technology, even 77 00:03:45,680 --> 00:03:48,960 Speaker 1: the legacy players, if they're not embracing technology, you know, 78 00:03:48,960 --> 00:03:50,360 Speaker 1: in a couple of years, it seems like they're going 79 00:03:50,400 --> 00:03:51,360 Speaker 1: to be left out in the cold. 80 00:03:51,760 --> 00:03:55,640 Speaker 2: We're and that's really accelerating. Like we've had a lot 81 00:03:55,680 --> 00:04:00,160 Speaker 2: of breakthroughs with AI in the last few months. The 82 00:04:00,280 --> 00:04:02,840 Speaker 2: building just literally in the last few months, the ability 83 00:04:02,880 --> 00:04:08,240 Speaker 2: to apply opeen AI and other large language models to 84 00:04:08,360 --> 00:04:12,880 Speaker 2: some of these unstructured data problems and get get remarkable results. 85 00:04:13,160 --> 00:04:15,720 Speaker 1: Could you talk about what you guys use AI to 86 00:04:15,880 --> 00:04:17,960 Speaker 1: do and to make your people more productive? 87 00:04:18,279 --> 00:04:20,640 Speaker 2: Yeah, well, there's there's a bunch of different use cases 88 00:04:20,640 --> 00:04:22,400 Speaker 2: that we've and some of this we've been doing for 89 00:04:22,440 --> 00:04:24,440 Speaker 2: a long time. We're build our own machine learning models. 90 00:04:24,440 --> 00:04:29,799 Speaker 2: So for that, for example, pricing, we have we optimize 91 00:04:30,200 --> 00:04:34,000 Speaker 2: pricing that we charge for spot market. It's very volatile 92 00:04:34,240 --> 00:04:36,839 Speaker 2: and it's hard for to stitch together all the rates 93 00:04:36,880 --> 00:04:39,359 Speaker 2: and then how much should you what markup should you 94 00:04:39,400 --> 00:04:43,960 Speaker 2: apply doing kind of conversion rate optimization. Uh, we've actually 95 00:04:44,000 --> 00:04:47,200 Speaker 2: managed to increase our net revenue per transaction and the 96 00:04:47,240 --> 00:04:50,599 Speaker 2: conversion rate with the AI, which is like you know, 97 00:04:50,680 --> 00:04:53,160 Speaker 2: usually you would think I'll accept the lower conversion rate 98 00:04:53,160 --> 00:04:55,800 Speaker 2: if I make more money per transaction, but when they 99 00:04:55,800 --> 00:04:57,560 Speaker 2: should be that result, I couldn't even believe it. It 100 00:04:57,600 --> 00:04:59,680 Speaker 2: was like, no, you can't make the conversion rate go 101 00:04:59,800 --> 00:05:02,159 Speaker 2: up and make more money. That's not possible, But it did. 102 00:05:02,240 --> 00:05:06,440 Speaker 2: It does. A more interesting example is in actually, and 103 00:05:06,480 --> 00:05:10,840 Speaker 2: it's related when you have a container that you need 104 00:05:10,880 --> 00:05:13,840 Speaker 2: to allocate and which ship should I put this container on? 105 00:05:15,520 --> 00:05:18,400 Speaker 2: Picking amongst all the world sailings of what's the best 106 00:05:18,480 --> 00:05:21,200 Speaker 2: route it will actually get this on time to where 107 00:05:21,200 --> 00:05:24,839 Speaker 2: the customer's warehouse is where they're shipping it to. That's 108 00:05:24,880 --> 00:05:26,880 Speaker 2: a problem that you know, humans can solve that problem. 109 00:05:26,920 --> 00:05:29,440 Speaker 2: They're they're reasonably good at it. It's not that hard 110 00:05:29,480 --> 00:05:34,200 Speaker 2: to pick. But what becomes very difficult is, on average 111 00:05:34,240 --> 00:05:37,840 Speaker 2: we're getting something like two thousand containers canceled by our 112 00:05:37,839 --> 00:05:41,720 Speaker 2: customers each week. Because they're none acts. He canceled, it's 113 00:05:41,720 --> 00:05:45,599 Speaker 2: wrong word reschedule it. Their production gets you know, production 114 00:05:45,720 --> 00:05:48,880 Speaker 2: is chaotic, and so if the cargo is not ready 115 00:05:48,960 --> 00:05:50,280 Speaker 2: when they thought it was going to be ready, they 116 00:05:50,320 --> 00:05:52,520 Speaker 2: have to cancel the booking they have with you and 117 00:05:52,640 --> 00:05:55,880 Speaker 2: do it for a week later or whatever. So that's 118 00:05:55,920 --> 00:05:59,200 Speaker 2: a hard problem. Because I've already got this ship, this 119 00:05:59,279 --> 00:06:02,359 Speaker 2: container alley cater to this sailing. You cancel on me. 120 00:06:02,400 --> 00:06:04,000 Speaker 2: I don't want to go back to the carrier, the 121 00:06:04,040 --> 00:06:06,320 Speaker 2: ocean carrier who owns the ship and cancel the sailing. 122 00:06:06,880 --> 00:06:08,599 Speaker 2: They don't like that. They lose money if the ship 123 00:06:08,600 --> 00:06:12,080 Speaker 2: doesn't sail full. And so it was very hard for 124 00:06:12,160 --> 00:06:14,440 Speaker 2: humans to do this. What software does is what the 125 00:06:14,480 --> 00:06:17,080 Speaker 2: AI does is ten times a day, it looks at 126 00:06:17,120 --> 00:06:20,240 Speaker 2: all every cancelation I have, and it finds another container 127 00:06:20,640 --> 00:06:22,880 Speaker 2: that was meant to go at a later date and 128 00:06:22,960 --> 00:06:26,800 Speaker 2: pulls it forward. And now I'm actually helping that customer 129 00:06:26,839 --> 00:06:32,680 Speaker 2: get there faster and reducing my cancelations, and it's selecting 130 00:06:32,680 --> 00:06:36,120 Speaker 2: for the cheapest option. So it's it's improving our transit time, 131 00:06:36,240 --> 00:06:39,120 Speaker 2: lowering our cancelation to our carrier, and it saved us 132 00:06:39,120 --> 00:06:42,400 Speaker 2: two percent of our freight costs. So there's all kinds 133 00:06:42,400 --> 00:06:44,640 Speaker 2: of things like this that machine learn. We've been building 134 00:06:44,640 --> 00:06:48,120 Speaker 2: machine learning models for a long time. The large language 135 00:06:48,120 --> 00:06:51,760 Speaker 2: model AI. We've worked with open ai, a company called 136 00:06:51,800 --> 00:06:55,120 Speaker 2: a Debt, which is actually the CEO of a Debut 137 00:06:55,200 --> 00:06:57,000 Speaker 2: used to be the head of engineering for open Ai. 138 00:06:57,240 --> 00:07:00,760 Speaker 2: This is all startup they were. We're having a lot 139 00:07:00,760 --> 00:07:05,560 Speaker 2: of success with these, with things like getting statuses for 140 00:07:05,640 --> 00:07:09,960 Speaker 2: a container from a carrier website, something that if I 141 00:07:09,960 --> 00:07:12,000 Speaker 2: don't have an integration, and we don't have integrations with 142 00:07:12,040 --> 00:07:14,760 Speaker 2: every port terminal in the world or every ocean carrier 143 00:07:14,840 --> 00:07:18,280 Speaker 2: on the world, there's hundreds of small carriers, we would 144 00:07:18,320 --> 00:07:20,080 Speaker 2: have to send a human there to check the status 145 00:07:20,120 --> 00:07:23,600 Speaker 2: and update it tell the customer what's happening. We're finding 146 00:07:23,600 --> 00:07:25,480 Speaker 2: that the AI is actually quite good at going to 147 00:07:25,480 --> 00:07:29,960 Speaker 2: find that data. Before we would build webscrapers and the 148 00:07:30,000 --> 00:07:32,600 Speaker 2: software would we would just write software and it would 149 00:07:32,600 --> 00:07:34,720 Speaker 2: be hard coded. Go to this website, look in this 150 00:07:35,320 --> 00:07:40,000 Speaker 2: corner of the website, grab this status, return it here. 151 00:07:40,200 --> 00:07:42,800 Speaker 2: That does break all the time because the website slight 152 00:07:42,880 --> 00:07:47,040 Speaker 2: change to the website in your software breaks. So webscrape 153 00:07:47,040 --> 00:07:49,320 Speaker 2: being is a really good example. We haven't started doing 154 00:07:49,400 --> 00:07:54,680 Speaker 2: it except in Hacketon, but Voice Agents and actually open 155 00:07:54,760 --> 00:07:58,440 Speaker 2: Eyes just put out their newest model that has a 156 00:07:58,520 --> 00:08:03,400 Speaker 2: really good voice agent technology. So this is a It 157 00:08:03,440 --> 00:08:07,040 Speaker 2: sounds like human calling you, and in our hackathon we 158 00:08:07,080 --> 00:08:10,239 Speaker 2: have it where a human actually in my voice will 159 00:08:10,280 --> 00:08:14,400 Speaker 2: call a vendor and negotiate cheaper freight rates. It was 160 00:08:14,480 --> 00:08:16,120 Speaker 2: kind of a dumb idea, but it was a cute 161 00:08:16,120 --> 00:08:18,080 Speaker 2: idea for Hacketon. I don't think it's a good business idea. 162 00:08:18,680 --> 00:08:21,880 Speaker 2: But what I think will actually work is having this 163 00:08:21,960 --> 00:08:24,000 Speaker 2: human call you don't need to pretend to be me. 164 00:08:24,040 --> 00:08:26,360 Speaker 2: It'd be weird if I, the CEO of the company 165 00:08:26,360 --> 00:08:29,040 Speaker 2: called and asked you the status of a shipman. But 166 00:08:29,560 --> 00:08:32,199 Speaker 2: being able to call out there and make a business 167 00:08:32,200 --> 00:08:34,439 Speaker 2: call and check, hey, what's going on with this shipman 168 00:08:34,559 --> 00:08:36,840 Speaker 2: and talk to a human at it. Because there's not 169 00:08:37,280 --> 00:08:41,600 Speaker 2: enough APIs, there's not enough web connectivity, especially at truckers 170 00:08:41,640 --> 00:08:46,680 Speaker 2: and warehouses. You know, you're waiting for someone to to 171 00:08:46,800 --> 00:08:48,240 Speaker 2: you have to call and find out what's going on, 172 00:08:48,360 --> 00:08:50,719 Speaker 2: and so having voice agents that can call and I 173 00:08:50,760 --> 00:08:52,200 Speaker 2: think you're gonna end up in a world where voice 174 00:08:52,200 --> 00:08:54,760 Speaker 2: agents talk to voice agents on the other side, right, 175 00:08:54,800 --> 00:08:56,719 Speaker 2: which will be kind of fun. Interesting. 176 00:08:56,920 --> 00:08:58,800 Speaker 1: Yeah, I heard a crazy stat the other day. It's 177 00:08:58,840 --> 00:09:01,400 Speaker 1: something like twenty five twenty percent of truckers still have 178 00:09:01,440 --> 00:09:02,240 Speaker 1: a flip phone. 179 00:09:02,480 --> 00:09:05,960 Speaker 2: Oh, that's pretty interesting. Yeah, that's that's probably changing pretty fast. 180 00:09:06,000 --> 00:09:08,720 Speaker 2: We have about we have about four hundred thousand truck 181 00:09:08,760 --> 00:09:11,680 Speaker 2: drivers on our mobile app that we can just dispatch 182 00:09:11,760 --> 00:09:15,400 Speaker 2: loads to. So they are you know, they use Android 183 00:09:15,520 --> 00:09:17,840 Speaker 2: phones mostly. You'll see a lot of that, right. 184 00:09:17,960 --> 00:09:21,600 Speaker 1: So where do you think AI? So you mentioned the voice. 185 00:09:23,520 --> 00:09:24,480 Speaker 2: Aspect of it, and. 186 00:09:26,160 --> 00:09:28,400 Speaker 1: In terms of where AI could go, where do you 187 00:09:28,559 --> 00:09:32,440 Speaker 1: see AI going beyond that for flex, foroard and the 188 00:09:32,440 --> 00:09:34,000 Speaker 1: broader transportation markets. 189 00:09:34,200 --> 00:09:36,880 Speaker 2: Yeah, Well, if you look at the freight forwarding industry, 190 00:09:36,920 --> 00:09:39,440 Speaker 2: which is again this like kind of coordinating layer on 191 00:09:39,520 --> 00:09:42,280 Speaker 2: top of the asset owners that's moving the cargo between 192 00:09:43,160 --> 00:09:45,800 Speaker 2: you know, between many asset owners on a single transaction. 193 00:09:46,480 --> 00:09:47,880 Speaker 2: If you look at the p and L of a 194 00:09:47,880 --> 00:09:50,640 Speaker 2: freight forwarder. So like, for every one hundred dollars in 195 00:09:50,760 --> 00:09:56,360 Speaker 2: revenue typically on average across these companies, about seventy five 196 00:09:56,440 --> 00:09:59,200 Speaker 2: dollars seventy five to eighty dollars of that is being 197 00:09:59,200 --> 00:10:02,400 Speaker 2: paid to the asset owners, right to the trucking companies, 198 00:10:02,400 --> 00:10:06,160 Speaker 2: the carriers, the ocean carriers, whoever, and then twenty to 199 00:10:06,160 --> 00:10:08,959 Speaker 2: twenty five dollars they get to keep as their effectively 200 00:10:08,960 --> 00:10:13,680 Speaker 2: their net revenue. There, that's their revenue. Within that twenty 201 00:10:13,679 --> 00:10:17,840 Speaker 2: five dollars, ten dollars of it is variable cost operating 202 00:10:18,080 --> 00:10:22,120 Speaker 2: labor to coordinate the transactions, and you'll get five to 203 00:10:22,160 --> 00:10:25,559 Speaker 2: ten dollars of sales and general other costs, rent and 204 00:10:25,600 --> 00:10:28,120 Speaker 2: everything else, and five dollars of profit. That's kind of 205 00:10:28,280 --> 00:10:30,439 Speaker 2: how to think about the p and L well of 206 00:10:30,440 --> 00:10:37,079 Speaker 2: that ten dollars of operating costs variable cost. We our 207 00:10:37,160 --> 00:10:39,840 Speaker 2: team thinks we can probably in the next five years 208 00:10:41,080 --> 00:10:44,920 Speaker 2: cut that by eighty percent. And then you have a choice, 209 00:10:44,960 --> 00:10:47,360 Speaker 2: do you are are you going to be eight percent 210 00:10:47,480 --> 00:10:50,000 Speaker 2: cheaper than everybody else or at eight percent of profit 211 00:10:50,120 --> 00:10:52,679 Speaker 2: margin to your you know, your bottom line, or or hey, 212 00:10:52,720 --> 00:10:55,640 Speaker 2: maybe competitors come along and everybody they're just the price 213 00:10:55,679 --> 00:10:58,000 Speaker 2: of transport goes down eight percent. You don't know, you 214 00:10:58,040 --> 00:11:00,720 Speaker 2: don't get to keep it right. We haven't built our 215 00:11:00,760 --> 00:11:02,600 Speaker 2: business model on that. We kind of see that as 216 00:11:02,600 --> 00:11:06,520 Speaker 2: a gravy and upside like that. We're We're not. It's 217 00:11:06,559 --> 00:11:08,640 Speaker 2: been more it's been harder over the last decade to 218 00:11:08,679 --> 00:11:11,040 Speaker 2: automated processes than I would have expected. But we're having 219 00:11:11,040 --> 00:11:13,599 Speaker 2: more breakthroughs in the last three to six months with 220 00:11:13,679 --> 00:11:16,199 Speaker 2: AI than we you know, all of a sudden, I'm 221 00:11:16,200 --> 00:11:18,480 Speaker 2: becoming quite optimistic that we will in fact be able 222 00:11:18,480 --> 00:11:22,160 Speaker 2: to realize some of those gains. So that's a huge impact. 223 00:11:22,160 --> 00:11:24,319 Speaker 2: If you can lower the cost of shipping anything internationally 224 00:11:24,400 --> 00:11:26,720 Speaker 2: by five to ten, you know, five or eight percent, 225 00:11:26,800 --> 00:11:27,880 Speaker 2: that's massive. 226 00:11:27,760 --> 00:11:30,560 Speaker 1: Right, and so flexport from my vantage point, you know, 227 00:11:30,559 --> 00:11:33,559 Speaker 1: I would consider like a technology first freight forward or 228 00:11:33,559 --> 00:11:36,520 Speaker 1: where you know the other ones are you know, the 229 00:11:36,600 --> 00:11:40,400 Speaker 1: legacy ones are are are human first, not being that 230 00:11:40,440 --> 00:11:42,719 Speaker 1: they treat humans first, but you know they they were 231 00:11:42,760 --> 00:11:45,920 Speaker 1: before technology came out, kind of to what you were 232 00:11:45,960 --> 00:11:49,719 Speaker 1: saying earlier. So I'm assuming you guys have freight brokers, 233 00:11:49,800 --> 00:11:53,120 Speaker 1: you have people that deal with customs in your organization. 234 00:11:54,200 --> 00:11:57,640 Speaker 1: How has AI increased their productivity. Can you give us 235 00:11:57,679 --> 00:12:01,160 Speaker 1: some examples of like like loads per day that they've 236 00:12:01,160 --> 00:12:03,720 Speaker 1: been able to do, uh with AI. 237 00:12:04,200 --> 00:12:08,200 Speaker 2: Yeah, we've are so we we do, we have and 238 00:12:08,240 --> 00:12:10,400 Speaker 2: we're still we still believe that this is a relationships 239 00:12:10,400 --> 00:12:12,240 Speaker 2: based business at the end of the day. Sure. And 240 00:12:12,280 --> 00:12:15,320 Speaker 2: I you know, when I hear relationships based business, I 241 00:12:15,360 --> 00:12:17,640 Speaker 2: often think it's kind of a code word for mediocrity. 242 00:12:17,720 --> 00:12:19,520 Speaker 2: Like if we have, if we do great work for 243 00:12:19,679 --> 00:12:22,080 Speaker 2: you, you will have a good relationship. It's not like some 244 00:12:22,160 --> 00:12:24,520 Speaker 2: good old boys club, like we've got to be really 245 00:12:24,559 --> 00:12:27,120 Speaker 2: good at our job. But it does very much matter 246 00:12:27,440 --> 00:12:29,040 Speaker 2: that you can pick up the phone and call I 247 00:12:29,040 --> 00:12:31,640 Speaker 2: talked about voice agents. We'll never do that towards our customers. 248 00:12:31,679 --> 00:12:34,120 Speaker 2: Like you need to call your customer and help them 249 00:12:34,160 --> 00:12:36,280 Speaker 2: out and solve their problem. They need to be able 250 00:12:36,280 --> 00:12:40,000 Speaker 2: to call us and have us solve their problem. But yeah, 251 00:12:40,000 --> 00:12:42,600 Speaker 2: we can make our operators a lot more efficient in 252 00:12:42,679 --> 00:12:46,640 Speaker 2: the processing of things that we do. On the custom side, 253 00:12:47,200 --> 00:12:51,760 Speaker 2: we were about double the number of customs entries per 254 00:12:52,200 --> 00:12:56,959 Speaker 2: month that uh that a traditional freight customs broker was 255 00:12:57,000 --> 00:13:00,480 Speaker 2: and that we were a few years ago. Uh. Through automation, 256 00:13:00,720 --> 00:13:04,280 Speaker 2: not just AI, but just writing good software to make 257 00:13:04,320 --> 00:13:09,679 Speaker 2: them efficient to process data, optical character recognition, so being 258 00:13:09,679 --> 00:13:11,720 Speaker 2: able to because a lot of customs that data comes 259 00:13:11,720 --> 00:13:15,319 Speaker 2: in on documents, excel files, PDFs. Whatever we do ingest 260 00:13:15,400 --> 00:13:18,720 Speaker 2: that structure it handed off to customs. So building models 261 00:13:18,720 --> 00:13:20,640 Speaker 2: that can go and extract that data and make the 262 00:13:20,640 --> 00:13:26,200 Speaker 2: operator very efficient. And then on the forwarding side, I 263 00:13:26,200 --> 00:13:29,760 Speaker 2: think what we've the work that we've done is taken us. 264 00:13:30,280 --> 00:13:33,800 Speaker 2: We really started in earnest on the operator productivity piece 265 00:13:34,440 --> 00:13:37,480 Speaker 2: in twenty nineteen. Before that, almost everything we've built was 266 00:13:37,559 --> 00:13:39,599 Speaker 2: kind of make the user experience amazing of shipped with 267 00:13:39,640 --> 00:13:44,679 Speaker 2: flexport really great, and we didn't spend enough time. In hindsight, 268 00:13:44,679 --> 00:13:47,120 Speaker 2: we should have done it earlier on just like really 269 00:13:47,120 --> 00:13:49,800 Speaker 2: hardcore focused on productivity. Twenty nineteen is what we got 270 00:13:49,840 --> 00:13:52,440 Speaker 2: really serious about. It's about five years ago. It's been 271 00:13:52,480 --> 00:13:56,120 Speaker 2: a journey. We built workflow software so the operator, if 272 00:13:56,120 --> 00:13:57,960 Speaker 2: you want to ship a container from door to door, 273 00:13:59,040 --> 00:14:02,880 Speaker 2: we now have it as a hundred and eight screens 274 00:14:02,920 --> 00:14:05,679 Speaker 2: like a workflow like with custom web form that how 275 00:14:05,720 --> 00:14:10,120 Speaker 2: to do it, and then software then automates aspects of 276 00:14:10,160 --> 00:14:13,400 Speaker 2: that process of each of those screens. And then this 277 00:14:13,480 --> 00:14:16,440 Speaker 2: is where we're seeing the best. Some other breakthroughs from AI, 278 00:14:16,679 --> 00:14:19,480 Speaker 2: from from these large language models is that because we've 279 00:14:19,480 --> 00:14:22,560 Speaker 2: made the screen so simple, so simple, even I could 280 00:14:22,760 --> 00:14:25,160 Speaker 2: do it. You know, we probably train youly in half 281 00:14:25,200 --> 00:14:27,360 Speaker 2: an hour to become a freight forwarder. It'll take you 282 00:14:27,400 --> 00:14:29,040 Speaker 2: longer than that. But for some of these pasts, they're 283 00:14:29,080 --> 00:14:35,000 Speaker 2: not that hard, right, so they kind of turnout taxi 284 00:14:35,160 --> 00:14:37,480 Speaker 2: like make it simple. You can follow them, follow along, 285 00:14:38,320 --> 00:14:40,160 Speaker 2: and then it's it's now simple enough to day I 286 00:14:40,200 --> 00:14:42,840 Speaker 2: can complete some of the tasks, like those things of 287 00:14:42,960 --> 00:14:45,520 Speaker 2: like where is this container? Track it? Track it for me, 288 00:14:46,680 --> 00:14:50,200 Speaker 2: get the status, or extract the data from this bill 289 00:14:50,240 --> 00:14:53,280 Speaker 2: of lading, or place this booking with this uh carrier, 290 00:14:53,440 --> 00:14:55,880 Speaker 2: those things. If you asked me a couple of years ago, 291 00:14:55,920 --> 00:14:58,240 Speaker 2: I'd be like, yeah, we're not better than everybody else 292 00:14:58,280 --> 00:14:59,840 Speaker 2: did this, yet we should be, and it's it's a 293 00:15:00,040 --> 00:15:02,640 Speaker 2: because of ours Now we're starting to see, Okay, the 294 00:15:02,720 --> 00:15:06,080 Speaker 2: software is making our operators a lot more efficient and 295 00:15:06,280 --> 00:15:09,880 Speaker 2: higher quality. And most of the inefficiencies and logistics come 296 00:15:09,880 --> 00:15:12,720 Speaker 2: from bad quality. Like if you make a mistake and 297 00:15:12,800 --> 00:15:16,000 Speaker 2: you have to undo it, that'll wipe out any efficiency 298 00:15:16,000 --> 00:15:19,360 Speaker 2: you've gained from people typing really fast right for the month. 299 00:15:19,960 --> 00:15:22,960 Speaker 1: So you guys are a private company, you know, I'm 300 00:15:22,960 --> 00:15:26,880 Speaker 1: assuming you're still in growth mode. Some of that growth 301 00:15:27,000 --> 00:15:29,520 Speaker 1: or organic, some through M and A. You guys spent 302 00:15:29,600 --> 00:15:31,680 Speaker 1: some interesting M and as in the last I guess 303 00:15:31,680 --> 00:15:32,360 Speaker 1: twelve months. 304 00:15:32,800 --> 00:15:34,920 Speaker 2: Can you talk about that and how those. 305 00:15:34,800 --> 00:15:40,360 Speaker 1: Businesses complement the legacy business, the Flexport of being a forwarder. 306 00:15:40,720 --> 00:15:43,120 Speaker 2: Yeah. So yeah, we started as in a very much 307 00:15:43,120 --> 00:15:46,600 Speaker 2: our roots is in this praight forwarding world. We acquired 308 00:15:46,600 --> 00:15:49,880 Speaker 2: two companies last year. We acquire Shopify Logistics, which is 309 00:15:49,880 --> 00:15:53,760 Speaker 2: a fulfillment business. Both e commerce fulfillment, so you're likely 310 00:15:53,800 --> 00:15:57,280 Speaker 2: if you're buying on a Shopify site website, you're likely 311 00:15:57,320 --> 00:16:00,360 Speaker 2: to have a package delivered by Flexport. You'll see Lexport 312 00:16:00,400 --> 00:16:03,040 Speaker 2: on the shipping label coming out of our fulfillment centers. 313 00:16:03,520 --> 00:16:06,920 Speaker 2: And then we also acquired Convoy, which is a trucking 314 00:16:07,080 --> 00:16:10,920 Speaker 2: brokerage software platform. And that's where I mentioned we had 315 00:16:10,960 --> 00:16:12,960 Speaker 2: these four Hunite thousand drivers on a mobile app. That's it. 316 00:16:13,000 --> 00:16:16,360 Speaker 2: Through an acquisition, we acquired Convoy, and that's allowing us 317 00:16:16,360 --> 00:16:19,720 Speaker 2: to do full truckload so shipping domestic in the United States. 318 00:16:19,760 --> 00:16:23,600 Speaker 2: Both of those businesses are just us focused today, and 319 00:16:23,640 --> 00:16:25,360 Speaker 2: the way to think about how it all ties together 320 00:16:25,440 --> 00:16:27,240 Speaker 2: is really well. On the one hand, it is a 321 00:16:27,240 --> 00:16:30,640 Speaker 2: one stop shop, like you can ship anything and now 322 00:16:30,680 --> 00:16:33,200 Speaker 2: a small including small package with the Shop of Vile 323 00:16:33,240 --> 00:16:38,840 Speaker 2: logistics acquisition or full truckload with the convoy acquisition, and 324 00:16:38,880 --> 00:16:42,440 Speaker 2: then from factory all the way to customers doors as 325 00:16:42,440 --> 00:16:46,360 Speaker 2: well as into retail stores. So we delivered to all 326 00:16:46,400 --> 00:16:50,000 Speaker 2: the major big box retailers in a nice automated way, 327 00:16:50,040 --> 00:16:52,360 Speaker 2: so we can store your product in our sites and 328 00:16:52,400 --> 00:16:55,040 Speaker 2: then distribute it out to your retail network. 329 00:16:55,480 --> 00:16:59,240 Speaker 1: In the Shop of five fulfilment. Do you have fulfillment 330 00:16:59,280 --> 00:17:01,480 Speaker 1: centers all over the United States? Is it North America? 331 00:17:01,560 --> 00:17:04,520 Speaker 2: Is it global? It's just it's just the US for today. 332 00:17:04,520 --> 00:17:07,480 Speaker 2: We want to take it global. We're focused on really 333 00:17:07,520 --> 00:17:11,000 Speaker 2: emphasizing the quality, getting that it wasn't a profitable business 334 00:17:11,000 --> 00:17:12,639 Speaker 2: when we required it, so we're sort of dialing the 335 00:17:13,280 --> 00:17:16,560 Speaker 2: unit economics and growing it to get it to be profitable. 336 00:17:17,080 --> 00:17:18,320 Speaker 2: Aim is by the end of the year and then 337 00:17:18,320 --> 00:17:21,879 Speaker 2: we'll start taking it global. We do we have five 338 00:17:22,080 --> 00:17:25,480 Speaker 2: massive fulfillment centers, so that gives us coverage to ship 339 00:17:25,520 --> 00:17:28,879 Speaker 2: to the US like eighty five or ninety percent of 340 00:17:28,880 --> 00:17:31,320 Speaker 2: the US population within two days out of one of 341 00:17:31,320 --> 00:17:35,280 Speaker 2: these sites. They're they're really massive. I was, they're kind 342 00:17:35,280 --> 00:17:37,679 Speaker 2: of blowed. They're they're a little terrifying if you're the 343 00:17:37,680 --> 00:17:42,640 Speaker 2: guy paying the rent. They're very big. The one point 344 00:17:42,960 --> 00:17:46,679 Speaker 2: two million square foot is it is the typical of 345 00:17:46,720 --> 00:17:49,840 Speaker 2: the of the five that's like kind of typical context. 346 00:17:49,840 --> 00:17:53,200 Speaker 2: That's half a mile long. The Pentagon is only point 347 00:17:53,200 --> 00:17:57,560 Speaker 2: two miles long. So it's like they're really big, big 348 00:17:57,640 --> 00:17:58,200 Speaker 2: to big building. 349 00:17:58,240 --> 00:18:02,320 Speaker 1: And are the customers like small to medium sized shippers 350 00:18:02,400 --> 00:18:05,119 Speaker 1: or are there retail names that most people would know. 351 00:18:05,960 --> 00:18:09,000 Speaker 2: We really run the gamuts and were We started with 352 00:18:09,200 --> 00:18:11,160 Speaker 2: being the best way for small business to ship things. 353 00:18:11,160 --> 00:18:14,119 Speaker 2: That's my background to run small business and importing stuff, 354 00:18:14,119 --> 00:18:16,320 Speaker 2: and I was very frustrated with the freight forwarding and 355 00:18:16,359 --> 00:18:19,960 Speaker 2: the lack of technology and the fulfillment piece. So that's 356 00:18:19,960 --> 00:18:21,800 Speaker 2: where we started, but we've really grown up. We worked 357 00:18:21,800 --> 00:18:23,480 Speaker 2: for some of the biggest companies in the world Fortune 358 00:18:23,520 --> 00:18:27,800 Speaker 2: tens that use us for various things, and yeah, really 359 00:18:27,840 --> 00:18:32,320 Speaker 2: any size company can can use something. There's something for everybody. 360 00:18:32,320 --> 00:18:39,480 Speaker 2: Between our ocean freight software and for managing contracts, customs clearance, fulfillment, trucking, 361 00:18:39,680 --> 00:18:43,240 Speaker 2: and these things can be purchased separately or you know, 362 00:18:43,359 --> 00:18:45,199 Speaker 2: the goal is to make it. The idea is to 363 00:18:45,240 --> 00:18:46,879 Speaker 2: make them. They work much better when you buy them 364 00:18:46,920 --> 00:18:50,040 Speaker 2: all together. But that is very much up to the 365 00:18:50,040 --> 00:18:52,320 Speaker 2: individual customer. How do we create value for you? And 366 00:18:52,359 --> 00:18:55,600 Speaker 2: we think we can generally create a solution by combining 367 00:18:55,960 --> 00:18:59,679 Speaker 2: ocean freight plus customs plus trucking into the fulfillment network. 368 00:18:59,720 --> 00:19:03,560 Speaker 1: Except or right, So you are a private company, so 369 00:19:03,720 --> 00:19:06,119 Speaker 1: you know your financials are your own. But you know 370 00:19:06,160 --> 00:19:09,159 Speaker 1: you mentioned you know the Shopify business. I want to 371 00:19:09,200 --> 00:19:10,840 Speaker 1: break it to profitability. Do you have any sort of 372 00:19:10,840 --> 00:19:14,720 Speaker 1: targets for the the company as a whole in terms 373 00:19:14,720 --> 00:19:16,600 Speaker 1: of any any financial targets you could share. 374 00:19:17,800 --> 00:19:20,800 Speaker 2: Well, definitely aiming to get profitable again, we've been profitable 375 00:19:20,840 --> 00:19:24,320 Speaker 2: in the past. We're we're all hands on deck. It's 376 00:19:24,320 --> 00:19:26,280 Speaker 2: a big focus for us right now and a lot 377 00:19:26,320 --> 00:19:29,000 Speaker 2: of it's basically through growth. Will be profitable in twenty 378 00:19:29,040 --> 00:19:30,640 Speaker 2: twenty five next year. 379 00:19:31,359 --> 00:19:33,240 Speaker 1: Okay, so it's about scale, but. 380 00:19:33,320 --> 00:19:36,000 Speaker 2: That's not automatic. That's through like everybody apples for working 381 00:19:36,040 --> 00:19:39,479 Speaker 2: super hard doing their job. Some of it's scale. Some 382 00:19:39,560 --> 00:19:43,320 Speaker 2: of its new technologies that are unlocking some cost savings 383 00:19:43,560 --> 00:19:46,080 Speaker 2: and revenue improvements, a lot of it. You know, we've 384 00:19:46,080 --> 00:19:49,240 Speaker 2: got some new products, the Shopify one. We have a 385 00:19:49,240 --> 00:19:52,400 Speaker 2: great new product that helps companies manage their contracts when 386 00:19:52,400 --> 00:19:56,920 Speaker 2: they sign directly ocean carrier contracts directly with the ocean carriers. 387 00:19:57,680 --> 00:19:59,560 Speaker 2: So we have to go grow these new products and 388 00:19:59,600 --> 00:20:00,479 Speaker 2: new product clients. 389 00:20:02,160 --> 00:20:05,200 Speaker 1: So you're taking a step back, you know, from from 390 00:20:05,240 --> 00:20:08,440 Speaker 1: the company. You know, you started talking about the freight 391 00:20:08,480 --> 00:20:11,000 Speaker 1: markets as a whole. Can you talk about what you're 392 00:20:11,000 --> 00:20:14,000 Speaker 1: seeing in the ocean markets. 393 00:20:14,119 --> 00:20:17,359 Speaker 2: Yeah, So the ocean market is it's a boom and 394 00:20:17,400 --> 00:20:19,720 Speaker 2: bus cycle, and I guess that's the way it's been 395 00:20:20,040 --> 00:20:22,439 Speaker 2: probably since the invention of the shipping container. You have 396 00:20:22,480 --> 00:20:26,200 Speaker 2: a long term secular growth. But it's the fundamental problem 397 00:20:26,240 --> 00:20:30,000 Speaker 2: in ocean phrase is that that the ships last for 398 00:20:30,080 --> 00:20:32,919 Speaker 2: thirty years, and so the supply side has to make 399 00:20:32,960 --> 00:20:35,879 Speaker 2: this projection for thirty years of how many you know, 400 00:20:36,000 --> 00:20:39,040 Speaker 2: how many seys you need, and just nobody can predict 401 00:20:39,040 --> 00:20:40,919 Speaker 2: anything more than eighteen months in the future, is what 402 00:20:40,960 --> 00:20:43,320 Speaker 2: we're finding out. Actually, predicting three months in the future 403 00:20:43,400 --> 00:20:46,520 Speaker 2: is hardy enough. So it's a booming bus cycle, like 404 00:20:46,520 --> 00:20:49,159 Speaker 2: anything that's asset heavy. You either have too many ships 405 00:20:49,200 --> 00:20:52,600 Speaker 2: or not enough and the price can go crazy. And 406 00:20:52,640 --> 00:20:54,960 Speaker 2: what's happened you know, we saw this in COVID, where 407 00:20:54,960 --> 00:20:56,679 Speaker 2: the price of a container went to like twenty thousand 408 00:20:56,680 --> 00:20:58,760 Speaker 2: dollars when there was congestion at the ports and not 409 00:20:58,960 --> 00:21:01,560 Speaker 2: enough not enough ships keep up with the consumer demand. 410 00:21:02,040 --> 00:21:06,120 Speaker 2: That was a consumer demand led cycle that then created 411 00:21:06,160 --> 00:21:11,720 Speaker 2: supply crunch as ships got congested at the ports. What 412 00:21:11,760 --> 00:21:13,840 Speaker 2: we're seeing right now with the Red Sea is a 413 00:21:13,920 --> 00:21:17,960 Speaker 2: demand side, but the ships having to go around the 414 00:21:18,080 --> 00:21:22,000 Speaker 2: Cape of Good Hope around Africa is really eating into 415 00:21:22,040 --> 00:21:24,640 Speaker 2: the supply side. So you have a lot of new capacity. 416 00:21:25,040 --> 00:21:26,920 Speaker 2: The carriers made a lot of money during COVID, that's 417 00:21:26,920 --> 00:21:30,919 Speaker 2: no secret. They bought a lot more ships, so everyone 418 00:21:31,000 --> 00:21:33,160 Speaker 2: thought there was going to be plenty of excess capacity 419 00:21:33,200 --> 00:21:35,119 Speaker 2: and the price of ocean free would be low, everyone 420 00:21:35,119 --> 00:21:38,920 Speaker 2: including me. Then all of a sudden, you have these 421 00:21:39,200 --> 00:21:43,480 Speaker 2: missile strikes in the Red Sea and ships going around. Well, 422 00:21:43,520 --> 00:21:45,840 Speaker 2: if you might have twenty percent more ships, but if 423 00:21:45,840 --> 00:21:50,320 Speaker 2: it takes them twenty percent longer, you're at no extra capacity. 424 00:21:51,000 --> 00:21:54,199 Speaker 2: And it's really down to the trade lane level. Do 425 00:21:54,240 --> 00:21:57,920 Speaker 2: you have enough containers enough chassis. Those are the trailers 426 00:21:57,920 --> 00:21:59,879 Speaker 2: that you move, and right now we have a supply crunch. 427 00:22:00,000 --> 00:22:03,600 Speaker 2: There's not enough capacity to keep up. The price of 428 00:22:03,680 --> 00:22:07,280 Speaker 2: freight in the spot market has gone almost we're looking 429 00:22:07,320 --> 00:22:12,240 Speaker 2: at five to six thousand dollars or more contracts. People 430 00:22:12,720 --> 00:22:16,760 Speaker 2: the carriers have imposed what we call peak season surcharge 431 00:22:17,359 --> 00:22:22,159 Speaker 2: that usually takes place like ahead of Christmas shipping, you know, 432 00:22:22,200 --> 00:22:25,400 Speaker 2: ahead of the later in the year. Uh, they've imposed 433 00:22:25,440 --> 00:22:29,000 Speaker 2: that now already. So we're getting even the contracted rates 434 00:22:29,000 --> 00:22:33,880 Speaker 2: that they're prices are spiking. There's not enough capacity. People 435 00:22:33,920 --> 00:22:36,359 Speaker 2: are having trouble getting their cargo in. So we'll see, 436 00:22:36,400 --> 00:22:39,080 Speaker 2: we'll see if this last month's are or through the 437 00:22:39,160 --> 00:22:40,840 Speaker 2: end of the year. It's very hard to predict. 438 00:22:41,359 --> 00:22:44,960 Speaker 1: Yeah, uh, you know, given the longer term supply issues 439 00:22:45,000 --> 00:22:48,280 Speaker 1: facing the industry, it doesn't seem like it's it's it's 440 00:22:48,320 --> 00:22:51,040 Speaker 1: long term sustainable in terms of the price. 441 00:22:51,160 --> 00:22:53,959 Speaker 2: And there's there's a lot that's that's my view too. 442 00:22:54,119 --> 00:22:56,480 Speaker 2: There's a lot even with going around the Red seat, 443 00:22:56,480 --> 00:22:58,240 Speaker 2: there's a lot of ships have been ordered will be 444 00:22:58,240 --> 00:23:00,680 Speaker 2: delivered throughout this year and in the next year, and 445 00:23:01,240 --> 00:23:03,760 Speaker 2: so probably there's that we're looking at excess capacity for 446 00:23:03,920 --> 00:23:06,560 Speaker 2: again for next year. The long run kind of rule 447 00:23:06,560 --> 00:23:09,159 Speaker 2: of thumb is it costs a little bit less than 448 00:23:09,160 --> 00:23:11,520 Speaker 2: two thousand dollars to ship a container from China to 449 00:23:11,520 --> 00:23:13,199 Speaker 2: the US, And so right now it's you're going five 450 00:23:13,200 --> 00:23:16,840 Speaker 2: to six thousand, and during COVID it was at peak 451 00:23:16,920 --> 00:23:19,119 Speaker 2: twenty thousand. And by the way, five or six thousands, 452 00:23:19,160 --> 00:23:20,880 Speaker 2: if you can get loaded, you might have to pay 453 00:23:20,920 --> 00:23:24,560 Speaker 2: ten thousand to get loaded. So it's really about the margin, 454 00:23:24,640 --> 00:23:26,520 Speaker 2: right It's like the average price is lower than that, 455 00:23:26,600 --> 00:23:28,560 Speaker 2: but if you have one extra container you need to 456 00:23:28,560 --> 00:23:30,440 Speaker 2: get loaded and you need it now, you're probably paying 457 00:23:30,480 --> 00:23:31,640 Speaker 2: ten thousand bucks. 458 00:23:32,560 --> 00:23:35,240 Speaker 1: So do you think the air market is better right now? 459 00:23:36,320 --> 00:23:39,440 Speaker 2: The air market is also really interesting right now because 460 00:23:39,480 --> 00:23:42,600 Speaker 2: of the rise of e commerce out of especially at 461 00:23:42,600 --> 00:23:47,800 Speaker 2: these Chinese e commerce providers. They're now somewhere depending on 462 00:23:47,840 --> 00:23:49,960 Speaker 2: the week, thirty to fifty percent of all the air 463 00:23:50,000 --> 00:23:53,280 Speaker 2: freight is parcels. All the air freight in the world 464 00:23:53,359 --> 00:23:56,720 Speaker 2: is parcel volume being flown direct to consumer out of 465 00:23:57,119 --> 00:24:00,800 Speaker 2: mostly out of China, and so that's a huge amount 466 00:24:00,800 --> 00:24:04,760 Speaker 2: of demand for air capacity. Similar problem. Actually, the air 467 00:24:04,800 --> 00:24:08,040 Speaker 2: market's really weird because fifty percent of all air freight 468 00:24:08,080 --> 00:24:12,159 Speaker 2: has flown in the belly of passenger planes, so that market. 469 00:24:12,240 --> 00:24:13,920 Speaker 2: Not only do you have the supply and demand, it's 470 00:24:13,920 --> 00:24:16,000 Speaker 2: hard to predict how many planes you need, but the 471 00:24:16,080 --> 00:24:18,720 Speaker 2: number of planes that you need is actually not driven 472 00:24:18,800 --> 00:24:22,000 Speaker 2: by how much cargoes being shipped, but also by how 473 00:24:22,000 --> 00:24:26,359 Speaker 2: many passengers want to travel, and so that can be 474 00:24:26,480 --> 00:24:27,919 Speaker 2: you know, and we've had all of a sudden the 475 00:24:27,960 --> 00:24:31,439 Speaker 2: resurgence of international travel as people couldn't travel for a 476 00:24:31,440 --> 00:24:36,360 Speaker 2: couple of years during the pandemic, so it's coming. There's 477 00:24:36,400 --> 00:24:42,480 Speaker 2: a lot of demand for package volume that air freight 478 00:24:42,520 --> 00:24:45,000 Speaker 2: prices are high this year after being pretty low last year. 479 00:24:45,960 --> 00:24:48,840 Speaker 1: Yeah, I got a package machine like once a month 480 00:24:48,880 --> 00:24:54,959 Speaker 1: for my fourteen year old daughter, not for me. So 481 00:24:55,359 --> 00:24:57,600 Speaker 1: when you're booking capacity in the air side, are you 482 00:24:57,640 --> 00:25:02,600 Speaker 1: doing it typically through a commercial air fleet or are 483 00:25:02,600 --> 00:25:07,520 Speaker 1: there like freighter specific carriers that you also contract. 484 00:25:07,080 --> 00:25:09,479 Speaker 2: With Both there's both. We do. We work with all 485 00:25:09,480 --> 00:25:12,640 Speaker 2: the major airlines the and in fact the major airlines 486 00:25:12,720 --> 00:25:16,119 Speaker 2: will run their own cargo flights as well, because if 487 00:25:16,160 --> 00:25:19,200 Speaker 2: you think about what the benefit of an airline having 488 00:25:19,960 --> 00:25:23,880 Speaker 2: broad network that they can serve on their passenger planes. 489 00:25:23,960 --> 00:25:30,159 Speaker 2: The small legs out to you know, take uh an 490 00:25:30,240 --> 00:25:35,880 Speaker 2: airline based in let's say in Taiwan, and they'll have 491 00:25:36,200 --> 00:25:39,679 Speaker 2: flights that go all over Asia that can bring cargo 492 00:25:39,720 --> 00:25:42,880 Speaker 2: in the belly. These are passenger flights. Then you can 493 00:25:42,880 --> 00:25:45,320 Speaker 2: bring it all to taype A and then you have 494 00:25:45,400 --> 00:25:49,320 Speaker 2: a cargo plane connecting or several many connecting from Taibei 495 00:25:49,560 --> 00:25:52,199 Speaker 2: out to the international legs. All of a sudden you 496 00:25:52,240 --> 00:25:54,880 Speaker 2: get you can ship anything from any of these countries 497 00:25:55,480 --> 00:25:58,560 Speaker 2: into the US or into Europe. And so it uh 498 00:25:59,080 --> 00:26:01,480 Speaker 2: the cargo line. The cargo flights tend to fly on 499 00:26:01,520 --> 00:26:06,679 Speaker 2: the big trunk lanes between the big the shag his Taibei, Soul, 500 00:26:07,560 --> 00:26:11,199 Speaker 2: Hong Kong, Singapore, and then passenger planes tends to be 501 00:26:11,840 --> 00:26:13,760 Speaker 2: to think might call it a feed or in the 502 00:26:13,760 --> 00:26:17,400 Speaker 2: ocean world that uh so, you know, it will really 503 00:26:17,400 --> 00:26:19,080 Speaker 2: depend on where the cargo is going, who's got the 504 00:26:19,080 --> 00:26:24,160 Speaker 2: best rates and service quality, the best timeliness and capacity available. 505 00:26:24,200 --> 00:26:27,760 Speaker 2: So it's very much an old kind of an ultiple industry. 506 00:26:27,840 --> 00:26:29,680 Speaker 2: What we can do to make it better is digitize 507 00:26:29,760 --> 00:26:33,479 Speaker 2: this data about who's flying where connect you know, what 508 00:26:33,560 --> 00:26:36,479 Speaker 2: is the schedules that being able to track the things 509 00:26:36,960 --> 00:26:39,719 Speaker 2: get rates into the system be bet faster at quoting, 510 00:26:39,760 --> 00:26:42,520 Speaker 2: et cetera, getting getting you uplift. But at the end 511 00:26:42,560 --> 00:26:44,840 Speaker 2: of the day, you got to have relationships with all 512 00:26:44,840 --> 00:26:48,000 Speaker 2: these different vendors and then ideally you're integrating and getting 513 00:26:48,040 --> 00:26:50,919 Speaker 2: data connected directly. But not all of the airlines are 514 00:26:50,960 --> 00:26:53,040 Speaker 2: as modern as you know some of them, some of 515 00:26:53,040 --> 00:26:54,920 Speaker 2: them have invested a lot more in tech than others. 516 00:26:55,359 --> 00:26:58,320 Speaker 1: Yeah, I mean going back to AI. You know, there's 517 00:26:58,359 --> 00:27:01,720 Speaker 1: some people question like, are people just talking about AI 518 00:27:01,760 --> 00:27:05,280 Speaker 1: to get another turn in their multiples? If you're a company, 519 00:27:06,080 --> 00:27:09,840 Speaker 1: you know, obviously the benefits are there. Machine learning has 520 00:27:09,840 --> 00:27:12,320 Speaker 1: been around for a long time, but it seems like 521 00:27:12,320 --> 00:27:16,240 Speaker 1: there's so much a focus on it, where maybe five 522 00:27:16,320 --> 00:27:19,080 Speaker 1: six years ago, maybe longer than that. Seven years ago, 523 00:27:19,080 --> 00:27:22,119 Speaker 1: we started talking about blockchain at nauseum and that didn't 524 00:27:22,119 --> 00:27:24,760 Speaker 1: really go anywhere. Yeah, do you think this is Do 525 00:27:24,800 --> 00:27:29,399 Speaker 1: you think this is different the discussion about AI versus blockchain. 526 00:27:29,680 --> 00:27:33,160 Speaker 2: Yeah, I think it's different. In these cases are way more obvious. 527 00:27:34,119 --> 00:27:36,840 Speaker 2: Some of them are live now. One of the things 528 00:27:36,840 --> 00:27:42,080 Speaker 2: about AI is about large language models in particular, is 529 00:27:42,080 --> 00:27:46,680 Speaker 2: that their performance can be predicted into the future based 530 00:27:46,720 --> 00:27:48,560 Speaker 2: on the amount of compute that you are able to 531 00:27:48,600 --> 00:27:51,960 Speaker 2: apply and you're gonna have emergent phenomenon and can do 532 00:27:52,040 --> 00:27:54,760 Speaker 2: things that surprises you. But we know that if you 533 00:27:54,800 --> 00:27:57,840 Speaker 2: apply a million times more commute compute, you're gonna get 534 00:27:58,440 --> 00:28:02,479 Speaker 2: way it's actually predicted. It's almost as this has been 535 00:28:02,480 --> 00:28:04,159 Speaker 2: explained to me by people much smarter than me. It 536 00:28:04,200 --> 00:28:07,560 Speaker 2: were really deep in the space, and there's there's very 537 00:28:07,600 --> 00:28:11,200 Speaker 2: few things as predictable in what returns you're gonna get 538 00:28:11,240 --> 00:28:14,679 Speaker 2: from applying more compute than this. It's it's almost like 539 00:28:14,760 --> 00:28:17,359 Speaker 2: Newton's laws in terms of how predictable it is. Like 540 00:28:17,440 --> 00:28:19,879 Speaker 2: you are, you know that if you apply a million 541 00:28:19,920 --> 00:28:22,760 Speaker 2: times more compute you will get way more results, way 542 00:28:22,800 --> 00:28:25,960 Speaker 2: better results. So I think it's going to keep surprising 543 00:28:26,040 --> 00:28:28,400 Speaker 2: us and the bottleneck will be compute for a long 544 00:28:28,440 --> 00:28:31,200 Speaker 2: period of time. Now what it means for any given company, 545 00:28:31,200 --> 00:28:35,000 Speaker 2: and how do you what is a competitive advantage? Who 546 00:28:35,040 --> 00:28:38,520 Speaker 2: wins in that space? It's like that doesn't make it 547 00:28:38,560 --> 00:28:40,520 Speaker 2: easy to predict at all. You know, the Internet was 548 00:28:41,160 --> 00:28:43,440 Speaker 2: you had all these dot com boom startups. It wasn't 549 00:28:43,440 --> 00:28:46,240 Speaker 2: obvious at all that Amazon or Google or somebody else 550 00:28:46,240 --> 00:28:50,040 Speaker 2: would be the winner, and just investing in AI could 551 00:28:50,080 --> 00:28:51,640 Speaker 2: be like investing in the air and that I don't 552 00:28:51,680 --> 00:28:54,040 Speaker 2: know that you necessarily win by doing it, but certainly 553 00:28:54,120 --> 00:28:55,719 Speaker 2: need to have a strategy around the internet. If you're 554 00:28:55,760 --> 00:28:57,760 Speaker 2: in two thousands and you didn't, you know, your company's 555 00:28:57,800 --> 00:29:01,160 Speaker 2: probably been disrupted pretty heavily by now, so you. 556 00:29:01,120 --> 00:29:04,120 Speaker 1: Know, disruptions are never a good thing. There's you know, 557 00:29:04,120 --> 00:29:06,280 Speaker 1: it seems like there's more and more black swan events 558 00:29:06,320 --> 00:29:11,320 Speaker 1: over the last five years. I'm assuming you know disruptions 559 00:29:11,320 --> 00:29:14,240 Speaker 1: are good for your business because shippers need alternatives and 560 00:29:14,280 --> 00:29:18,160 Speaker 1: they need help. Do you see a surgeon volume not 561 00:29:18,200 --> 00:29:22,480 Speaker 1: necessarily in rates, but like in volume, when we see disruptions. 562 00:29:22,960 --> 00:29:26,280 Speaker 2: Not necessarily in volume, that's that tends to be just 563 00:29:26,360 --> 00:29:30,480 Speaker 2: driven by the macroeconomy, consumer demand trends. You know, you 564 00:29:30,520 --> 00:29:33,880 Speaker 2: saw a lot with COVID where people couldn't go out 565 00:29:33,920 --> 00:29:36,600 Speaker 2: to restaurants and travel and spend money on services, so 566 00:29:36,640 --> 00:29:40,280 Speaker 2: they just bought. They shifted consumption, they shifted the dollars 567 00:29:40,280 --> 00:29:45,920 Speaker 2: they're spend onto goods, so that I don't really call 568 00:29:45,960 --> 00:29:48,360 Speaker 2: that disruption. It led to supply chain disruptions because of 569 00:29:48,400 --> 00:29:51,320 Speaker 2: the economy. The supply side of the economy couldn't keep 570 00:29:51,400 --> 00:29:53,960 Speaker 2: up with all that demand. But yeah, we are sort 571 00:29:53,960 --> 00:29:55,760 Speaker 2: of in the volatility business. I'd like to think we're 572 00:29:55,760 --> 00:29:59,760 Speaker 2: anti fragile. Things go wrong and tend to find that's 573 00:29:59,760 --> 00:30:02,360 Speaker 2: when you tend to need someone to help you solve 574 00:30:02,360 --> 00:30:04,400 Speaker 2: these problems. You know, if the world's just static and 575 00:30:04,400 --> 00:30:08,120 Speaker 2: always the same, you could probably actually just contract directly 576 00:30:08,720 --> 00:30:11,120 Speaker 2: with an ocean carrier and a trucker and a customs 577 00:30:11,120 --> 00:30:13,840 Speaker 2: broker and just set the network up and forget about it. 578 00:30:14,320 --> 00:30:16,760 Speaker 2: But in a world that's volatile and always changing and 579 00:30:16,800 --> 00:30:19,880 Speaker 2: getting disrupted in this you know, the Panama Canal is 580 00:30:20,440 --> 00:30:23,200 Speaker 2: only operating at two thirds capacity right now because of 581 00:30:23,240 --> 00:30:26,360 Speaker 2: a drought, the lack of water in system. The Baltimore 582 00:30:26,360 --> 00:30:29,640 Speaker 2: Bridge cut off the port to Baltimore for over a month. 583 00:30:29,920 --> 00:30:33,200 Speaker 2: Like that's when you need someone with solutions to help 584 00:30:33,240 --> 00:30:36,200 Speaker 2: you route around it and find alternatives and better prices 585 00:30:36,200 --> 00:30:36,600 Speaker 2: and stuff. 586 00:30:36,640 --> 00:30:39,080 Speaker 1: So you could you give an example of what you 587 00:30:39,120 --> 00:30:41,520 Speaker 1: guys said for customers when the Baltimore when the Friends 588 00:30:41,560 --> 00:30:44,200 Speaker 1: of Scott Key Bridge fell down. 589 00:30:45,240 --> 00:30:47,400 Speaker 2: Yeah, well, the first thing we had containers on that 590 00:30:47,440 --> 00:30:49,200 Speaker 2: We still have containers that are on that ship, so 591 00:30:50,000 --> 00:30:52,800 Speaker 2: we had insurance claims to file. Was the very first 592 00:30:52,800 --> 00:30:54,200 Speaker 2: thing we had to do for the customers who were 593 00:30:54,200 --> 00:30:59,600 Speaker 2: on there right the what we've we've had we had 594 00:30:59,640 --> 00:31:02,080 Speaker 2: about two twelve hundred containers that were routed to the 595 00:31:02,120 --> 00:31:04,120 Speaker 2: Port of Baltimore that we're going to arrive within two 596 00:31:04,120 --> 00:31:05,680 Speaker 2: weeks of that, So we had to get all new 597 00:31:05,720 --> 00:31:09,960 Speaker 2: routings for all of those. UH immediately informed the customer, Hey, 598 00:31:09,960 --> 00:31:12,360 Speaker 2: you're you know, it's not gonna arrive in Baltimore. It's 599 00:31:12,360 --> 00:31:14,080 Speaker 2: not gonna arrive on time. It's gonna be a different 600 00:31:14,080 --> 00:31:17,040 Speaker 2: time after arrange trucking. It's gonna cost you more. You 601 00:31:17,160 --> 00:31:19,400 Speaker 2: have to pay for a longer trucking leg that's not free. 602 00:31:20,080 --> 00:31:22,480 Speaker 2: So a big part of his communication like getting in 603 00:31:22,520 --> 00:31:26,160 Speaker 2: front of the customer explaining what's happening, What does this 604 00:31:26,240 --> 00:31:28,959 Speaker 2: mean for you? What does it mean for your your customers? 605 00:31:29,000 --> 00:31:31,480 Speaker 2: If you have cargo with an ETA, when is it 606 00:31:31,520 --> 00:31:34,360 Speaker 2: gonna arrive? You need to know that. So a lot 607 00:31:34,360 --> 00:31:38,440 Speaker 2: of it is around communication. Baltimore is a major port 608 00:31:38,480 --> 00:31:40,280 Speaker 2: for the mid Atlantic region of the US. It's not 609 00:31:40,320 --> 00:31:43,200 Speaker 2: a major port in the global scheme of things, all right, 610 00:31:43,760 --> 00:31:46,480 Speaker 2: It's nothing like when the Ever Given got stuck in 611 00:31:46,480 --> 00:31:49,640 Speaker 2: the SEUs Canal. That ship that got stuck. We had 612 00:31:49,960 --> 00:31:52,520 Speaker 2: you know, forty five containers or so on that ship 613 00:31:53,040 --> 00:31:57,080 Speaker 2: and another several thousand lined up behind it, or even 614 00:31:57,160 --> 00:32:00,920 Speaker 2: just now with the Suez getting disrupted again, being able 615 00:32:00,960 --> 00:32:03,120 Speaker 2: to go communicate to everybody what is happening, When is 616 00:32:03,160 --> 00:32:04,960 Speaker 2: your cargo going to arrive, what does this mean, what 617 00:32:05,000 --> 00:32:08,760 Speaker 2: is it going to cost, et cetera. These are it's 618 00:32:08,800 --> 00:32:10,600 Speaker 2: hard to do without the databases. Like I don't know 619 00:32:10,600 --> 00:32:13,800 Speaker 2: how traditional freight forwarders do this. We get in an hour, 620 00:32:14,040 --> 00:32:17,120 Speaker 2: find all the shipment's affected and send everybody an email 621 00:32:17,160 --> 00:32:19,360 Speaker 2: and then have a plan for you know, a call 622 00:32:19,400 --> 00:32:21,160 Speaker 2: down plan to go call everybody and let them know 623 00:32:21,200 --> 00:32:21,719 Speaker 2: what's happening. 624 00:32:22,800 --> 00:32:24,800 Speaker 1: Right, this might be getting too much into the weeds. 625 00:32:24,840 --> 00:32:27,000 Speaker 1: But what happens to the containers that are stuck on 626 00:32:27,040 --> 00:32:29,440 Speaker 1: that ship by the bridge in Baltimore. 627 00:32:29,880 --> 00:32:32,520 Speaker 2: It's still TBD. They're going to be stuck in litigation 628 00:32:32,720 --> 00:32:38,800 Speaker 2: for for many years. What fundamentally, you have this rule 629 00:32:38,960 --> 00:32:45,720 Speaker 2: in maritime law called general average ancient law which says 630 00:32:45,760 --> 00:32:50,480 Speaker 2: that the damage is shared. If cargoes damage had shared 631 00:32:50,520 --> 00:32:54,320 Speaker 2: across everybody's all the customers, all the containers to share 632 00:32:54,360 --> 00:32:57,520 Speaker 2: that Parada Parada. So like some of the containers in 633 00:32:57,560 --> 00:32:59,840 Speaker 2: the front were damaged. There's perishable goods on there that 634 00:33:00,200 --> 00:33:03,360 Speaker 2: bad that are damaged, So those people will be made 635 00:33:03,440 --> 00:33:06,640 Speaker 2: whole by everybody else on the ship, and then it's 636 00:33:06,680 --> 00:33:11,080 Speaker 2: all gonna get tied up in litigation. So you know, 637 00:33:12,720 --> 00:33:14,920 Speaker 2: we advise the customer, hey, you should just forget about 638 00:33:14,920 --> 00:33:17,440 Speaker 2: those containers. We pay you use the insurance claim. Just 639 00:33:17,520 --> 00:33:19,800 Speaker 2: pretend they never you know, you hopefully they show up. 640 00:33:20,160 --> 00:33:23,480 Speaker 2: Both of them. Sadly, they were both exports for nonprofits. 641 00:33:23,480 --> 00:33:25,160 Speaker 2: We had this scrip called flexport dot org that does 642 00:33:25,240 --> 00:33:28,920 Speaker 2: humanitarian shipping, right they were. One of them was the 643 00:33:28,920 --> 00:33:33,280 Speaker 2: playground being shipped to in school in Africa. Yeah goodness. Uh, 644 00:33:33,320 --> 00:33:35,600 Speaker 2: they got paid in full for the values of their shipping. 645 00:33:35,600 --> 00:33:38,000 Speaker 2: Another one out there, but I forget the other one 646 00:33:38,040 --> 00:33:41,960 Speaker 2: was it was also goods headed to Africa. Forget the 647 00:33:41,960 --> 00:33:46,480 Speaker 2: exact contents of it, but there. Yeah, you sort of 648 00:33:46,520 --> 00:33:48,320 Speaker 2: advised them, Hey, you know, hopefully we get a free 649 00:33:48,320 --> 00:33:50,280 Speaker 2: playground out of this. In a few years. When you 650 00:33:50,360 --> 00:33:52,760 Speaker 2: got an insurance claim, I guess it gets handed over 651 00:33:52,760 --> 00:33:55,360 Speaker 2: to the insurance company and they liquidated or something. We'll see. 652 00:33:55,640 --> 00:34:00,400 Speaker 1: So you know, you started this company, how did you 653 00:34:00,480 --> 00:34:03,480 Speaker 1: get into transports? Because a lot of people. They're either 654 00:34:03,600 --> 00:34:05,880 Speaker 1: born into it or they kind of stumbled into it. 655 00:34:06,920 --> 00:34:09,960 Speaker 1: How did you get into the freight transportation logistics world? 656 00:34:12,200 --> 00:34:14,760 Speaker 2: So I was I was a customer of freight forwarders. 657 00:34:14,760 --> 00:34:16,880 Speaker 2: I ran this business. My brother actually ran the business. 658 00:34:16,920 --> 00:34:18,840 Speaker 2: I worked for him and then eventually became a partners 659 00:34:18,880 --> 00:34:21,520 Speaker 2: in a business with him and another guy named Michael Kanko. 660 00:34:22,000 --> 00:34:26,480 Speaker 2: We ran this business buying products in Asia, mostly motorcycles 661 00:34:26,480 --> 00:34:29,759 Speaker 2: and motorsports products and selling them on the internet in 662 00:34:29,760 --> 00:34:32,280 Speaker 2: the early two thousands. They started in the late nineties 663 00:34:32,320 --> 00:34:35,080 Speaker 2: when I was in selling college and joined them, and 664 00:34:35,120 --> 00:34:37,200 Speaker 2: so we would import products from China and I was 665 00:34:37,280 --> 00:34:41,280 Speaker 2: very frustrated. We're a software team, like we built software 666 00:34:41,320 --> 00:34:43,640 Speaker 2: for the business, and so we didn't call ourselves a 667 00:34:43,640 --> 00:34:46,799 Speaker 2: software company, but we built software. It was pre shopify. 668 00:34:46,880 --> 00:34:48,880 Speaker 2: So we made our own website to sell stuff on 669 00:34:48,920 --> 00:34:54,480 Speaker 2: the internet e commerce, checkout solutions, payments, inventory management, and 670 00:34:54,560 --> 00:34:57,040 Speaker 2: we had software. In the early two thousand, my brother 671 00:34:57,080 --> 00:35:02,880 Speaker 2: wrote the software that could check the rates for shipping 672 00:35:03,000 --> 00:35:06,560 Speaker 2: a product a motorcycle to a customer. It would go 673 00:35:06,680 --> 00:35:10,040 Speaker 2: on the carrier, the trucking carriers websites and log in 674 00:35:10,080 --> 00:35:13,640 Speaker 2: as us as our account find the cheapest option, check 675 00:35:13,719 --> 00:35:18,520 Speaker 2: multiple sites, book it, print the bill of landing, print 676 00:35:18,560 --> 00:35:20,920 Speaker 2: the shipping label, and a truck would show up and 677 00:35:21,440 --> 00:35:23,799 Speaker 2: we'd be able to ship it without having a law 678 00:35:23,880 --> 00:35:25,600 Speaker 2: even go on to their website and do anything. And 679 00:35:25,640 --> 00:35:27,799 Speaker 2: that was in the early two thousands when we try 680 00:35:27,840 --> 00:35:31,160 Speaker 2: to do similar stuff with the import side customs, and 681 00:35:31,200 --> 00:35:32,640 Speaker 2: it was just nowhere, you got no where, You couldn't 682 00:35:32,640 --> 00:35:35,560 Speaker 2: even get a company to help us on the phone 683 00:35:35,600 --> 00:35:40,400 Speaker 2: almost and so eventually we decided today this is probably Aventually. 684 00:35:40,400 --> 00:35:42,240 Speaker 2: There's a lot of other companies that have this problem 685 00:35:42,520 --> 00:35:48,000 Speaker 2: like customs and customs really complicated clear knowing what paperwork 686 00:35:48,040 --> 00:35:50,319 Speaker 2: you need, what the regulations are there should be The 687 00:35:50,360 --> 00:35:52,680 Speaker 2: original idea was like kind of turbo attacks for customs. 688 00:35:52,719 --> 00:35:55,319 Speaker 2: There should be a simple way to understand what you 689 00:35:55,400 --> 00:35:58,520 Speaker 2: need to clear customs and get the paperwork handled and 690 00:35:58,520 --> 00:36:00,800 Speaker 2: then we'll tell you freight solutions and other stuff. And 691 00:36:00,840 --> 00:36:03,920 Speaker 2: that's basically been the journey that we started over ten 692 00:36:04,000 --> 00:36:04,960 Speaker 2: years ago, my brother. 693 00:36:04,760 --> 00:36:08,640 Speaker 1: And I and you know, how would you describe your 694 00:36:08,880 --> 00:36:12,280 Speaker 1: kind of your management style as an entrepreneur and as 695 00:36:12,400 --> 00:36:14,719 Speaker 1: as someone that's leading a business that's growing. 696 00:36:16,000 --> 00:36:18,319 Speaker 2: Well, It's evolved a lot you know, I think in 697 00:36:18,400 --> 00:36:22,759 Speaker 2: the beginning, early days, I was really growth oriented and 698 00:36:23,600 --> 00:36:26,040 Speaker 2: really just tried to hire great people and get out 699 00:36:26,080 --> 00:36:31,160 Speaker 2: of their way. And I think now I'm much more 700 00:36:31,640 --> 00:36:33,799 Speaker 2: down into details, and I don't believe in getting out 701 00:36:33,800 --> 00:36:34,120 Speaker 2: of the way. 702 00:36:34,200 --> 00:36:38,680 Speaker 3: I'm I'm I'm learning everything that's going on and try 703 00:36:38,719 --> 00:36:40,960 Speaker 3: to be a ball of energy, showing up at everybody's 704 00:36:40,960 --> 00:36:43,920 Speaker 3: meetings and reading all the documents and the reports and 705 00:36:43,960 --> 00:36:46,120 Speaker 3: asking questions and challenging. 706 00:36:45,680 --> 00:36:48,960 Speaker 2: And much more hands on than I was in the past. 707 00:36:49,040 --> 00:36:54,400 Speaker 2: And I you know Toby Toby from Shopify as a 708 00:36:54,400 --> 00:36:57,640 Speaker 2: friend of mine and our big investor, and I've heard 709 00:36:57,680 --> 00:37:02,319 Speaker 2: him say that one of the worst most pernicious cliches 710 00:37:02,360 --> 00:37:05,400 Speaker 2: ever was that or the idea is that micro management 711 00:37:05,440 --> 00:37:09,240 Speaker 2: is bad. It's just like a terrible lesson that everybody's 712 00:37:09,280 --> 00:37:11,560 Speaker 2: been taught, and that actually you need a lot of micromanagement. 713 00:37:11,600 --> 00:37:13,239 Speaker 2: You should really be involved in what's going on in 714 00:37:13,239 --> 00:37:15,200 Speaker 2: your company. And I sort of took that to heart, 715 00:37:15,239 --> 00:37:17,040 Speaker 2: and I get good results from me. 716 00:37:18,120 --> 00:37:21,279 Speaker 1: So, you know, you mentioned Shopify as an investor. Are 717 00:37:21,280 --> 00:37:23,520 Speaker 1: you mostly or most of your investors private. 718 00:37:23,239 --> 00:37:26,000 Speaker 2: Equity funds like bet your capital funds? 719 00:37:26,080 --> 00:37:30,120 Speaker 1: Yeah, So is there a book that you've read about 720 00:37:30,239 --> 00:37:33,360 Speaker 1: the transportation, space or leadership that kind of left to 721 00:37:33,440 --> 00:37:37,200 Speaker 1: Mark and you that you would recommend to anyone listening 722 00:37:37,200 --> 00:37:38,759 Speaker 1: to us today, Oh too many. 723 00:37:38,880 --> 00:37:42,560 Speaker 2: I mean, we're very lucky that most of the world's 724 00:37:42,560 --> 00:37:45,160 Speaker 2: smartest humans who ever lived well at least in the 725 00:37:45,239 --> 00:37:47,719 Speaker 2: last five hundred years, like, took the time to write 726 00:37:47,760 --> 00:37:50,319 Speaker 2: down their ideas and you get to read them. So 727 00:37:50,880 --> 00:37:53,319 Speaker 2: I think that the difference between who you are now 728 00:37:53,320 --> 00:37:55,080 Speaker 2: and who you'll be in five years is almost entirely 729 00:37:55,120 --> 00:37:56,880 Speaker 2: made up of the books that you read and the 730 00:37:56,920 --> 00:38:01,239 Speaker 2: podcast that you listen to. And yeah, actually, one of 731 00:38:01,239 --> 00:38:03,520 Speaker 2: my best tricks is that if you don't no one 732 00:38:03,560 --> 00:38:05,080 Speaker 2: has enough time to read all the books. And I 733 00:38:05,120 --> 00:38:07,000 Speaker 2: have one of those bad habits of buying more books 734 00:38:07,000 --> 00:38:11,360 Speaker 2: than I can read. But if you search YouTube for 735 00:38:11,480 --> 00:38:16,400 Speaker 2: almost any book author and nonfiction, you'll find an interview 736 00:38:16,440 --> 00:38:20,040 Speaker 2: with the author in twenty minutes. You could get eighty 737 00:38:20,040 --> 00:38:21,920 Speaker 2: percent of the ideas in twenty minutes. You know, you 738 00:38:21,920 --> 00:38:23,719 Speaker 2: can listen to it on two XP if you don't 739 00:38:23,719 --> 00:38:26,040 Speaker 2: have times. I do a lot of audio books and 740 00:38:26,080 --> 00:38:30,120 Speaker 2: a lot of listening on YouTube to smart people, but 741 00:38:30,160 --> 00:38:31,600 Speaker 2: I do I do read a ton of books as 742 00:38:31,600 --> 00:38:36,280 Speaker 2: well transportation. The classic is The Box by Mark Limenson. 743 00:38:36,280 --> 00:38:37,839 Speaker 3: If you haven't had it on your podcast, you should 744 00:38:37,880 --> 00:38:39,400 Speaker 3: get him. 745 00:38:39,440 --> 00:38:42,000 Speaker 2: For management style, I really like this book called Certain 746 00:38:42,040 --> 00:38:47,719 Speaker 2: to Win by Chet Richards and he was an acolyte 747 00:38:47,760 --> 00:38:52,239 Speaker 2: of John Boyd, who was the probably the greatest military 748 00:38:52,280 --> 00:38:57,480 Speaker 2: strategist of the twentieth century of the United States. He 749 00:38:57,640 --> 00:39:03,920 Speaker 2: was the architect of the Gulf War One strategy. He 750 00:39:04,040 --> 00:39:09,560 Speaker 2: also designed the F fifteen like prototypes. Like he's a 751 00:39:09,360 --> 00:39:12,600 Speaker 2: a true military genius, and Certain to Win is the 752 00:39:12,920 --> 00:39:16,520 Speaker 2: application of his ideas to business, which is always a 753 00:39:16,600 --> 00:39:19,759 Speaker 2: very interesting as a history I like history a lot, 754 00:39:19,840 --> 00:39:21,319 Speaker 2: so being able to see, like, okay, how do I 755 00:39:21,400 --> 00:39:24,440 Speaker 2: take lessons from the military who they deal with, a 756 00:39:24,480 --> 00:39:27,600 Speaker 2: lot of complexity, a lot of chaos, a lot of uncertainty. 757 00:39:28,320 --> 00:39:31,560 Speaker 2: That's pretty applicable in global logistics and transportation. 758 00:39:32,400 --> 00:39:35,520 Speaker 1: And so so being a leader of a company and 759 00:39:35,600 --> 00:39:39,200 Speaker 1: an entrepreneur. You know, before I let you go, is 760 00:39:39,360 --> 00:39:41,879 Speaker 1: what keeps you up at night related to work? 761 00:39:43,760 --> 00:39:46,760 Speaker 2: We keep other people up at night. No, I'm just kidding. 762 00:39:46,800 --> 00:39:50,960 Speaker 2: That's a that's a that's a military line as well. No, 763 00:39:51,160 --> 00:39:53,680 Speaker 2: you know what, it's a very hard business because we are. 764 00:39:54,560 --> 00:39:58,040 Speaker 2: We're very dependent on our on our vendors to do 765 00:39:58,080 --> 00:40:00,600 Speaker 2: a good job for our customers, and there's a lot 766 00:40:00,640 --> 00:40:04,239 Speaker 2: of volatility and getting enough For example, in the current world, 767 00:40:04,440 --> 00:40:07,280 Speaker 2: can I get enough space. We did a great job 768 00:40:07,920 --> 00:40:11,560 Speaker 2: this year of winning business from new customers and we 769 00:40:11,680 --> 00:40:14,239 Speaker 2: if we can get enough space on the ships, I 770 00:40:14,239 --> 00:40:16,120 Speaker 2: think we might double the size of our business in 771 00:40:16,160 --> 00:40:19,480 Speaker 2: one year, which would be crazy. I don't think we 772 00:40:19,520 --> 00:40:21,200 Speaker 2: can get enough space to serve all the demand that 773 00:40:21,280 --> 00:40:25,799 Speaker 2: we have, so going around making sure, going to work 774 00:40:25,840 --> 00:40:28,719 Speaker 2: with all the vendors. It's a very global business. We 775 00:40:28,760 --> 00:40:31,759 Speaker 2: have two thousand employees. We ship cargo to and from 776 00:40:31,800 --> 00:40:36,440 Speaker 2: one hundred countries, so that's kind of hard to be everywhere. 777 00:40:38,160 --> 00:40:39,840 Speaker 2: I can answer the question literally what keeps me up 778 00:40:39,880 --> 00:40:42,160 Speaker 2: and now is that every time zone of the world. 779 00:40:43,080 --> 00:40:45,839 Speaker 2: But yeah, it's kind ofel It can feel unwieldy, right, 780 00:40:45,880 --> 00:40:48,399 Speaker 2: Like how do you keep a handle on such a big, 781 00:40:48,440 --> 00:40:52,560 Speaker 2: complex organization that's so global finding out what's happening. We 782 00:40:52,600 --> 00:40:56,239 Speaker 2: do a lot of document writing in part it helps 783 00:40:56,239 --> 00:40:59,920 Speaker 2: with the time zones. But people write monthly business reviews 784 00:41:00,239 --> 00:41:03,600 Speaker 2: patres about their business unit and keeping track of it all. 785 00:41:05,400 --> 00:41:07,000 Speaker 2: Knowing if everybody's doing a good job, and you have 786 00:41:07,040 --> 00:41:13,200 Speaker 2: the best people empowered fighting bureaucracy on their behalf. The 787 00:41:13,239 --> 00:41:16,160 Speaker 2: hatred of bureaucracy is not enough to make buiocracy go away. 788 00:41:16,719 --> 00:41:18,319 Speaker 2: So you have to live in reality the fact that 789 00:41:18,320 --> 00:41:20,400 Speaker 2: we do have bureaucracy and some of it's good and 790 00:41:20,440 --> 00:41:22,799 Speaker 2: some of it's bad. Trying to understand how you can 791 00:41:22,840 --> 00:41:25,640 Speaker 2: break down the friction that our employees face every day 792 00:41:26,080 --> 00:41:27,200 Speaker 2: so they can do a great job. 793 00:41:27,600 --> 00:41:30,839 Speaker 1: All right, great, thanks Ryan, and I really appreciate your 794 00:41:30,840 --> 00:41:34,080 Speaker 1: time today. It was a great learning more about flex 795 00:41:34,080 --> 00:41:36,520 Speaker 1: sport and here in front of you. So thanks again 796 00:41:36,560 --> 00:41:37,040 Speaker 1: for the time. 797 00:41:37,680 --> 00:41:39,160 Speaker 2: Yeah, well, thanks for the opportunity to big fans of 798 00:41:39,200 --> 00:41:41,080 Speaker 2: Bloomberg over here, so we appreciate. 799 00:41:40,760 --> 00:41:43,399 Speaker 1: It, okay, and hopefully the Talking Transports podcasts as. 800 00:41:43,320 --> 00:41:46,280 Speaker 2: Well obviously, yes, yeah, yeah. 801 00:41:45,640 --> 00:41:47,400 Speaker 1: Well, I also want to thank everyone for tuning in. 802 00:41:47,440 --> 00:41:50,080 Speaker 1: If you like the episode, please subscribe and leave a review. 803 00:41:50,239 --> 00:41:53,400 Speaker 1: We've lined up a number of great guests for the podcast. 804 00:41:54,200 --> 00:41:58,799 Speaker 1: Check back to hear conversations with C suite executives, shippers, regulators, 805 00:41:58,800 --> 00:42:02,759 Speaker 1: and decision makers within the frank transportation markets. Also, if 806 00:42:02,760 --> 00:42:04,960 Speaker 1: you have an idea for a future episode, please hit me. 807 00:42:04,960 --> 00:42:06,320 Speaker 2: Up on the Bloomberg terminal. 808 00:42:06,520 --> 00:42:09,680 Speaker 1: We're on Twitter at Logistics late. Thanks everyone, and take care.