1 00:00:08,200 --> 00:00:08,680 Speaker 1: Pushkin. 2 00:00:13,960 --> 00:00:15,920 Speaker 2: I never went to a Formula one race as a 3 00:00:16,000 --> 00:00:18,479 Speaker 2: kid because we lived in southern Ontario and there was 4 00:00:18,560 --> 00:00:21,320 Speaker 2: just one F one race in Canada. That race was 5 00:00:21,360 --> 00:00:25,160 Speaker 2: in Montreal, a good seven hour drive away. I never 6 00:00:25,200 --> 00:00:28,560 Speaker 2: watched F one race on television either, because we didn't 7 00:00:28,560 --> 00:00:31,960 Speaker 2: have a television. But what I did have was a 8 00:00:32,000 --> 00:00:35,160 Speaker 2: subscription to the car magazine Road and Track, and Roadent 9 00:00:35,200 --> 00:00:38,959 Speaker 2: Track took F one very seriously. Every month my new 10 00:00:38,960 --> 00:00:42,040 Speaker 2: issue would arrive, I would turn immediately to the long 11 00:00:42,240 --> 00:00:46,080 Speaker 2: detailed account of that month's race, and I fell in love. 12 00:00:49,200 --> 00:00:51,879 Speaker 2: It's been fifty years, but I can still rattle off 13 00:00:51,920 --> 00:00:53,880 Speaker 2: the names of all the top drivers of that era 14 00:00:53,960 --> 00:00:58,720 Speaker 2: from memory, James Hunt, Mario Andretti, Carlos Pace, Jacqueslafitte, and 15 00:00:58,800 --> 00:01:01,920 Speaker 2: of course the greatest of them all, my adolescent idol, 16 00:01:02,200 --> 00:01:05,560 Speaker 2: Nikki Lauda, who won two world championships in the mid 17 00:01:05,680 --> 00:01:08,560 Speaker 2: nineteen seventies with Scuderia Ferrari. 18 00:01:09,400 --> 00:01:12,440 Speaker 3: He was world championship material in the moment he joined 19 00:01:12,480 --> 00:01:15,679 Speaker 3: the Ferrari team. 20 00:01:16,120 --> 00:01:19,160 Speaker 4: There's no question that in seventy five seventy six I 21 00:01:19,280 --> 00:01:23,200 Speaker 4: was really dominating the whole thing without any mistake. So 22 00:01:23,319 --> 00:01:26,160 Speaker 4: I did nothing wrong I mean this was perfect driving. 23 00:01:26,880 --> 00:01:29,319 Speaker 2: If you had met skinny, pre adolescent Malcolm in the 24 00:01:29,319 --> 00:01:32,440 Speaker 2: mid nineteen seventies in rural Ontario, there's a good chance 25 00:01:32,480 --> 00:01:35,040 Speaker 2: you would have seen me in my prize Ferrari T shirt. 26 00:01:35,400 --> 00:01:38,560 Speaker 2: I was a fan, one of what the Italians called tifosi, 27 00:01:38,959 --> 00:01:41,760 Speaker 2: a Ferrari devote, And that's what it meant to be 28 00:01:41,800 --> 00:01:45,960 Speaker 2: a fan fifty years ago, t shirts, magazine stories and 29 00:01:46,000 --> 00:01:49,000 Speaker 2: a big Nikki lauda poster on your wall. But what 30 00:01:49,040 --> 00:01:52,040 Speaker 2: does it mean to be a fan today? Today we 31 00:01:52,120 --> 00:01:54,680 Speaker 2: have the Internet and streaming and big data and AI 32 00:01:54,760 --> 00:01:57,480 Speaker 2: and all the other accouterment of the digital age. Is 33 00:01:57,520 --> 00:02:06,520 Speaker 2: there a chance to reinvent the meaning of fan? My 34 00:02:06,600 --> 00:02:09,359 Speaker 2: name is Malcolm Gladwell. You're listening to the latest episode 35 00:02:09,400 --> 00:02:12,359 Speaker 2: of Smart Talks with IBM, where we offer our listeners 36 00:02:12,360 --> 00:02:14,959 Speaker 2: a glimpse behind the curtain of the world of technology. 37 00:02:15,720 --> 00:02:18,480 Speaker 2: In our first episode, we talked about how an AI 38 00:02:18,520 --> 00:02:24,240 Speaker 2: assistant created with IBM watsonex helps future teachers practice responsive teaching. 39 00:02:24,680 --> 00:02:27,959 Speaker 2: Our second episode was how a custom AI model could 40 00:02:28,000 --> 00:02:31,160 Speaker 2: help Loreel's researchers shape the future of what we put 41 00:02:31,200 --> 00:02:36,079 Speaker 2: on our faces every morning. In this episode, how IBM 42 00:02:36,320 --> 00:02:39,720 Speaker 2: one of the world's pre eminent technology companies, is joining 43 00:02:39,800 --> 00:02:42,359 Speaker 2: up with one of the world's pre eminent racing brands 44 00:02:42,720 --> 00:02:50,800 Speaker 2: to fundamentally change how fans interact with their favorite team. 45 00:02:51,080 --> 00:02:55,120 Speaker 2: The size of the Scuderia Ferrari HB fan base is staggering. 46 00:02:55,720 --> 00:02:58,959 Speaker 2: Three hundred and ninety six million people around the world 47 00:02:59,000 --> 00:03:03,240 Speaker 2: identify as Ferrari fans. Three hundred and ninety six million. 48 00:03:03,760 --> 00:03:06,120 Speaker 2: The only other fan base is that big belong to 49 00:03:06,160 --> 00:03:10,000 Speaker 2: the iconic Premier League Football teams like Manchester United or 50 00:03:10,080 --> 00:03:14,080 Speaker 2: Chelsea FC. I don't believe there is any other Formula 51 00:03:14,160 --> 00:03:18,520 Speaker 2: One team that inspires that kind of devotion. Ferrari's job, then, 52 00:03:18,639 --> 00:03:21,720 Speaker 2: isn't to necessarily grow its fan base. Three hundred and 53 00:03:21,880 --> 00:03:24,960 Speaker 2: ninety six million is more than enough fans. Their job 54 00:03:25,080 --> 00:03:28,239 Speaker 2: is to deepen the connection people feel with the scudery 55 00:03:28,320 --> 00:03:31,400 Speaker 2: A Ferrari team. But if I'm Ferrari, how do I 56 00:03:31,520 --> 00:03:34,000 Speaker 2: find out more about who my fans are, what they 57 00:03:34,040 --> 00:03:36,360 Speaker 2: care about, what they want? How do I use my 58 00:03:36,480 --> 00:03:39,880 Speaker 2: archives and data to create experiences that matter to them? 59 00:03:40,280 --> 00:03:42,160 Speaker 2: How do I say to the guy who spent his 60 00:03:42,280 --> 00:03:46,280 Speaker 2: childhood eagerly reading roadent track every month? Here are other ways? 61 00:03:46,320 --> 00:03:49,040 Speaker 2: You can get involved with your favorite F one team. 62 00:03:49,240 --> 00:03:49,680 Speaker 1: Today. 63 00:03:50,640 --> 00:03:53,960 Speaker 2: The task of deepening an emotional connection in the digital 64 00:03:54,000 --> 00:03:58,920 Speaker 2: age begins as an information problem, which is where IBM 65 00:03:58,960 --> 00:04:01,880 Speaker 2: comes in. How would you describe what you do? 66 00:04:02,760 --> 00:04:05,240 Speaker 3: I describe it as the probably the best job at IBM. 67 00:04:05,480 --> 00:04:07,040 Speaker 2: Yeah, I was going to say, I was going to 68 00:04:07,120 --> 00:04:09,120 Speaker 2: ask you, do you have the best job at IBM? 69 00:04:10,120 --> 00:04:10,680 Speaker 3: I think so. 70 00:04:11,320 --> 00:04:14,280 Speaker 2: I'm talking to Fred Baker, who leads sports and Entertainment 71 00:04:14,280 --> 00:04:17,640 Speaker 2: for IBM Consulting in Europe, the Middle East and Africa. 72 00:04:18,400 --> 00:04:21,400 Speaker 2: You can probably guess from the accent he's from New Zealand. 73 00:04:21,760 --> 00:04:24,120 Speaker 3: We've had a really interesting range of experience over the 74 00:04:24,160 --> 00:04:26,640 Speaker 3: past sort of five six years. We've worked with Premier 75 00:04:26,720 --> 00:04:30,480 Speaker 3: League clubs like Liverpool Football. We've worked with England Rugby, 76 00:04:30,880 --> 00:04:34,640 Speaker 3: Saint Andrew's Links. We also globally, we've got a global team, 77 00:04:34,720 --> 00:04:38,200 Speaker 3: so we work with the Masters, the US Open, ESPN, 78 00:04:38,279 --> 00:04:40,359 Speaker 3: Fantasy Football, the Grammys. 79 00:04:40,800 --> 00:04:43,240 Speaker 1: You do the tennis stuff. Is it all under Europe? 80 00:04:43,279 --> 00:04:45,360 Speaker 3: Yeah, so we do Wimbledon as well. Yep, that's under 81 00:04:45,400 --> 00:04:45,799 Speaker 3: my remit. 82 00:04:46,200 --> 00:04:48,880 Speaker 2: If you've ever watched Wimbledon on television, I'm sure you've 83 00:04:48,920 --> 00:04:51,760 Speaker 2: seen at various moments a little IBM logo on the 84 00:04:51,760 --> 00:04:55,680 Speaker 2: bottom of the screen. That's because IBM has been Wimbledon's 85 00:04:55,720 --> 00:05:01,039 Speaker 2: official information technology partner since nineteen ninety. When the idea 86 00:05:01,040 --> 00:05:04,120 Speaker 2: of a collaboration between Ferrari and IBM was first broached, 87 00:05:04,560 --> 00:05:07,200 Speaker 2: Bicker actually took people from Ferrari on a tour of 88 00:05:07,279 --> 00:05:10,479 Speaker 2: IBM's Wimbledon operation just so they could see what a 89 00:05:10,520 --> 00:05:13,960 Speaker 2: tech company like IBM could do for a sports franchise. 90 00:05:14,760 --> 00:05:15,480 Speaker 1: Which Wimbledon. 91 00:05:15,560 --> 00:05:18,919 Speaker 3: Did you take them to last year's champs? 92 00:05:19,080 --> 00:05:21,040 Speaker 2: Tell me what you showed them. 93 00:05:21,520 --> 00:05:24,080 Speaker 3: We take them into what we call the bunker, so 94 00:05:24,320 --> 00:05:27,880 Speaker 3: it's literally underground at the Champs, and showed them how 95 00:05:27,920 --> 00:05:31,720 Speaker 3: we bring everything to life from the data capture off 96 00:05:31,760 --> 00:05:36,440 Speaker 3: the courts, how we real time categorize, serve all those 97 00:05:36,440 --> 00:05:38,880 Speaker 3: points to broadcasters and serve them into the app the 98 00:05:38,880 --> 00:05:41,240 Speaker 3: website for millions of fans around the world. They were 99 00:05:41,279 --> 00:05:42,240 Speaker 3: really impressed by that. 100 00:05:42,839 --> 00:05:46,080 Speaker 2: I'm also impressed by that IBM trained it on the 101 00:05:46,160 --> 00:05:48,920 Speaker 2: language of tennis, and not only the language of tennis, 102 00:05:49,200 --> 00:05:53,200 Speaker 2: but specifically the language of tennis at Wimbledon. 103 00:05:53,400 --> 00:05:57,880 Speaker 3: So it can then decipher what an unforced era or 104 00:05:57,920 --> 00:06:01,320 Speaker 3: a winner or a lob or you know, idiosyncrasies in 105 00:06:01,360 --> 00:06:03,160 Speaker 3: the language. It can decipher all of that, and then 106 00:06:03,200 --> 00:06:05,280 Speaker 3: it can also tell what is a broadcast like to 107 00:06:05,320 --> 00:06:08,240 Speaker 3: talk about that is interesting to a fan. You know, 108 00:06:08,360 --> 00:06:10,839 Speaker 3: we've trained it so it can not only analyze everything 109 00:06:10,880 --> 00:06:13,799 Speaker 3: going on in the match. It can analyze past performances 110 00:06:13,839 --> 00:06:18,760 Speaker 3: and rationalize results based on conditions or form and then 111 00:06:18,800 --> 00:06:21,040 Speaker 3: make predictions that fans can learn from. But it can 112 00:06:21,040 --> 00:06:25,960 Speaker 3: also pull out on the spot really interesting milestones, moments, 113 00:06:26,040 --> 00:06:28,479 Speaker 3: data points that then come out of the mouth of 114 00:06:28,480 --> 00:06:29,120 Speaker 3: a broadcaster. 115 00:06:30,000 --> 00:06:32,599 Speaker 2: IBM is running an AI model that has been trained 116 00:06:32,680 --> 00:06:35,840 Speaker 2: on huge amounts of tennis data in order to give 117 00:06:35,920 --> 00:06:39,520 Speaker 2: human broadcasters ideas on what they can talk about. And 118 00:06:39,600 --> 00:06:43,040 Speaker 2: it all takes place underground, right near the courts. 119 00:06:43,800 --> 00:06:47,680 Speaker 3: It's literally like it's the underground floor of the broadcast 120 00:06:47,680 --> 00:06:50,680 Speaker 3: center at Wimbledon. It's literally almost under the courts. 121 00:06:50,960 --> 00:06:52,520 Speaker 1: Is IBM got the entire bunker? 122 00:06:53,200 --> 00:06:53,680 Speaker 3: Yeah? 123 00:06:53,720 --> 00:06:55,200 Speaker 1: How big is the room? 124 00:06:55,960 --> 00:06:58,520 Speaker 3: Oh? I'm sure our team would like it to be bigger, 125 00:06:58,520 --> 00:07:02,400 Speaker 3: but it's it's big enough. There's probably thirty forty IBM 126 00:07:02,480 --> 00:07:05,800 Speaker 3: is down there. Man in the four saying it live 127 00:07:05,960 --> 00:07:08,960 Speaker 3: is just really impressive when you see how much work 128 00:07:09,160 --> 00:07:12,640 Speaker 3: and intelligence goes on to then make an end experience 129 00:07:12,640 --> 00:07:16,200 Speaker 3: for a fan that is really beautiful and representative of 130 00:07:16,800 --> 00:07:18,040 Speaker 3: their brand and tradition. 131 00:07:19,040 --> 00:07:22,640 Speaker 2: IBM's goal in taking Ferrari to the Wimbledon Bunker was 132 00:07:22,680 --> 00:07:25,080 Speaker 2: to show them what it looks like to harness the 133 00:07:25,120 --> 00:07:28,080 Speaker 2: power of data and how this could help shape Scuderia 134 00:07:28,160 --> 00:07:32,840 Speaker 2: Ferrari's fan and digital experiences. Could AI learn the language 135 00:07:33,080 --> 00:07:36,559 Speaker 2: of Scuderia Ferrari. What was the original app like before 136 00:07:36,600 --> 00:07:40,320 Speaker 2: IBM got involved. I'm speaking with Stefano Pollard, who runs 137 00:07:40,400 --> 00:07:42,720 Speaker 2: fan development for Ferrari's F one team. 138 00:07:42,840 --> 00:07:46,280 Speaker 5: It was quite a good app, a very good digital product, 139 00:07:46,280 --> 00:07:49,840 Speaker 5: but just an editorial product. So we were providing fans 140 00:07:50,120 --> 00:07:53,840 Speaker 5: news and videos, articles and it was mainly about that. 141 00:07:54,520 --> 00:07:57,600 Speaker 5: The strategy and the idea was trying to use the 142 00:07:57,640 --> 00:08:00,680 Speaker 5: app to have a deeper connection and an interaction with 143 00:08:00,760 --> 00:08:04,760 Speaker 5: our fans, make it more interactive, so turning it from 144 00:08:04,760 --> 00:08:08,280 Speaker 5: an editorial product, which was a very good editorial product, 145 00:08:08,800 --> 00:08:11,880 Speaker 5: to a more interactive product, digital product. 146 00:08:11,680 --> 00:08:15,040 Speaker 2: With such a massive undertaking. I asked Stephen how it 147 00:08:15,120 --> 00:08:17,480 Speaker 2: all started once IBM got involved. 148 00:08:18,360 --> 00:08:21,160 Speaker 5: We started really with a very long couple of months 149 00:08:21,240 --> 00:08:25,000 Speaker 5: of discovery phase. So looking at the current app, looking 150 00:08:25,040 --> 00:08:28,119 Speaker 5: at fans, looking at what fans wanted from an app. 151 00:08:28,400 --> 00:08:30,320 Speaker 2: Tell me a little bit more about that phrase something 152 00:08:30,360 --> 00:08:33,160 Speaker 2: a fan wanted, What is it that the super fan 153 00:08:34,360 --> 00:08:38,040 Speaker 2: wasn't getting before? That was something that would tie them 154 00:08:38,080 --> 00:08:39,400 Speaker 2: even closer to Ferrari. 155 00:08:39,760 --> 00:08:44,560 Speaker 5: Having run some focused group, having having read of market research, 156 00:08:44,600 --> 00:08:47,479 Speaker 5: having spoken to fans, and being a fan the strongest 157 00:08:47,520 --> 00:08:51,400 Speaker 5: inside is fry. Fans and super fans want to be 158 00:08:51,520 --> 00:08:54,719 Speaker 5: part of something, want to belong to something, so they 159 00:08:54,760 --> 00:08:57,199 Speaker 5: want to be part of a community, and ultimately they 160 00:08:57,200 --> 00:09:00,400 Speaker 5: want to be part of a winning team, so they 161 00:09:00,440 --> 00:09:10,600 Speaker 5: want to feel closer and get access. 162 00:09:07,840 --> 00:09:11,480 Speaker 2: The way Stefano said, the opportunity wasn't with race days 163 00:09:12,040 --> 00:09:14,480 Speaker 2: when the cars are on the track the tafosi are 164 00:09:14,520 --> 00:09:17,800 Speaker 2: already locked in, but there was an opportunity to engage 165 00:09:17,800 --> 00:09:20,880 Speaker 2: Ferrari fans on the other days of the week or 166 00:09:20,960 --> 00:09:21,920 Speaker 2: during the off season. 167 00:09:22,280 --> 00:09:25,720 Speaker 3: Formula One is so much more than just the race. 168 00:09:26,040 --> 00:09:27,240 Speaker 1: This is Fred Baker again. 169 00:09:27,559 --> 00:09:31,320 Speaker 3: What we can do is relive the race and bring 170 00:09:31,360 --> 00:09:34,560 Speaker 3: it to life after the facts. We can help them prepare, 171 00:09:34,600 --> 00:09:36,520 Speaker 3: we can help them relive the past, and we can 172 00:09:36,559 --> 00:09:39,440 Speaker 3: also bring the experience around race weekend to life as 173 00:09:39,480 --> 00:09:40,640 Speaker 3: well that, of. 174 00:09:40,559 --> 00:09:43,680 Speaker 2: Course, maybe wonder how do you engage fans when there's 175 00:09:43,720 --> 00:09:47,720 Speaker 2: not a race happening. Baker says, it all comes back 176 00:09:47,800 --> 00:09:51,800 Speaker 2: to data and information. Talk a little about data collection, 177 00:09:51,960 --> 00:09:55,960 Speaker 2: because you're talking about a brand with tentacles everywhere, and 178 00:09:56,000 --> 00:09:57,640 Speaker 2: you're trying to bring a lot of that stuff together 179 00:09:57,640 --> 00:09:58,200 Speaker 2: in the app. 180 00:09:58,720 --> 00:10:01,960 Speaker 3: This is an organization that has for decades used data 181 00:10:02,320 --> 00:10:06,840 Speaker 3: for racing the performance. It's not historically use that data 182 00:10:06,880 --> 00:10:10,600 Speaker 3: for everyone in the world to see. What we're trying 183 00:10:10,600 --> 00:10:12,080 Speaker 3: to do is expose as much of it as we 184 00:10:12,120 --> 00:10:14,880 Speaker 3: can to fans. So part of collecting the data, the 185 00:10:14,960 --> 00:10:17,400 Speaker 3: challenge with around how you go across all the disparate 186 00:10:17,559 --> 00:10:21,440 Speaker 3: different groups that collect data for different purposes. The team 187 00:10:21,480 --> 00:10:24,000 Speaker 3: that collects data on tires, the team that has data 188 00:10:24,040 --> 00:10:27,439 Speaker 3: on drivers, on whether or on competitors, and so on. 189 00:10:28,120 --> 00:10:30,280 Speaker 3: So you're trying to bring all that together and source 190 00:10:30,320 --> 00:10:33,240 Speaker 3: it and make sense of it and train our AI 191 00:10:33,320 --> 00:10:36,920 Speaker 3: to understand what it means, what things on team radio mean, 192 00:10:37,160 --> 00:10:41,640 Speaker 3: what nicknames mean, what abbreviations and slang and idiosyncrasies on 193 00:10:42,160 --> 00:10:45,480 Speaker 3: car specifics and track specifics and so on mean. And 194 00:10:45,520 --> 00:10:49,160 Speaker 3: you're also trying to design for something that is going 195 00:10:49,240 --> 00:10:53,640 Speaker 3: to be fan engaging, but also appropriate to all the 196 00:10:53,679 --> 00:10:58,240 Speaker 3: sensitivities of the privacy. That's necessary. So you want it 197 00:10:58,280 --> 00:10:59,920 Speaker 3: to be able to do all of that, collect all 198 00:10:59,920 --> 00:11:03,360 Speaker 3: the data, produce something for fans in an automated way. 199 00:11:03,640 --> 00:11:07,319 Speaker 2: But in order to design something to expertly engage the tafosi, 200 00:11:07,679 --> 00:11:11,080 Speaker 2: it's necessary to understand more about the passion and the 201 00:11:11,160 --> 00:11:15,480 Speaker 2: type of national identity behind the fan base. You need 202 00:11:15,679 --> 00:11:17,239 Speaker 2: to get inside the mind. 203 00:11:17,000 --> 00:11:17,719 Speaker 1: Of the super fan. 204 00:11:19,160 --> 00:11:21,440 Speaker 2: If you wanted to meet some modern day tafosi in 205 00:11:21,480 --> 00:11:23,720 Speaker 2: the United States, you could head to a bar in 206 00:11:23,800 --> 00:11:28,319 Speaker 2: Midtown Manhattan called Feala. Every race day Formula One fans 207 00:11:28,360 --> 00:11:32,120 Speaker 2: gathered Fala to cheer on their favorite drivers, their favorite teams, 208 00:11:32,160 --> 00:11:37,600 Speaker 2: and I mean really cheer. I sent our producer Jake 209 00:11:37,640 --> 00:11:40,280 Speaker 2: Harper to Fala on the day of the Canadian Grand 210 00:11:40,320 --> 00:11:43,400 Speaker 2: Prix so he could see the fandom up close. The 211 00:11:43,440 --> 00:11:47,360 Speaker 2: bar gets loud and so crowded it's hard to move Today. 212 00:11:47,559 --> 00:11:50,480 Speaker 2: The room is packed with Scuderia Ferrari HP fans. 213 00:11:50,800 --> 00:11:53,439 Speaker 3: Even your glasses are Ferrari. I just noticed that. 214 00:11:53,360 --> 00:11:55,760 Speaker 2: Jake talked to a Ferrari fan named Gino, who was 215 00:11:55,840 --> 00:11:59,600 Speaker 2: dressed head to toe in Ferrari's signature red and black. 216 00:12:00,160 --> 00:12:03,400 Speaker 3: My shoes are Ferrari fully chucked out. They were making 217 00:12:03,440 --> 00:12:04,640 Speaker 3: fun of me last time I was here. 218 00:12:04,640 --> 00:12:07,120 Speaker 6: They're like, is your underwear Ferrari? And I texted my 219 00:12:07,120 --> 00:12:09,600 Speaker 6: girlfriend like, babe, I need Ferrari underwear. 220 00:12:10,480 --> 00:12:11,560 Speaker 3: Did you get it yet? 221 00:12:11,640 --> 00:12:13,160 Speaker 6: Not yet, I'll work on it. 222 00:12:13,679 --> 00:12:16,319 Speaker 2: Gino's fandom started with Ferrari as a brand. 223 00:12:17,240 --> 00:12:19,280 Speaker 6: I love the cars. I think the four fifty eight 224 00:12:19,320 --> 00:12:22,520 Speaker 6: Scodaria is like the pinnacle of automotive engineering. That's my 225 00:12:22,640 --> 00:12:25,440 Speaker 6: dream car. The four point thirty with the glass house 226 00:12:25,440 --> 00:12:28,160 Speaker 6: for the engine, I mean, that's They're all gorgeous. There's 227 00:12:28,200 --> 00:12:31,719 Speaker 6: always been an aspiration of mine to own one, so 228 00:12:31,920 --> 00:12:35,760 Speaker 6: that naturally made me gravitate towards Ferrari. Even when the 229 00:12:35,800 --> 00:12:39,400 Speaker 6: company I worked for a sponsored AMG Petronis, I was 230 00:12:39,480 --> 00:12:44,440 Speaker 6: secretly like hiding my TPOSI at. 231 00:12:44,320 --> 00:12:47,400 Speaker 3: The races, like Clark Kenton Superman. You're just hiding the 232 00:12:47,480 --> 00:12:48,880 Speaker 3: uniform underneath. I love that. 233 00:12:49,000 --> 00:12:50,800 Speaker 6: I love that. Yeah, I was wearing a Ferrari shirt 234 00:12:50,880 --> 00:12:51,800 Speaker 6: underneath my suit. 235 00:12:52,480 --> 00:12:54,920 Speaker 2: In one sense, Gino is typical of what Ferrari has 236 00:12:55,000 --> 00:12:57,680 Speaker 2: learned about its followers. A lot of F one fans 237 00:12:57,960 --> 00:13:01,400 Speaker 2: especially newer fans, are fans of drivers, but the. 238 00:13:01,400 --> 00:13:03,280 Speaker 1: Tafosi love Ferrari. 239 00:13:03,840 --> 00:13:06,280 Speaker 2: It's the oldest brand in Formula One, the only team 240 00:13:06,320 --> 00:13:08,880 Speaker 2: that has stuck around since the series was founded in 241 00:13:08,960 --> 00:13:13,280 Speaker 2: nineteen fifty. But in another sense, Gino is not typical. 242 00:13:13,760 --> 00:13:15,880 Speaker 2: He lives in New York. He can go to Fala 243 00:13:15,920 --> 00:13:17,880 Speaker 2: to celebrate f one with other tefosi. 244 00:13:18,120 --> 00:13:21,080 Speaker 6: I'm a big racing fan and coming to this bar, 245 00:13:21,920 --> 00:13:23,559 Speaker 6: I found a bunch of people that were in a 246 00:13:23,840 --> 00:13:24,200 Speaker 6: f one. 247 00:13:24,240 --> 00:13:24,880 Speaker 1: Now I'm at this. 248 00:13:24,880 --> 00:13:28,680 Speaker 6: Bar every weekend, just about with four or five friends 249 00:13:28,679 --> 00:13:30,120 Speaker 6: that I made just through racings. 250 00:13:31,640 --> 00:13:34,319 Speaker 2: But lots and lots of Scuderia Ferrari's three hundred and 251 00:13:34,400 --> 00:13:37,199 Speaker 2: ninety six million fans don't live in a big city 252 00:13:37,200 --> 00:13:39,920 Speaker 2: with a Ferrari bar, and lots of those three hundred 253 00:13:39,920 --> 00:13:42,760 Speaker 2: and ninety six million fans aren't the kind of hardcore 254 00:13:42,840 --> 00:13:45,439 Speaker 2: fan who dressed head to toe in Ferrari's signature red 255 00:13:45,440 --> 00:13:50,360 Speaker 2: and black. A group that large is diverse, necessarily, and 256 00:13:50,400 --> 00:13:52,880 Speaker 2: one of the first tasks that IBM and Ferrari set 257 00:13:52,880 --> 00:13:55,920 Speaker 2: out to do was to understand the full range of 258 00:13:55,960 --> 00:14:00,199 Speaker 2: the Tafosi phenomenon. People like Gino, hardcore fans. 259 00:14:00,440 --> 00:14:01,040 Speaker 1: They were easy. 260 00:14:01,320 --> 00:14:03,800 Speaker 2: They would follow the scudery of Ferrari hb anywhere it 261 00:14:03,840 --> 00:14:07,240 Speaker 2: wanted to go. But who else was out there? The 262 00:14:07,320 --> 00:14:10,360 Speaker 2: most interesting addition to the f one fan base were 263 00:14:10,360 --> 00:14:14,880 Speaker 2: those who watched the phenomenally successful Netflix documentary Drive to Survive. 264 00:14:15,679 --> 00:14:19,160 Speaker 2: These tended to be newcomers to the sport, more Americans 265 00:14:19,440 --> 00:14:24,040 Speaker 2: than Europeans. What was their emotional perspective? What did they want? 266 00:14:24,800 --> 00:14:27,320 Speaker 2: Here's Fred Baker again, the guy with the coolest job 267 00:14:27,360 --> 00:14:27,880 Speaker 2: at IBM. 268 00:14:28,120 --> 00:14:29,840 Speaker 3: If I'm a passionate fan, I want to read a 269 00:14:29,840 --> 00:14:32,920 Speaker 3: totally different thing on the app to a casual fan 270 00:14:33,000 --> 00:14:36,520 Speaker 3: who is of the Netflix Drive to Survive generation versus 271 00:14:37,000 --> 00:14:39,120 Speaker 3: you know, some really niche personas that we found that 272 00:14:39,400 --> 00:14:42,640 Speaker 3: are super interested but don't find it accessible yet until 273 00:14:42,640 --> 00:14:45,560 Speaker 3: we start to deliver to quite different needs that they have. 274 00:14:45,840 --> 00:14:48,920 Speaker 2: Working with IBM WATSONEX, Baker and his team began to 275 00:14:49,000 --> 00:14:54,080 Speaker 2: develop personas archetypes of all the possible kinds of Ferrari fans. 276 00:14:54,640 --> 00:14:56,880 Speaker 2: Because if Ferrari wanted to get better at talking to 277 00:14:56,920 --> 00:15:00,000 Speaker 2: their fans, they had to understand who the fans were, 278 00:15:00,760 --> 00:15:04,400 Speaker 2: and the personas are helping Ferrari and IBM create an 279 00:15:04,480 --> 00:15:08,280 Speaker 2: app that caters to the tafosi in all their iterations. 280 00:15:09,440 --> 00:15:11,800 Speaker 1: How many personas did you come up with? 281 00:15:12,480 --> 00:15:14,760 Speaker 3: I think we had over ten in the end, maybe 282 00:15:14,800 --> 00:15:17,560 Speaker 3: a dozen. And this is different archetypes of people. Even 283 00:15:17,600 --> 00:15:20,520 Speaker 3: that process is helped by AI, So we train AI 284 00:15:20,680 --> 00:15:23,360 Speaker 3: to help us develop out a persona. We can get 285 00:15:23,920 --> 00:15:27,720 Speaker 3: really detailed as to what each archetype is and their 286 00:15:28,400 --> 00:15:31,239 Speaker 3: hobbies and backgrounds and so on. So our own WATSONEX 287 00:15:31,280 --> 00:15:35,200 Speaker 3: helped us in developing those personas, like our research helped 288 00:15:35,240 --> 00:15:38,720 Speaker 3: us uncover a segment of middle aged women in China 289 00:15:38,800 --> 00:15:42,720 Speaker 3: who Ferrari is a real status symbol and they're really 290 00:15:42,760 --> 00:15:45,160 Speaker 3: interested in the Scuderia Ferrari brand and now they can 291 00:15:45,160 --> 00:15:49,320 Speaker 3: engage more with it, but it wasn't yet accessible or 292 00:15:49,360 --> 00:15:51,880 Speaker 3: inclusive enough for them to feel comfortable doing so. Real 293 00:15:51,920 --> 00:15:55,400 Speaker 3: spectrums of fans across those dozen personas that we had 294 00:15:55,400 --> 00:15:56,280 Speaker 3: to design for. 295 00:15:56,880 --> 00:16:00,960 Speaker 2: Give me some more examples of personas. Can you give 296 00:16:00,960 --> 00:16:02,360 Speaker 2: me a couple more just so get a flavor? 297 00:16:02,520 --> 00:16:04,560 Speaker 3: Yeah? Sure. So the other obvious one is the drive 298 00:16:04,640 --> 00:16:07,800 Speaker 3: to survive fan and that they're probably not a diehard 299 00:16:08,120 --> 00:16:10,280 Speaker 3: all their lives Scuterier Ferrari fan, but they've really got 300 00:16:10,280 --> 00:16:12,720 Speaker 3: into the more social side of Formula one that's been 301 00:16:13,200 --> 00:16:16,040 Speaker 3: born out of the really popular series Drive to Survive 302 00:16:16,120 --> 00:16:19,200 Speaker 3: on Netflix. You then have gamer personas who are into 303 00:16:19,520 --> 00:16:23,200 Speaker 3: you know, esports is growing massively in motorsport, and they're 304 00:16:23,200 --> 00:16:26,000 Speaker 3: probably not necessarily into the real life racing quite so much, 305 00:16:26,040 --> 00:16:27,920 Speaker 3: but they're certainly into gaming. So how do you appeal 306 00:16:27,960 --> 00:16:30,480 Speaker 3: to them? Then casual fans who are sort of into 307 00:16:30,480 --> 00:16:33,160 Speaker 3: the luxury of scudo Ferrari but not the sport necessarily. 308 00:16:33,800 --> 00:16:35,200 Speaker 1: Do the personas have names? 309 00:16:36,320 --> 00:16:38,520 Speaker 3: Yeah, I mean we give them human names. So we 310 00:16:38,560 --> 00:16:41,640 Speaker 3: had a Max, we had an Alfonso. I think we 311 00:16:41,720 --> 00:16:42,480 Speaker 3: had a Pedro. 312 00:16:43,280 --> 00:16:46,360 Speaker 2: The woman in China, is she watching F one or 313 00:16:46,400 --> 00:16:48,880 Speaker 2: is she interested more in the brand and what it signifies? 314 00:16:49,000 --> 00:16:51,600 Speaker 3: Yeah, more in the brand and being part of a community. 315 00:16:51,960 --> 00:16:54,560 Speaker 3: If I'm that persona in China, then I probably don't 316 00:16:54,600 --> 00:16:58,720 Speaker 3: feel like I belong to it truly yet, but I'd 317 00:16:58,800 --> 00:17:00,600 Speaker 3: love to feel like I do, so I could start 318 00:17:00,640 --> 00:17:03,120 Speaker 3: to become a part of a digital community, learn more 319 00:17:03,120 --> 00:17:07,639 Speaker 3: about the brand, probably get access to exclusive merchandise or 320 00:17:08,400 --> 00:17:11,320 Speaker 3: you know, if I can't necessarily own a Ferrari car, 321 00:17:11,320 --> 00:17:13,639 Speaker 3: which let's face it, not many people can, and if 322 00:17:13,640 --> 00:17:15,520 Speaker 3: we're relyinged only on the people who can own a car, 323 00:17:15,560 --> 00:17:17,800 Speaker 3: then we're probably not going to get much engagement. So 324 00:17:17,880 --> 00:17:20,280 Speaker 3: how do we make others feel that they're still a 325 00:17:20,280 --> 00:17:21,879 Speaker 3: part of that community. 326 00:17:22,640 --> 00:17:24,320 Speaker 2: This is what I mean when I say the task 327 00:17:24,359 --> 00:17:27,000 Speaker 2: of relating to the Ferrari fan base is a data 328 00:17:27,160 --> 00:17:31,840 Speaker 2: and information problem. It's about collecting, organizing, and analyzing the 329 00:17:31,880 --> 00:17:35,520 Speaker 2: needs and wants of an enormous pool of people and 330 00:17:35,560 --> 00:17:39,040 Speaker 2: speaking to each of them in their own emotional language. 331 00:17:40,560 --> 00:17:43,480 Speaker 2: Giving all the work Ford put into understanding Ferrari's fan base, 332 00:17:43,880 --> 00:17:47,000 Speaker 2: I was curious to know how his framework would categorize me. 333 00:17:48,080 --> 00:17:50,960 Speaker 2: I want to figure out which persona I am. So 334 00:17:51,160 --> 00:17:53,679 Speaker 2: I'll describe to you my relationship to Ferrari. 335 00:17:53,800 --> 00:17:59,400 Speaker 1: You tell me so what I am is a huge car? 336 00:17:59,600 --> 00:18:00,679 Speaker 3: Not so? 337 00:18:00,760 --> 00:18:06,320 Speaker 2: I like all cars, obsessively collect on a very limited 338 00:18:06,680 --> 00:18:11,960 Speaker 2: stage vintage cars, read serious car magazines, spend a lot 339 00:18:11,960 --> 00:18:15,159 Speaker 2: of time my car websites. Have a historical relationship to 340 00:18:15,240 --> 00:18:18,600 Speaker 2: have one. Because I grew up with Nikki Lauda battling 341 00:18:18,680 --> 00:18:22,119 Speaker 2: James Hunt in loudest for our years, I have a 342 00:18:22,200 --> 00:18:26,960 Speaker 2: gret nostalgic connection. Went to Italy with my nephew and 343 00:18:27,040 --> 00:18:30,080 Speaker 2: went to the Ferrari factory and rented one of those 344 00:18:31,160 --> 00:18:34,040 Speaker 2: to drive around, you know, and I follow that F one, 345 00:18:34,040 --> 00:18:35,600 Speaker 2: but I wouldn't I would don't think I would ever 346 00:18:36,400 --> 00:18:39,080 Speaker 2: go to an I wouldn't fly to Miami for F 347 00:18:39,080 --> 00:18:42,440 Speaker 2: one Miami. I wouldn't go that far. And I don't 348 00:18:42,480 --> 00:18:44,199 Speaker 2: have time to watch F one on TV on a 349 00:18:44,200 --> 00:18:48,960 Speaker 2: regular basis. But I'm interested. And I have a red 350 00:18:49,200 --> 00:18:52,080 Speaker 2: Ferrari T shirt which I've been known to wear. And 351 00:18:52,200 --> 00:18:54,600 Speaker 2: if you if I ever got really rich, would I 352 00:18:54,600 --> 00:18:55,080 Speaker 2: buy it for aur? 353 00:18:55,240 --> 00:18:55,400 Speaker 5: Yes? 354 00:18:55,400 --> 00:18:55,760 Speaker 1: I would. 355 00:18:56,359 --> 00:19:00,120 Speaker 2: Okay, So where am I? Where am I in your breakdown? 356 00:19:00,240 --> 00:19:00,480 Speaker 5: Yeah? 357 00:19:00,560 --> 00:19:03,880 Speaker 3: I think you're probably a combination of the I think 358 00:19:03,920 --> 00:19:09,000 Speaker 3: it's casual loyalist. It's not going to overtly go out 359 00:19:09,040 --> 00:19:11,440 Speaker 3: of their way to sort of spend money on the racing, 360 00:19:11,480 --> 00:19:13,640 Speaker 3: but they are loyal to the Ferrari brand and they 361 00:19:13,680 --> 00:19:16,040 Speaker 3: have nostalgia with it or whatever it might be. And 362 00:19:16,080 --> 00:19:19,000 Speaker 3: then the luxury enthusiasts as well, so and that type 363 00:19:19,000 --> 00:19:22,120 Speaker 3: of fan. You're right, We're probably not going to engage 364 00:19:22,119 --> 00:19:24,119 Speaker 3: you by doing a ton more on race weekend, but 365 00:19:24,160 --> 00:19:28,600 Speaker 3: we can engage you by bringing this hugely rich amount 366 00:19:28,640 --> 00:19:34,320 Speaker 3: of archive material, footage, feelings, and past drivers of yesteryear 367 00:19:34,480 --> 00:19:37,680 Speaker 3: by bringing them to life. 368 00:19:43,200 --> 00:19:47,919 Speaker 2: In zero an app that you saw another brand doing 369 00:19:48,320 --> 00:19:50,120 Speaker 2: that served as a kind of model. I don't mean 370 00:19:50,160 --> 00:19:53,679 Speaker 2: within f one, I'm talking about it from any other film. 371 00:19:53,880 --> 00:19:59,000 Speaker 5: On top of being a very sport passionate, I'm, let's say, 372 00:19:59,119 --> 00:20:03,320 Speaker 5: a marketing per digital passionate guy. So I have a 373 00:20:03,359 --> 00:20:05,439 Speaker 5: lot of apps, and he also for my job, I 374 00:20:05,640 --> 00:20:08,080 Speaker 5: tried to look at different markets and different apps. 375 00:20:08,240 --> 00:20:11,320 Speaker 2: As we were talking, I was thinking about Strava. I'm 376 00:20:11,320 --> 00:20:14,200 Speaker 2: a huge Stravahead. It's my favorite app. If you don't 377 00:20:14,240 --> 00:20:17,240 Speaker 2: already know, Strava is used by millions of active people 378 00:20:17,320 --> 00:20:19,800 Speaker 2: around the world. I'm a runner, and the app shows 379 00:20:19,840 --> 00:20:22,640 Speaker 2: me a map of where I went, how fast I ran, 380 00:20:23,119 --> 00:20:25,159 Speaker 2: what my heart ray was, what the weather was on 381 00:20:25,280 --> 00:20:27,280 Speaker 2: and on a you're a cyclostore. 382 00:20:26,840 --> 00:20:28,359 Speaker 1: Runner, I'm a runner. I'm a runner. 383 00:20:28,560 --> 00:20:32,600 Speaker 5: I run marathons and ultra marathons. I did let's see 384 00:20:32,680 --> 00:20:33,760 Speaker 5: one hundred kilometers. 385 00:20:33,920 --> 00:20:37,720 Speaker 2: As it turns out, Stefano is a Stravahead too. Right 386 00:20:37,720 --> 00:20:40,200 Speaker 2: after I spoke to him, I followed him on Strava. 387 00:20:40,280 --> 00:20:42,320 Speaker 2: He and I run roughly the same distance every day 388 00:20:42,359 --> 00:20:44,560 Speaker 2: at the same pace. And if I'm ever in Milan. 389 00:20:44,800 --> 00:20:46,800 Speaker 2: I'm almost certainly going to look him up to see 390 00:20:46,840 --> 00:20:48,479 Speaker 2: if he'll take me out on one of his favorite 391 00:20:48,520 --> 00:20:51,480 Speaker 2: routes through the city. This is what I love about Strava. 392 00:20:51,840 --> 00:20:54,479 Speaker 2: You can find people to run with and interact with. 393 00:20:54,920 --> 00:20:58,119 Speaker 2: Strava is a community of like minded people and for 394 00:20:58,160 --> 00:21:01,400 Speaker 2: those like me, the Strava app become a regular part 395 00:21:01,480 --> 00:21:04,639 Speaker 2: of my daily routine. And that's what Stephano wanted for 396 00:21:04,680 --> 00:21:07,920 Speaker 2: the Ferrari a. Are you interested in allowing creating sort 397 00:21:07,920 --> 00:21:12,280 Speaker 2: of robust forums for Ferrari fans to communicate with each other? 398 00:21:13,600 --> 00:21:17,040 Speaker 5: I think you have to work in three directions. So 399 00:21:17,160 --> 00:21:21,320 Speaker 5: direction number one is a Ferrari to fans, so providing 400 00:21:21,400 --> 00:21:24,800 Speaker 5: them something which is compelling, which has value, and this 401 00:21:25,040 --> 00:21:26,800 Speaker 5: I think we're already doing and we're working on it. 402 00:21:27,280 --> 00:21:32,200 Speaker 5: Second way is fans to Ferrari, so help like allowing 403 00:21:32,240 --> 00:21:35,239 Speaker 5: fans to better interact with us, which was something we 404 00:21:35,240 --> 00:21:37,800 Speaker 5: were not doing with the previous app. For example, we 405 00:21:38,040 --> 00:21:41,520 Speaker 5: have just introduced two features which are polls, so basic ones, 406 00:21:41,560 --> 00:21:45,159 Speaker 5: but polls and the possibility like the submit your message feature. 407 00:21:45,400 --> 00:21:48,600 Speaker 5: So really to work on the way fans to Ferrari. 408 00:21:48,720 --> 00:21:51,760 Speaker 5: And then the third important way to build a community 409 00:21:51,800 --> 00:21:54,400 Speaker 5: and nurture community is like fans to fans. 410 00:21:54,640 --> 00:21:55,639 Speaker 3: So if you were. 411 00:21:55,480 --> 00:21:59,119 Speaker 5: Able to work on those trae dimensions of Ferrari to fans, 412 00:21:59,160 --> 00:22:02,680 Speaker 5: fans to Ferrari, fans to fans, that's how you could 413 00:22:02,680 --> 00:22:06,320 Speaker 5: really create a strong community and start really monetizing and 414 00:22:06,359 --> 00:22:09,119 Speaker 5: creating value. I think we're very strong in the first 415 00:22:09,119 --> 00:22:12,760 Speaker 5: dimension right now. We're building the second one, so fans 416 00:22:12,760 --> 00:22:16,080 Speaker 5: to Ferrari, and then definitely the third one has to 417 00:22:16,119 --> 00:22:19,560 Speaker 5: be there in order to have a complete community engagement. 418 00:22:20,119 --> 00:22:21,639 Speaker 1: So let's talk about results. 419 00:22:22,040 --> 00:22:25,240 Speaker 2: Scuddery of Ferrari HP launched the new app at the 420 00:22:25,240 --> 00:22:29,400 Speaker 2: Miami Grand Prix in twenty twenty five, incorporating AI elements 421 00:22:29,600 --> 00:22:35,359 Speaker 2: and tailoring it to those archetypes. Was talking about, are 422 00:22:35,400 --> 00:22:38,640 Speaker 2: more people using the app? Are users spending more time 423 00:22:38,680 --> 00:22:41,080 Speaker 2: on it they did on the older version of the app? 424 00:22:41,680 --> 00:22:45,440 Speaker 5: Yes, we doubled these months the daily active users we 425 00:22:45,440 --> 00:22:48,159 Speaker 5: were having last season, so compared to the average of 426 00:22:48,160 --> 00:22:51,119 Speaker 5: twenty twenty four season, we have more than double of 427 00:22:51,359 --> 00:22:56,520 Speaker 5: daily active users. Also, we're doubling normal months down down loads, 428 00:22:56,560 --> 00:23:00,000 Speaker 5: so we did in these months more than two times 429 00:23:00,119 --> 00:23:02,520 Speaker 5: down load we are doing in a normal months. We 430 00:23:02,840 --> 00:23:06,639 Speaker 5: are increasing by thirty five percent the average time spent 431 00:23:06,720 --> 00:23:08,080 Speaker 5: on app. So keepy eyes are good. 432 00:23:09,400 --> 00:23:16,680 Speaker 2: If you build the right app, they will come for generations. 433 00:23:16,880 --> 00:23:20,600 Speaker 2: Fans of all varieties have met in public places, the 434 00:23:20,640 --> 00:23:23,719 Speaker 2: stands of stadiums, in bars, to watch races and matches 435 00:23:23,720 --> 00:23:27,080 Speaker 2: on television. But there's a chance now for fandom to 436 00:23:27,119 --> 00:23:30,760 Speaker 2: exist on a higher and broader level, for a community 437 00:23:30,920 --> 00:23:33,959 Speaker 2: to be created over the Internet, even when the fans 438 00:23:34,040 --> 00:23:41,000 Speaker 2: are vastly different people who live vast distances apart. I 439 00:23:41,000 --> 00:23:44,200 Speaker 2: could imagine Gino in his Ferrari Red and Black, using 440 00:23:44,280 --> 00:23:46,919 Speaker 2: the scudery of Ferrari app relating to me as I 441 00:23:46,960 --> 00:23:49,760 Speaker 2: relive my memories of Niki Lauder from the nineteen seventies. 442 00:23:50,520 --> 00:23:52,400 Speaker 2: Maybe I could use the app to learn something from 443 00:23:52,440 --> 00:23:56,000 Speaker 2: the woman in China, the Tefosie newcomer, or some seventeen 444 00:23:56,080 --> 00:23:58,840 Speaker 2: year old who got sucked in first by drive to survive. 445 00:24:00,119 --> 00:24:04,480 Speaker 2: Imagine myself as part of that vision, taking my lifelong 446 00:24:04,520 --> 00:24:21,040 Speaker 2: obsession to the next level. Smart Talks with IBM is 447 00:24:21,040 --> 00:24:25,600 Speaker 2: produced by Matt Ramano, Amy Gains McQuaid, Trina Menino, and 448 00:24:25,800 --> 00:24:30,200 Speaker 2: Jake Harper were edited by Lacy Roberts. Engineering by Nina 449 00:24:30,200 --> 00:24:35,000 Speaker 2: Bird Lawrence Mastering by Sarah Buguer, music by Gramoscope, Strategy 450 00:24:35,040 --> 00:24:39,680 Speaker 2: by Tatiana Lieberman and Cassidy Meyer. Special thanks to Scuderia 451 00:24:39,760 --> 00:24:43,479 Speaker 2: Ferrari HP and the bar and restaurant Feala in New 452 00:24:43,560 --> 00:24:46,440 Speaker 2: York City. Smart Talks with IBM is a production of 453 00:24:46,560 --> 00:24:50,800 Speaker 2: Pushkin Industries and Ruby Studio at iHeartMedia. To find more 454 00:24:50,840 --> 00:24:55,240 Speaker 2: Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts, or 455 00:24:55,280 --> 00:24:59,919 Speaker 2: wherever you listen to podcasts. I'm Malcolm Glavo. This is 456 00:25:00,160 --> 00:25:04,480 Speaker 2: paid advertisement from IBM. The conversations on this podcast don't 457 00:25:04,520 --> 00:25:19,720 Speaker 2: necessarily represent IBM's positions, strategies, or opinions. I asked Fred 458 00:25:19,720 --> 00:25:23,119 Speaker 2: Baker to come up with a hypothetical something that could 459 00:25:23,119 --> 00:25:27,399 Speaker 2: fulfill my childhood dreams, something that this type of technology 460 00:25:27,640 --> 00:25:30,760 Speaker 2: could theoretically do that might appeal to a fan like me, 461 00:25:31,600 --> 00:25:34,159 Speaker 2: someone who's interested in the sport went back fifty years. 462 00:25:34,800 --> 00:25:39,320 Speaker 2: He said, what about using AI to bring historical cars 463 00:25:39,359 --> 00:25:39,920 Speaker 2: to life? 464 00:25:40,720 --> 00:25:43,560 Speaker 3: Bringing to live cars of the past and allows fans 465 00:25:43,600 --> 00:25:49,639 Speaker 3: to simulate a nineteen fifty Ferrari race versus nineteen seventy 466 00:25:49,680 --> 00:25:51,879 Speaker 3: one to see who which car would be faster. So 467 00:25:51,920 --> 00:25:53,119 Speaker 3: it's those sorts of trade offs. 468 00:25:53,800 --> 00:25:55,520 Speaker 1: Wait, you could do it? Wait you could do that? 469 00:25:55,560 --> 00:25:58,240 Speaker 2: Tell me about last thing you said you can run 470 00:25:58,280 --> 00:26:01,040 Speaker 2: simulations out of the app. 471 00:26:01,440 --> 00:26:03,680 Speaker 3: You can't out of the app at this point, So. 472 00:26:04,480 --> 00:26:06,840 Speaker 1: No, take I know potentially potentially. 473 00:26:06,400 --> 00:26:09,439 Speaker 3: Yeah, yeah, it can, you know, simulates based on a 474 00:26:09,440 --> 00:26:11,520 Speaker 3: whole range of factors that we can feed and train 475 00:26:11,560 --> 00:26:11,840 Speaker 3: it on. 476 00:26:12,720 --> 00:26:16,040 Speaker 2: Wait, so I could hypothetically you could allow me to 477 00:26:16,080 --> 00:26:22,000 Speaker 2: compare Niki Lauder, for example, to a contemporary driver, and 478 00:26:22,000 --> 00:26:23,919 Speaker 2: and I could say if I put Nicki Lauder in 479 00:26:23,960 --> 00:26:27,480 Speaker 2: a contemporary car, what you're saying is that there is 480 00:26:27,520 --> 00:26:32,959 Speaker 2: a scenario where I could recreate that era in modern 481 00:26:32,960 --> 00:26:36,280 Speaker 2: cars and get a sense of how my childhood heroes 482 00:26:36,280 --> 00:26:39,040 Speaker 2: were performing would have performed a breast day. 483 00:26:39,400 --> 00:26:41,639 Speaker 3: Yeah, yeah, So you can analyze and understand how you 484 00:26:41,680 --> 00:26:44,399 Speaker 3: would rank all drivers of all time based on the 485 00:26:44,400 --> 00:26:46,320 Speaker 3: different traits of a driver, right, So you can say 486 00:26:46,320 --> 00:26:48,760 Speaker 3: who's the best late breaking, who's who was typically the 487 00:26:48,760 --> 00:26:51,240 Speaker 3: best on a tight track with limited overtaking opportunities, who 488 00:26:51,240 --> 00:26:53,040 Speaker 3: was the best overtaker, who was the best of all 489 00:26:53,080 --> 00:26:55,840 Speaker 3: these traits? You then apply those traits and rankings to 490 00:26:56,200 --> 00:26:59,359 Speaker 3: different tracks and different cars where you know different some 491 00:26:59,440 --> 00:27:01,720 Speaker 3: different cars are better for a late breaker, some different 492 00:27:01,760 --> 00:27:03,679 Speaker 3: cars are better for a you know, on straights and 493 00:27:03,680 --> 00:27:06,680 Speaker 3: so on. So you can simulate. You could hypothetically allow 494 00:27:06,760 --> 00:27:09,879 Speaker 3: fans to simulate any scenario. You could say, who's going 495 00:27:09,920 --> 00:27:14,359 Speaker 3: to win in Monaco on a nineteen eighty model car. 496 00:27:15,080 --> 00:27:17,000 Speaker 3: You can put a current driver in a nineteen to 497 00:27:17,040 --> 00:27:18,920 Speaker 3: eighty car equally, So you can do all sorts of 498 00:27:19,040 --> 00:27:20,080 Speaker 3: fun and simulations. 499 00:27:21,600 --> 00:27:22,760 Speaker 1: And that's just the beginning.