1 00:00:08,039 --> 00:00:11,240 Speaker 1: Hi everyone, this is Lee Clasgow when We're Talking Transports. 2 00:00:11,320 --> 00:00:14,600 Speaker 1: Welcome to Bloomberg Intelligence Talking Transport Podcast. I'm your host, 3 00:00:14,720 --> 00:00:18,680 Speaker 1: Lee Claskow, Senior free transportation Logistics Analysts at Bloomberg Intelligence, 4 00:00:18,960 --> 00:00:21,919 Speaker 1: Bloomberg's in house research arm of almost five hundred analysts 5 00:00:21,960 --> 00:00:25,520 Speaker 1: and strategists around the globe. Before diving in little public 6 00:00:25,560 --> 00:00:29,360 Speaker 1: service announcement. Your support is instrumental to keep bringing great 7 00:00:29,440 --> 00:00:31,920 Speaker 1: guests onto the podcast like the one we have today. 8 00:00:32,320 --> 00:00:34,360 Speaker 1: If you haven't already, please do take a moment to 9 00:00:34,440 --> 00:00:39,200 Speaker 1: follow rate and share the Talking Transports podcast with your family, friends, 10 00:00:39,240 --> 00:00:41,599 Speaker 1: and colleagues. We really appreciate your support. 11 00:00:42,040 --> 00:00:42,400 Speaker 2: Today. 12 00:00:42,520 --> 00:00:45,000 Speaker 1: I'm very excited to have Benjamin Gordon with us. He's 13 00:00:45,040 --> 00:00:49,120 Speaker 1: the founder of BGSA Holdings and Cambridge Capital. Ben draws 14 00:00:49,159 --> 00:00:53,120 Speaker 1: on a career spent investing in advising and building supply 15 00:00:53,240 --> 00:00:57,400 Speaker 1: chain and technology companies. At BGSA Holdings, he led the 16 00:00:57,400 --> 00:01:00,520 Speaker 1: firm's efforts advising on over one billion worth of supply 17 00:01:00,720 --> 00:01:06,680 Speaker 1: chain transactions. Prior to BGSA Holdings, Ben founded three Plex, 18 00:01:06,800 --> 00:01:11,120 Speaker 1: the internet solution enabling third party logistics companies to automate 19 00:01:11,200 --> 00:01:15,720 Speaker 1: their businesses. Three Plex was later acquired by Bersk Prior 20 00:01:15,760 --> 00:01:20,280 Speaker 1: to three Plex, Ben advised transportation logistics clients at Mercer 21 00:01:20,360 --> 00:01:23,920 Speaker 1: Management Consulting, and prior to Mercer, Ben worked in his 22 00:01:24,000 --> 00:01:28,440 Speaker 1: family's transportation business AMI, where he helped the company expand 23 00:01:28,480 --> 00:01:32,920 Speaker 1: its logistics operation. He received a master's in Business administration 24 00:01:33,040 --> 00:01:35,919 Speaker 1: from Harvard Business School and a Bachelor of Arts degree 25 00:01:35,920 --> 00:01:39,640 Speaker 1: from Yale. Ben, thanks for joining us on the podcast. 26 00:01:39,720 --> 00:01:40,840 Speaker 1: Thanks so much for being here. 27 00:01:41,360 --> 00:01:42,880 Speaker 3: Thanks Ley, great to be with you again. 28 00:01:44,080 --> 00:01:48,480 Speaker 1: And you know Cambridge Capital BGSA. I know them well, 29 00:01:48,520 --> 00:01:50,560 Speaker 1: but maybe not a household name for our listeners. Can 30 00:01:50,560 --> 00:01:52,840 Speaker 1: you tell us a little about the organizations that you founded? 31 00:01:53,640 --> 00:01:57,520 Speaker 3: Sure, glad to so. BGSA is an investment bank. I 32 00:01:57,560 --> 00:01:59,920 Speaker 3: started it in two thousand and two after we saw 33 00:02:00,120 --> 00:02:04,520 Speaker 3: three Plex, the SAASTMS company that you mentioned. The reason 34 00:02:04,560 --> 00:02:07,360 Speaker 3: I started it was I had all these investment bankers 35 00:02:07,360 --> 00:02:10,920 Speaker 3: calling on me when I was running my logistics software company, 36 00:02:10,960 --> 00:02:14,840 Speaker 3: three Plex, and most of them didn't really understand the 37 00:02:14,880 --> 00:02:18,040 Speaker 3: business as well as we did. Now, maybe that's to 38 00:02:18,080 --> 00:02:21,040 Speaker 3: be expected, because an operator should know is business better 39 00:02:21,080 --> 00:02:23,359 Speaker 3: than somebody on the outside. But I thought there was 40 00:02:23,400 --> 00:02:26,840 Speaker 3: an opportunity to build an investment bank that could provide 41 00:02:27,320 --> 00:02:30,840 Speaker 3: M and A and advisory services for CEOs of logistics 42 00:02:30,840 --> 00:02:34,400 Speaker 3: and supply chain companies based on deep domain expertise and 43 00:02:35,120 --> 00:02:38,399 Speaker 3: over the course of twenty two twenty three years done 44 00:02:38,440 --> 00:02:41,160 Speaker 3: over fifty M and A transactions. We've worked with lots 45 00:02:41,200 --> 00:02:47,720 Speaker 3: of top companies giants like ups, FED, XDHL, Quintin, Augleshanker, Panel, PENA, 46 00:02:47,880 --> 00:02:55,359 Speaker 3: NFI and many others, and that business continues and continues 47 00:02:55,400 --> 00:02:57,760 Speaker 3: to work with lots of trific companies in supply chain. 48 00:02:58,160 --> 00:03:02,519 Speaker 3: Cambridge Capital is the private equity firm that we started 49 00:03:02,560 --> 00:03:07,800 Speaker 3: in twenty ten. Cambridge invests in outstanding logistics and supply 50 00:03:07,880 --> 00:03:12,280 Speaker 3: chain companies and the premise there is use what we 51 00:03:12,360 --> 00:03:18,400 Speaker 3: know as operators and investors to pick great people, building 52 00:03:18,440 --> 00:03:21,760 Speaker 3: great companies and then bring them extra resources to help 53 00:03:21,800 --> 00:03:26,520 Speaker 3: them scale up faster. The model at Cambridge is invest 54 00:03:26,880 --> 00:03:32,040 Speaker 3: both growth equity and buyout capital in terrific businesses. We 55 00:03:32,080 --> 00:03:35,600 Speaker 3: had the great fortune of meeting Brad Jacobs and being 56 00:03:35,960 --> 00:03:39,600 Speaker 3: an original investor with Brad in XPO back in twenty eleven. 57 00:03:40,160 --> 00:03:42,880 Speaker 3: We've invested in a number of companies since then's day. 58 00:03:42,880 --> 00:03:46,280 Speaker 3: We've got a portfolio of nine companies across multiple areas 59 00:03:46,360 --> 00:03:51,280 Speaker 3: of the supply chain sector and so on the BJSA side, 60 00:03:51,920 --> 00:03:54,920 Speaker 3: provide advice on the Cambridge Capital side, put our money 61 00:03:54,960 --> 00:03:57,880 Speaker 3: where our mouth is and get involved in supporting great 62 00:03:57,920 --> 00:03:59,120 Speaker 3: people building great businesses. 63 00:04:00,000 --> 00:04:04,520 Speaker 1: Sounds like that keeps you extremely extremely busy. Now I 64 00:04:04,560 --> 00:04:09,240 Speaker 1: know BGSA they hold a big conference every January down 65 00:04:09,280 --> 00:04:12,240 Speaker 1: in Florida, so we're just coming off of that. Can 66 00:04:12,280 --> 00:04:14,360 Speaker 1: you talk about the takeaways from the conference? 67 00:04:15,120 --> 00:04:19,320 Speaker 3: Sure? Absolutely so. This is our nineteenth annual BJSA Supply 68 00:04:19,440 --> 00:04:22,839 Speaker 3: Chain CEO conference and biggest and best yet. We had 69 00:04:22,880 --> 00:04:25,880 Speaker 3: three hundred and eighty CEOs and other supply chain leaders 70 00:04:26,279 --> 00:04:30,640 Speaker 3: here in Palm Beach at the Breakers apparently the epicenter 71 00:04:30,640 --> 00:04:34,120 Speaker 3: of the universe now given the proxity tomorrow Lago, So 72 00:04:34,640 --> 00:04:39,320 Speaker 3: a lot of people here and a few interesting takeaways. 73 00:04:39,440 --> 00:04:44,600 Speaker 3: First of all, the level of bullishness enthusiasm, I would 74 00:04:44,600 --> 00:04:48,400 Speaker 3: say is exceptionally high. One way to measure that is 75 00:04:48,960 --> 00:04:53,200 Speaker 3: we actually survey the CEOs and attendees that join us 76 00:04:53,760 --> 00:04:56,680 Speaker 3: and question that. We asked, what was the growth for 77 00:04:56,800 --> 00:04:59,320 Speaker 3: your company last year and what do you expect for 78 00:04:59,400 --> 00:05:04,160 Speaker 3: this year? And more than seventy percent expect over ten 79 00:05:04,200 --> 00:05:10,039 Speaker 3: percent growth this year, which is noteworthy certainly, you know, 80 00:05:10,200 --> 00:05:13,880 Speaker 3: looking at the public comps. That means much stronger growth 81 00:05:14,120 --> 00:05:17,400 Speaker 3: than what the past year produced for the sector. And 82 00:05:17,400 --> 00:05:20,400 Speaker 3: then a second data point is we ask people about 83 00:05:20,400 --> 00:05:23,960 Speaker 3: their appetite for M and A both in terms of 84 00:05:23,960 --> 00:05:26,280 Speaker 3: their opinion. So we ask what level of M and 85 00:05:26,320 --> 00:05:28,640 Speaker 3: A activity do you expect for the industry this year? 86 00:05:29,360 --> 00:05:31,640 Speaker 3: And then we also ask how likely are you to 87 00:05:31,760 --> 00:05:37,560 Speaker 3: consider doing something mergor acquisition, investment, or otherwise. About seventy 88 00:05:37,800 --> 00:05:41,240 Speaker 3: three percent expect more M and A activity in the 89 00:05:41,240 --> 00:05:45,680 Speaker 3: industry this year, and more than seventy percent say they 90 00:05:45,680 --> 00:05:48,480 Speaker 3: are either very likely or somewhat likely to do something 91 00:05:48,640 --> 00:05:51,360 Speaker 3: themselves this year. So I think that tells you something 92 00:05:51,400 --> 00:05:58,240 Speaker 3: about both overall industry enthusiasm and bollishness after a long, 93 00:05:58,360 --> 00:06:01,920 Speaker 3: deep record freight recession. It also tells you something about 94 00:06:01,960 --> 00:06:04,840 Speaker 3: the appetite for M and A and deal activity. 95 00:06:05,800 --> 00:06:07,120 Speaker 2: And you know, when. 96 00:06:07,000 --> 00:06:10,360 Speaker 1: Talking about appetite for more deals this year, are there 97 00:06:10,360 --> 00:06:13,400 Speaker 1: any sub sectors that you know you think that are 98 00:06:13,440 --> 00:06:16,600 Speaker 1: more ripe than others for transactions? 99 00:06:17,360 --> 00:06:21,760 Speaker 3: Yeah, definitely. So I mean, first of all, let me 100 00:06:21,800 --> 00:06:25,480 Speaker 3: address that in two ways. First of all, thematically, Okay, 101 00:06:25,560 --> 00:06:29,920 Speaker 3: So thematically, I mean there's lots of M and A activity. 102 00:06:30,640 --> 00:06:35,080 Speaker 3: But I would highlight three areas in particular. One is divestitures, 103 00:06:35,440 --> 00:06:38,080 Speaker 3: where big companies are shedding what they perceive as non 104 00:06:38,080 --> 00:06:41,040 Speaker 3: core assets. So an example would be UPS selling the 105 00:06:41,120 --> 00:06:45,240 Speaker 3: Coyugi Logistics division to RXO to get out of the 106 00:06:45,240 --> 00:06:48,240 Speaker 3: truck broke region to focus on the core parcel and 107 00:06:48,320 --> 00:06:52,239 Speaker 3: an e commerce logistics business. Similarly FedEx spinning off FedEx Freight, 108 00:06:52,920 --> 00:06:56,040 Speaker 3: h Robinson selling it to European division to Sender, Marist 109 00:06:56,080 --> 00:07:01,039 Speaker 3: divesting Sply Chain Service you know as well. So divestitures 110 00:07:01,080 --> 00:07:05,479 Speaker 3: I think are one theme, particularly for public companies. Second 111 00:07:05,560 --> 00:07:08,680 Speaker 3: is consolidation. Guys that are great at one thing, they're saying, 112 00:07:08,760 --> 00:07:11,600 Speaker 3: let me do more of it. A great example would 113 00:07:11,640 --> 00:07:16,880 Speaker 3: be DSB buying Schenker for fourteen billion, the RXO deal, 114 00:07:17,880 --> 00:07:20,560 Speaker 3: RXO buying Coyote. It was a divestiture for UPS, it 115 00:07:20,680 --> 00:07:25,840 Speaker 3: was a consolidation for RXO, Wistech buying a string of companies, 116 00:07:25,840 --> 00:07:29,040 Speaker 3: and Global Trade Management, so those are all consolidators. And 117 00:07:29,080 --> 00:07:31,880 Speaker 3: then a third theme would be specialists, people that are 118 00:07:31,880 --> 00:07:35,920 Speaker 3: saying I'm going to go deeper in niche areas. For instance, 119 00:07:36,000 --> 00:07:41,440 Speaker 3: in reverse logistics, DHL bought inmar which followed on moves 120 00:07:41,480 --> 00:07:44,320 Speaker 3: that FedEx and UPS had made in the reverse logistics arena, 121 00:07:44,960 --> 00:07:48,320 Speaker 3: UPS going deeper in colt Chaine buying vpl and frigotrons. 122 00:07:49,240 --> 00:07:52,120 Speaker 3: And so I think all three of those themes, divestitures, 123 00:07:52,160 --> 00:07:56,280 Speaker 3: consolidation and specialists, those are all things that we're going 124 00:07:56,360 --> 00:07:58,560 Speaker 3: to see a lot more of. The Other thing that 125 00:07:58,600 --> 00:08:01,400 Speaker 3: I would add is I think you will see more 126 00:08:01,520 --> 00:08:04,960 Speaker 3: M and A from a sector standpoint in a variety 127 00:08:04,960 --> 00:08:09,840 Speaker 3: of fragmented areas. Truck brokerage certainly an obvious one, you know, 128 00:08:09,920 --> 00:08:14,240 Speaker 3: freight forwarding, warehousing, etc. But interestingly, one of the things 129 00:08:14,280 --> 00:08:17,600 Speaker 3: that I learned from watching Brad Jacobs, Brad took a 130 00:08:17,600 --> 00:08:21,200 Speaker 3: fragmented market and consolidated. Even though there's still much more 131 00:08:21,200 --> 00:08:24,680 Speaker 3: to do on truck brokeridge and other areas of logistics, 132 00:08:25,280 --> 00:08:28,080 Speaker 3: nobody has really done that to the same extent. On 133 00:08:28,160 --> 00:08:32,360 Speaker 3: the tech enabled logistics or supply chain software side. Certainly 134 00:08:32,400 --> 00:08:36,280 Speaker 3: wise tech Descarte and others have been serial acquirers, but 135 00:08:36,360 --> 00:08:40,199 Speaker 3: I think you're going to see more XPO like consolidation 136 00:08:40,360 --> 00:08:45,600 Speaker 3: strategies applied to the tech enabled and supply chain software side. 137 00:08:45,800 --> 00:08:49,000 Speaker 1: And when you say tech enable software side, can you 138 00:08:49,200 --> 00:08:53,440 Speaker 1: talk about you know, companies that might be consolidators. 139 00:08:53,600 --> 00:08:57,040 Speaker 2: And I don't know if. 140 00:08:56,160 --> 00:08:59,520 Speaker 1: There's an obvious company that might be benefit from being 141 00:08:59,720 --> 00:09:01,520 Speaker 1: being bought by a larger player. 142 00:09:02,480 --> 00:09:04,720 Speaker 3: Yeah. Well, so I'll give you two examples, one on 143 00:09:04,760 --> 00:09:07,400 Speaker 3: the software side, one on the tech enabled service aside. 144 00:09:07,440 --> 00:09:11,280 Speaker 3: So software, if you look at and this is one 145 00:09:11,320 --> 00:09:14,000 Speaker 3: of the things that came up at the BJSA supply 146 00:09:14,040 --> 00:09:17,040 Speaker 3: chain conference, if you look at the last nine years, 147 00:09:17,440 --> 00:09:19,360 Speaker 3: if you invested in the S and P five hundred, 148 00:09:19,760 --> 00:09:21,200 Speaker 3: you would have made a two hundred and eighty eight 149 00:09:21,240 --> 00:09:25,480 Speaker 3: percent return. Great, if you'd invested in Descartes you would 150 00:09:25,480 --> 00:09:28,560 Speaker 3: have made almost triple that. You would have made just 151 00:09:28,640 --> 00:09:32,040 Speaker 3: under seven hundred percent. And if you invested in wise 152 00:09:32,040 --> 00:09:35,199 Speaker 3: Tech you would have made over three thousand percent thirty 153 00:09:35,240 --> 00:09:39,720 Speaker 3: one twelve. And so both wise Tech and Discard are 154 00:09:39,760 --> 00:09:43,680 Speaker 3: examples of consolidators and supply chain software. To give you 155 00:09:43,679 --> 00:09:48,480 Speaker 3: an example, Descartes over the last nine years has bought 156 00:09:48,840 --> 00:09:53,160 Speaker 3: something like thirty over thirty companies. Yeah, typically three to 157 00:09:53,200 --> 00:09:56,720 Speaker 3: five companies a year every year as they're you know, 158 00:09:56,800 --> 00:09:59,360 Speaker 3: incrementally expanding what they do. But really what they have 159 00:09:59,400 --> 00:10:03,200 Speaker 3: focused on is global trade management software. So for example, 160 00:10:04,280 --> 00:10:08,160 Speaker 3: automating cross border trade, making compliance easier, things like that, 161 00:10:08,920 --> 00:10:12,360 Speaker 3: And so that's a good example of a consolidation strategy 162 00:10:12,360 --> 00:10:15,199 Speaker 3: and supply chain software. I think you will see that 163 00:10:15,640 --> 00:10:20,280 Speaker 3: kind of approach in other areas, whether it's TMS or 164 00:10:20,960 --> 00:10:26,080 Speaker 3: analytics and intelligence or a whole host of other areas. 165 00:10:26,120 --> 00:10:28,920 Speaker 3: So I think that's the software side on the circuit. 166 00:10:29,360 --> 00:10:31,480 Speaker 1: Can I just ask on the software side, so when 167 00:10:31,480 --> 00:10:35,040 Speaker 1: they're buying something, they're just buying something to add to 168 00:10:35,080 --> 00:10:37,040 Speaker 1: a speed of what they're offering, or are they getting 169 00:10:37,040 --> 00:10:42,840 Speaker 1: into totally different markets When these kind of companies are inquisitive, well. 170 00:10:42,760 --> 00:10:46,720 Speaker 3: In Descartes's case, they mostly focused on doing one thing 171 00:10:46,800 --> 00:10:50,040 Speaker 3: really well, which is global trade management, So most of 172 00:10:50,040 --> 00:10:52,800 Speaker 3: the acquisitions have had something to do with that, automating 173 00:10:53,080 --> 00:10:57,679 Speaker 3: cross border trade or compliance or something like that. Then 174 00:10:57,720 --> 00:11:01,920 Speaker 3: there are other companies like in For. In For has 175 00:11:01,960 --> 00:11:04,680 Speaker 3: made a string of acquisitions really over the last two decades, 176 00:11:05,600 --> 00:11:09,720 Speaker 3: combining TMS and WMS and analytics and a whole host 177 00:11:09,720 --> 00:11:12,880 Speaker 3: of other capabilities. So I think you've got both one 178 00:11:12,880 --> 00:11:15,720 Speaker 3: which is the deeper strategy, and one which is the 179 00:11:15,720 --> 00:11:20,959 Speaker 3: broader strategy. My personal view is you could see winners 180 00:11:21,000 --> 00:11:24,199 Speaker 3: on both sides. I would say, the deeper strategy is 181 00:11:24,440 --> 00:11:27,880 Speaker 3: illustrated by Descartes has been the one that's been the 182 00:11:27,880 --> 00:11:30,480 Speaker 3: most successful in the public markets. But there are plenty 183 00:11:30,480 --> 00:11:33,400 Speaker 3: of private equity back companies that have done a broader strategy, 184 00:11:34,080 --> 00:11:36,760 Speaker 3: uh In for being example, among others that have done 185 00:11:36,840 --> 00:11:37,280 Speaker 3: quite well. 186 00:11:38,880 --> 00:11:41,120 Speaker 2: Gotcha, and then you were you were mentioning on the 187 00:11:41,120 --> 00:11:42,319 Speaker 2: services side. 188 00:11:42,760 --> 00:11:45,079 Speaker 3: Yeah, so second would be services. So what would be 189 00:11:45,120 --> 00:11:49,360 Speaker 3: a good example tech enabled services? If you think about it, 190 00:11:50,960 --> 00:11:53,080 Speaker 3: all the companies that you cover in the in the 191 00:11:53,080 --> 00:11:56,199 Speaker 3: public markets, you know, C. H. Robinson, Expediors, et cetera. 192 00:11:57,559 --> 00:12:01,520 Speaker 3: You know, those are great services companies. Increasingly, over the 193 00:12:01,559 --> 00:12:05,880 Speaker 3: last five years you've seen the emergence of companies, many 194 00:12:05,920 --> 00:12:09,200 Speaker 3: times venture backed, that are trying to automate as much 195 00:12:09,200 --> 00:12:11,880 Speaker 3: of that as possible. Uh, and that are more tech 196 00:12:11,960 --> 00:12:16,360 Speaker 3: enabled versions of those kinds of services companies. Flex Sport 197 00:12:16,360 --> 00:12:20,040 Speaker 3: would be a classic examples a tech enabled freight forwarder. Uh, 198 00:12:20,160 --> 00:12:24,880 Speaker 3: you know, competing against extraditors and you know some some 199 00:12:25,000 --> 00:12:29,280 Speaker 3: have of course done poorly, I mean legendary unfortunately example 200 00:12:29,400 --> 00:12:32,800 Speaker 3: like Convoy you know, which failed, But plenty of others 201 00:12:32,800 --> 00:12:37,000 Speaker 3: that are succeeding that are tech enabled versions of services. Companies. 202 00:12:38,040 --> 00:12:42,520 Speaker 3: I think you will see acquisitions that lead to combinations 203 00:12:42,679 --> 00:12:44,520 Speaker 3: in that arena. I'll give you a couple of examples 204 00:12:44,800 --> 00:12:48,240 Speaker 3: on the tech enabled freight forwarding front. You know, flexport 205 00:12:48,240 --> 00:12:51,680 Speaker 3: has made a series of acquisitions as as they've you know, 206 00:12:51,720 --> 00:12:56,040 Speaker 3: broadened their footprint in other areas. Look, I mean you 207 00:12:56,040 --> 00:13:01,680 Speaker 3: have tech enabled truck brokers. Uh yeah, tech enabled warehousing 208 00:13:01,720 --> 00:13:07,760 Speaker 3: companies companies like Flex and stored and cart dot Com 209 00:13:07,760 --> 00:13:11,720 Speaker 3: and others you have, Look, I mean a tech enabled 210 00:13:11,720 --> 00:13:15,400 Speaker 3: truck borkridge company. An example would be the Uber Freight business. 211 00:13:15,920 --> 00:13:19,720 Speaker 3: For instance, Uber Freate's acquisition of Transplats as a you know, 212 00:13:20,040 --> 00:13:22,120 Speaker 3: as a case study, and how they're you know, trying 213 00:13:22,120 --> 00:13:24,200 Speaker 3: to expand that. So I think you'll see both of 214 00:13:24,240 --> 00:13:26,560 Speaker 3: those groups of companies make more acquisitions. 215 00:13:26,600 --> 00:13:30,800 Speaker 1: This year got youa and I guess, as you know, 216 00:13:30,920 --> 00:13:35,000 Speaker 1: because you're you're a banker and an investor, are you 217 00:13:35,080 --> 00:13:39,439 Speaker 1: actively looking to buy and consolidate companies? 218 00:13:39,960 --> 00:13:43,400 Speaker 3: So so I spend most of my time focused on 219 00:13:43,480 --> 00:13:48,040 Speaker 3: Cambridge Capital and a Cambridge Capital, we have night portfolio companies. 220 00:13:49,280 --> 00:13:54,200 Speaker 3: Some of them are outstanding pure organic growth companies. Some 221 00:13:54,240 --> 00:13:58,120 Speaker 3: of them are also looking for acquisitions to accelerate beyond that. 222 00:13:58,240 --> 00:14:00,520 Speaker 3: So I'll give you a couple of examples. We have 223 00:14:00,559 --> 00:14:05,640 Speaker 3: a platform and truck brokeridge called Everest. Everest is in 224 00:14:05,679 --> 00:14:09,200 Speaker 3: a very good position to acquire and expand. One reason 225 00:14:09,240 --> 00:14:14,400 Speaker 3: why is because they figured out how to create competitive 226 00:14:14,400 --> 00:14:17,640 Speaker 3: advantage in a couple of ways, one with technology, but 227 00:14:17,679 --> 00:14:21,920 Speaker 3: two with labor. So they actually have a labor base 228 00:14:22,240 --> 00:14:27,000 Speaker 3: in Eastern Europe, Poland in Ukraine that allows them to 229 00:14:27,040 --> 00:14:30,680 Speaker 3: do several things. One, it gives them more time zone coverage. Two, 230 00:14:31,200 --> 00:14:35,680 Speaker 3: it gives them a high quality pool of people who 231 00:14:35,720 --> 00:14:41,920 Speaker 3: are loyal. The churt is a lot lower there than 232 00:14:41,960 --> 00:14:45,200 Speaker 3: it is here in the US. Also, obviously there's a 233 00:14:45,240 --> 00:14:48,840 Speaker 3: cost advantage associated with that. That cost in part leads 234 00:14:48,880 --> 00:14:51,560 Speaker 3: to being able to share better price with the customer 235 00:14:51,840 --> 00:14:54,440 Speaker 3: as well as have better margin for the company for Everest. 236 00:14:54,800 --> 00:14:57,480 Speaker 3: And part of what that means is when Everest acquires 237 00:14:57,480 --> 00:15:01,440 Speaker 3: a company, they can automatically give that company higher margin. 238 00:15:01,800 --> 00:15:04,520 Speaker 3: That makes them a very good candidate to be a 239 00:15:04,600 --> 00:15:09,240 Speaker 3: buyer or merger partner for other US truck brokerage companies. 240 00:15:09,480 --> 00:15:11,640 Speaker 3: So Everest is in the middle of executing on that 241 00:15:12,480 --> 00:15:17,880 Speaker 3: and talking with multiple companies right now about acquisitions or 242 00:15:17,920 --> 00:15:20,000 Speaker 3: mergers that will help them scale up. They be good 243 00:15:20,000 --> 00:15:22,280 Speaker 3: for them, but also be good for the companies that 244 00:15:22,280 --> 00:15:24,760 Speaker 3: they buy or merge with because they get all those benefits. 245 00:15:25,520 --> 00:15:30,280 Speaker 3: So that's one example. Another example would be we have 246 00:15:30,320 --> 00:15:35,880 Speaker 3: another platform company called Stat Recovery Services STAT, which is 247 00:15:37,000 --> 00:15:41,800 Speaker 3: focused on a niche called audit recovery. It helps brands 248 00:15:42,760 --> 00:15:47,440 Speaker 3: reduce the audit and other transportation related fees that they 249 00:15:47,480 --> 00:15:51,160 Speaker 3: pay to Walmart and other major retailers. So it turns 250 00:15:51,200 --> 00:15:54,920 Speaker 3: out that most transportation people that are listening to this 251 00:15:55,880 --> 00:15:59,520 Speaker 3: will know. Most other people will not know what the 252 00:16:00,000 --> 00:16:04,160 Speaker 3: acronym otif means. Otiff stands for on time in full. Now, 253 00:16:04,200 --> 00:16:07,400 Speaker 3: if you don't deliver on time in full as verified 254 00:16:07,760 --> 00:16:11,920 Speaker 3: by the auditors at a retailer like Walmart, you have 255 00:16:11,960 --> 00:16:14,480 Speaker 3: to pay fees. Walmart charges over two billion dollars a 256 00:16:14,520 --> 00:16:19,040 Speaker 3: year in those fees. Well, Stat Recovery figured out how 257 00:16:19,120 --> 00:16:23,400 Speaker 3: to solve that problem and to build a software based 258 00:16:23,440 --> 00:16:27,400 Speaker 3: solution for major brands, you know, companies like Craft Highs 259 00:16:27,440 --> 00:16:30,680 Speaker 3: and p ANDNG and others, and help them to boost 260 00:16:30,680 --> 00:16:36,480 Speaker 3: their compliance, reduce the otiff penalties, and you know, help 261 00:16:36,560 --> 00:16:40,040 Speaker 3: those clients make millions of dollars more in bottom line 262 00:16:40,040 --> 00:16:42,920 Speaker 3: profit and so stats shot up and has been in 263 00:16:43,040 --> 00:16:46,760 Speaker 3: an INK five thousand high growth company for years to 264 00:16:46,840 --> 00:16:50,200 Speaker 3: become the leader in that arena. They're also in a 265 00:16:50,240 --> 00:16:53,040 Speaker 3: good position to do acquisitions to continue to expand that 266 00:16:53,360 --> 00:16:56,080 Speaker 3: and to provide more value. So that's another example of 267 00:16:56,120 --> 00:16:58,760 Speaker 3: an area where we are putting our money where I'm 268 00:16:58,760 --> 00:17:02,280 Speaker 3: out this and sponsor acquisition. So the short answer, and 269 00:17:02,320 --> 00:17:05,280 Speaker 3: then lastly I'll add lee, we're also looking at investing 270 00:17:05,280 --> 00:17:09,280 Speaker 3: in new platforms in a variety of areas. So Cambridge Capital, 271 00:17:09,320 --> 00:17:12,359 Speaker 3: we're bullish and we're putting more money into these kinds 272 00:17:12,359 --> 00:17:12,880 Speaker 3: of ames. 273 00:17:13,119 --> 00:17:13,640 Speaker 2: Got Yeah. 274 00:17:13,760 --> 00:17:16,960 Speaker 1: I mean you're talking a lot about technology, and obviously 275 00:17:17,000 --> 00:17:20,960 Speaker 1: technology is very important for supply chains and transportation companies. 276 00:17:21,920 --> 00:17:25,800 Speaker 1: You know, what are the biggest innovations in the industry. 277 00:17:26,440 --> 00:17:28,480 Speaker 2: That you see down the road? 278 00:17:28,600 --> 00:17:35,520 Speaker 1: You know, visit AI, autonomous trucking, what's on your radar? 279 00:17:36,680 --> 00:17:40,880 Speaker 3: Yeah, so clearly AI is a huge theme at our conference. 280 00:17:42,000 --> 00:17:44,359 Speaker 3: You know, just a few days ago, we have something 281 00:17:44,400 --> 00:17:48,200 Speaker 3: called the bjsa supply chain shark tank, and over the 282 00:17:48,280 --> 00:17:50,280 Speaker 3: last several years, winners of the shark tank you have 283 00:17:50,320 --> 00:17:54,280 Speaker 3: gone under raise hundreds of millions in capital to sell 284 00:17:54,359 --> 00:17:58,280 Speaker 3: in some cases to other CEOs in the audience. And 285 00:17:58,680 --> 00:18:02,359 Speaker 3: the winner this year was a company using AI to 286 00:18:02,600 --> 00:18:06,760 Speaker 3: help automate part of the transportation process called Happy Robot. 287 00:18:06,920 --> 00:18:10,919 Speaker 3: What they do is they use AI to basically produce 288 00:18:13,480 --> 00:18:18,800 Speaker 3: voice robots. So if you, as a customer, are calling 289 00:18:18,920 --> 00:18:22,600 Speaker 3: a truck broker to get a status update, you think 290 00:18:22,640 --> 00:18:24,520 Speaker 3: you're talking to a person, but really you're talking to 291 00:18:24,560 --> 00:18:29,040 Speaker 3: an AI you know, voice spot and it's it's pretty neat. 292 00:18:29,080 --> 00:18:32,800 Speaker 3: They did a demo on the phone that certainly captured 293 00:18:32,800 --> 00:18:37,119 Speaker 3: everybody's attention. And I think that's just one illustration of 294 00:18:37,160 --> 00:18:40,400 Speaker 3: where AI works in supply chain. You could apply AI 295 00:18:40,480 --> 00:18:42,520 Speaker 3: to a lot of areas. The area that I think 296 00:18:42,600 --> 00:18:47,479 Speaker 3: is the most interesting is using AI to create superior data, 297 00:18:47,760 --> 00:18:53,080 Speaker 3: analytics and intelligence. And we put our money where our 298 00:18:53,080 --> 00:18:56,040 Speaker 3: mouth is because we invested in and co founded a 299 00:18:56,040 --> 00:18:59,199 Speaker 3: company called green Screens five years ago and green Screens 300 00:18:59,200 --> 00:19:02,359 Speaker 3: has grown to become the market leader in using AI 301 00:19:02,600 --> 00:19:06,679 Speaker 3: to create predictive pricing in the trucking arena. So if 302 00:19:06,680 --> 00:19:09,679 Speaker 3: you're a truck broker, you would use green Screens to 303 00:19:09,720 --> 00:19:12,520 Speaker 3: tell you on a real time basis right now at 304 00:19:12,680 --> 00:19:14,920 Speaker 3: ten thirty in the morning, how much should it cost 305 00:19:14,960 --> 00:19:18,120 Speaker 3: to ship a truckload of freight from New York to Atlanta. 306 00:19:18,640 --> 00:19:21,600 Speaker 3: And the reason why green Screens has become the market 307 00:19:21,680 --> 00:19:24,040 Speaker 3: leader in that arena, first of all, is because of 308 00:19:24,080 --> 00:19:27,760 Speaker 3: an amazing team of engineers. Second of all, all the 309 00:19:27,840 --> 00:19:30,800 Speaker 3: data that's been used to train the data model. So 310 00:19:30,840 --> 00:19:35,040 Speaker 3: green Screens now processes over thirty billion approaching forty billion 311 00:19:35,080 --> 00:19:39,199 Speaker 3: dollars of freight. And the more data you feed it, 312 00:19:39,240 --> 00:19:43,080 Speaker 3: the more accurate the system becomes. So, for example, one 313 00:19:43,240 --> 00:19:47,520 Speaker 3: of Greenscreens customers, a multi billion dollar logistics company, found 314 00:19:47,760 --> 00:19:50,879 Speaker 3: that in comparison with the pricing system that they were 315 00:19:50,920 --> 00:19:55,040 Speaker 3: using previously, green Screens cut the error rate by more 316 00:19:55,119 --> 00:19:57,960 Speaker 3: than five x. That's a big deal because if you 317 00:19:58,000 --> 00:20:01,040 Speaker 3: think about it, pure truck broker, your margin, your gross 318 00:20:01,119 --> 00:20:04,720 Speaker 3: margin might be twelve percent. And if the average error 319 00:20:04,800 --> 00:20:07,879 Speaker 3: rate with the old system in this case sort of 320 00:20:07,920 --> 00:20:10,920 Speaker 3: a de facto standard in the industry, was twenty percent, 321 00:20:11,240 --> 00:20:14,639 Speaker 3: that means that most brokers are actively losing money on 322 00:20:14,680 --> 00:20:16,720 Speaker 3: a portion of their transactions, but they don't know it 323 00:20:16,760 --> 00:20:19,879 Speaker 3: at the time. So green Screens cuts that down. It 324 00:20:19,920 --> 00:20:24,160 Speaker 3: allows truck brokers to number one, make decisions faster. Number 325 00:20:24,160 --> 00:20:28,879 Speaker 3: two price more accurately. Number three push more decisions to 326 00:20:28,920 --> 00:20:30,520 Speaker 3: the front line so they don't have to go up 327 00:20:30,520 --> 00:20:33,360 Speaker 3: the chain of command and have multiple people involved. Number 328 00:20:33,400 --> 00:20:37,040 Speaker 3: four is a result. Ultimately, the typical broker not only 329 00:20:37,080 --> 00:20:41,360 Speaker 3: makes millions more profit, but also can grow without having 330 00:20:41,440 --> 00:20:44,280 Speaker 3: to add people. So it's an amazing example of how 331 00:20:44,280 --> 00:20:46,960 Speaker 3: AI can create tremendous value and it's the reason why 332 00:20:46,960 --> 00:20:49,720 Speaker 3: green Screens now powers more than two hundred the top 333 00:20:49,720 --> 00:20:53,240 Speaker 3: three hundred truck brokers. So AI is really an opportunity 334 00:20:53,320 --> 00:20:57,760 Speaker 3: to help companies and logistics do things smarter, better and 335 00:20:57,800 --> 00:20:58,520 Speaker 3: more profitably. 336 00:20:59,280 --> 00:21:03,080 Speaker 1: And you know, outside of AI, you know, do you 337 00:21:03,119 --> 00:21:06,359 Speaker 1: have any thoughts on autonomous vehicles? 338 00:21:08,480 --> 00:21:12,119 Speaker 3: I do? I mean, on the one hand, you know, 339 00:21:12,160 --> 00:21:19,119 Speaker 3: I have a friend who described somebody that was well, 340 00:21:19,280 --> 00:21:22,960 Speaker 3: I won't name names, but was a member prominent political 341 00:21:23,000 --> 00:21:27,040 Speaker 3: family who was running for office. And I asked my 342 00:21:27,080 --> 00:21:28,760 Speaker 3: friend what he thought of him, and he said, well, 343 00:21:29,240 --> 00:21:31,280 Speaker 3: he's a man who has a lot of potential and 344 00:21:31,320 --> 00:21:38,199 Speaker 3: you always will great line, right, So you know, you 345 00:21:38,320 --> 00:21:41,159 Speaker 3: wonder whether autonomous is one of those areas that has 346 00:21:41,200 --> 00:21:44,560 Speaker 3: a lot of potential that never gets fully fulfilled or not. 347 00:21:45,000 --> 00:21:49,240 Speaker 3: I happen to be optimistic because we're seeing autonomous work 348 00:21:49,280 --> 00:21:51,919 Speaker 3: in certain areas already, right. We see it work for 349 00:21:52,000 --> 00:21:54,640 Speaker 3: long haul, right, and if you're doing long haul trucking, 350 00:21:55,080 --> 00:21:57,040 Speaker 3: not going to do the first while on the last mile, 351 00:21:57,160 --> 00:21:59,480 Speaker 3: but the part that's on ninety five or you know, 352 00:21:59,600 --> 00:22:02,160 Speaker 3: or another you know long call highway stretch that can 353 00:22:02,200 --> 00:22:05,760 Speaker 3: be done on an autonomous basis today. And then similarly, 354 00:22:06,720 --> 00:22:09,520 Speaker 3: if you look at trucks that are operated in places 355 00:22:09,520 --> 00:22:13,520 Speaker 3: that don't have people, like, for instance, trucks that operate 356 00:22:13,520 --> 00:22:17,119 Speaker 3: in underground mines in Australia, you know those are autonomous today. 357 00:22:17,400 --> 00:22:21,280 Speaker 3: So some of this is already happening. But I think 358 00:22:21,359 --> 00:22:24,359 Speaker 3: the hard part is areas where there's high density of 359 00:22:24,400 --> 00:22:28,160 Speaker 3: population first mile, last mile, major cities, etc. That's taken 360 00:22:28,240 --> 00:22:31,800 Speaker 3: much longer than people originally predicted, and I think it 361 00:22:31,840 --> 00:22:33,960 Speaker 3: will continue to be a tough met to solve. But 362 00:22:34,000 --> 00:22:38,600 Speaker 3: in the right applications, the right niches, it's definitely it's 363 00:22:38,600 --> 00:22:42,080 Speaker 3: definitely real. The problem is an investor is it's hard 364 00:22:42,080 --> 00:22:45,280 Speaker 3: for you to invest in autonomous and make money because 365 00:22:48,280 --> 00:22:51,320 Speaker 3: the failure rate is high. It's likely to be a 366 00:22:51,320 --> 00:22:54,639 Speaker 3: winner take all market where you know, one company comes 367 00:22:54,640 --> 00:22:57,560 Speaker 3: to dominate. So if there were fifty companies and you 368 00:22:57,600 --> 00:23:00,440 Speaker 3: invested in forty of them, you might there's money and 369 00:23:00,480 --> 00:23:02,679 Speaker 3: all of them unless you pick the winner. So for 370 00:23:02,880 --> 00:23:05,200 Speaker 3: as a consumer, it's great. I can't wait to see 371 00:23:05,200 --> 00:23:09,000 Speaker 3: autonomous come and I'm you know, certainly encouraging that. As 372 00:23:09,000 --> 00:23:12,840 Speaker 3: an investor, we have not invested in anything autonomous because 373 00:23:12,880 --> 00:23:15,320 Speaker 3: it's just really hard to know who the one big 374 00:23:15,320 --> 00:23:16,040 Speaker 3: winner is going to be. 375 00:23:16,880 --> 00:23:19,520 Speaker 1: Do you see technology, like any technology that are out 376 00:23:19,560 --> 00:23:24,560 Speaker 1: there making parts of supply chain obsolete or companies obsolete. 377 00:23:24,880 --> 00:23:31,480 Speaker 3: Definitely. So look, one good example is in the area 378 00:23:31,640 --> 00:23:36,760 Speaker 3: of in the area of truck brokerage. I don't believe 379 00:23:36,800 --> 00:23:40,000 Speaker 3: technology will make the brokers obsolete, but I do believe 380 00:23:40,440 --> 00:23:47,240 Speaker 3: technology is massively reducing the headcount needed right and streamlining 381 00:23:47,240 --> 00:23:49,639 Speaker 3: the process. So you know, it might be that the 382 00:23:49,680 --> 00:23:53,840 Speaker 3: truck broker of twenty thirty does the same level of 383 00:23:53,880 --> 00:23:57,280 Speaker 3: revenue as the company of today, but with half the headcount. 384 00:23:58,560 --> 00:24:02,280 Speaker 3: As an example, similarly, I mean there are jobs, like, 385 00:24:02,359 --> 00:24:05,399 Speaker 3: you know, people that make phone calls for the check 386 00:24:05,440 --> 00:24:09,200 Speaker 3: call function, you know, calling to see where are things well, 387 00:24:09,560 --> 00:24:13,800 Speaker 3: you know, happy robot illustration shows how that function should 388 00:24:13,880 --> 00:24:16,199 Speaker 3: probably just be automated by by the way, I mean 389 00:24:16,440 --> 00:24:19,160 Speaker 3: it could be automated with technology if you have tracking 390 00:24:19,200 --> 00:24:21,719 Speaker 3: on the vehicles, or it could be automated because you're 391 00:24:21,800 --> 00:24:24,679 Speaker 3: using an AI chatbot. But either way, I think certain 392 00:24:25,440 --> 00:24:29,480 Speaker 3: functions will be automated and will go away. On the 393 00:24:29,520 --> 00:24:32,080 Speaker 3: other hand, I don't see entire industries. I mean, like 394 00:24:32,359 --> 00:24:35,399 Speaker 3: there are some people that have said there's some Silicon Valley, 395 00:24:35,520 --> 00:24:38,720 Speaker 3: you know, startups that said, oh, my startup's going to 396 00:24:38,800 --> 00:24:40,680 Speaker 3: put the truck proker in a business. I don't think 397 00:24:40,680 --> 00:24:44,920 Speaker 3: that's happening any time soon, but it will certainly massively 398 00:24:45,000 --> 00:24:47,639 Speaker 3: reduce workflow. I'm just curious. 399 00:24:47,640 --> 00:24:49,359 Speaker 1: I don't know if you remember this, but during the 400 00:24:49,359 --> 00:24:52,080 Speaker 1: conference in your shark tank the Happy Robot Company, what 401 00:24:52,600 --> 00:24:54,480 Speaker 1: kind of accent did the broker have? 402 00:24:54,840 --> 00:24:57,879 Speaker 2: Did they sound like from the south or an english woman? 403 00:24:58,080 --> 00:25:01,320 Speaker 3: Like? What was the accent? Well, it was funny. The 404 00:25:01,320 --> 00:25:05,480 Speaker 3: the accent was fairly neutral. But but just to give 405 00:25:05,480 --> 00:25:07,560 Speaker 3: you a sense of of the you know, the real 406 00:25:07,640 --> 00:25:11,479 Speaker 3: time give and take the you know, the co founder 407 00:25:11,520 --> 00:25:14,000 Speaker 3: of the of the company who's who's doing the demo's 408 00:25:14,080 --> 00:25:17,760 Speaker 3: name is Pablo, And in the middle of his questioning, uh, 409 00:25:17,840 --> 00:25:19,800 Speaker 3: you know, where's the truck what's happening? He said, By 410 00:25:19,840 --> 00:25:22,280 Speaker 3: the way, what's your name? My name is Pablo, and 411 00:25:22,320 --> 00:25:25,119 Speaker 3: the and the the chatbots said, oh, my name is 412 00:25:25,119 --> 00:25:29,320 Speaker 3: Pablo too. So look, I mean it's uh you could 413 00:25:29,359 --> 00:25:32,560 Speaker 3: train a voice uh chatbot to do anything, right. You 414 00:25:32,560 --> 00:25:35,240 Speaker 3: could give it a Southern accent or an Indian accent, 415 00:25:35,320 --> 00:25:39,199 Speaker 3: or a French accent or whatever you want. But but 416 00:25:39,280 --> 00:25:41,840 Speaker 3: I think you'll see most of these are they're kind 417 00:25:41,880 --> 00:25:44,879 Speaker 3: of like PBX voice systems, you know, they're they're trained 418 00:25:44,920 --> 00:25:47,080 Speaker 3: on on neutral accents. Right. 419 00:25:47,720 --> 00:25:50,240 Speaker 1: So you know, earlier in the conversation we talked about 420 00:25:50,359 --> 00:25:52,680 Speaker 1: M and A, you know, but we didn't talk about valuations, 421 00:25:52,680 --> 00:25:55,359 Speaker 1: and that's always an important part. You know, what what 422 00:25:55,480 --> 00:26:02,960 Speaker 1: evaluations look like right now relative through his historical measures. 423 00:26:01,560 --> 00:26:04,600 Speaker 3: Well, I think they're they're lower than they were during 424 00:26:04,640 --> 00:26:07,520 Speaker 3: peak COVID, they're higher than they were prior to COVID, 425 00:26:07,640 --> 00:26:11,159 Speaker 3: and I think that's some level of balance in between. 426 00:26:11,280 --> 00:26:14,760 Speaker 3: So I'll give you an example. If you took a 427 00:26:14,800 --> 00:26:18,840 Speaker 3: ten to twenty million dollar EBITA asset light logistics company, 428 00:26:18,840 --> 00:26:22,720 Speaker 3: a truck brokeridge or freight forward in company twenty years ago, 429 00:26:22,880 --> 00:26:26,800 Speaker 3: on average, that company might have traded for five times ebatah. 430 00:26:28,560 --> 00:26:33,240 Speaker 3: Ten years ago, a company might have traded for seven 431 00:26:33,280 --> 00:26:36,639 Speaker 3: times ebatah, eight times ebatah again just on average. You know, 432 00:26:36,680 --> 00:26:41,280 Speaker 3: plenty of examples higher and lower. During peak COVID, when 433 00:26:42,000 --> 00:26:47,159 Speaker 3: everything was scarce and growth shot up, you know, multiples 434 00:26:47,200 --> 00:26:49,639 Speaker 3: went up, and they went up on inflated ebata So 435 00:26:50,000 --> 00:26:53,640 Speaker 3: that same company might have traded for you know, thirteen 436 00:26:53,720 --> 00:26:57,560 Speaker 3: times in twenty twenty one, and today that company might 437 00:26:57,560 --> 00:27:01,320 Speaker 3: trade back at at somewhere closer to the you know, 438 00:27:01,359 --> 00:27:04,720 Speaker 3: twenty fifteen, sixteen seventeen range, maybe a little bit higher, 439 00:27:04,760 --> 00:27:08,600 Speaker 3: you know, like eight times or so again as an average. 440 00:27:08,720 --> 00:27:12,400 Speaker 3: Of course, the problem is deals only happen when buyers 441 00:27:12,400 --> 00:27:15,480 Speaker 3: and sellers agree on a price, right, you've got to 442 00:27:15,520 --> 00:27:20,600 Speaker 3: have a zone of agreement. The sellers who remembered inflated 443 00:27:20,680 --> 00:27:25,040 Speaker 3: multiples off of inflated EBITDA in twenty one, you know, 444 00:27:26,080 --> 00:27:27,879 Speaker 3: say why would I transact now? I mean, if your 445 00:27:27,920 --> 00:27:30,560 Speaker 3: EBITDA was double what it might have otherwise been, and 446 00:27:30,600 --> 00:27:32,879 Speaker 3: the multiples double what it might have otherwise been, there 447 00:27:32,920 --> 00:27:35,040 Speaker 3: was a brief window of about a year where you 448 00:27:35,080 --> 00:27:37,600 Speaker 3: could have gotten four times the money that you might 449 00:27:37,600 --> 00:27:42,320 Speaker 3: have otherwise gotten for the business. But you know, memories 450 00:27:42,440 --> 00:27:45,720 Speaker 3: tend to be sticky and selective, and so if you 451 00:27:45,840 --> 00:27:49,040 Speaker 3: remembered that you could get four times more money in 452 00:27:49,160 --> 00:27:51,960 Speaker 3: twenty one, why would you take what you perceive as 453 00:27:52,000 --> 00:27:54,879 Speaker 3: a seventy five percent discount today? Even though it's not 454 00:27:54,920 --> 00:27:57,720 Speaker 3: really a discount, it's more of a regression to the mean, 455 00:27:58,720 --> 00:28:01,520 Speaker 3: and so it's taken a while for buyer and seller 456 00:28:01,560 --> 00:28:04,960 Speaker 3: expectations to reset. I think that's what a lot of 457 00:28:05,000 --> 00:28:07,640 Speaker 3: what happened in twenty twenty four. But I think we've 458 00:28:07,680 --> 00:28:11,399 Speaker 3: reached that level of alignment, which is why we're starting 459 00:28:11,400 --> 00:28:14,439 Speaker 3: to see such a surgeon deal activity. And examples like 460 00:28:14,760 --> 00:28:16,640 Speaker 3: some of the ones that I mentioned, the Shanker deal, 461 00:28:16,720 --> 00:28:20,119 Speaker 3: the Coyote deal, illustrate the fact that a lot of 462 00:28:20,359 --> 00:28:23,720 Speaker 3: large buyers and sellers have finally come to some consensus 463 00:28:23,720 --> 00:28:24,280 Speaker 3: around what's. 464 00:28:24,240 --> 00:28:28,440 Speaker 1: Fair right and inflated multiples probably led to inflated egos 465 00:28:28,440 --> 00:28:32,480 Speaker 1: of some of the selling companies. So you know, in trucking, 466 00:28:33,800 --> 00:28:36,560 Speaker 1: a seller is usually motivated because it's a family business 467 00:28:36,560 --> 00:28:38,880 Speaker 1: and no one wanted in the family wants to continue. 468 00:28:39,360 --> 00:28:45,880 Speaker 1: What motivates most of the sellers that you are involved with, well, I. 469 00:28:45,880 --> 00:28:48,840 Speaker 3: Think there are a few scenarios. I think one scenario 470 00:28:49,000 --> 00:28:53,120 Speaker 3: is it's a founder who's built something great but wants 471 00:28:53,160 --> 00:28:56,640 Speaker 3: to do something different. It's the life transition, right, So 472 00:28:56,680 --> 00:29:00,680 Speaker 3: maybe it's an age and stage. You know, worked hard, 473 00:29:00,680 --> 00:29:03,280 Speaker 3: built the business, want to step back and retire, or 474 00:29:03,320 --> 00:29:05,400 Speaker 3: want to go start something different, or want to make 475 00:29:05,880 --> 00:29:08,959 Speaker 3: a different life choice. I mean, as an example, one 476 00:29:09,000 --> 00:29:11,800 Speaker 3: of the great CEOs that I worked with earlier in 477 00:29:11,800 --> 00:29:13,960 Speaker 3: my career was Lewis D. Joy. Lewis to Joy built 478 00:29:14,040 --> 00:29:17,640 Speaker 3: New Breed, grew it over the course of two decades 479 00:29:18,200 --> 00:29:21,040 Speaker 3: to over six hundred million dollar value company, and then 480 00:29:21,080 --> 00:29:23,480 Speaker 3: he sold to XPO in twenty fifteen. Because you want 481 00:29:23,520 --> 00:29:26,320 Speaker 3: to do something totally different. That totally different thing was 482 00:29:26,360 --> 00:29:30,560 Speaker 3: going into government and public policy and public service and 483 00:29:30,600 --> 00:29:36,160 Speaker 3: he's now the Postmaster General and so look, not everybody 484 00:29:36,200 --> 00:29:38,760 Speaker 3: gets to do that. But it's a good illustration to 485 00:29:38,800 --> 00:29:41,360 Speaker 3: the point, which is sometimes people get to a stage 486 00:29:41,360 --> 00:29:43,680 Speaker 3: where it's like, all right, I'm ready for something different. 487 00:29:44,720 --> 00:29:48,440 Speaker 3: The second reason is sometimes seller gets to the point 488 00:29:48,440 --> 00:29:51,320 Speaker 3: where here she says, you know what, my business is good, 489 00:29:51,960 --> 00:29:55,120 Speaker 3: but I'm in an industry where there's consolidation, and if 490 00:29:55,160 --> 00:29:59,120 Speaker 3: I'm whatever running one hundred million dollar truck brokerage business, 491 00:29:59,160 --> 00:30:02,320 Speaker 3: but I'm competing against multi billion dollar giants. I got 492 00:30:02,320 --> 00:30:04,320 Speaker 3: to be a part of something bigger. And so that 493 00:30:04,800 --> 00:30:07,680 Speaker 3: owner might say, I'm going to sell or merge with 494 00:30:07,800 --> 00:30:10,680 Speaker 3: another company in order to be a part of something bigger. 495 00:30:10,680 --> 00:30:13,520 Speaker 3: And that's certainly part of what happened with XPO. It's 496 00:30:13,560 --> 00:30:15,720 Speaker 3: part of what we're seeing and doing with Everest. It's 497 00:30:15,760 --> 00:30:20,280 Speaker 3: a good example where where CEOs or owners of companies, 498 00:30:20,600 --> 00:30:22,800 Speaker 3: it's not like they're cashing out because they're done and 499 00:30:22,800 --> 00:30:26,040 Speaker 3: they're retired. It's because they recognize that they will have 500 00:30:26,120 --> 00:30:30,360 Speaker 3: a stronger, better combined business if they sell or merge 501 00:30:30,440 --> 00:30:35,240 Speaker 3: into a consolidator. And I think that's important. And then look, 502 00:30:35,280 --> 00:30:37,800 Speaker 3: I think there are also, you know, third scenarios where 503 00:30:37,800 --> 00:30:41,320 Speaker 3: people realize I mean, as my friend Hurbsheer likes to say, 504 00:30:41,600 --> 00:30:45,320 Speaker 3: most people who own their business think it's an heirloom, right, 505 00:30:45,400 --> 00:30:47,720 Speaker 3: I'm going to pass it on for generations. But then 506 00:30:47,720 --> 00:30:50,120 Speaker 3: when they look at it more closely, they realize, no, 507 00:30:50,240 --> 00:30:53,480 Speaker 3: it's an asset. And so an heirloom is something you 508 00:30:53,560 --> 00:30:56,720 Speaker 3: keep forever, but an asset is something that you could sell, right. 509 00:30:56,720 --> 00:30:58,960 Speaker 3: I mean, if you've built a great business and you're 510 00:30:59,000 --> 00:31:01,560 Speaker 3: thinking what do I do for kids? You know, maybe 511 00:31:01,600 --> 00:31:04,480 Speaker 3: your kids don't want to be in the logistics world, right, 512 00:31:04,560 --> 00:31:07,920 Speaker 3: and maybe they're passionate something else, and maybe, you know, 513 00:31:08,200 --> 00:31:10,880 Speaker 3: sell the business and giving them money and freedom and 514 00:31:10,960 --> 00:31:13,520 Speaker 3: choice to do whatever they want is, you know, is 515 00:31:13,560 --> 00:31:15,400 Speaker 3: better for you and better for them. So I think 516 00:31:15,640 --> 00:31:18,760 Speaker 3: all those are factors that play into it. But ultimately, 517 00:31:19,160 --> 00:31:21,440 Speaker 3: and I guess lastly, there's one fourth thing that I'll mention, 518 00:31:23,000 --> 00:31:28,400 Speaker 3: which is market market conditions, including tax Right now, we're 519 00:31:28,400 --> 00:31:32,920 Speaker 3: in an ecosystem where the tax rates which were lowered 520 00:31:32,960 --> 00:31:36,000 Speaker 3: in twenty seventeen are set to expire at the end 521 00:31:36,000 --> 00:31:40,200 Speaker 3: of this year. Now we'll probably be extended in some 522 00:31:40,320 --> 00:31:43,640 Speaker 3: form because the Trump administration wants to, and there's a 523 00:31:43,720 --> 00:31:47,000 Speaker 3: Republican Senate, Republican Congress that supports that as well, But 524 00:31:47,080 --> 00:31:49,040 Speaker 3: you don't know. I think one thing that's clear is 525 00:31:49,080 --> 00:31:52,960 Speaker 3: this is an unpredictable climate. And so you know, if 526 00:31:53,120 --> 00:31:55,400 Speaker 3: somebody who's built a business and wants to sell and 527 00:31:55,800 --> 00:31:59,560 Speaker 3: do so under you know, favorable market conditions or tax regimes, 528 00:31:59,600 --> 00:32:02,440 Speaker 3: certainly that that's an important consideration as well. So I'd 529 00:32:02,440 --> 00:32:05,320 Speaker 3: say those are four profiles and factors. 530 00:32:05,840 --> 00:32:08,280 Speaker 1: Yeah, and you mentioned you know that the M and 531 00:32:08,320 --> 00:32:11,640 Speaker 1: A market is poised to heat up this year. 532 00:32:12,280 --> 00:32:13,440 Speaker 2: How about the IPO market. 533 00:32:13,480 --> 00:32:16,120 Speaker 1: Are there any large private companies that you know that 534 00:32:16,120 --> 00:32:19,320 Speaker 1: are on your radar that might go public or talked 535 00:32:19,320 --> 00:32:21,400 Speaker 1: about going to public. Do you think that that market's 536 00:32:21,440 --> 00:32:23,160 Speaker 1: going to heat up as well in twenty twenty five? 537 00:32:23,720 --> 00:32:28,600 Speaker 3: Yeah, yeah, I do. On the on the classic logistics side, 538 00:32:28,640 --> 00:32:32,120 Speaker 3: there are some major companies, some terrific companies that are 539 00:32:32,160 --> 00:32:35,800 Speaker 3: poised to go public. Keen An Advantage would be one example, 540 00:32:36,800 --> 00:32:40,560 Speaker 3: and they're certainly in a position where they're big enough 541 00:32:40,680 --> 00:32:47,600 Speaker 3: and yeah, high quality business that I would expect should 542 00:32:47,600 --> 00:32:51,440 Speaker 3: go public this year. Worldwide Express would be another another, 543 00:32:51,560 --> 00:32:54,800 Speaker 3: you know, act light consolidator in the logistics arena. And 544 00:32:54,840 --> 00:32:59,480 Speaker 3: then on the software side and tech enabled services side, 545 00:33:00,360 --> 00:33:03,160 Speaker 3: ship Bob is a candidate to do so in e commerce, fulfillment, 546 00:33:04,000 --> 00:33:06,920 Speaker 3: software and take enabled services. So look, I think there 547 00:33:06,960 --> 00:33:08,960 Speaker 3: are a number of companies in the supply chain world 548 00:33:09,000 --> 00:33:12,880 Speaker 3: that are poised to go public in the next couple 549 00:33:12,920 --> 00:33:13,520 Speaker 3: of quarters. 550 00:33:13,960 --> 00:33:16,680 Speaker 2: Right, and does does BGSA do they? 551 00:33:16,840 --> 00:33:17,320 Speaker 3: You guys? 552 00:33:17,360 --> 00:33:18,440 Speaker 2: Do I pos as well? 553 00:33:18,800 --> 00:33:22,200 Speaker 3: BGSA does full service investment banking, so you know, certainly 554 00:33:22,200 --> 00:33:26,240 Speaker 3: can help on yet that and all the above, Most 555 00:33:26,240 --> 00:33:28,840 Speaker 3: of what BGSA has done historically has been M and 556 00:33:28,880 --> 00:33:33,960 Speaker 3: A and you know, private capital markets activity for the 557 00:33:33,960 --> 00:33:36,920 Speaker 3: simple reason that if you think about the logistics industry, 558 00:33:37,640 --> 00:33:40,280 Speaker 3: you know, something like ninety nine percent of all logistics 559 00:33:40,360 --> 00:33:45,120 Speaker 3: companies are private. But yes, we've certainly done plenty of 560 00:33:45,160 --> 00:33:47,320 Speaker 3: work on the public side as well as the private side, 561 00:33:47,320 --> 00:33:49,240 Speaker 3: and I think there's there's plenty of opportunity there. 562 00:33:49,360 --> 00:33:52,120 Speaker 1: You know, when I was reading through your bio, you 563 00:33:52,160 --> 00:33:54,600 Speaker 1: know you had in there that you know, you started 564 00:33:54,760 --> 00:33:58,800 Speaker 1: transportation working for a family business. Did you know, as 565 00:33:58,840 --> 00:34:01,520 Speaker 1: like you knows as a fourteen year old kid that 566 00:34:01,560 --> 00:34:03,920 Speaker 1: you were going to go into your family's business, And 567 00:34:03,960 --> 00:34:06,280 Speaker 1: maybe you can talk about, you know, exactly what kind 568 00:34:06,320 --> 00:34:07,160 Speaker 1: of business that was. 569 00:34:07,920 --> 00:34:11,160 Speaker 3: You know. I remember watching an interview with a college 570 00:34:11,160 --> 00:34:15,280 Speaker 3: basketball coach from small school who made it into the NBA, 571 00:34:15,600 --> 00:34:19,640 Speaker 3: excuse me, the NCAA sweet sixteen, and somebody asked him, 572 00:34:19,640 --> 00:34:22,040 Speaker 3: in your wildest dreams, did you think this would happen? 573 00:34:22,400 --> 00:34:25,040 Speaker 3: And he said, sudden, my wildest dreams are not about basketball. 574 00:34:25,600 --> 00:34:30,040 Speaker 3: So my dreams as a fourteen year old boy were 575 00:34:30,040 --> 00:34:33,480 Speaker 3: not about logistics. Good, but I did have exposure to 576 00:34:33,520 --> 00:34:38,200 Speaker 3: the industry early on, and a turning point for me was, 577 00:34:38,640 --> 00:34:41,880 Speaker 3: you know, look, when I was in college, I worked 578 00:34:41,880 --> 00:34:45,600 Speaker 3: for a you know, summer job in Ami and the 579 00:34:45,719 --> 00:34:48,920 Speaker 3: you know, truck leasing business, and yeah, struve to be 580 00:34:48,960 --> 00:34:51,240 Speaker 3: the you know, first and last out work you know hard, 581 00:34:51,520 --> 00:34:53,960 Speaker 3: worked on opening a new market in Baltimore and a 582 00:34:54,000 --> 00:34:57,160 Speaker 3: couple of other things. And what struck me was it 583 00:34:57,239 --> 00:35:02,240 Speaker 3: was an interesting business, it was growing, and it was Look, 584 00:35:02,719 --> 00:35:06,480 Speaker 3: there are some businesses where there's an incredible amount of 585 00:35:06,880 --> 00:35:08,560 Speaker 3: things that have to go right in order for you 586 00:35:08,600 --> 00:35:12,359 Speaker 3: to succeed, like, for example, biotech. You know, if you're 587 00:35:12,440 --> 00:35:16,279 Speaker 3: in the biotech world, you could spend a decade of 588 00:35:16,280 --> 00:35:19,120 Speaker 3: your life working on a drug and spend over a 589 00:35:19,120 --> 00:35:20,839 Speaker 3: billion dollars, you know, trying to go through the FD 590 00:35:20,920 --> 00:35:23,520 Speaker 3: approval process, and then in the end it might fail. 591 00:35:23,680 --> 00:35:26,239 Speaker 3: You'd say, well, wow, I just wasted ten years of 592 00:35:26,239 --> 00:35:29,239 Speaker 3: my life for nothing because there's this high data. On 593 00:35:29,239 --> 00:35:31,160 Speaker 3: the other hand, if you get it right and you're 594 00:35:31,200 --> 00:35:35,440 Speaker 3: the next you know, maderta amazing. But what struck me 595 00:35:35,640 --> 00:35:39,520 Speaker 3: was in the logistics arena, if you work hard and 596 00:35:39,560 --> 00:35:42,640 Speaker 3: you're disciplined and your focus, you could be successful. And 597 00:35:43,840 --> 00:35:46,399 Speaker 3: there's sure I mean, there are lots of areas of 598 00:35:46,400 --> 00:35:49,840 Speaker 3: innovation in areas of risk, but I just saw a 599 00:35:49,840 --> 00:35:53,880 Speaker 3: pretty linear path to success there. And so when I 600 00:35:53,920 --> 00:35:57,879 Speaker 3: went into strategy consulting, I looked for opportunities to work 601 00:35:57,880 --> 00:36:01,280 Speaker 3: in the transportation arena. So I worked at Extra Trailer 602 00:36:01,360 --> 00:36:03,640 Speaker 3: Leasing was one of our clients, and you know, I 603 00:36:03,680 --> 00:36:08,439 Speaker 3: remember studying companies like C. Trobinson and Hub Group and saying, hey, 604 00:36:08,480 --> 00:36:11,040 Speaker 3: this is something that's taking off. It's really going to 605 00:36:11,080 --> 00:36:14,239 Speaker 3: be successful. And of course that was you know, in 606 00:36:14,280 --> 00:36:16,960 Speaker 3: the nineties, in the early days of the explosion of 607 00:36:17,440 --> 00:36:20,840 Speaker 3: outsourcing and asset light logistics, and so, you know, I 608 00:36:20,880 --> 00:36:22,759 Speaker 3: made the decision when I was in business school. I 609 00:36:22,800 --> 00:36:26,560 Speaker 3: was watching all these amazing startups in the internet arena. 610 00:36:27,040 --> 00:36:29,680 Speaker 3: There's a guy named Dave Perry who built something called Kendex. 611 00:36:29,719 --> 00:36:32,319 Speaker 3: It was the first B to B exchange, and in 612 00:36:32,360 --> 00:36:35,319 Speaker 3: his case, it was automating the chemical industry. And I thought, 613 00:36:35,320 --> 00:36:37,799 Speaker 3: wouldn't it be great if I could apply what he's 614 00:36:37,840 --> 00:36:40,839 Speaker 3: doing in the transportation world. And that's really what led 615 00:36:40,840 --> 00:36:44,080 Speaker 3: me to start Threeplex over twenty five years ago, and 616 00:36:45,280 --> 00:36:48,799 Speaker 3: here I am today. So that's how it really it started. 617 00:36:48,480 --> 00:36:51,719 Speaker 1: For me and what is you know, you've kind of 618 00:36:52,280 --> 00:36:55,520 Speaker 1: built two very successful financial institutions. 619 00:36:55,560 --> 00:36:58,279 Speaker 2: You know what's your favorite part of your job? 620 00:36:59,480 --> 00:37:02,319 Speaker 3: I mean, for me, it's the opportunity to work with 621 00:37:02,400 --> 00:37:07,799 Speaker 3: great people in supportive innovation to build amazing things. Green 622 00:37:07,840 --> 00:37:10,080 Speaker 3: Streine is a really good example. I mean, when I 623 00:37:10,160 --> 00:37:14,240 Speaker 3: first met Felix, who is you know, really the founder 624 00:37:15,040 --> 00:37:20,120 Speaker 3: behind the business. You know, they had amazing technology, they 625 00:37:20,120 --> 00:37:24,240 Speaker 3: had some great ideas, but they were doing too much 626 00:37:24,520 --> 00:37:27,360 Speaker 3: and so you know, we had a conversation around focus 627 00:37:27,440 --> 00:37:29,759 Speaker 3: and well, what's the one thing that matters the most. Well, 628 00:37:29,760 --> 00:37:32,360 Speaker 3: the one thing that matters the most was using AI 629 00:37:32,440 --> 00:37:35,120 Speaker 3: to solve the problem of predictive pricing. And so the 630 00:37:35,160 --> 00:37:38,719 Speaker 3: whole thought process, the innovation, the creativity around saying, hey, 631 00:37:38,719 --> 00:37:40,799 Speaker 3: there are all these great things you could do and 632 00:37:40,880 --> 00:37:44,120 Speaker 3: all these great capabilities. Let's pick the best one. Then 633 00:37:44,200 --> 00:37:47,399 Speaker 3: let's figure out a path to turning that into something 634 00:37:47,480 --> 00:37:50,160 Speaker 3: much bigger. Who are the perfect customers and you know, 635 00:37:50,280 --> 00:37:54,880 Speaker 3: talking to companies like NFI and Werner and a host 636 00:37:54,960 --> 00:37:58,360 Speaker 3: of others, and who are the perfect people and talking 637 00:37:58,400 --> 00:38:01,920 Speaker 3: to outstanding people with experience and the transportation and tech arena, 638 00:38:02,600 --> 00:38:05,600 Speaker 3: you know, like Don Salvucci Fabia who became the CEO, 639 00:38:05,680 --> 00:38:08,719 Speaker 3: and it's done an amazing job, and what's the big 640 00:38:08,760 --> 00:38:11,680 Speaker 3: market opportunity and how to focus on that and then 641 00:38:11,760 --> 00:38:14,759 Speaker 3: partner with others in areas that are adjacent to that, 642 00:38:15,120 --> 00:38:20,280 Speaker 3: and then ultimately seeing all that strategic and creative work 643 00:38:20,640 --> 00:38:24,560 Speaker 3: turning into something that really blossoms. I mean that's really rewarding. 644 00:38:24,800 --> 00:38:29,160 Speaker 3: And it's rewarding because when you first of all work 645 00:38:29,200 --> 00:38:31,279 Speaker 3: with other people that have big dreams, and then you 646 00:38:31,320 --> 00:38:33,919 Speaker 3: can help those dreams come to life and then put 647 00:38:33,960 --> 00:38:37,480 Speaker 3: resources behind them and then have that translate into something 648 00:38:37,520 --> 00:38:41,320 Speaker 3: that really makes a difference for companies and customers for 649 00:38:41,440 --> 00:38:45,960 Speaker 3: the industry. I mean, I think that's fantastic, and it's intense, 650 00:38:46,000 --> 00:38:47,759 Speaker 3: and there's of course a massive amount of work, but 651 00:38:48,200 --> 00:38:51,040 Speaker 3: I mean in the end, having a dream and then 652 00:38:51,080 --> 00:38:54,160 Speaker 3: doing the work to put it into reality in partnership 653 00:38:54,200 --> 00:38:56,680 Speaker 3: with other great people is really rewarding. 654 00:38:58,000 --> 00:39:00,520 Speaker 1: And I always like to ask the guests on the 655 00:39:00,640 --> 00:39:04,080 Speaker 1: Talking Transport podcast, you know about books that they've read, 656 00:39:04,400 --> 00:39:08,239 Speaker 1: whether it's on leadership, investing or transportation that's kind of 657 00:39:08,280 --> 00:39:09,000 Speaker 1: close to your heart. 658 00:39:10,160 --> 00:39:15,560 Speaker 3: Absolutely So in my office, I organize the library by 659 00:39:15,680 --> 00:39:18,000 Speaker 3: theme and so seven feet to my left. I have 660 00:39:18,520 --> 00:39:20,799 Speaker 3: the shelf of biographies. And if I look at the 661 00:39:21,160 --> 00:39:24,480 Speaker 3: great biographies that I read and draw inspiration from, I 662 00:39:24,480 --> 00:39:27,520 Speaker 3: mean there's Type by Ron Churno, which is the story 663 00:39:27,520 --> 00:39:30,840 Speaker 3: of how Rockefeller built his empire. Of course, all the 664 00:39:30,880 --> 00:39:35,680 Speaker 3: Walter Isaacson books, including Elon Musk, Steve Jobs, and a 665 00:39:35,680 --> 00:39:38,680 Speaker 3: host of others. There's the Ben Franklin biography as well 666 00:39:38,680 --> 00:39:44,319 Speaker 3: that Isaacson wrote, Coolidge, et cetera. But a couple of things, 667 00:39:44,480 --> 00:39:49,719 Speaker 3: and then of course the the Winston Churchill biography by 668 00:39:49,719 --> 00:39:54,400 Speaker 3: Paul Johnson. What I love about biographies is if you 669 00:39:54,440 --> 00:39:57,560 Speaker 3: can get the accumulated wisdom of other people who have 670 00:39:57,600 --> 00:40:01,160 Speaker 3: accomplished great things over seven eighty ninety years of life, 671 00:40:01,239 --> 00:40:03,560 Speaker 3: and you know you read the book in a day 672 00:40:04,320 --> 00:40:07,160 Speaker 3: and capture all that wisdom. How amazing is that? And so, 673 00:40:08,080 --> 00:40:10,800 Speaker 3: But just to give you an example, I mean the 674 00:40:11,239 --> 00:40:16,399 Speaker 3: Paul Johnson biography of Winston Churchill. People know Churchill as 675 00:40:16,800 --> 00:40:20,359 Speaker 3: the great statesman who rallied Britain during World War Two, 676 00:40:21,360 --> 00:40:24,000 Speaker 3: but the truth is Churchill had some real low points. 677 00:40:24,600 --> 00:40:28,280 Speaker 3: For example, he was responsible for leading his men into 678 00:40:28,719 --> 00:40:32,400 Speaker 3: a calamitous battle in World War One that that of 679 00:40:32,440 --> 00:40:36,359 Speaker 3: course led to many of his trips being killed. It 680 00:40:36,400 --> 00:40:40,560 Speaker 3: could have been the end of his career. But what 681 00:40:40,600 --> 00:40:44,880 Speaker 3: did he do. He basically, he retreated, he reflected, he 682 00:40:44,960 --> 00:40:48,520 Speaker 3: wrote books, he thought, you know, and he came back bigger, 683 00:40:48,520 --> 00:40:51,680 Speaker 3: better and stronger. And look, we've all had our setbacks. 684 00:40:51,719 --> 00:40:55,200 Speaker 3: I certainly have. But you know, the idea that the 685 00:40:55,239 --> 00:40:59,920 Speaker 3: greatest leaders of the last century went through a hero's 686 00:41:00,080 --> 00:41:03,520 Speaker 3: journey of failure before getting to success. For me, he's 687 00:41:03,520 --> 00:41:07,279 Speaker 3: remotivating because during the times when things aren't going the 688 00:41:07,360 --> 00:41:09,680 Speaker 3: right way, and that happens all the time, it's great 689 00:41:09,680 --> 00:41:13,400 Speaker 3: to draw on that for strength. And so while this 690 00:41:13,520 --> 00:41:17,120 Speaker 3: might not be the transportation book that you're expecting, I 691 00:41:17,480 --> 00:41:21,160 Speaker 3: think learning from people like the Church biography for me 692 00:41:21,200 --> 00:41:22,120 Speaker 3: has been pretty powerful. 693 00:41:22,600 --> 00:41:27,160 Speaker 2: No, that's great, that's great. You know I've read that 694 00:41:27,200 --> 00:41:27,560 Speaker 2: one too. 695 00:41:27,800 --> 00:41:32,600 Speaker 1: That's it's very interesting journey that his life slash political 696 00:41:32,640 --> 00:41:33,319 Speaker 1: career took. 697 00:41:34,400 --> 00:41:36,239 Speaker 2: Well, Ben, I really want to thank you for your time. 698 00:41:36,320 --> 00:41:38,200 Speaker 2: This is a great conversation, Lee. 699 00:41:38,320 --> 00:41:40,759 Speaker 3: Thank You're great talking with you. Really enjoyed it and 700 00:41:40,920 --> 00:41:43,719 Speaker 3: appreciate all the great work that you're doing at Bloomberg Intelligence. 701 00:41:44,280 --> 00:41:44,920 Speaker 3: Thanks so much. 702 00:41:45,000 --> 00:41:46,560 Speaker 1: And I don't want to thank you for tuning in. 703 00:41:46,640 --> 00:41:49,480 Speaker 1: If you liked the episode, please subscribe and leave a review. 704 00:41:49,960 --> 00:41:52,640 Speaker 1: We've lined up a number of great guests for the podcast, 705 00:41:52,680 --> 00:41:58,040 Speaker 1: so please check back to hear conversations with C suite executives, shippers, regulators, 706 00:41:58,040 --> 00:42:00,880 Speaker 1: and decision makers within the freight market. Also, if you 707 00:42:00,920 --> 00:42:03,000 Speaker 1: have an idea for a future episode, please hit me 708 00:42:03,080 --> 00:42:06,320 Speaker 1: up on the terminal or on Twitter at logistics Lee. 709 00:42:06,360 --> 00:42:15,360 Speaker 1: Thanks so much everyone, and have a great day.