1 00:00:00,200 --> 00:00:04,400 Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. This season 2 00:00:04,440 --> 00:00:08,000 Speaker 1: on Smart Talks with IBM, Malcolm Glabwell is back, and 3 00:00:08,039 --> 00:00:10,680 Speaker 1: this time he's taking the show on the road. Malcolm 4 00:00:10,720 --> 00:00:14,840 Speaker 1: is stepping outside the studio to explore how IBM clients 5 00:00:14,880 --> 00:00:18,880 Speaker 1: are using artificial intelligence to solve real world challenges and 6 00:00:18,960 --> 00:00:23,120 Speaker 1: transform the way they do business, from accelerating scientific breakthroughs 7 00:00:23,160 --> 00:00:27,640 Speaker 1: to reimagining education. It's a fresh look at innovation in action, 8 00:00:28,080 --> 00:00:31,800 Speaker 1: where big ideas meet cutting edge solutions. You'll hear from 9 00:00:31,840 --> 00:00:36,120 Speaker 1: industry leaders, creative thinkers, and of course Malcolm Glabwell himself 10 00:00:36,360 --> 00:00:40,000 Speaker 1: as he guides you through each story. New episodes of 11 00:00:40,040 --> 00:00:43,400 Speaker 1: Smart Talks with IBM drop every month on the iHeartRadio app, 12 00:00:43,560 --> 00:00:47,360 Speaker 1: Apple Podcasts, or wherever you get your podcasts. Learn more 13 00:00:47,400 --> 00:00:50,080 Speaker 1: at IBM dot com, slash smart. 14 00:00:49,680 --> 00:01:00,600 Speaker 2: Talks, pushkin. 15 00:01:05,880 --> 00:01:07,840 Speaker 3: I never went to a Formula one race as a 16 00:01:07,920 --> 00:01:10,399 Speaker 3: kid because we lived in southern Ontario and there was 17 00:01:10,480 --> 00:01:12,120 Speaker 3: just one F one race in Canada. 18 00:01:12,640 --> 00:01:13,440 Speaker 4: That race was in. 19 00:01:13,440 --> 00:01:17,480 Speaker 3: Montreal, a good seven hour drive away. I never watched 20 00:01:17,600 --> 00:01:20,600 Speaker 3: F one race on television either, because we didn't have 21 00:01:20,640 --> 00:01:24,480 Speaker 3: a television, But what I did have was a subscription 22 00:01:24,600 --> 00:01:27,320 Speaker 3: to the car magazine Road and Track and Roadent Track 23 00:01:27,440 --> 00:01:31,119 Speaker 3: took f one very seriously. Every month my new issue 24 00:01:31,120 --> 00:01:34,600 Speaker 3: would arrive, I would turn immediately to the long detailed 25 00:01:34,600 --> 00:01:38,000 Speaker 3: account of that month's race, and I fell in love. 26 00:01:41,120 --> 00:01:43,840 Speaker 3: It's been fifty years, but I can still rattle off 27 00:01:43,840 --> 00:01:45,800 Speaker 3: the names of all the top drivers of that era 28 00:01:45,880 --> 00:01:50,640 Speaker 3: from memory, James Hunt, Mario Andretti, Carlos Pace, Jacqueslafitte, and 29 00:01:50,720 --> 00:01:53,800 Speaker 3: of course the greatest of them all, my adolescent idol, 30 00:01:54,120 --> 00:01:57,480 Speaker 3: Nikki Lauda, who won two world championships in the mid 31 00:01:57,600 --> 00:02:00,480 Speaker 3: nineteen seventies with Scoodery Ferrari. 32 00:02:01,320 --> 00:02:04,360 Speaker 5: He was world championship material from the moment he joined 33 00:02:04,400 --> 00:02:07,360 Speaker 5: the Ferrari team. 34 00:02:08,040 --> 00:02:11,080 Speaker 6: There's no question that in seventy five seventy six I 35 00:02:11,200 --> 00:02:14,960 Speaker 6: was reallydominating the whole thing without any mistake. 36 00:02:15,040 --> 00:02:16,080 Speaker 5: So I did nothing wrong. 37 00:02:16,200 --> 00:02:18,120 Speaker 6: I mean, this was perfect driving. 38 00:02:18,800 --> 00:02:21,240 Speaker 3: If you had met skinny pre adolescent Malcolm in the 39 00:02:21,240 --> 00:02:24,359 Speaker 3: mid nineteen seventies in rural Ontario, there's a good chance 40 00:02:24,400 --> 00:02:27,040 Speaker 3: you would have seen me in my prize Ferrari T shirt. 41 00:02:27,320 --> 00:02:30,480 Speaker 3: I was a fan, one of what the Italians called tifosi, 42 00:02:30,880 --> 00:02:33,639 Speaker 3: a Ferrari devote, and that's what it meant to be 43 00:02:33,720 --> 00:02:37,880 Speaker 3: a fan fifty years ago, t shirts, magazine stories and 44 00:02:37,919 --> 00:02:40,760 Speaker 3: a big Nikki laut a poster on your wall. But 45 00:02:40,800 --> 00:02:43,880 Speaker 3: what does it mean to be a fan today? Today 46 00:02:43,919 --> 00:02:46,280 Speaker 3: we have the Internet and streaming and big data and 47 00:02:46,320 --> 00:02:48,840 Speaker 3: AI and all the other accouterment of the digital age. 48 00:02:49,320 --> 00:02:57,880 Speaker 3: Is there a chance to reinvent the meaning of fandom? 49 00:02:58,360 --> 00:02:58,919 Speaker 5: My name is. 50 00:02:58,840 --> 00:03:02,120 Speaker 3: Malcolm Gladwell listening to the latest episode of Smart Talks 51 00:03:02,120 --> 00:03:05,160 Speaker 3: with IBM, where we offer our listeners a glimpse behind 52 00:03:05,200 --> 00:03:08,920 Speaker 3: the curtain of the world of technology. In our first episode, 53 00:03:08,960 --> 00:03:12,320 Speaker 3: we talked about how an AI assistant created with IBM 54 00:03:12,360 --> 00:03:17,720 Speaker 3: watsonex helps future teachers practice responsive teaching. Our second episode 55 00:03:17,919 --> 00:03:21,359 Speaker 3: was how a custom AI model could help Loreel's researchers 56 00:03:21,600 --> 00:03:23,760 Speaker 3: shape the future of what we put on our faces 57 00:03:23,760 --> 00:03:28,560 Speaker 3: every morning. In this episode, how IBM, one of the 58 00:03:28,560 --> 00:03:32,080 Speaker 3: world's pre eminent technology companies, is joining up with one 59 00:03:32,120 --> 00:03:35,840 Speaker 3: of the world's pre eminent racing brands to fundamentally change 60 00:03:35,840 --> 00:03:43,720 Speaker 3: how fans interact with their favorite team. The size of 61 00:03:43,720 --> 00:03:48,280 Speaker 3: the Scuderia Ferrari HP fan base is staggering. Three hundred 62 00:03:48,320 --> 00:03:51,800 Speaker 3: and ninety six million people around the world identify as 63 00:03:51,880 --> 00:03:56,000 Speaker 3: Ferrari fans three hundred and ninety six million. The only 64 00:03:56,040 --> 00:03:58,960 Speaker 3: other fan bases that big belong to the iconic Premier 65 00:03:59,040 --> 00:04:04,120 Speaker 3: League football team like Manchester United or Chelsea FC. I 66 00:04:04,120 --> 00:04:06,920 Speaker 3: don't believe there is any other Formula one team that 67 00:04:07,040 --> 00:04:10,880 Speaker 3: inspires that kind of devotion. Ferrari's job, then, isn't to 68 00:04:10,920 --> 00:04:14,280 Speaker 3: necessarily grow its fan base. Three hundred and ninety six 69 00:04:14,280 --> 00:04:17,240 Speaker 3: million is more than enough fans. Their job is to 70 00:04:17,320 --> 00:04:21,560 Speaker 3: deepen the connection people feel with the scuderya Ferrari team. 71 00:04:21,760 --> 00:04:24,080 Speaker 3: But if I'm Ferrari, how do I find out more 72 00:04:24,080 --> 00:04:26,840 Speaker 3: about who my fans are, what they care about, what 73 00:04:26,880 --> 00:04:29,480 Speaker 3: they want? How do I use my archives and data 74 00:04:29,560 --> 00:04:32,599 Speaker 3: to create experiences that matter to them? How do I 75 00:04:32,640 --> 00:04:35,280 Speaker 3: say to the guy who spent his childhood eagerly reading 76 00:04:35,400 --> 00:04:38,480 Speaker 3: roadent Track every month? Here are other ways you can 77 00:04:38,520 --> 00:04:42,680 Speaker 3: get involved with your favorite F one team today. The 78 00:04:42,720 --> 00:04:46,160 Speaker 3: task of deepening an emotional connection in the digital age 79 00:04:46,520 --> 00:04:51,320 Speaker 3: begins as an information problem, which is where IBM comes in. 80 00:04:52,400 --> 00:04:53,800 Speaker 3: How would you describe what you do? 81 00:04:54,680 --> 00:04:57,160 Speaker 5: I describe it as the probably the best job that IBM. 82 00:04:57,440 --> 00:04:58,960 Speaker 3: Yeah, I was going to say, I was going to 83 00:04:59,040 --> 00:05:01,039 Speaker 3: ask you, do you have the best job at IBM. 84 00:05:02,040 --> 00:05:02,599 Speaker 5: I think so. 85 00:05:03,240 --> 00:05:06,200 Speaker 3: I'm talking to Fred Baker, who leads sports and Entertainment 86 00:05:06,200 --> 00:05:09,560 Speaker 3: for IBM consulting in Europe, the Middle East and Africa. 87 00:05:10,320 --> 00:05:13,320 Speaker 3: You can probably guess from the accent he's FN New Zealand. 88 00:05:13,680 --> 00:05:16,000 Speaker 5: We've had a really interesting range of experience over the 89 00:05:16,080 --> 00:05:18,560 Speaker 5: past sort of five six years. We've worked with Premier 90 00:05:18,640 --> 00:05:22,400 Speaker 5: League clubs like Liverpool Football. We've worked with England Rugby, 91 00:05:22,800 --> 00:05:26,560 Speaker 5: Saint Andrew's Links. We also globally, we've got a global team, 92 00:05:26,640 --> 00:05:30,119 Speaker 5: so we work with the Masters, the US Open, ESPN, 93 00:05:30,200 --> 00:05:32,800 Speaker 5: Fantasy Football, the Grammys. 94 00:05:32,720 --> 00:05:35,160 Speaker 4: You do the tennis stuff. Is it all under Europe? 95 00:05:35,200 --> 00:05:37,719 Speaker 5: Yeah, so we do Wimbledon as well. Yep, that's under myrima. 96 00:05:38,120 --> 00:05:40,800 Speaker 3: If you've ever watched Wimbledon on television, I'm sure you've 97 00:05:40,839 --> 00:05:43,680 Speaker 3: seen at various moments a little IBM logo on the 98 00:05:43,680 --> 00:05:47,600 Speaker 3: bottom of the screen. That's because IBM has been Wimbledon's 99 00:05:47,640 --> 00:05:52,960 Speaker 3: official information technology partner since nineteen ninety. When the idea 100 00:05:52,960 --> 00:05:55,480 Speaker 3: of a collaboration between Ferrari and IBM. 101 00:05:55,240 --> 00:05:56,039 Speaker 4: Was first broached. 102 00:05:56,480 --> 00:05:59,120 Speaker 3: Baker actually took people from Ferrari on a tour of 103 00:05:59,200 --> 00:06:02,240 Speaker 3: IBM's wimbled An operation just so they could see what 104 00:06:02,320 --> 00:06:05,880 Speaker 3: a tech company like IBM could do for a sports franchise. 105 00:06:06,680 --> 00:06:07,400 Speaker 4: Which Wimbledon. 106 00:06:07,480 --> 00:06:10,839 Speaker 5: Did you take them to last year's champs? 107 00:06:11,000 --> 00:06:12,920 Speaker 3: Tell me what you showed them. 108 00:06:13,440 --> 00:06:16,000 Speaker 5: We take them into what we call the bunker, so 109 00:06:16,240 --> 00:06:19,800 Speaker 5: it's literally underground at the Champs, and showed them how 110 00:06:19,839 --> 00:06:23,680 Speaker 5: we bring everything to life from the data capture off 111 00:06:23,680 --> 00:06:28,360 Speaker 5: the courts, how we real time categorize, serve all those 112 00:06:28,360 --> 00:06:30,800 Speaker 5: points to broadcasters and serve them into the app the 113 00:06:30,800 --> 00:06:33,160 Speaker 5: website for millions of fans around the world. They were 114 00:06:33,200 --> 00:06:34,080 Speaker 5: really impressed by that. 115 00:06:34,760 --> 00:06:37,880 Speaker 3: I'm also impressed by that IBM trained to say on 116 00:06:37,960 --> 00:06:40,840 Speaker 3: the language of tennis, and not only the language of tennis, 117 00:06:41,160 --> 00:06:45,120 Speaker 3: but specifically the language of tennis at Wimbledon. 118 00:06:45,320 --> 00:06:49,800 Speaker 5: So it can then decipher what an unforced era or 119 00:06:49,839 --> 00:06:53,240 Speaker 5: a winner or a lob or you know, idiosyncrasies in 120 00:06:53,279 --> 00:06:55,080 Speaker 5: the language. It can decipher all of that, and then 121 00:06:55,120 --> 00:06:57,200 Speaker 5: it can also tell what is a broadcast like to 122 00:06:57,240 --> 00:07:00,839 Speaker 5: talk about that is interesting to a fan. We've trained 123 00:07:00,839 --> 00:07:03,160 Speaker 5: it so it can not only analyze everything going on 124 00:07:03,200 --> 00:07:07,160 Speaker 5: in the match, it can analyze past performances and rationalize 125 00:07:07,440 --> 00:07:11,480 Speaker 5: results based on conditions or form and then make predictions 126 00:07:11,520 --> 00:07:13,800 Speaker 5: that fans can learn from, but it can also pull 127 00:07:13,840 --> 00:07:18,600 Speaker 5: out on the spot really interesting milestones, moments, data points 128 00:07:18,640 --> 00:07:21,040 Speaker 5: that then come out of the mouth of a broadcaster. 129 00:07:21,920 --> 00:07:24,520 Speaker 3: IBM is running an AI model that has been trained 130 00:07:24,600 --> 00:07:27,760 Speaker 3: on huge amounts of tennis data in order to give 131 00:07:27,840 --> 00:07:31,480 Speaker 3: human broadcasters ideas on what they can talk about. And 132 00:07:31,520 --> 00:07:35,240 Speaker 3: it all takes place underground, right near the courts. 133 00:07:35,720 --> 00:07:39,600 Speaker 5: It's literally like it's the underground floor of the broadcast 134 00:07:39,600 --> 00:07:42,600 Speaker 5: center at Wimbledon. It's literally almost under the courts. 135 00:07:42,880 --> 00:07:44,440 Speaker 4: Is IBM got the entire bunker? 136 00:07:45,160 --> 00:07:45,600 Speaker 5: Yeah? 137 00:07:45,640 --> 00:07:46,600 Speaker 4: How big is the room? 138 00:07:48,400 --> 00:07:50,440 Speaker 5: I'm sure our team would like it to be bigger, 139 00:07:50,440 --> 00:07:54,480 Speaker 5: but it's big enough. There's probably thirty forty IBM is 140 00:07:54,520 --> 00:07:59,040 Speaker 5: down there. Man. Seeing it live is just really impressive 141 00:07:59,040 --> 00:08:02,200 Speaker 5: when you see how much which work and intelligence goes 142 00:08:02,240 --> 00:08:05,040 Speaker 5: on to then make an end experience for a fan 143 00:08:05,080 --> 00:08:09,960 Speaker 5: that is really beautiful and representative of their brand and tradition. 144 00:08:10,960 --> 00:08:14,560 Speaker 3: IBM's goal in taking Ferrari to the Wimbledon bunker was 145 00:08:14,600 --> 00:08:17,040 Speaker 3: to show them what it looks like to harness the 146 00:08:17,040 --> 00:08:20,040 Speaker 3: power of data and how this could help shape Scuderia 147 00:08:20,080 --> 00:08:24,760 Speaker 3: Ferraris fan and digital experiences. Could AI learn the language 148 00:08:25,000 --> 00:08:28,480 Speaker 3: of Scuderia Ferrari. What was the original app like before 149 00:08:28,520 --> 00:08:32,320 Speaker 3: IBM got involved. I'm speaking with Stefano Pollard, who runs 150 00:08:32,320 --> 00:08:34,640 Speaker 3: fan development for Ferrari's F one team. 151 00:08:34,800 --> 00:08:38,199 Speaker 6: It was quite a good app, a very good digital product, 152 00:08:38,200 --> 00:08:41,760 Speaker 6: but just an editorial product. So we were providing fans 153 00:08:42,040 --> 00:08:45,760 Speaker 6: news and videos, articles and it was mainly about that. 154 00:08:46,440 --> 00:08:49,520 Speaker 6: The strategy and the idea was trying to use the 155 00:08:49,559 --> 00:08:53,280 Speaker 6: app to have a deeper connection and interaction with our fans, 156 00:08:53,320 --> 00:08:57,960 Speaker 6: make it more interactive, So turning it from an editorial product, 157 00:08:58,200 --> 00:09:01,200 Speaker 6: which was a very good editorial product, to a more 158 00:09:01,559 --> 00:09:03,800 Speaker 6: interactive product, digital product. 159 00:09:03,600 --> 00:09:07,000 Speaker 3: With such a massive undertaking. I asked Stephen how it 160 00:09:07,040 --> 00:09:09,400 Speaker 3: all started once IBM got involved. 161 00:09:10,280 --> 00:09:13,080 Speaker 6: We started really with a very long couple of months 162 00:09:13,160 --> 00:09:16,920 Speaker 6: of discovery phase. So looking at the current app, looking 163 00:09:16,960 --> 00:09:20,040 Speaker 6: at fans, looking at what fans wanted from an app. 164 00:09:20,320 --> 00:09:22,240 Speaker 3: Tell me a little bit more about that phrase something 165 00:09:22,280 --> 00:09:25,080 Speaker 3: a fan wanted? What is it that the super fan 166 00:09:26,280 --> 00:09:30,000 Speaker 3: wasn't getting before? That was something that would tie them 167 00:09:30,000 --> 00:09:31,040 Speaker 3: even closer to Ferrari. 168 00:09:31,679 --> 00:09:36,480 Speaker 6: Having run some focused group, having having read of market research, 169 00:09:36,520 --> 00:09:39,400 Speaker 6: having spoken to fans, and being a fan. The strongest 170 00:09:39,480 --> 00:09:43,320 Speaker 6: inside is Feridy Fans and super fans want to be 171 00:09:43,480 --> 00:09:46,640 Speaker 6: part of something, want to belong to something, so they 172 00:09:46,679 --> 00:09:49,120 Speaker 6: want to be part of a community, and ultimately they 173 00:09:49,120 --> 00:09:52,319 Speaker 6: want to be part of a winning team, so they 174 00:09:52,360 --> 00:09:55,160 Speaker 6: want to feel closer and get access. 175 00:09:59,720 --> 00:10:03,400 Speaker 3: The way Steffan aside, The opportunity wasn't with race days 176 00:10:03,960 --> 00:10:06,400 Speaker 3: when the cars are on the track, the tafosi are 177 00:10:06,440 --> 00:10:09,720 Speaker 3: already locked in, but there was an opportunity to engage 178 00:10:09,720 --> 00:10:12,800 Speaker 3: Ferrari fans on the other days of the week or 179 00:10:12,880 --> 00:10:13,840 Speaker 3: during the off season. 180 00:10:14,200 --> 00:10:17,640 Speaker 5: Formula One is so much more than just the race. 181 00:10:17,960 --> 00:10:19,160 Speaker 4: This is Fred Baker again. 182 00:10:19,480 --> 00:10:23,240 Speaker 5: What we can do is relive the race and bring 183 00:10:23,280 --> 00:10:26,480 Speaker 5: it to life after the fact. We can help them prepare, 184 00:10:26,520 --> 00:10:28,440 Speaker 5: we can help them relive the past, and we can 185 00:10:28,480 --> 00:10:31,640 Speaker 5: also bring the experience around race weekend to life as well. 186 00:10:32,200 --> 00:10:35,160 Speaker 3: That of course, maybe wonder how do you engage fans 187 00:10:35,200 --> 00:10:39,040 Speaker 3: when there's not a race happening. Baker says it all 188 00:10:39,080 --> 00:10:43,080 Speaker 3: comes back to data and information. Talk a little about 189 00:10:43,160 --> 00:10:46,840 Speaker 3: data collection, because you're talking about a brand with tentacles 190 00:10:46,840 --> 00:10:48,920 Speaker 3: everywhere and you're trying to bring a lot of that 191 00:10:48,960 --> 00:10:50,120 Speaker 3: stuff together in the app. 192 00:10:50,640 --> 00:10:53,880 Speaker 5: This is an organization that has for decades used data 193 00:10:54,240 --> 00:10:58,720 Speaker 5: for racing the performance, it's not historically use that data 194 00:10:58,800 --> 00:11:02,520 Speaker 5: for one in the world to see. What we're trying 195 00:11:02,520 --> 00:11:04,000 Speaker 5: to do is expose as much of it as we 196 00:11:04,040 --> 00:11:06,800 Speaker 5: can to fans. So part of collecting the data the 197 00:11:06,880 --> 00:11:09,320 Speaker 5: challenge with around how you go across all the disparate 198 00:11:09,480 --> 00:11:13,360 Speaker 5: different groups that collect data for different purposes. The team 199 00:11:13,400 --> 00:11:15,920 Speaker 5: that collects data on tires, the team that has data 200 00:11:15,960 --> 00:11:19,360 Speaker 5: on drivers, on whether or on competitors, and so on. 201 00:11:20,040 --> 00:11:22,200 Speaker 5: So you're trying to bring all that together and source 202 00:11:22,240 --> 00:11:25,320 Speaker 5: it and make sense of it and train ouri to 203 00:11:25,440 --> 00:11:28,839 Speaker 5: understand what it means, what things on team radio mean, 204 00:11:29,080 --> 00:11:33,560 Speaker 5: what nicknames mean, what abbreviations and slang and idiosyncrasies on 205 00:11:34,080 --> 00:11:37,400 Speaker 5: car specifics and track specifics and so on mean. And 206 00:11:37,440 --> 00:11:41,120 Speaker 5: you're also trying to design for something that is going 207 00:11:41,160 --> 00:11:45,560 Speaker 5: to be fan engaging but also appropriate to all the 208 00:11:45,600 --> 00:11:50,160 Speaker 5: sensitivities of the privacy that's necessary. So you want it 209 00:11:50,200 --> 00:11:51,880 Speaker 5: to be able to do all of that, collect all 210 00:11:51,920 --> 00:11:55,719 Speaker 5: the data, produce something for fans in an automated way, but. 211 00:11:55,720 --> 00:11:59,240 Speaker 3: In order to design something to expertly engage the tafosi 212 00:12:00,000 --> 00:12:03,360 Speaker 3: necessary to understand more about the passion and the type 213 00:12:03,360 --> 00:12:07,680 Speaker 3: of national identity behind the fan base. You need to 214 00:12:07,720 --> 00:12:09,160 Speaker 3: get inside the mind. 215 00:12:08,920 --> 00:12:09,640 Speaker 4: Of the super fan. 216 00:12:11,080 --> 00:12:13,240 Speaker 3: If you wanted to meet some modern day to Fosie 217 00:12:13,280 --> 00:12:15,439 Speaker 3: in the United States, you could head to a bar 218 00:12:15,600 --> 00:12:19,920 Speaker 3: in Midtown Manhattan called Fela. Every race day, Formula One 219 00:12:19,960 --> 00:12:23,319 Speaker 3: fans gathered Feala to cheer on their favorite drivers, their 220 00:12:23,320 --> 00:12:28,880 Speaker 3: favorite teams, and I mean really cheer. I sent our 221 00:12:28,880 --> 00:12:31,480 Speaker 3: producer Jake Harper to Feala on the day of the 222 00:12:31,480 --> 00:12:34,520 Speaker 3: Canadian Grand Prix so you could see the fandom up close. 223 00:12:35,240 --> 00:12:38,280 Speaker 3: The bar gets loud and so crowded it's hard to move. 224 00:12:38,880 --> 00:12:42,400 Speaker 3: Today the room is packed with Scuderia Ferrari HP fans. 225 00:12:42,720 --> 00:12:44,200 Speaker 5: Even your glasses are Ferrari. 226 00:12:44,280 --> 00:12:46,480 Speaker 3: I just noticed that Jake talked to a Ferrari fan 227 00:12:46,559 --> 00:12:49,920 Speaker 3: named Gino who was dressed head to toe in Ferrari's 228 00:12:50,040 --> 00:12:51,840 Speaker 3: signature red and black. 229 00:12:51,760 --> 00:12:54,760 Speaker 5: And my shoes are Ferraria. Fully checked out. 230 00:12:54,800 --> 00:12:56,559 Speaker 7: They were making fun of me last time I was here. 231 00:12:56,600 --> 00:12:59,040 Speaker 7: They're like, is your underwear Ferrari? And I texted my 232 00:12:59,040 --> 00:13:01,480 Speaker 7: girlfriend like, I need Ferrari underwear? 233 00:13:02,400 --> 00:13:05,400 Speaker 5: Did you get it yet? Not yet, I'll work on it. 234 00:13:05,600 --> 00:13:08,240 Speaker 3: Gino's fandom started with Ferrari as a brand. 235 00:13:09,160 --> 00:13:10,040 Speaker 5: I love the cars. 236 00:13:10,160 --> 00:13:12,200 Speaker 7: I think the four fifty eight sCOD areas like the 237 00:13:12,280 --> 00:13:15,880 Speaker 7: pinnacle of automotive engineering. That's my dream car. The four 238 00:13:15,920 --> 00:13:18,280 Speaker 7: point thirty with the glass house for the engine, I mean, 239 00:13:18,280 --> 00:13:21,520 Speaker 7: that's They're all gorgeous. There's always been an aspiration of 240 00:13:21,559 --> 00:13:26,000 Speaker 7: mine the own one, so that naturally made me gravitate 241 00:13:26,080 --> 00:13:28,640 Speaker 7: towards Ferrari. Even when the company I worked for a 242 00:13:28,760 --> 00:13:34,880 Speaker 7: sponsored AMG Petronis, I was secretly like hiding my taposi 243 00:13:36,040 --> 00:13:37,120 Speaker 7: at the races, like. 244 00:13:37,200 --> 00:13:40,480 Speaker 5: Clark Kenton Superman. You're just hiding the uniform underneath. I 245 00:13:40,520 --> 00:13:41,280 Speaker 5: love that. I love that. 246 00:13:41,360 --> 00:13:43,720 Speaker 7: Yeah, I was wearing a Ferrari shirt underneath my suit. 247 00:13:44,400 --> 00:13:46,840 Speaker 3: In one sense, Gino is typical of what Ferrari has 248 00:13:46,920 --> 00:13:49,600 Speaker 3: learned about its followers. A lot of F one fans, 249 00:13:49,880 --> 00:13:53,880 Speaker 3: especially newer fans, are fans of drivers, but the tafosi 250 00:13:54,400 --> 00:13:57,640 Speaker 3: love Ferrari. It's the oldest brand in Formula One, the 251 00:13:57,679 --> 00:14:00,240 Speaker 3: only team that has stuck around since the series was 252 00:14:00,280 --> 00:14:04,360 Speaker 3: founded in nineteen fifty. But in another sense, Gino is 253 00:14:04,440 --> 00:14:07,280 Speaker 3: not typical. He lives in New York. He can go 254 00:14:07,320 --> 00:14:09,800 Speaker 3: to Fala to celebrate F one with other tefhosi. 255 00:14:10,040 --> 00:14:13,000 Speaker 7: I'm a big racing fan, and coming to this bar, 256 00:14:13,840 --> 00:14:15,520 Speaker 7: I found a bunch of people that were in a 257 00:14:15,760 --> 00:14:16,120 Speaker 7: F one. 258 00:14:16,160 --> 00:14:16,800 Speaker 5: Now I'm at this. 259 00:14:16,800 --> 00:14:20,600 Speaker 7: Bar every weekend, just about with four or five friends 260 00:14:20,600 --> 00:14:22,040 Speaker 7: that I made just through racings. 261 00:14:23,560 --> 00:14:26,240 Speaker 3: But lots and lots of Scuderia Ferraris three hundred and 262 00:14:26,320 --> 00:14:29,120 Speaker 3: ninety six million fans don't live in a big city 263 00:14:29,120 --> 00:14:31,840 Speaker 3: with a Ferrari bar, and lots of those three hundred 264 00:14:31,840 --> 00:14:34,680 Speaker 3: and ninety six million fans aren't the kind of hardcore 265 00:14:34,760 --> 00:14:37,360 Speaker 3: fan who dressed head to toe in Ferrari's signature red 266 00:14:37,360 --> 00:14:42,280 Speaker 3: and black. A group that large is diverse, necessarily, and 267 00:14:42,320 --> 00:14:44,800 Speaker 3: one of the first tasks that IBM and Ferrari set 268 00:14:44,800 --> 00:14:47,840 Speaker 3: out to do was to understand the full range of 269 00:14:47,880 --> 00:14:53,000 Speaker 3: the tafosi phenomenon. People like Gino hardcore fans. They were easy, 270 00:14:53,240 --> 00:14:56,320 Speaker 3: they would follow Scuderia Ferrari HB anywhere it wanted to go. 271 00:14:56,920 --> 00:15:00,359 Speaker 4: But who else was out there? The most interesting. 272 00:15:00,080 --> 00:15:02,720 Speaker 3: Addition to the F one fan base were those who 273 00:15:02,720 --> 00:15:07,880 Speaker 3: watched the phenomenally successful Netflix documentary Drive to Survive. These 274 00:15:07,960 --> 00:15:12,200 Speaker 3: tended to be newcomers to the sport, more Americans than Europeans. 275 00:15:12,800 --> 00:15:17,000 Speaker 3: What was their emotional perspective, what did they want here's 276 00:15:17,000 --> 00:15:19,800 Speaker 3: Fred Baker again, the guy with the coolest job at IBM. 277 00:15:20,040 --> 00:15:21,760 Speaker 5: If I'm a passionate fan, I want to read a 278 00:15:21,760 --> 00:15:24,880 Speaker 5: totally different thing on the app to a casual fan 279 00:15:24,920 --> 00:15:28,440 Speaker 5: who is of the Netflix Drive to Survive generation versus 280 00:15:28,920 --> 00:15:31,040 Speaker 5: you know, some really niche personas that we found that 281 00:15:31,320 --> 00:15:34,560 Speaker 5: are super interested but don't find it accessible yet until 282 00:15:34,560 --> 00:15:37,520 Speaker 5: we start to deliver to quite different needs that they have. 283 00:15:37,760 --> 00:15:40,840 Speaker 3: Working with IBM WATSONEX, Baker and his team began to 284 00:15:40,920 --> 00:15:46,000 Speaker 3: develop personas archetypes of all the possible kinds of Ferrari fans, 285 00:15:46,560 --> 00:15:48,800 Speaker 3: because if Ferrari wanted to get better at talking to 286 00:15:48,840 --> 00:15:52,040 Speaker 3: their fans, they had to understand who the fans were. 287 00:15:52,680 --> 00:15:56,400 Speaker 3: And the personas are helping Ferrari and IBM create an 288 00:15:56,400 --> 00:15:59,880 Speaker 3: app that caters to the tafosi in all their iteration. 289 00:16:01,400 --> 00:16:03,720 Speaker 4: How many personas did you come up with? 290 00:16:04,400 --> 00:16:06,680 Speaker 5: I think we had over ten in the end, maybe 291 00:16:06,720 --> 00:16:09,480 Speaker 5: a dozen. And this is different archetypes of people. Even 292 00:16:09,520 --> 00:16:12,440 Speaker 5: that process is helped by AI, so we train AI 293 00:16:12,600 --> 00:16:15,240 Speaker 5: to help us develop out a persona. We can get 294 00:16:15,840 --> 00:16:19,640 Speaker 5: really detailed as to what each archetype is and their 295 00:16:20,320 --> 00:16:23,160 Speaker 5: hobbies and backgrounds and so on. So our own WATSONEX 296 00:16:23,200 --> 00:16:27,120 Speaker 5: helped us in developing those personas, like our research helped 297 00:16:27,160 --> 00:16:30,640 Speaker 5: us uncover a segment of middle aged women in China 298 00:16:30,720 --> 00:16:34,680 Speaker 5: who Ferrari is a real status symbol and they're really 299 00:16:34,680 --> 00:16:37,080 Speaker 5: interested in the Scuderia Ferrari brand and now they can 300 00:16:37,080 --> 00:16:41,240 Speaker 5: engage more with it, but it wasn't yet accessible or 301 00:16:41,280 --> 00:16:43,800 Speaker 5: inclusive enough for them to feel comfortable doing so. Real 302 00:16:43,840 --> 00:16:47,320 Speaker 5: spectrums of fans across those dozen personas that we had 303 00:16:47,320 --> 00:16:47,800 Speaker 5: to design. 304 00:16:47,840 --> 00:16:52,720 Speaker 3: For give me some more examples of personas. Can you 305 00:16:52,760 --> 00:16:54,280 Speaker 3: give me a couple more just so get a flavor? 306 00:16:54,440 --> 00:16:56,520 Speaker 5: Yeah? Sure. So the other obvious one is the Drive 307 00:16:56,560 --> 00:16:59,480 Speaker 5: to Survive fan and that they're probably not a die 308 00:16:59,520 --> 00:17:01,960 Speaker 5: hard all their lives Scouterier Ferrari fan, but they've really 309 00:17:02,000 --> 00:17:04,440 Speaker 5: got into the more social side of formula one that's 310 00:17:04,480 --> 00:17:07,680 Speaker 5: been born out of the really popular series Drive to 311 00:17:07,720 --> 00:17:10,800 Speaker 5: Survive on Netflix. You then have gamer personas who are 312 00:17:10,840 --> 00:17:15,479 Speaker 5: into esports is growing massively in motorsport, and they're probably 313 00:17:15,480 --> 00:17:17,920 Speaker 5: not necessarily into the real life racing quite so much, 314 00:17:17,960 --> 00:17:19,840 Speaker 5: but they're certainly into gaming, So how do you appeal 315 00:17:19,880 --> 00:17:22,400 Speaker 5: to them. Then casual fans who are sort of into 316 00:17:22,400 --> 00:17:25,080 Speaker 5: the luxury of scudo Ferrari, but not the sport necessarily 317 00:17:25,720 --> 00:17:29,000 Speaker 5: do it. Personas have names, Yeah, I mean we give 318 00:17:29,000 --> 00:17:31,479 Speaker 5: them human names too. We had a Max, we had 319 00:17:31,520 --> 00:17:34,400 Speaker 5: an Alfonso. I think we had a Pedro. 320 00:17:35,200 --> 00:17:38,240 Speaker 3: Two women in China. Is she watching F one or 321 00:17:38,320 --> 00:17:40,800 Speaker 3: is she interested more in the brand and what it signifies? 322 00:17:40,920 --> 00:17:43,560 Speaker 5: Yeah, more in the brand and being part of a community. 323 00:17:43,880 --> 00:17:46,480 Speaker 5: If I'm that persona in China, then I probably don't 324 00:17:46,520 --> 00:17:50,680 Speaker 5: feel like I belong to it truly yet, but I'd 325 00:17:50,720 --> 00:17:52,560 Speaker 5: love to feel like I do, so I could start 326 00:17:52,560 --> 00:17:55,040 Speaker 5: to become a part of a digital community, learn more 327 00:17:55,040 --> 00:17:59,560 Speaker 5: about the brand, probably get access to exclusive merchandise, or 328 00:18:00,320 --> 00:18:03,200 Speaker 5: you know, if I can't necessarily own a Ferrari car, 329 00:18:03,240 --> 00:18:05,560 Speaker 5: which let's face it, not many people can. And if 330 00:18:05,560 --> 00:18:07,440 Speaker 5: we're relyinged only on the people who can own a car, 331 00:18:07,480 --> 00:18:09,720 Speaker 5: then we're probably not going to get much engagement. So 332 00:18:09,800 --> 00:18:12,199 Speaker 5: how do we make others feel that they're still a 333 00:18:12,240 --> 00:18:13,800 Speaker 5: part of that community. 334 00:18:14,560 --> 00:18:16,240 Speaker 3: This is what I mean when I say the task 335 00:18:16,280 --> 00:18:18,960 Speaker 3: of relating to the Ferrari fan base is a data 336 00:18:19,080 --> 00:18:23,760 Speaker 3: and information problem. It's about collecting, organizing, and analyzing the 337 00:18:23,800 --> 00:18:27,440 Speaker 3: needs and wants of an enormous pool of people and 338 00:18:27,520 --> 00:18:31,640 Speaker 3: speaking to each of them in their own emotional language. 339 00:18:32,480 --> 00:18:33,280 Speaker 4: Giving all the work. 340 00:18:33,160 --> 00:18:36,520 Speaker 3: Fred put into understanding Ferrari's fan base, I was curious 341 00:18:36,560 --> 00:18:40,280 Speaker 3: to know how his framework would categorize me. I want 342 00:18:40,320 --> 00:18:43,800 Speaker 3: to figure out which persona I am, So I'll describe 343 00:18:43,800 --> 00:18:44,200 Speaker 3: to my. 344 00:18:44,640 --> 00:18:45,600 Speaker 4: Relationship to Ferrari. 345 00:18:45,720 --> 00:18:51,320 Speaker 3: You tell me so what I am is a huge car, 346 00:18:51,560 --> 00:18:57,639 Speaker 3: not so like all cars. Yeah, obsessively collect on a 347 00:18:57,720 --> 00:19:03,679 Speaker 3: very limited stage, benaged cars, read serious car magazines, been 348 00:19:03,720 --> 00:19:06,480 Speaker 3: a lot of time. My car websites have a historical 349 00:19:06,520 --> 00:19:08,960 Speaker 3: relationship to have one because I grew up with Nikki 350 00:19:09,000 --> 00:19:13,919 Speaker 3: Lauder battling James Hunt in loudest for our years. I 351 00:19:13,920 --> 00:19:17,919 Speaker 3: have a get nostalgic connection. Went to Italy with my 352 00:19:18,160 --> 00:19:21,639 Speaker 3: nephew and went to the Ferrari factory and rented one 353 00:19:21,640 --> 00:19:25,600 Speaker 3: of those to drive around, you know, and I follow 354 00:19:25,640 --> 00:19:25,960 Speaker 3: that F one. 355 00:19:25,960 --> 00:19:26,399 Speaker 4: But I wouldn't. 356 00:19:26,400 --> 00:19:28,760 Speaker 3: I would don't think I would ever go to an 357 00:19:28,920 --> 00:19:31,360 Speaker 3: I wouldn't fly to Miami for F one Miam. 358 00:19:31,400 --> 00:19:34,720 Speaker 4: I wouldn't go that far. And I don't have time 359 00:19:34,760 --> 00:19:37,160 Speaker 4: to watch F one on TV on a regular basis. 360 00:19:37,600 --> 00:19:41,560 Speaker 3: But I'm interested, and I have a red Ferrari T 361 00:19:41,640 --> 00:19:44,399 Speaker 3: shirt which I've been known to wear and if you 362 00:19:44,760 --> 00:19:46,800 Speaker 3: if I ever got really rich, would I buy a 363 00:19:46,800 --> 00:19:47,000 Speaker 3: for Aur? 364 00:19:47,160 --> 00:19:49,800 Speaker 4: Yes? I would. Okay, So where am I? Where am 365 00:19:49,840 --> 00:19:51,879 Speaker 4: I in your breakdown? 366 00:19:52,160 --> 00:19:55,600 Speaker 5: Yeah? I think you're probably a combination of the I 367 00:19:55,640 --> 00:19:59,879 Speaker 5: think it's casual loyalist. It's not gonna not gonna overtly 368 00:20:00,600 --> 00:20:02,840 Speaker 5: go out of their way to sort of spend money 369 00:20:02,840 --> 00:20:04,840 Speaker 5: on the racing, but they are loyal to the Ferrari 370 00:20:04,920 --> 00:20:07,439 Speaker 5: brand and they have nostalgia with it or whatever it 371 00:20:07,480 --> 00:20:09,959 Speaker 5: might be. And then the luxury enthusiasts as well, so 372 00:20:10,320 --> 00:20:13,359 Speaker 5: and that type of fan. You're right, We're probably not 373 00:20:13,359 --> 00:20:15,240 Speaker 5: going to engage you by doing a ton more on 374 00:20:15,320 --> 00:20:18,639 Speaker 5: race weekend, but we can engage you by bringing this 375 00:20:18,880 --> 00:20:24,920 Speaker 5: hugely rich amount of archive material, footage, feelings and past 376 00:20:25,080 --> 00:20:29,600 Speaker 5: drivers of yesteryear, by bringing them to life. 377 00:20:35,119 --> 00:20:39,840 Speaker 3: In zero an app that you saw another brand doing 378 00:20:40,240 --> 00:20:42,040 Speaker 3: that served as a kind of model. I don't mean 379 00:20:42,080 --> 00:20:45,520 Speaker 3: within f one, I'm talking about it from any other film. 380 00:20:45,800 --> 00:20:50,920 Speaker 6: On top of being a very sport passionate, I'm let's say, 381 00:20:51,040 --> 00:20:55,119 Speaker 6: a marketing passionate, a digital passionate guy. So I have 382 00:20:55,160 --> 00:20:57,240 Speaker 6: a lot of apps and he also for my job, 383 00:20:57,280 --> 00:20:59,960 Speaker 6: I tried to look at different markets in different apps. 384 00:21:00,160 --> 00:21:03,240 Speaker 3: As we were talking, I was thinking about Strava. I'm 385 00:21:03,240 --> 00:21:06,120 Speaker 3: a huge Stravahead. It's my favorite app. If you don't 386 00:21:06,160 --> 00:21:09,200 Speaker 3: already know, Strava is used by millions of active people 387 00:21:09,240 --> 00:21:11,760 Speaker 3: around the world. I'm a runner and the app shows 388 00:21:11,760 --> 00:21:14,600 Speaker 3: me a map of where I went, how fast I ran, 389 00:21:15,040 --> 00:21:17,119 Speaker 3: what my heart ray was, what the weather was on 390 00:21:17,200 --> 00:21:17,480 Speaker 3: and on. 391 00:21:17,840 --> 00:21:19,040 Speaker 4: Are you a cyclostore runner? 392 00:21:19,080 --> 00:21:19,639 Speaker 6: I'm a runner. 393 00:21:19,680 --> 00:21:20,240 Speaker 5: I'm a runner. 394 00:21:20,480 --> 00:21:24,520 Speaker 6: I run marathons and ultra marathons. I did lights see 395 00:21:24,600 --> 00:21:25,719 Speaker 6: one hundred kilometers. 396 00:21:25,840 --> 00:21:29,640 Speaker 3: As it turns out, Stefano is a Stravahead too. Right 397 00:21:29,640 --> 00:21:31,960 Speaker 3: after I spoke to him, I followed him on Strava. 398 00:21:32,200 --> 00:21:34,239 Speaker 3: He and I run roughly the same distance every day 399 00:21:34,280 --> 00:21:36,479 Speaker 3: at the same pace, and if I'm ever in Milan, 400 00:21:36,720 --> 00:21:38,720 Speaker 3: I'm almost certainly going to look him up to see 401 00:21:38,760 --> 00:21:40,399 Speaker 3: if he'll take me out on one of his favorite 402 00:21:40,440 --> 00:21:43,400 Speaker 3: routes through the city. This is what I love about Strava. 403 00:21:43,760 --> 00:21:46,399 Speaker 3: You can find people to run with and interact with. 404 00:21:46,840 --> 00:21:50,040 Speaker 3: Strava is a community of like minded people, and for 405 00:21:50,080 --> 00:21:53,320 Speaker 3: those like me, the Strava app becomes a regular part 406 00:21:53,400 --> 00:21:56,560 Speaker 3: of my daily routine, and that's what Stefano wanted for 407 00:21:56,600 --> 00:21:59,800 Speaker 3: the Ferrari app. Are you interested in allowing creating sort 408 00:21:59,800 --> 00:22:04,200 Speaker 3: of vast forums for our evans to communicate with each other? 409 00:22:05,520 --> 00:22:08,960 Speaker 6: I think you have to work in three directions. So 410 00:22:09,080 --> 00:22:13,240 Speaker 6: direction number one is a Ferrari to fans, so providing 411 00:22:13,320 --> 00:22:16,720 Speaker 6: them something which is compelling, which has value, and this 412 00:22:16,960 --> 00:22:18,720 Speaker 6: I think we're already doing and we're working on it. 413 00:22:19,200 --> 00:22:24,119 Speaker 6: Second way is fans to Ferrari, so help like allowing 414 00:22:24,160 --> 00:22:27,159 Speaker 6: fans to better interact with us, which was something we 415 00:22:27,160 --> 00:22:30,040 Speaker 6: were not doing with the previous app. For example, we've 416 00:22:30,240 --> 00:22:33,480 Speaker 6: just introduced two features which are polls, so basic ones, 417 00:22:33,480 --> 00:22:37,080 Speaker 6: but polls and the possibility like the submit your message feature. 418 00:22:37,320 --> 00:22:40,480 Speaker 6: So really to work on the way fans to Ferrari. 419 00:22:40,640 --> 00:22:43,679 Speaker 6: And then the third important way to build a community 420 00:22:43,680 --> 00:22:47,080 Speaker 6: and nurture community is like fans to fans. So if 421 00:22:47,119 --> 00:22:50,000 Speaker 6: you were able to work on those trae dimensions of 422 00:22:50,080 --> 00:22:52,800 Speaker 6: Ferrari to fans, fans to Ferrari, and fans to fans, 423 00:22:53,440 --> 00:22:56,840 Speaker 6: that's how you could really create a strong community and 424 00:22:56,920 --> 00:23:00,000 Speaker 6: start really monetizing and creating value. I think we're very 425 00:23:00,000 --> 00:23:03,439 Speaker 6: strong in the first dimension right now. We're building the 426 00:23:03,480 --> 00:23:06,800 Speaker 6: second one, So fans to Ferrari and then definitely the 427 00:23:06,800 --> 00:23:09,639 Speaker 6: third one has to be there in order to have 428 00:23:09,680 --> 00:23:11,480 Speaker 6: a complete community engagement. 429 00:23:12,040 --> 00:23:13,560 Speaker 4: So let's talk about results. 430 00:23:13,960 --> 00:23:17,159 Speaker 3: Scuddery of Ferrari HP launched the new app at the 431 00:23:17,200 --> 00:23:21,399 Speaker 3: Miami Grand Prix in twenty twenty five, incorporating AI elements 432 00:23:21,520 --> 00:23:24,720 Speaker 3: and tailoring it to those archetypes Thread was talking about. 433 00:23:27,160 --> 00:23:30,280 Speaker 3: Are more people using the app? Are users spending more 434 00:23:30,320 --> 00:23:32,600 Speaker 3: time on it they did on the older version of 435 00:23:32,640 --> 00:23:33,000 Speaker 3: the app? 436 00:23:33,600 --> 00:23:37,360 Speaker 6: Yes, we doubled these months the daily active users we 437 00:23:37,359 --> 00:23:40,040 Speaker 6: were having last season, so compared to the average of 438 00:23:40,080 --> 00:23:43,040 Speaker 6: twenty twenty four season, we have more than double of 439 00:23:43,280 --> 00:23:48,439 Speaker 6: daily active users. Also, we're doubling normal months down downloads, 440 00:23:48,480 --> 00:23:51,879 Speaker 6: so we did in these months more than two times 441 00:23:51,880 --> 00:23:54,439 Speaker 6: the download we are doing in a normal months. We 442 00:23:54,760 --> 00:23:58,560 Speaker 6: are increasing by thirty five percent the average time spent 443 00:23:58,640 --> 00:23:59,960 Speaker 6: on app. So KPIs are good. 444 00:24:01,320 --> 00:24:08,640 Speaker 3: If you build the right app, they will come for generations. 445 00:24:08,800 --> 00:24:12,520 Speaker 3: Fans of all varieties have met in public places, the 446 00:24:12,560 --> 00:24:15,639 Speaker 3: stands of stadiums, in bars to watch races and matches 447 00:24:15,640 --> 00:24:19,000 Speaker 3: on television, but there's a chance now for fandom to 448 00:24:19,040 --> 00:24:22,680 Speaker 3: exist on a higher and broader level, for a community 449 00:24:22,840 --> 00:24:25,920 Speaker 3: to be created over the Internet, even when the fans 450 00:24:25,960 --> 00:24:32,919 Speaker 3: are vastly different people who live vast distances apart. I 451 00:24:32,920 --> 00:24:36,200 Speaker 3: could imagine Gino in his Ferrari Red and Black, using 452 00:24:36,200 --> 00:24:38,840 Speaker 3: the scudery of Ferrari app relating to me as I 453 00:24:38,880 --> 00:24:41,720 Speaker 3: relive my memories of Niki Lauda from the nineteen seventies. 454 00:24:42,440 --> 00:24:44,320 Speaker 3: Maybe I could use the app to learn something from 455 00:24:44,359 --> 00:24:47,920 Speaker 3: the woman in China, the Tafosie newcomer, or some seventeen 456 00:24:48,000 --> 00:24:49,920 Speaker 3: year old who got sucked in first by. 457 00:24:49,880 --> 00:24:50,760 Speaker 4: Drive to survive. 458 00:24:51,680 --> 00:24:55,760 Speaker 3: I can imagine myself as part of that vision, taking 459 00:24:55,800 --> 00:25:12,439 Speaker 3: my lifelong obsession to the next level. Smart Talks with 460 00:25:12,480 --> 00:25:16,880 Speaker 3: IBM is produced by Matt Ramano, Amy Gains, McQuaid, Trina Menino, 461 00:25:17,400 --> 00:25:21,800 Speaker 3: and Jake Harper. Were edited by Lacy Roberts. Engineering by 462 00:25:21,840 --> 00:25:26,120 Speaker 3: Nina Bird Lawrence, mastering by Sarah Buguier, music by Gramoscope, 463 00:25:26,359 --> 00:25:31,080 Speaker 3: Strategy by Tatiana Lieberman and Cassidy Meyer. Special thanks to 464 00:25:31,119 --> 00:25:35,199 Speaker 3: Scuderia Ferrari HP and the bar and restaurant Feala in 465 00:25:35,280 --> 00:25:38,240 Speaker 3: New York City. Smart Talks with IBM is a production 466 00:25:38,320 --> 00:25:42,560 Speaker 3: of Pushkin Industries and Ruby Studio at iHeartMedia. To find 467 00:25:42,600 --> 00:25:46,960 Speaker 3: more Pushkin podcasts, listen on the iHeartRadio app, Apple podcasts, 468 00:25:47,080 --> 00:25:48,320 Speaker 3: or wherever. 469 00:25:47,960 --> 00:25:50,679 Speaker 4: You listen to podcasts. I'm Malcolm Glavo. 470 00:25:51,480 --> 00:25:55,199 Speaker 3: This is a paid advertisement from IBM. The conversations on 471 00:25:55,240 --> 00:26:09,640 Speaker 3: this podcast don't necessarily represent IBM's positions, strategies, or opinions. 472 00:26:11,080 --> 00:26:13,440 Speaker 3: I asked Fred Baker to come up with a hypothetical 473 00:26:14,080 --> 00:26:18,240 Speaker 3: something that could fulfill my childhood dreams, something that this 474 00:26:18,359 --> 00:26:21,879 Speaker 3: type of technology could theoretically do that might appeal to 475 00:26:21,920 --> 00:26:24,879 Speaker 3: a fan like me, someone who's interested in the sport 476 00:26:24,960 --> 00:26:29,280 Speaker 3: went back fifty years. He said, what about using AI 477 00:26:29,840 --> 00:26:31,800 Speaker 3: to bring historical cars to life? 478 00:26:32,640 --> 00:26:35,480 Speaker 5: Bringing to live cars of the past and allows fans 479 00:26:35,520 --> 00:26:41,560 Speaker 5: to simulate a nineteen fifty Ferrari race versus nineteen seventy 480 00:26:41,600 --> 00:26:43,800 Speaker 5: one to see who which car would be faster. So 481 00:26:43,840 --> 00:26:45,280 Speaker 5: it's those sorts of trade offs. 482 00:26:45,720 --> 00:26:47,439 Speaker 4: Wait, you could do it. You could do that. 483 00:26:47,480 --> 00:26:50,160 Speaker 3: Tell me about last thing you said you can run 484 00:26:50,200 --> 00:26:52,960 Speaker 3: simulations race simulations out of the app. 485 00:26:53,400 --> 00:26:56,960 Speaker 5: You can't out of the app at this point, So I. 486 00:26:56,920 --> 00:26:58,760 Speaker 4: Know, potentially potentially. 487 00:26:58,320 --> 00:27:01,359 Speaker 5: Yeah, yeah, I can you know, simulate based on a 488 00:27:01,359 --> 00:27:03,440 Speaker 5: whole range of factors that we can feed and train 489 00:27:03,480 --> 00:27:03,760 Speaker 5: it on. 490 00:27:04,640 --> 00:27:07,960 Speaker 3: Wait, so I could hypothetically you could allow me to 491 00:27:08,040 --> 00:27:12,960 Speaker 3: compare Niki Lauder, for example, to a contemporary driver. Ye, 492 00:27:13,200 --> 00:27:15,760 Speaker 3: and and I could say if I put Nicki Lauder 493 00:27:15,760 --> 00:27:19,280 Speaker 3: in a contemporary car, What you're saying is that there 494 00:27:19,320 --> 00:27:24,439 Speaker 3: is a scenario where I could recreate that era in 495 00:27:24,520 --> 00:27:27,800 Speaker 3: modern cars and get a sense of how my childhood 496 00:27:27,840 --> 00:27:30,960 Speaker 3: heroes were performing, would have performed a break day. 497 00:27:31,320 --> 00:27:31,520 Speaker 4: Yeah. 498 00:27:31,600 --> 00:27:33,800 Speaker 5: Yeah, So you can analyze and understand how you would 499 00:27:33,880 --> 00:27:36,679 Speaker 5: rank all drivers of all time based on the different 500 00:27:36,680 --> 00:27:38,440 Speaker 5: traits of a driver, right, So you can say who's 501 00:27:38,480 --> 00:27:40,880 Speaker 5: the best late breaking, Who's who was typically the best 502 00:27:40,960 --> 00:27:43,240 Speaker 5: on a tight track with limited overtaking opportunities, who was 503 00:27:43,240 --> 00:27:45,520 Speaker 5: the best overtaker, who was the best of all these traits. 504 00:27:45,720 --> 00:27:48,920 Speaker 5: You then apply those traits and rankings to different tracks 505 00:27:49,440 --> 00:27:52,000 Speaker 5: and different cars where you know different Some different cars 506 00:27:52,000 --> 00:27:54,040 Speaker 5: are better for a late breaker, some different cars are 507 00:27:54,040 --> 00:27:55,840 Speaker 5: better for a you know, on straights and so on. 508 00:27:55,920 --> 00:27:59,080 Speaker 5: So you can simulate, You could hypothetically allow fans to 509 00:27:59,119 --> 00:28:02,080 Speaker 5: simulate any scenario. You could say who's going to win 510 00:28:02,600 --> 00:28:07,239 Speaker 5: and Monaco on a nineteen eighty model car. You can 511 00:28:07,320 --> 00:28:09,840 Speaker 5: put a current driver in a nineteen eighty car equally, 512 00:28:09,880 --> 00:28:12,040 Speaker 5: So you can do all sorts of fun and simulations, 513 00:28:13,520 --> 00:28:14,680 Speaker 5: and that's just the beginning.