1 00:00:02,720 --> 00:00:10,600 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the 2 00:00:10,600 --> 00:00:14,560 Speaker 1: Bloomberg Intelligence podcast. Catch us live weekdays at ten am 3 00:00:14,600 --> 00:00:18,520 Speaker 1: Eastern on Applecarplay and Android Auto with the Bloomberg Business App. 4 00:00:18,640 --> 00:00:21,840 Speaker 1: Listen on demand wherever you get your podcasts, or watch 5 00:00:21,960 --> 00:00:23,520 Speaker 1: us live on YouTube. 6 00:00:24,079 --> 00:00:27,720 Speaker 2: FedEx reported some numbers last night. I thought there were 7 00:00:27,720 --> 00:00:29,880 Speaker 2: some pretty good numbers, given what's going on out there 8 00:00:29,920 --> 00:00:32,319 Speaker 2: in the world. Sex kind of unchanged on the day. 9 00:00:32,320 --> 00:00:34,400 Speaker 2: It's up thirty five percent year to date. Let's break 10 00:00:34,400 --> 00:00:38,159 Speaker 2: it down because we all deal with FedEx almost on 11 00:00:38,200 --> 00:00:41,000 Speaker 2: a daily basis. It seems like Lee Klasical, Senior Transport 12 00:00:41,080 --> 00:00:44,280 Speaker 2: Logistics and Chipping analyst from Bloomberg Intelligence. He's also a 13 00:00:44,360 --> 00:00:48,760 Speaker 2: host of a weekly podcast, Bloomberg Intelligence Talking Transports. Lee 14 00:00:48,800 --> 00:00:50,760 Speaker 2: talked to U about FedEx. What did we learn from 15 00:00:50,760 --> 00:00:51,280 Speaker 2: their earnings? 16 00:00:52,120 --> 00:00:53,880 Speaker 3: Yeah, hey, Paul, I thought they did a pretty good 17 00:00:54,160 --> 00:00:57,520 Speaker 3: print for their physical fourth quarter. You know, what they've 18 00:00:57,520 --> 00:01:01,120 Speaker 3: been able to show is that their car controls, they've 19 00:01:01,120 --> 00:01:06,080 Speaker 3: had a number of different programs in place that are 20 00:01:06,120 --> 00:01:09,720 Speaker 3: really starting to pay off, and that kind of helped 21 00:01:09,760 --> 00:01:14,200 Speaker 3: them with their beat. They also painted a relatively okay 22 00:01:14,440 --> 00:01:17,800 Speaker 3: demand picture for us. They noted that you know, they 23 00:01:17,800 --> 00:01:20,959 Speaker 3: didn't see much demand destruction from higher fuel costs, which is, 24 00:01:21,600 --> 00:01:24,199 Speaker 3: you know, a very good thing to hear. And also 25 00:01:24,240 --> 00:01:26,920 Speaker 3: they mentioned their B to B business has been benefiting 26 00:01:26,959 --> 00:01:29,440 Speaker 3: from the built out of data centers and all the 27 00:01:29,480 --> 00:01:32,360 Speaker 3: stuff that goes into that. You know, they're helping bringing 28 00:01:32,760 --> 00:01:36,480 Speaker 3: those technologies to the places where they're building those centers. 29 00:01:36,480 --> 00:01:39,600 Speaker 3: So you know, they pointed to you know, decent top 30 00:01:39,680 --> 00:01:42,720 Speaker 3: line growth. And also, you know what they also did, 31 00:01:43,040 --> 00:01:45,959 Speaker 3: which is kind of probably why the stock is down 32 00:01:46,000 --> 00:01:48,840 Speaker 3: so much of the last twenty four hours, is is 33 00:01:48,880 --> 00:01:52,560 Speaker 3: you know, they provided their first calendar year outlook because 34 00:01:52,640 --> 00:01:54,960 Speaker 3: they were on a physical calendar. Now they're going on 35 00:01:55,000 --> 00:01:59,560 Speaker 3: a regular, good old fashion calendar reporting schedule, and so 36 00:01:59,680 --> 00:02:02,960 Speaker 3: I think that might have raised some confusion. Also, you know, 37 00:02:03,240 --> 00:02:06,960 Speaker 3: the guide, which topped out at eighteen dollars and ten 38 00:02:07,040 --> 00:02:09,160 Speaker 3: cents at the top of the range, I think was 39 00:02:09,200 --> 00:02:13,520 Speaker 3: slightly below whisper numbers that we were hearing, you know, 40 00:02:14,320 --> 00:02:16,639 Speaker 3: call it a dime or a quarter. So nothing terrible. 41 00:02:16,919 --> 00:02:18,800 Speaker 3: So I think, you know, analysts are going to be 42 00:02:19,200 --> 00:02:23,520 Speaker 3: rejiggering their numbers to probably fit within management's expectations. But 43 00:02:23,600 --> 00:02:26,480 Speaker 3: what I would note is that management recently has been 44 00:02:26,480 --> 00:02:29,560 Speaker 3: doing a pretty good job of providing some sort of 45 00:02:29,600 --> 00:02:32,800 Speaker 3: modest guidance, if you will, and then beating that. And 46 00:02:32,800 --> 00:02:35,120 Speaker 3: that is something that the street definitely wants to see 47 00:02:35,200 --> 00:02:37,880 Speaker 3: because prior to i would say, the last couple of quarters, 48 00:02:37,880 --> 00:02:40,359 Speaker 3: they had a history of doing the opposite, maybe over 49 00:02:40,440 --> 00:02:44,160 Speaker 3: delivering and under over promising it under delivering. 50 00:02:44,240 --> 00:02:49,800 Speaker 4: So investors tend not to forget that certainly when it 51 00:02:49,800 --> 00:02:52,560 Speaker 4: comes to competition. I mean, there's only so much Effetex 52 00:02:52,600 --> 00:02:56,040 Speaker 4: can do about things like oil prices and tariffs, and 53 00:02:56,320 --> 00:02:59,160 Speaker 4: demand is tied to how the economy is doing when 54 00:02:59,200 --> 00:03:01,800 Speaker 4: it comes to competition. And Amazon, of course plants to 55 00:03:01,960 --> 00:03:06,320 Speaker 4: expands logistics business, how well positioned or perhaps not so 56 00:03:06,400 --> 00:03:09,520 Speaker 4: well positioned is FedEx and along with its peers for that. 57 00:03:10,840 --> 00:03:13,280 Speaker 3: Yeah, so you know, Amazon made an announcement a couple 58 00:03:13,280 --> 00:03:16,680 Speaker 3: of weeks ago that it was broadening out its logistics 59 00:03:17,040 --> 00:03:21,520 Speaker 3: services to third parties. This really wasn't new news. They've 60 00:03:21,520 --> 00:03:24,040 Speaker 3: were kind of doing these services for a while, they 61 00:03:24,160 --> 00:03:27,200 Speaker 3: kind of more or less free packaged that. Yes, they 62 00:03:27,240 --> 00:03:30,360 Speaker 3: are a competitor to FedEx, but fed X really doesn't 63 00:03:30,400 --> 00:03:33,480 Speaker 3: want the same business that Amazon's going after. You know, 64 00:03:33,520 --> 00:03:36,320 Speaker 3: Amazon first and foremost is starting these business to reduce 65 00:03:36,320 --> 00:03:39,480 Speaker 3: their overall costs to deliver their packages, and if they 66 00:03:39,480 --> 00:03:42,960 Speaker 3: can offset that cost by putting other freight in their 67 00:03:44,200 --> 00:03:47,400 Speaker 3: into their system, They're they're absolutely for it. You know, 68 00:03:47,480 --> 00:03:52,080 Speaker 3: FedEx would rather have you know, obviously all growth is 69 00:03:52,400 --> 00:03:54,680 Speaker 3: relatively good growth, but if they had their choice, they'd 70 00:03:54,720 --> 00:03:56,360 Speaker 3: rather have B to B business with tend to be 71 00:03:56,440 --> 00:03:58,600 Speaker 3: higher margin business than B two C business. 72 00:03:59,560 --> 00:04:00,320 Speaker 5: Stay with us. 73 00:04:00,320 --> 00:04:02,520 Speaker 4: More from Bloomberg Intelligence coming up after this. 74 00:04:06,280 --> 00:04:10,000 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 75 00:04:10,080 --> 00:04:13,120 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 76 00:04:13,160 --> 00:04:16,479 Speaker 1: Auto with the Bloomberg Business app. Listen on demand wherever 77 00:04:16,520 --> 00:04:20,080 Speaker 1: you get your podcasts, or watch us live on YouTube. 78 00:04:20,760 --> 00:04:23,000 Speaker 2: All I know is the weather apps on my phone. 79 00:04:23,240 --> 00:04:26,039 Speaker 2: They're getting better and better and better. It tells you now, 80 00:04:26,279 --> 00:04:28,800 Speaker 2: which is so cool, particularly when you're a beach guy 81 00:04:28,920 --> 00:04:31,479 Speaker 2: when you need weather forecast for boating and fishing. Is 82 00:04:31,839 --> 00:04:35,280 Speaker 2: rains can start in twelve minutes? Sure enough, twelve minutes 83 00:04:35,320 --> 00:04:36,159 Speaker 2: later it starts raining. 84 00:04:36,240 --> 00:04:38,279 Speaker 4: Yeah, it's pretty impressive. Gone are the days where you 85 00:04:38,320 --> 00:04:40,800 Speaker 4: had to stay up and watch the eleven PM news 86 00:04:40,839 --> 00:04:42,719 Speaker 4: to figure out what things would look like the next day. 87 00:04:42,880 --> 00:04:45,960 Speaker 2: You would think some guys people actually get PhDs in weather. 88 00:04:46,480 --> 00:04:48,840 Speaker 2: That's the thing. You know. Our next guest does that, 89 00:04:48,960 --> 00:04:52,000 Speaker 2: Ryan Ward. He's a weather associate b n EF. Ryan 90 00:04:52,040 --> 00:04:55,039 Speaker 2: talk to us about AI is hitting all parts of 91 00:04:55,040 --> 00:04:58,320 Speaker 2: our life. It's got to be impacting your business forecasting 92 00:04:59,000 --> 00:05:02,520 Speaker 2: weather because it's all about the models and what this 93 00:05:02,640 --> 00:05:05,279 Speaker 2: model is saying. How is AI being used in the 94 00:05:05,279 --> 00:05:05,960 Speaker 2: world of weather. 95 00:05:06,400 --> 00:05:08,760 Speaker 6: Yeah, definitely. I think a lot of people think, you know, 96 00:05:08,800 --> 00:05:10,640 Speaker 6: they hear AI and the thinking like Claude or chat 97 00:05:10,680 --> 00:05:13,280 Speaker 6: GPT or whatever. We have very specific models now that 98 00:05:13,360 --> 00:05:16,360 Speaker 6: are emerging for a variety of purposes and weather, and 99 00:05:16,400 --> 00:05:19,120 Speaker 6: I think the one that we're I think really shocked 100 00:05:19,120 --> 00:05:21,200 Speaker 6: and excited by right now is tropical cyclones. Right it's 101 00:05:21,279 --> 00:05:25,120 Speaker 6: hurricane season right now. We're expecting probably a lower than 102 00:05:25,120 --> 00:05:28,080 Speaker 6: average count of hurricanes this year. But surprisingly, you know, 103 00:05:28,160 --> 00:05:30,440 Speaker 6: last year was basically the first year that AI models 104 00:05:30,480 --> 00:05:34,480 Speaker 6: were deployed to actually model hurricanes, and they did really 105 00:05:34,560 --> 00:05:37,960 Speaker 6: well insofar as they did basically better than every model 106 00:05:37,960 --> 00:05:40,159 Speaker 6: that already exists, models that have been here for thirty 107 00:05:40,279 --> 00:05:40,919 Speaker 6: plus years. 108 00:05:41,160 --> 00:05:43,680 Speaker 4: Yeah, I guess the question is how far back do 109 00:05:43,720 --> 00:05:45,800 Speaker 4: the models go when it comes to data, because I 110 00:05:46,000 --> 00:05:48,880 Speaker 4: remember how often we hear that this was the storm 111 00:05:48,880 --> 00:05:51,479 Speaker 4: of the century, or we haven't had this kind of 112 00:05:51,520 --> 00:05:53,680 Speaker 4: storm in X number of years, And if your data 113 00:05:53,720 --> 00:05:56,960 Speaker 4: only goes back, say fifteen, thirty, even fifty years, that 114 00:05:57,000 --> 00:05:58,120 Speaker 4: doesn't take you far back enough. 115 00:05:58,400 --> 00:06:00,320 Speaker 6: Yeah, definitely, I think this is one of the major 116 00:06:00,320 --> 00:06:03,480 Speaker 6: concerns is that we're concerned that if AI models are 117 00:06:03,520 --> 00:06:06,640 Speaker 6: only trained on previous data and storms are getting more extreme, 118 00:06:06,760 --> 00:06:09,839 Speaker 6: maybe they won't be able to actually predict those future 119 00:06:09,839 --> 00:06:12,200 Speaker 6: storms that are you know, the most extreme stored on record. 120 00:06:12,240 --> 00:06:14,120 Speaker 6: And yet last year we had one of those, it 121 00:06:14,120 --> 00:06:17,440 Speaker 6: was Hurricane Melissa. And yet you know, the Google Deep 122 00:06:17,440 --> 00:06:19,520 Speaker 6: Mind model, which is an AI based model, did better 123 00:06:19,560 --> 00:06:24,000 Speaker 6: than every other hurricane model. Basically got its intensity basically correct, 124 00:06:24,040 --> 00:06:27,360 Speaker 6: and its path you know, where it exactly went, also correct. 125 00:06:27,560 --> 00:06:30,080 Speaker 6: And it's a storm that it could never have seen 126 00:06:30,120 --> 00:06:30,800 Speaker 6: in the trading data. 127 00:06:30,920 --> 00:06:31,000 Speaker 2: Right. 128 00:06:31,120 --> 00:06:33,360 Speaker 6: So that was I think really remarkable and exciting for us, 129 00:06:33,400 --> 00:06:35,760 Speaker 6: as you know, meteorologists, that these models are doing so 130 00:06:35,880 --> 00:06:37,960 Speaker 6: exceptionally well given they're like, you know a couple of 131 00:06:38,000 --> 00:06:38,680 Speaker 6: years old. 132 00:06:38,960 --> 00:06:41,080 Speaker 2: What is El Nino and how's it going to impact 133 00:06:41,200 --> 00:06:41,919 Speaker 2: hurricane season? 134 00:06:41,960 --> 00:06:43,960 Speaker 6: This year. Yeah, a lot of people are talking about 135 00:06:43,960 --> 00:06:46,120 Speaker 6: on Nina right now. It's you know, it's this large 136 00:06:46,120 --> 00:06:49,839 Speaker 6: scale climate phenomenon. It basically all it means is that 137 00:06:49,880 --> 00:06:51,800 Speaker 6: the Pacific is warmer than average, But that has a 138 00:06:51,800 --> 00:06:56,279 Speaker 6: lot of downstream implications for whether across across the world. Really, 139 00:06:56,640 --> 00:06:59,000 Speaker 6: as far as hurricanes are concerned, we nominally expect that 140 00:06:59,000 --> 00:07:01,560 Speaker 6: there will be fewerhurricanes than average. You know, if there's 141 00:07:01,560 --> 00:07:04,880 Speaker 6: maybe average seven hurricanes in the Atlantic that this year, 142 00:07:05,279 --> 00:07:08,640 Speaker 6: no expects maybe somewhere between three and six something like that. 143 00:07:08,720 --> 00:07:11,800 Speaker 6: And so you know that basically just results from the 144 00:07:11,800 --> 00:07:13,960 Speaker 6: fact that the wind patterns change of the Atlantic and 145 00:07:13,960 --> 00:07:16,480 Speaker 6: it's less conducive for hurricanes this year. 146 00:07:17,280 --> 00:07:20,760 Speaker 4: And what is a relationship between hurricane season here in 147 00:07:21,000 --> 00:07:24,880 Speaker 4: North America versus say, the typhoon season in Asia. Does 148 00:07:24,920 --> 00:07:25,800 Speaker 4: it affect that. 149 00:07:26,000 --> 00:07:29,720 Speaker 6: Yeah, definitely. So what we generally expect is that the 150 00:07:29,960 --> 00:07:32,520 Speaker 6: relationship between al Nino and what happens in the Atlantic, 151 00:07:32,560 --> 00:07:35,880 Speaker 6: that's a pretty tried and true impact. We can look 152 00:07:35,920 --> 00:07:37,960 Speaker 6: over fifty years of date and see usually during al 153 00:07:38,040 --> 00:07:40,480 Speaker 6: Nino years there's less hurricanes in the Pacific. You kind 154 00:07:40,480 --> 00:07:43,280 Speaker 6: of see different effects in fact on the Eastern side 155 00:07:43,320 --> 00:07:46,200 Speaker 6: of the Pacific, closer to the United States, you see 156 00:07:46,680 --> 00:07:49,480 Speaker 6: perhaps more hurricanes than average. Right, So the way that 157 00:07:49,480 --> 00:07:53,600 Speaker 6: all Nino impacts globally, hurricanes and the circulation patterns, it's 158 00:07:53,640 --> 00:07:56,000 Speaker 6: different depending on where you are, and so we may, 159 00:07:56,320 --> 00:07:57,840 Speaker 6: you know, just because we see less here, we might 160 00:07:57,880 --> 00:08:00,400 Speaker 6: still see more in the Pacific depending on where you are. 161 00:08:00,560 --> 00:08:04,200 Speaker 2: So who's is who does who predicts like our hurricanes 162 00:08:04,240 --> 00:08:05,880 Speaker 2: and tells us where they're going and all that kinds 163 00:08:05,880 --> 00:08:08,000 Speaker 2: of Is that the government or is that a private thing? 164 00:08:08,080 --> 00:08:09,480 Speaker 6: Yeah? I think it's a lot of people are So 165 00:08:09,520 --> 00:08:12,120 Speaker 6: there's there are a lot of government agencies which have 166 00:08:12,280 --> 00:08:13,720 Speaker 6: these models because the models are. 167 00:08:13,600 --> 00:08:15,240 Speaker 2: Really the Canadian model, American model. 168 00:08:15,280 --> 00:08:18,080 Speaker 6: Yeah, exactly, hear about exactly exactly. I think as far 169 00:08:18,080 --> 00:08:21,520 Speaker 6: as tropical cyclones are concerned, American stakeholders are more interested because, 170 00:08:21,560 --> 00:08:23,760 Speaker 6: you know, the Canada, the Canadians don't really have an 171 00:08:23,760 --> 00:08:26,480 Speaker 6: incentive to make a really great chopical cyclone model because 172 00:08:26,480 --> 00:08:28,160 Speaker 6: there's not like a lot of hurricanes in Canada, you know. 173 00:08:28,240 --> 00:08:30,400 Speaker 6: But Noah, I think is usually the gold standard for. 174 00:08:30,360 --> 00:08:33,160 Speaker 2: This kind of north Who is it national? 175 00:08:33,240 --> 00:08:36,280 Speaker 6: Noah, The National Oceanic and Atmospheric Administration. 176 00:08:36,000 --> 00:08:38,040 Speaker 4: Are they are they still funded. I mean, you know, 177 00:08:38,200 --> 00:08:40,920 Speaker 4: Doage took a you know, cut a lot of jobs, 178 00:08:40,920 --> 00:08:43,760 Speaker 4: and I'm guessing that you know, something that forecasts weather 179 00:08:43,880 --> 00:08:46,600 Speaker 4: is not high on the current White Houses list party lists. 180 00:08:46,679 --> 00:08:50,360 Speaker 6: Yeah, definitely. I think we've seen pretty big heights and lows, 181 00:08:50,400 --> 00:08:53,560 Speaker 6: probably as far as Noah's concerned. You'll see there's hiring 182 00:08:53,600 --> 00:08:57,840 Speaker 6: pushes very recently to bring back meteorologists because some were 183 00:08:58,000 --> 00:09:00,320 Speaker 6: you know cut in that you know that of it, 184 00:09:00,320 --> 00:09:03,600 Speaker 6: all right, and so you know, I think but you know, 185 00:09:03,760 --> 00:09:06,080 Speaker 6: all that aside, like the media all just started doing 186 00:09:06,200 --> 00:09:09,040 Speaker 6: Hurricane prediction and general weather prediction are like super important, right, 187 00:09:09,080 --> 00:09:11,599 Speaker 6: so these feel a little bit more like jobs that 188 00:09:11,679 --> 00:09:13,680 Speaker 6: have a little more staying power, I would hope. 189 00:09:13,880 --> 00:09:17,280 Speaker 2: All Right, so we're fewer than average hurricanes. 190 00:09:17,480 --> 00:09:20,200 Speaker 4: That is good news. That's good news for everyone. Just 191 00:09:20,240 --> 00:09:22,559 Speaker 4: a quick question. You have your PhD in atmospheric science. 192 00:09:22,559 --> 00:09:25,400 Speaker 4: When you were growing up, what did you want to be? Like? 193 00:09:25,440 --> 00:09:27,520 Speaker 4: You knew you loved weather, but like how did you 194 00:09:27,640 --> 00:09:28,559 Speaker 4: see things playing out? 195 00:09:29,080 --> 00:09:31,960 Speaker 6: Yeah? I think for me, there was two things. I 196 00:09:32,000 --> 00:09:34,040 Speaker 6: was always like really interested just like we I grew 197 00:09:34,120 --> 00:09:37,079 Speaker 6: up in Florida and so Florida is known like it's 198 00:09:37,120 --> 00:09:39,000 Speaker 6: in this area where like the clouds are crazy. Like 199 00:09:39,000 --> 00:09:40,520 Speaker 6: I lived in La for five years, there's like no 200 00:09:40,600 --> 00:09:43,720 Speaker 6: clouds there ever, But in Florida you look up outside 201 00:09:43,760 --> 00:09:45,800 Speaker 6: any day and just like the scale and scope of 202 00:09:45,840 --> 00:09:48,200 Speaker 6: the clouds are so crazy. And I was just like 203 00:09:48,200 --> 00:09:50,880 Speaker 6: always fascinated by that. And then also the BP oil 204 00:09:50,920 --> 00:09:53,280 Speaker 6: spill was like such a huge impact on like all 205 00:09:53,280 --> 00:09:55,600 Speaker 6: of our like golf communities, and I think just like 206 00:09:56,000 --> 00:09:58,080 Speaker 6: that really got me turned on to like, you know, 207 00:09:58,200 --> 00:09:59,920 Speaker 6: environmental like engineering science. 208 00:10:00,679 --> 00:10:03,600 Speaker 4: Stay with us more from Bloomberg Intelligence coming up after this. 209 00:10:07,400 --> 00:10:11,079 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 210 00:10:11,160 --> 00:10:14,199 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 211 00:10:14,200 --> 00:10:17,480 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 212 00:10:17,559 --> 00:10:20,640 Speaker 1: you get your podcasts, or watch us live on YouTube. 213 00:10:21,320 --> 00:10:25,200 Speaker 2: Well in the historical time coming up for ESPN that 214 00:10:25,240 --> 00:10:27,480 Speaker 2: we definitely want to spend some time talking about Linda 215 00:10:27,480 --> 00:10:33,920 Speaker 2: Cone Joints, a sports center anchor for ESPN, retiring after 216 00:10:34,200 --> 00:10:37,680 Speaker 2: thirty four years from ESPN. Linda, thank you so much 217 00:10:38,160 --> 00:10:40,679 Speaker 2: for joining us. I guess I'd love to just get 218 00:10:40,720 --> 00:10:45,760 Speaker 2: a sense since you've seen everything in sports television, for 219 00:10:45,800 --> 00:10:48,920 Speaker 2: better or worse, how has it changed? How has sports 220 00:10:49,280 --> 00:10:52,680 Speaker 2: television changed? Because ESPN changed everything. 221 00:10:54,080 --> 00:10:56,120 Speaker 5: Yeah, first of all, thanks for having me, Paul and 222 00:10:56,200 --> 00:10:59,160 Speaker 5: Scarlett really appreciate it. Yeah, it's been a fun time 223 00:10:59,200 --> 00:11:02,880 Speaker 5: for me. The big shows, the final shows or this Friday, 224 00:11:05,160 --> 00:11:10,040 Speaker 5: and so I have seen everything, and most of it's 225 00:11:10,080 --> 00:11:13,040 Speaker 5: been for the great you know. I like telling this story. 226 00:11:13,080 --> 00:11:15,520 Speaker 5: You know, I started in nineteen ninety two, July nineteen 227 00:11:15,600 --> 00:11:18,560 Speaker 5: ninety two, for those who can do the math, thirty 228 00:11:18,600 --> 00:11:24,200 Speaker 5: four years and when they first instituted the Institute of 229 00:11:24,240 --> 00:11:27,959 Speaker 5: the bottom line, that ticker at the bottom before that, 230 00:11:28,080 --> 00:11:31,199 Speaker 5: you know, telling the scores before we gave them the information. 231 00:11:31,880 --> 00:11:34,360 Speaker 5: I share this story because many of us on camera 232 00:11:34,559 --> 00:11:38,719 Speaker 5: a sports center anchors were horrified about this because we 233 00:11:39,080 --> 00:11:44,079 Speaker 5: really cared about our writing and telling a story, setting 234 00:11:44,160 --> 00:11:48,160 Speaker 5: up the highlight of the game you're about to see, 235 00:11:48,800 --> 00:11:51,760 Speaker 5: but you're giving away the score. And this was I 236 00:11:51,760 --> 00:11:53,280 Speaker 5: don't even know what year this was. It might have 237 00:11:53,320 --> 00:11:56,920 Speaker 5: been late nineties or whatever, but it was it was like, 238 00:11:57,720 --> 00:12:00,440 Speaker 5: you know, Swilight Zone ish for us, so you know 239 00:12:00,480 --> 00:12:02,959 Speaker 5: what I mean. And it was like and we thought 240 00:12:03,400 --> 00:12:05,520 Speaker 5: there was a talent meeting and all of us like 241 00:12:05,559 --> 00:12:08,720 Speaker 5: stood up and made our case how awful this would be. 242 00:12:09,520 --> 00:12:11,640 Speaker 5: Of course we didn't get our way, and then it 243 00:12:11,679 --> 00:12:14,520 Speaker 5: turned out to be again one of these things that 244 00:12:14,600 --> 00:12:18,720 Speaker 5: if we don't see, you know, what's wrong with the TV, 245 00:12:18,880 --> 00:12:21,319 Speaker 5: there's no bottom line that tells us a store before 246 00:12:21,360 --> 00:12:23,680 Speaker 5: we talk about it. So that's always the first thing 247 00:12:24,080 --> 00:12:26,640 Speaker 5: that jumps up. But then technically all the stuff we 248 00:12:26,640 --> 00:12:28,960 Speaker 5: see when we watch live football get any kind of 249 00:12:29,000 --> 00:12:33,960 Speaker 5: sporting event, making it easy to watch and all this. 250 00:12:34,080 --> 00:12:37,880 Speaker 5: But for Sports Center ESPN, yes, we were always ahead 251 00:12:37,880 --> 00:12:41,120 Speaker 5: of it. And I'm really proud that I, you know, 252 00:12:41,160 --> 00:12:42,880 Speaker 5: I played a you know, small part of it with 253 00:12:42,960 --> 00:12:44,480 Speaker 5: my colleagues from the golden era. 254 00:12:44,800 --> 00:12:46,520 Speaker 4: I feel like you played an instrumental part of it, 255 00:12:46,600 --> 00:12:47,440 Speaker 4: not a small part of it. 256 00:12:47,520 --> 00:12:50,160 Speaker 5: And the fact that Scarlett, the fact that. 257 00:12:50,120 --> 00:12:52,560 Speaker 4: You started in radio that's near and dear to our parts. 258 00:12:52,559 --> 00:12:55,240 Speaker 4: Of course, because we're talking to you on radio right now, 259 00:12:56,320 --> 00:12:58,200 Speaker 4: just give us a little bit of background about you, 260 00:12:58,320 --> 00:13:01,000 Speaker 4: because I'm in chiage by the fact that you played 261 00:13:01,040 --> 00:13:04,880 Speaker 4: hockey as a teenager with the boys and against the boys. 262 00:13:06,080 --> 00:13:08,960 Speaker 4: How much did that factor into your wanting to go 263 00:13:09,040 --> 00:13:10,280 Speaker 4: into sports broadcasting. 264 00:13:11,400 --> 00:13:14,040 Speaker 5: Yeah, and thanks Scarlett for saying that. Yeah, first, I 265 00:13:14,040 --> 00:13:16,600 Speaker 5: don't want to forget to say this. How you know 266 00:13:16,760 --> 00:13:20,000 Speaker 5: young women like yourself. All these women have reached out 267 00:13:20,040 --> 00:13:23,280 Speaker 5: to me that are in the business, and that is like, 268 00:13:23,559 --> 00:13:27,680 Speaker 5: I'm really proud of that professional legacy of inspiring all 269 00:13:27,679 --> 00:13:30,079 Speaker 5: these young women who are in kindergarten, first grade, second 270 00:13:30,160 --> 00:13:33,520 Speaker 5: grade watching the sports in early years before they jumped 271 00:13:33,559 --> 00:13:37,160 Speaker 5: on the boss, realizing that they too could do it 272 00:13:37,240 --> 00:13:40,319 Speaker 5: if they saw a woman up there telling them about 273 00:13:40,320 --> 00:13:44,239 Speaker 5: what happened in the game. Right. So I'm really professionally 274 00:13:44,240 --> 00:13:47,680 Speaker 5: almost proud of that the most. So getting back to 275 00:13:47,720 --> 00:13:50,240 Speaker 5: the hockey thing, I was a kid with very low 276 00:13:50,280 --> 00:13:53,360 Speaker 5: self esteem. I wore very thick glasses. Now the glasses 277 00:13:53,360 --> 00:13:57,679 Speaker 5: are not as thick, thank god. But I love sports, 278 00:13:57,720 --> 00:14:00,440 Speaker 5: watched it with my dad. Sports gave me something to 279 00:14:00,440 --> 00:14:03,960 Speaker 5: look forward to. That's how I always phrase it. Because 280 00:14:04,040 --> 00:14:05,800 Speaker 5: I didn't really have a lot of friends. I mean, 281 00:14:05,960 --> 00:14:08,520 Speaker 5: I mean I listened to the Carpenters growing up. I 282 00:14:08,600 --> 00:14:11,840 Speaker 5: love depressing stuff. It was it was all it wasn't 283 00:14:11,960 --> 00:14:14,240 Speaker 5: you know, I would don't worry no what happen emailfore 284 00:14:14,320 --> 00:14:21,560 Speaker 5: your time? Yeah, exactly exactly, Scarlet. So anyway, you know, 285 00:14:21,760 --> 00:14:24,240 Speaker 5: then I then I started playing street hockey with the boys, 286 00:14:24,360 --> 00:14:25,840 Speaker 5: and I realized, you know, I'm really good at this 287 00:14:25,880 --> 00:14:28,480 Speaker 5: goalie thing. And what I loved about that position is 288 00:14:28,520 --> 00:14:31,400 Speaker 5: that I could determine here's this girl, low self esteem, 289 00:14:31,920 --> 00:14:34,800 Speaker 5: shy as a as a wallflower, you know, just kind 290 00:14:34,800 --> 00:14:39,160 Speaker 5: of melted into the wall and playing goalie, realizing that 291 00:14:39,200 --> 00:14:42,080 Speaker 5: I was good at it. And I got contact lenses 292 00:14:42,160 --> 00:14:45,400 Speaker 5: by the way, that helped my vision, and you know, 293 00:14:46,080 --> 00:14:49,320 Speaker 5: and then I was like, Wow, people are noticing me. 294 00:14:49,960 --> 00:14:52,680 Speaker 5: And it wasn't like, you know, conceited kind of way, 295 00:14:52,840 --> 00:14:55,400 Speaker 5: but I felt like I was, Wow, I'm helping people, 296 00:14:55,800 --> 00:14:58,600 Speaker 5: I'm winning games, I'm making the safe at the right time. 297 00:14:58,880 --> 00:15:03,160 Speaker 5: I'm suddenly like somebody. And so I rode that emotion 298 00:15:03,360 --> 00:15:05,560 Speaker 5: and I added that to just my love of sports 299 00:15:05,560 --> 00:15:08,840 Speaker 5: and my sports teams and watching the games with my dad, 300 00:15:09,240 --> 00:15:11,360 Speaker 5: but being a goalie. I liked telling this story too, 301 00:15:11,400 --> 00:15:15,480 Speaker 5: because I don't think I ever would have had this 302 00:15:15,640 --> 00:15:19,960 Speaker 5: career in broadcasting and then onto ESPN, you know, because 303 00:15:19,960 --> 00:15:22,200 Speaker 5: I start out in radio, like you said, in New York, 304 00:15:22,240 --> 00:15:25,760 Speaker 5: working seven days a week. Loved it. Then I you know, 305 00:15:25,800 --> 00:15:30,320 Speaker 5: hosting three hour solo sports talk shows. It's the best thing. 306 00:15:30,360 --> 00:15:32,400 Speaker 5: People don't get and that has made me a better 307 00:15:32,440 --> 00:15:37,120 Speaker 5: sports center anchor. Oh, by the way, so about getting 308 00:15:37,120 --> 00:15:39,760 Speaker 5: back to the goalie story quickly. You know, when I 309 00:15:39,840 --> 00:15:41,920 Speaker 5: was playing with the boys. When I first started, my 310 00:15:41,960 --> 00:15:45,360 Speaker 5: mom found a league on Long Island that you know, 311 00:15:45,480 --> 00:15:47,920 Speaker 5: I was fourteen. I wanted to play ice hockey. I 312 00:15:48,000 --> 00:15:50,760 Speaker 5: learned to skate with forty pounds of goalie equipment on 313 00:15:50,840 --> 00:15:53,400 Speaker 5: me and I played. They didn't let me play with 314 00:15:53,440 --> 00:15:55,320 Speaker 5: fourteen year old boys. That I had to play with 315 00:15:55,360 --> 00:15:57,240 Speaker 5: eight year old boys back in the day. It was 316 00:15:57,480 --> 00:15:59,880 Speaker 5: you know, I'm dating myself, but you can just google me. 317 00:16:00,000 --> 00:16:04,720 Speaker 5: And it was nineteen seventy, nineteen seventy, like mid seventies, 318 00:16:04,760 --> 00:16:09,120 Speaker 5: five seventy six, this kind of time. And so I 319 00:16:09,160 --> 00:16:12,600 Speaker 5: heard the moms, like I heard the moms whispering, well, 320 00:16:12,600 --> 00:16:14,920 Speaker 5: what is that a girl in that? I see a 321 00:16:14,920 --> 00:16:17,320 Speaker 5: ponytail out of behind her mask? All this kind of 322 00:16:17,840 --> 00:16:20,000 Speaker 5: and I had to block out the noise. I had 323 00:16:20,000 --> 00:16:22,600 Speaker 5: to block out the critics. I had to block out 324 00:16:22,600 --> 00:16:24,720 Speaker 5: the people who are like, what is she doing here? 325 00:16:25,240 --> 00:16:29,800 Speaker 5: Type of verbiage, type of words that I heard and overheard, 326 00:16:30,440 --> 00:16:32,800 Speaker 5: And man, if that doesn't prepare you for, you know, 327 00:16:32,800 --> 00:16:35,400 Speaker 5: breaking into the boys club, like I did. And I 328 00:16:35,400 --> 00:16:38,320 Speaker 5: wrote a book years ago called Conehead and I kind 329 00:16:38,320 --> 00:16:40,640 Speaker 5: of put that tagline on it. I don't know what 330 00:16:40,800 --> 00:16:43,880 Speaker 5: does because I learned to block out the noise. I 331 00:16:43,960 --> 00:16:46,560 Speaker 5: had my moments, I had my days, I had my 332 00:16:46,640 --> 00:16:51,440 Speaker 5: crying episodes in ladies' rooms, in various workplaces. I'm only human. 333 00:16:52,520 --> 00:16:55,160 Speaker 5: But you know what, you just believe in yourself and 334 00:16:55,200 --> 00:16:58,800 Speaker 5: you get enough validation to keep going, right. You know, 335 00:16:58,840 --> 00:17:02,240 Speaker 5: we always need a little extra validation like Okay, this, that, 336 00:17:02,440 --> 00:17:05,520 Speaker 5: this that, but you uh, you know, it's just when 337 00:17:05,560 --> 00:17:07,840 Speaker 5: I look back, it's been a great, great ride. 338 00:17:08,000 --> 00:17:10,639 Speaker 2: And now, Linda, I mean your business, the business of 339 00:17:10,760 --> 00:17:15,159 Speaker 2: sports cable TV. Now it's streaming, it's changing yet again. 340 00:17:15,359 --> 00:17:18,480 Speaker 2: But yeah, what do you think the future is of 341 00:17:18,960 --> 00:17:20,840 Speaker 2: ESPN and this new world we're in. 342 00:17:21,840 --> 00:17:24,600 Speaker 5: Yeah, I don't have that crystal ball. I just know that, 343 00:17:24,880 --> 00:17:28,080 Speaker 5: you know, full disclosure. I'm glad I'm not starting out 344 00:17:28,080 --> 00:17:32,040 Speaker 5: on the business now. It would be very things have changed, 345 00:17:32,040 --> 00:17:34,720 Speaker 5: would be very challenging. We know about AI, we know 346 00:17:34,760 --> 00:17:38,960 Speaker 5: about all that. I'm not saying they're gonna take the 347 00:17:39,119 --> 00:17:40,560 Speaker 5: I don't know. I'm not gonna take the play, but 348 00:17:40,640 --> 00:17:43,240 Speaker 5: not gonna see robots and sports, that or chairs, But 349 00:17:43,680 --> 00:17:46,360 Speaker 5: I'm not sure what we're gonna see. On one hand, 350 00:17:46,400 --> 00:17:49,719 Speaker 5: streaming is exciting. On one hand, AI is exciting. Uh. 351 00:17:49,880 --> 00:17:53,080 Speaker 5: But you know, kudos to ESPN and with all these 352 00:17:53,160 --> 00:17:56,239 Speaker 5: super in the last five, six, seven years, as you 353 00:17:56,240 --> 00:18:00,720 Speaker 5: guys have witnessed, it's really jumped leaps and bounds with 354 00:18:00,800 --> 00:18:03,920 Speaker 5: all these technical changes and adjustments you have to make, 355 00:18:04,200 --> 00:18:07,760 Speaker 5: and the competition what I love about When I broke 356 00:18:07,880 --> 00:18:10,960 Speaker 5: in and then, you know, probably the first twenty five 357 00:18:11,000 --> 00:18:13,840 Speaker 5: of the thirty four years, maybe the first twenty eight 358 00:18:13,920 --> 00:18:16,800 Speaker 5: of the thirty four years, you know, there wasn't a 359 00:18:16,800 --> 00:18:21,399 Speaker 5: lot of super competition to ESPN, right and especially the 360 00:18:21,440 --> 00:18:23,560 Speaker 5: early days that was a big thrill for all of 361 00:18:23,640 --> 00:18:26,240 Speaker 5: us that we were the only game in town, like 362 00:18:26,560 --> 00:18:30,080 Speaker 5: meaning that's all you could turn to to see anything. 363 00:18:30,119 --> 00:18:32,760 Speaker 5: There was, you know, no internet, blah blah blah. So 364 00:18:33,240 --> 00:18:37,280 Speaker 5: how all are these changes going to affect ESPN? Honestly, guys, 365 00:18:37,480 --> 00:18:40,760 Speaker 5: I don't I don't know, but I will be watching. 366 00:18:41,520 --> 00:18:44,280 Speaker 4: They'll be watching as well, we all be, as will 367 00:18:44,320 --> 00:18:47,320 Speaker 4: all be. One question I do have for you, Linda 368 00:18:47,440 --> 00:18:49,720 Speaker 4: is that you have always said you approach your job 369 00:18:49,760 --> 00:18:52,920 Speaker 4: as a fan first, because you are not a professional 370 00:18:53,000 --> 00:18:56,960 Speaker 4: athlete who then transitioned into becoming an on air talent. 371 00:18:57,640 --> 00:19:01,080 Speaker 4: Now that most of the commentators on live sports are 372 00:19:01,320 --> 00:19:05,440 Speaker 4: former athletes themselves, what do normal people like a Linda 373 00:19:05,480 --> 00:19:11,160 Speaker 4: Cone offer that you know, a Charles Barkley doesn't. I mean, 374 00:19:11,200 --> 00:19:13,679 Speaker 4: what can you say? What can you bring to the 375 00:19:13,720 --> 00:19:17,080 Speaker 4: table too audiences that former athletes can't and don't. 376 00:19:17,920 --> 00:19:20,159 Speaker 5: Yeah, and you're right about your observation. And a lot 377 00:19:20,200 --> 00:19:23,119 Speaker 5: of former athletes have become analysts and that's great. And 378 00:19:23,520 --> 00:19:26,280 Speaker 5: you know, being in a very lower level a college athlete, 379 00:19:26,320 --> 00:19:28,680 Speaker 5: you know, I get that, and I always I always 380 00:19:28,720 --> 00:19:32,159 Speaker 5: been athlete friendly, player friendly, that type of thing. But 381 00:19:32,359 --> 00:19:35,119 Speaker 5: you know, the young people, the young men and women 382 00:19:35,160 --> 00:19:37,760 Speaker 5: coming out of college still want to be in the business. 383 00:19:38,040 --> 00:19:40,800 Speaker 5: There's so many great things that can hone their skills, 384 00:19:40,800 --> 00:19:43,919 Speaker 5: whether it's YouTube, whether it's TikTok, all these things you 385 00:19:43,920 --> 00:19:46,240 Speaker 5: know that I didn't have, that many of us didn't have, 386 00:19:46,760 --> 00:19:50,240 Speaker 5: so there are I would I would really concentrate, even 387 00:19:50,280 --> 00:19:53,480 Speaker 5: though you know AI is there and all that, and 388 00:19:53,520 --> 00:19:56,840 Speaker 5: it's all about videos now. But the writing, I mean 389 00:19:56,840 --> 00:20:01,480 Speaker 5: that is something, the journalistic skill, the writing, you can 390 00:20:02,960 --> 00:20:06,359 Speaker 5: be way ahead of the former athletes. You can guide 391 00:20:06,359 --> 00:20:08,920 Speaker 5: them and the way you speak, you know, the one 392 00:20:08,920 --> 00:20:11,440 Speaker 5: thing I really had to learn, and thank God goodness 393 00:20:11,440 --> 00:20:18,159 Speaker 5: for radio guys, because I learned to communicate naturally, not 394 00:20:18,400 --> 00:20:20,600 Speaker 5: feel like I was reading something, you know, because I 395 00:20:20,600 --> 00:20:22,760 Speaker 5: started out doing updates, and then I started hosting radio 396 00:20:22,800 --> 00:20:26,320 Speaker 5: shows and then you have a suddenly like, Okay, be conversational, 397 00:20:26,840 --> 00:20:30,199 Speaker 5: and so that was great and that helped me in 398 00:20:30,359 --> 00:20:32,639 Speaker 5: TV and that helped me for thirty four years on 399 00:20:32,680 --> 00:20:36,920 Speaker 5: the Sports Center set. So back to your original question, yes, 400 00:20:37,040 --> 00:20:39,760 Speaker 5: I mean that's where you could bring it, being conversational, 401 00:20:40,119 --> 00:20:42,400 Speaker 5: connecting to the fans. Yes, I say I'm a fan 402 00:20:42,440 --> 00:20:45,240 Speaker 5: first because I am. I'm just as nuts about my 403 00:20:45,359 --> 00:20:48,040 Speaker 5: teams than they are the people watching me, and I 404 00:20:48,080 --> 00:20:51,560 Speaker 5: tried to show that. But you can also channel that 405 00:20:52,240 --> 00:20:57,680 Speaker 5: into Okay, you be smart, you know what you're talking about. Prepared. 406 00:20:58,080 --> 00:21:01,399 Speaker 5: You have to be prepared. When I'm prepared, man, my 407 00:21:01,520 --> 00:21:04,920 Speaker 5: confidence boost when I'm not prepared, right, I mean, when 408 00:21:04,960 --> 00:21:07,199 Speaker 5: I'm not prepared, I'm doing something like I don't know 409 00:21:07,200 --> 00:21:10,320 Speaker 5: about this assignment. Oh, I'm getting nervous about it? All 410 00:21:10,359 --> 00:21:14,000 Speaker 5: comes on, you know, right, Linda, exactly scall it. 411 00:21:15,400 --> 00:21:20,080 Speaker 1: This is the Bloomberg Intelligence podcast, available on Apple, Spotify, 412 00:21:20,280 --> 00:21:24,240 Speaker 1: and anywhere else you get your podcasts. Listen live each weekday, 413 00:21:24,440 --> 00:21:27,720 Speaker 1: ten am to noon Eastern on Bloomberg dot com, the 414 00:21:27,800 --> 00:21:31,679 Speaker 1: iHeartRadio app tune In, and the Bloomberg Business app. You 415 00:21:31,720 --> 00:21:35,000 Speaker 1: can also watch us live every weekday on YouTube and 416 00:21:35,240 --> 00:21:37,160 Speaker 1: always on the Bloomberg terminal