1 00:00:00,100 --> 00:00:02,033 Yeah, it's not a little different. 2 00:00:02,033 --> 00:00:05,066 You know, I think even if you took out 90% of the hype, 3 00:00:05,766 --> 00:00:09,266 you've got a capability that is 4 00:00:10,266 --> 00:00:12,566 twice as big as electricity. 5 00:00:12,566 --> 00:00:14,500 If you believe we can change the narrative. 6 00:00:14,500 --> 00:00:16,700 If you believe, we can change our communities. 7 00:00:16,700 --> 00:00:20,066 If you believe we can change the outcomes, then we can change the world. 8 00:00:20,800 --> 00:00:22,366 I'm Rob Richardson. 9 00:00:22,366 --> 00:00:24,266 Welcome to disruption. Now. 10 00:00:24,266 --> 00:00:25,466 Welcome to Disruption Now. 11 00:00:25,466 --> 00:00:27,700 I'm your host and moderator, Rob Richardson. 12 00:00:27,700 --> 00:00:29,666 As always, please like subscribe. 13 00:00:29,666 --> 00:00:30,966 We're here at 1819. 14 00:00:30,966 --> 00:00:34,033 We're honored to have them as a sponsor with me, a special guest, 15 00:00:34,200 --> 00:00:37,466 Pete Blackshaw, who is the founder of Brand Rank I. 16 00:00:37,566 --> 00:00:40,200 Also, he was the former leader of CentriFuze 17 00:00:40,200 --> 00:00:43,433 which is the central ecosystem here in Cincinnati. 18 00:00:43,700 --> 00:00:45,800 And before that, I think he did stuff with Nestle 19 00:00:45,800 --> 00:00:48,000 and was just doing things in digital innovation. 20 00:00:48,000 --> 00:00:50,366 So he's been innovating and disrupting for a while. 21 00:00:50,366 --> 00:00:52,100 Pete, great to have you shake. It have you? 22 00:00:52,100 --> 00:00:53,433 Yeah, I mean. 23 00:00:53,433 --> 00:00:57,866 So, Pete, you know, you this is your second startup, right? 24 00:00:58,966 --> 00:01:00,866 Like, what made you 25 00:01:00,866 --> 00:01:03,866 jump into the startup world again at this point? 26 00:01:04,733 --> 00:01:07,733 In both instances, you know, I was in a moment. 27 00:01:08,100 --> 00:01:08,366 Yeah. 28 00:01:08,366 --> 00:01:10,333 kind of an inflection point 29 00:01:10,333 --> 00:01:14,300 in the digital world where you just knew that a disruption. 30 00:01:14,366 --> 00:01:14,900 Yeah. 31 00:01:14,900 --> 00:01:17,733 You know, as you would put it, was upon us. 32 00:01:17,733 --> 00:01:19,933 And, you know, I struggled on the, 33 00:01:19,933 --> 00:01:23,866 I think, trying to convince myself that it was hype, shiny new object. 34 00:01:23,900 --> 00:01:26,900 And as I 35 00:01:27,166 --> 00:01:30,433 use the tool myself, which has been personally transformative, 36 00:01:30,700 --> 00:01:35,300 I concluded that this was the time to take the plunge. 37 00:01:35,300 --> 00:01:36,333 And it's a little bit scary. 38 00:01:36,333 --> 00:01:39,333 You know, my age, two kids going to college, but 39 00:01:39,866 --> 00:01:42,866 I concluded that I would never forgive myself if I didn't. 40 00:01:43,933 --> 00:01:45,766 You know, 41 00:01:45,766 --> 00:01:47,266 take a shot at it. Right. 42 00:01:47,266 --> 00:01:51,266 There's some I'm working on an idea that I'm particularly passionate about. 43 00:01:51,266 --> 00:01:54,566 So you can kind of align a historic moment with the personal passion. 44 00:01:54,866 --> 00:01:56,066 You know why not. 45 00:01:56,066 --> 00:01:58,933 And then the other thing I was just thinking about was 46 00:01:58,933 --> 00:02:02,800 I just think, you know, I expertise is going to be sell demand. 47 00:02:02,833 --> 00:02:06,566 There's no there's no downside if you jump into it 48 00:02:06,566 --> 00:02:09,000 because you're going to learn ten times faster in startup mode. 49 00:02:09,000 --> 00:02:11,566 You may come out with no cash. You may come out of failure. 50 00:02:11,566 --> 00:02:12,600 It happens technically. 51 00:02:12,600 --> 00:02:17,666 But there's going to be a lot of opportunities, post that experience. 52 00:02:17,666 --> 00:02:19,600 And so for me, it was an acceptable risk. 53 00:02:19,600 --> 00:02:22,566 And but I'm really excited about kind of shaping the space. 54 00:02:22,566 --> 00:02:26,066 So, hey, I've been around for a long time like, you know, so 55 00:02:26,066 --> 00:02:29,900 but it was obviously generative AI that really kind of sparked you. 56 00:02:30,700 --> 00:02:33,500 What do you see as this moment? 57 00:02:33,500 --> 00:02:36,866 Like, I know a lot of people always say this is going to change everything. 58 00:02:37,133 --> 00:02:39,133 And at many moments in time. 59 00:02:39,133 --> 00:02:40,966 And there's been a couple times where that's been true. 60 00:02:42,500 --> 00:02:43,833 but this time feels a little different. 61 00:02:43,833 --> 00:02:47,000 I want to get your perspective on how you think AI 62 00:02:47,000 --> 00:02:50,000 is going to shape and change business in our world in general. 63 00:02:50,200 --> 00:02:52,133 Yeah, it's not a little different. 64 00:02:52,133 --> 00:02:55,166 You know, I think even if you took out 90% of the hype, 65 00:02:55,866 --> 00:02:59,366 you've got a capability that is 66 00:03:00,366 --> 00:03:02,266 twice as big as electricity. 67 00:03:02,266 --> 00:03:04,466 And I often say this is as much twice. 68 00:03:04,466 --> 00:03:05,500 As big as electricity. 69 00:03:05,500 --> 00:03:07,733 Yeah, I really I really believe that. 70 00:03:07,733 --> 00:03:11,233 and I've often said that, you know, I think one of the biggest historic 71 00:03:11,233 --> 00:03:13,700 moments was a big print and printing press. Yeah. 72 00:03:13,700 --> 00:03:16,700 You know, all the knowledge was kind of stored in the hills with the monks, 73 00:03:16,700 --> 00:03:19,500 you know, and that liberated everything. 74 00:03:19,500 --> 00:03:21,366 It was almost like social media on steroids. 75 00:03:21,366 --> 00:03:22,166 Right. 76 00:03:22,166 --> 00:03:25,500 And, you know, this is, I mean, 77 00:03:25,500 --> 00:03:29,566 this is like intelligence that's highly accessible, right? 78 00:03:29,700 --> 00:03:32,700 It is iterating at a scary pace. 79 00:03:32,700 --> 00:03:35,700 so facet policy can't keep up with it. 80 00:03:36,033 --> 00:03:38,666 none of us can really kind of keep up with it. 81 00:03:38,666 --> 00:03:40,500 And we're not really sure where it's going to lead. 82 00:03:40,500 --> 00:03:43,833 And there's always a dark side of technology that we need to be on top of. 83 00:03:43,833 --> 00:03:47,733 That's why you and I are taking, a proactive and important lead 84 00:03:47,733 --> 00:03:50,133 on responsible AI. 85 00:03:50,133 --> 00:03:52,733 but yeah, this is absolutely huge. 86 00:03:52,733 --> 00:03:54,433 I mean, just think about it from a, 87 00:03:55,433 --> 00:03:57,466 you know, businesses are going to be transformed. 88 00:03:57,466 --> 00:04:02,533 there will, in fact be a lot of job dislocation. 89 00:04:02,533 --> 00:04:04,700 That doesn't mean net jobs will go down. 90 00:04:04,700 --> 00:04:07,000 But a nice way of saying job lost. 91 00:04:07,000 --> 00:04:09,600 Yeah, but but but I think as leaders 92 00:04:09,600 --> 00:04:13,933 we have to get really in front of what the workforce could look like. 93 00:04:13,933 --> 00:04:14,333 Yeah. 94 00:04:14,333 --> 00:04:18,533 You know, what does it mean to copilot with this new capability? 95 00:04:18,533 --> 00:04:21,433 And what does it mean to say bye bye to a certain industry? 96 00:04:21,433 --> 00:04:23,433 But hello to a new one. 97 00:04:23,433 --> 00:04:25,500 This is where universities play a really big role. 98 00:04:25,500 --> 00:04:28,566 Universities can also be part of the problem if they don't kind of get in. 99 00:04:28,866 --> 00:04:29,633 If they don't. Evolve. 100 00:04:29,633 --> 00:04:29,833 Yeah. 101 00:04:29,833 --> 00:04:35,033 That's why I think this kind of this island of the future 1819 digital futures 102 00:04:35,033 --> 00:04:38,400 is really, really important at this critical moment in time. 103 00:04:38,400 --> 00:04:41,200 But it's big. It's it's it's going to change everything. 104 00:04:41,200 --> 00:04:41,366 Yeah. 105 00:04:41,366 --> 00:04:45,866 I, I have I'm obviously pro innovation across the board. 106 00:04:45,866 --> 00:04:49,133 You know that of course I do have concerns because, 107 00:04:49,133 --> 00:04:52,766 I think this I think this is different than past technologies. 108 00:04:52,966 --> 00:04:55,600 So past technologies have been sometimes industry 109 00:04:56,666 --> 00:04:57,500 specific. 110 00:04:57,500 --> 00:05:02,033 We've had we've had very little data on, you know, general purpose technology. 111 00:05:02,066 --> 00:05:04,333 The internet was a general purpose technology. 112 00:05:04,333 --> 00:05:06,866 so that's one example. There's not many. 113 00:05:06,866 --> 00:05:11,933 I think AI is a general purpose technology where it's not getting rid of 114 00:05:12,466 --> 00:05:15,000 or modernizing one industry. 115 00:05:15,000 --> 00:05:17,433 It's not the Luddites where it's just like across the board. 116 00:05:17,433 --> 00:05:18,900 It's across the board. Right? 117 00:05:18,900 --> 00:05:22,300 It's like we've gotten to the point where we're think Mark Cuban said this. 118 00:05:22,300 --> 00:05:24,833 We're automating automation, 119 00:05:24,833 --> 00:05:27,266 and we don't know what that looks like in terms of the job, 120 00:05:27,266 --> 00:05:30,266 in terms of the workforce, in terms of what it looks like. 121 00:05:30,333 --> 00:05:33,133 It's a to your point, it's ever changing. 122 00:05:33,133 --> 00:05:37,566 And systems and, businesses and everyone else have to adapt quickly. 123 00:05:37,833 --> 00:05:42,066 So I like to hear from you like what is your what is your biggest concern. 124 00:05:42,533 --> 00:05:47,333 That business that people aren't realizing about AI real. 125 00:05:47,400 --> 00:05:49,400 And then we can talk about the biggest opportunity. Yeah. 126 00:05:49,400 --> 00:05:52,466 Well the the biggest concern is if people are going to kind of miss the boat 127 00:05:52,466 --> 00:05:56,100 and they're and when you're not kind of in front of it, 128 00:05:56,366 --> 00:06:00,066 you know, you get a little bit brash and reckless and maybe 129 00:06:00,066 --> 00:06:03,866 you, pushed the wrong policies out of fear and paranoia. 130 00:06:03,866 --> 00:06:06,866 And that's understandable because there's a lot of unknown. Yep. 131 00:06:08,433 --> 00:06:10,766 I've been since the very day it came out. 132 00:06:10,766 --> 00:06:15,900 I think this can cut in a couple different ways on the access and equity side. 133 00:06:15,900 --> 00:06:19,866 There's a part of me that believes that knows that this is a revolution in access. 134 00:06:20,233 --> 00:06:23,233 And so therefore, if we get ahead of the curve, we could 135 00:06:23,266 --> 00:06:27,033 redefine the rules of access for entrepreneurs. 136 00:06:27,333 --> 00:06:31,066 I've co-led workshops with minority small businesses where 137 00:06:31,566 --> 00:06:35,866 these tools have allowed these businesses, which have not had access 138 00:06:35,866 --> 00:06:40,033 to a lot of resources, tools and the like to kind of get a lot of them up front. 139 00:06:40,366 --> 00:06:43,366 The job issue is almost moot at the small business level. 140 00:06:43,366 --> 00:06:46,766 AI gets complicated at the big level. 141 00:06:46,766 --> 00:06:47,100 It gets 142 00:06:47,100 --> 00:06:51,466 complicated at the white collar level, it gets complicated at the service level. 143 00:06:51,466 --> 00:06:53,666 And that's one area that I think is disproportionately 144 00:06:53,666 --> 00:06:57,933 represented by minorities and that's where and we just got to get in front of it. 145 00:06:57,966 --> 00:06:59,866 Yeah. You know, and this is where I think leadership is. 146 00:06:59,866 --> 00:07:01,866 I agree with that. Important I. Think. 147 00:07:01,866 --> 00:07:05,600 And I would say I would give credit to the state of Ohio, 148 00:07:05,600 --> 00:07:07,233 kind of both sides of the aisle, I agree. 149 00:07:07,233 --> 00:07:10,566 Or, you know, thinking ahead again, I think that's why you're creating. 150 00:07:10,566 --> 00:07:12,700 These absolutely. Innovation hubs and the like. 151 00:07:12,700 --> 00:07:17,366 But we have to shape the dialog and we need to yeah, we need to. 152 00:07:17,366 --> 00:07:19,533 You want a little bit of paranoia right. 153 00:07:19,533 --> 00:07:22,366 I think that keeps people on guard. 154 00:07:22,366 --> 00:07:25,066 But we also have to help people see possibilities. 155 00:07:25,066 --> 00:07:26,233 I mean, what makes me 156 00:07:27,200 --> 00:07:27,700 different? 157 00:07:27,700 --> 00:07:28,400 Well, not different. 158 00:07:28,400 --> 00:07:30,766 I think you're I, I probably put you in the same camp. 159 00:07:30,766 --> 00:07:32,633 Yeah. You know, I'm an urgent optimist. Yeah. 160 00:07:32,633 --> 00:07:33,433 So I too. 161 00:07:33,433 --> 00:07:35,966 I see possibilities. I'm not a total cynic. 162 00:07:35,966 --> 00:07:39,100 I'm not on the dark side of Armageddon, you know? But. 163 00:07:40,400 --> 00:07:43,766 But I recognize that if you want the optimism, you have to get it. 164 00:07:44,033 --> 00:07:45,666 But the urgency of cannot you. 165 00:07:45,666 --> 00:07:48,000 And then this is what we're kind of doing right now. Yeah. 166 00:07:48,000 --> 00:07:50,966 What happens if we get it wrong? 167 00:07:50,966 --> 00:07:52,900 Listen, I think it's going to be. 168 00:07:52,900 --> 00:07:55,700 It'll probably change the political environment. 169 00:07:55,700 --> 00:07:59,333 You know, you've just got a lot of polar extremes 170 00:07:59,433 --> 00:08:02,566 in the marketplace and the political environment on both sides. 171 00:08:02,566 --> 00:08:07,600 And I think where there's fear, there's division and, where there's the unknown. 172 00:08:07,633 --> 00:08:10,433 and so, yeah, this could get really ugly. 173 00:08:10,433 --> 00:08:15,466 And, I think, I don't think we've, you know, we've dodged 174 00:08:15,466 --> 00:08:19,366 a few bullets with a couple of the strikes that have taken place. 175 00:08:19,400 --> 00:08:23,466 You know, I think the Hollywood thing landed, you know, it landed. 176 00:08:23,466 --> 00:08:24,066 You know? Yeah. 177 00:08:24,066 --> 00:08:28,800 You know, and I think they kind of found that the hybrid model, but, 178 00:08:29,133 --> 00:08:32,800 yeah, it could get there could be some industries that really, create 179 00:08:33,233 --> 00:08:38,266 a lot of, political instability and absolutely just got to get in front of it. 180 00:08:38,433 --> 00:08:42,233 And I think there's going to potentially there's some, you know, 181 00:08:42,233 --> 00:08:44,100 some racial issues. We're going 182 00:08:44,100 --> 00:08:47,866 to have to be really sensitive, like who is helped and who is not. 183 00:08:47,866 --> 00:08:48,600 And this is where we need 184 00:08:48,600 --> 00:08:52,333 to be really attentive to, you know, the different sectors. 185 00:08:52,366 --> 00:08:57,200 I thought McKinsey did a really good job about four months ago, looking at 186 00:08:57,233 --> 00:09:00,666 which groups will be impacted or not, but they also had a 187 00:09:01,333 --> 00:09:02,900 they kind of same thing we said earlier. 188 00:09:02,900 --> 00:09:05,833 Like if we're proactive when we lead that we can get in front of it. 189 00:09:05,833 --> 00:09:08,233 Yeah. so I, I agree with that completely. 190 00:09:08,233 --> 00:09:12,166 And I, we already have data on the issue, like, we, we know 191 00:09:13,066 --> 00:09:15,400 what, what algorithms do without 192 00:09:15,400 --> 00:09:18,566 any type of thought process guardrails. 193 00:09:18,866 --> 00:09:20,666 That's what social media became. 194 00:09:20,666 --> 00:09:22,833 And that's the that's how we got 195 00:09:23,800 --> 00:09:24,533 we these are 196 00:09:24,533 --> 00:09:27,600 part of our human nature for on is we have these part of our human nature. 197 00:09:27,600 --> 00:09:29,866 To be prejudiced against others is part of our human nature. 198 00:09:29,866 --> 00:09:33,666 To, de-emphasizing is sometimes to do things. 199 00:09:33,666 --> 00:09:36,666 These things it depends upon, 200 00:09:37,600 --> 00:09:39,466 what we want to emphasize and unfortunately. 201 00:09:39,466 --> 00:09:40,166 Right. 202 00:09:40,166 --> 00:09:44,200 You know, a lot of the algorithms focus on outrage and conspiracy. 203 00:09:44,500 --> 00:09:45,866 So those things tend to divide. 204 00:09:45,866 --> 00:09:47,366 And they do that not because they're trying 205 00:09:47,366 --> 00:09:50,800 to focus on those things, because their goal is singular. 206 00:09:51,366 --> 00:09:52,833 Is algorithms are rules. 207 00:09:52,833 --> 00:09:56,266 And all rules, are written in different ways. 208 00:09:56,266 --> 00:09:58,966 Correct. All rules manifest themselves in dial. 209 00:09:58,966 --> 00:10:02,066 That may be correct a little bit to the right or a little bit to the left. 210 00:10:02,066 --> 00:10:04,500 In fact, this is kind of the heart of what we're trying to measure. 211 00:10:04,500 --> 00:10:06,433 As part of my new startup. 212 00:10:06,433 --> 00:10:06,900 And we are 213 00:10:06,900 --> 00:10:11,666 when we just need to be hypersensitive to where we think the dial needs to. 214 00:10:11,733 --> 00:10:15,800 Yeah, to sit in a way that it, positively impacts, 215 00:10:15,833 --> 00:10:18,433 the economy, consumer. Just have to be aware, 216 00:10:18,433 --> 00:10:20,866 you have to be aware that there could be issues. 217 00:10:20,866 --> 00:10:21,700 Oh no question. 218 00:10:21,700 --> 00:10:22,033 Right. 219 00:10:22,033 --> 00:10:25,333 Like and that's what it gets to because they, it's it's algorithms are rules. 220 00:10:25,333 --> 00:10:29,366 But you know AI is also the maybe you're training on the data 221 00:10:29,800 --> 00:10:33,066 and it's it it's going to reflect what you input to it. 222 00:10:33,066 --> 00:10:34,800 This is which is why it's so important, 223 00:10:34,800 --> 00:10:36,566 which is why it's so important to be inclusive. 224 00:10:36,566 --> 00:10:41,433 So but tell us okay, but you entered into AI wanting to solve a specific problem. 225 00:10:41,433 --> 00:10:43,566 What? What got you up at night? 226 00:10:43,566 --> 00:10:47,366 What problem are you trying to solve within with using AI as a tool? 227 00:10:47,366 --> 00:10:50,366 Because I tell people it's not the technology is the problem you're solving. 228 00:10:50,666 --> 00:10:52,533 So what problem are you solving? 229 00:10:52,533 --> 00:10:54,266 Well, let me take a step back. So I 230 00:10:55,666 --> 00:10:56,133 came to 231 00:10:56,133 --> 00:10:59,966 Cincinnati after eight years in Switzerland, leading 232 00:11:00,500 --> 00:11:05,666 digital work for NASA, and had a chance to, go all over the world and really kind 233 00:11:05,666 --> 00:11:09,066 of see the digital revolution sure place in different ways. 234 00:11:09,066 --> 00:11:12,066 And so I came back to Cincinnati to lead 235 00:11:12,300 --> 00:11:15,366 this startup accelerator 236 00:11:16,033 --> 00:11:20,066 under funds to really kind of grow the local and economy. 237 00:11:21,200 --> 00:11:25,566 And in the process of trying to figure out, like where 238 00:11:26,433 --> 00:11:32,133 we could lead a big believer that internet economies where there's a concentration 239 00:11:32,133 --> 00:11:36,166 of talent, technology and capital, yeah, have kind of an outsize advantage. 240 00:11:36,166 --> 00:11:40,000 And so the areas that have been not just keeping me up at night, 241 00:11:40,000 --> 00:11:41,766 but actually making me want to go to work. 242 00:11:41,766 --> 00:11:42,066 Yeah. 243 00:11:42,066 --> 00:11:44,700 Or areas like sustainability. Sure. 244 00:11:44,700 --> 00:11:47,166 Or resilience supply chains. 245 00:11:47,166 --> 00:11:49,833 That was a big theme that came out of Covid. 246 00:11:49,833 --> 00:11:54,433 a lot of things on the health front, but and then and then 247 00:11:54,766 --> 00:11:58,866 as the AI stuff started to develop, especially in the last 18 months, 248 00:11:59,400 --> 00:12:02,766 thinking a lot about the power of responsible AI, 249 00:12:02,766 --> 00:12:07,566 could a region that has really good consumer credentials. 250 00:12:07,600 --> 00:12:10,766 Again, you got the world's largest advertiser here, the world's third largest 251 00:12:10,766 --> 00:12:13,800 retailer here, and the whole ecosystem that supports that. 252 00:12:14,366 --> 00:12:17,100 So could we leverage 253 00:12:17,100 --> 00:12:20,266 those consumer chops credentials to 254 00:12:21,433 --> 00:12:23,700 get in front of these very complicated issues? 255 00:12:23,700 --> 00:12:27,633 And moreover, could we cultivate a lot of entrepreneurs 256 00:12:27,633 --> 00:12:30,633 that would want to be the problem solvers or the heroes in that area? 257 00:12:31,433 --> 00:12:35,366 And I found myself as a pretty loud cheerleader. 258 00:12:35,366 --> 00:12:36,333 Let's do it. 259 00:12:36,333 --> 00:12:39,533 And then and then I kind of got to that moment of truth. 260 00:12:39,533 --> 00:12:42,533 And I just said, I'm just going to do it myself, 261 00:12:42,566 --> 00:12:45,566 you know, give up this really big salary and, 262 00:12:45,666 --> 00:12:49,633 be part of the solution and is a whole nother element of wealth creation. 263 00:12:49,633 --> 00:12:51,100 If you're successful. 264 00:12:51,100 --> 00:12:54,866 And, but and what I've been thinking about, you know, 265 00:12:54,866 --> 00:12:58,000 the areas that I get excited about is really the collision of 266 00:12:59,133 --> 00:13:01,200 AI and sustainability. 267 00:13:01,200 --> 00:13:06,133 So I actually think I'm a, I, I do lose sleep about the world, 268 00:13:06,133 --> 00:13:09,133 not kind of keeping up with its, its carbon commitments. 269 00:13:09,333 --> 00:13:13,866 And I think AI is a huge, opportunity to kind of move that ahead. 270 00:13:13,866 --> 00:13:17,066 And some of my inspiration has come from local companies like 80 acres, 271 00:13:17,066 --> 00:13:20,700 where they use 99% less water because of the way they use 272 00:13:20,700 --> 00:13:24,833 artificial intelligence, you know, and so there's some inspiration. 273 00:13:24,833 --> 00:13:29,466 So I think as we get smarter, the use of AI, AI plus sensors, 274 00:13:29,466 --> 00:13:32,633 I mean, we could dramatically change the green landscape. 275 00:13:32,633 --> 00:13:35,900 And I would like to see it cultivated, you know, here in our backyard. 276 00:13:36,700 --> 00:13:36,933 Yeah. 277 00:13:36,933 --> 00:13:39,933 So it sounds like you were working to be an entrepreneur, 278 00:13:40,166 --> 00:13:42,933 but, like, you decided the best way to do that. 279 00:13:42,933 --> 00:13:45,733 You had to go. Actually, I've always I've always kind of worked. 280 00:13:45,733 --> 00:13:46,666 And I was thinking about this the other day. 281 00:13:46,666 --> 00:13:49,666 I've always worked in like, five year cycles, so I was always get 282 00:13:49,700 --> 00:13:53,133 everybody thinks as a P&G veteran, I was only there for like 5 or 6 years. 283 00:13:53,133 --> 00:13:56,766 And then I, you know, after we got interactive Marketer the year 284 00:13:56,766 --> 00:14:00,066 I left to do a startup and then I did the startup was a bit of a slog. 285 00:14:00,066 --> 00:14:00,666 It took us a while 286 00:14:00,666 --> 00:14:03,866 before we got sold to Nielsen, but then I went to Nestlé and then I went. 287 00:14:03,866 --> 00:14:05,333 Startups are usually a bit of a thing. 288 00:14:05,333 --> 00:14:08,666 Is just kind of a hybrid between big company and startup. 289 00:14:08,666 --> 00:14:11,166 There are elements of that. We're kind of pretty big and bureaucratic. 290 00:14:11,166 --> 00:14:13,366 The other parts that we're kind of startup nimble. Yeah. 291 00:14:13,366 --> 00:14:14,133 And now I feel. 292 00:14:14,133 --> 00:14:16,766 Very like I those probably sounds, but it feels very corporate some time. 293 00:14:16,766 --> 00:14:17,933 But go ahead. That's my opinion. 294 00:14:17,933 --> 00:14:19,966 Yeah. I mean it sparks a big debate. 295 00:14:19,966 --> 00:14:22,000 I mean, listen, we have a store front for services, 296 00:14:22,000 --> 00:14:25,300 but we also work with corporates and we're trying to figure out how to, 297 00:14:26,500 --> 00:14:26,733 you know, 298 00:14:26,733 --> 00:14:30,600 how to unlock that startup corporate connection. 299 00:14:30,600 --> 00:14:33,266 And it's a lot. Of hard challenges. 300 00:14:33,266 --> 00:14:34,366 Yeah I know and it's hard. 301 00:14:34,366 --> 00:14:35,966 Because there's different cultures. 302 00:14:35,966 --> 00:14:36,866 Yeah. Exactly. 303 00:14:36,866 --> 00:14:38,933 Yeah. Now what's different today. 304 00:14:38,933 --> 00:14:40,900 It's like again you have to exploit the moment. 305 00:14:40,900 --> 00:14:44,133 There's always moments where the organization need to be. 306 00:14:44,133 --> 00:14:45,266 It needs to be stimulated. 307 00:14:45,266 --> 00:14:48,433 And I think AI is a moment where startups might have a chance 308 00:14:48,433 --> 00:14:49,633 to kind of get in the door, 309 00:14:49,633 --> 00:14:53,766 because there's a lot the big cows don't really fully understand. 310 00:14:54,500 --> 00:14:56,900 And I'm hoping that I'll be one of those folks that does 311 00:14:56,900 --> 00:14:58,900 get in the door, because I'm bringing an outside 312 00:14:58,900 --> 00:15:02,866 in perspective on a very, very fast evolving, world. 313 00:15:02,866 --> 00:15:05,700 It's just very, very hard to move fast in an organization. 314 00:15:05,700 --> 00:15:08,900 And organizations are really good at like bottling up their learning 315 00:15:08,900 --> 00:15:11,900 and everything about disseminating and executing against the learning. 316 00:15:11,933 --> 00:15:13,733 But now in the world, generative AI, 317 00:15:13,733 --> 00:15:16,733 you got a whole different playbook that's being developed. Yep. 318 00:15:16,800 --> 00:15:19,400 So real brand rank. yeah. 319 00:15:19,400 --> 00:15:20,633 Trade this. Like what? 320 00:15:20,633 --> 00:15:24,500 Again, I can go back to that question like what is brand rank seeking to do? 321 00:15:25,333 --> 00:15:27,700 So I'll tell you all great ideas. 322 00:15:27,700 --> 00:15:29,733 Start with a big insight. Yes. 323 00:15:29,733 --> 00:15:31,466 And it's like that. 324 00:15:31,466 --> 00:15:34,066 Oh. Like moment. 325 00:15:34,066 --> 00:15:37,066 And for me, I was like 326 00:15:37,066 --> 00:15:40,266 you and everybody just playing around with generative AI. 327 00:15:41,366 --> 00:15:42,266 And I started to do 328 00:15:42,266 --> 00:15:45,266 searches on sustainability. 329 00:15:45,366 --> 00:15:49,466 And I would ask questions like, are campus diapers sustainable 330 00:15:49,933 --> 00:15:53,766 or is Unilever's Dove brand sustainable? 331 00:15:54,500 --> 00:15:57,500 And I was shocked how 332 00:15:58,166 --> 00:16:01,466 granular and size the responses were. 333 00:16:02,533 --> 00:16:06,433 And in some cases, some of that data would come from the brand websites. 334 00:16:06,433 --> 00:16:08,600 But, you know, generative AI is a weird way. 335 00:16:08,600 --> 00:16:12,233 Imagine like a blender that just kind of takes best available knowledge, 336 00:16:12,233 --> 00:16:15,400 puts it in a blender, and then pours it in a mold that you then access. 337 00:16:15,400 --> 00:16:16,566 Absolutely. Yeah. Okay. Yeah. 338 00:16:16,566 --> 00:16:19,100 And that that's a really good metaphor doesn't really change. 339 00:16:19,100 --> 00:16:19,933 Now you can 340 00:16:19,933 --> 00:16:22,900 you can change the how you can say, give it to me as a Jay-Z rap 341 00:16:22,900 --> 00:16:25,400 or give it to me as haiku or to me as a sonnet. 342 00:16:25,400 --> 00:16:26,966 But the what? 343 00:16:26,966 --> 00:16:29,100 It's like one shot. What? Yeah. 344 00:16:29,100 --> 00:16:32,666 And and I thought to myself, and it was funny because I typed in 345 00:16:33,300 --> 00:16:34,266 our Pampers sustainable. 346 00:16:34,266 --> 00:16:38,766 And it was interesting because they said they're trying but they're not. 347 00:16:38,766 --> 00:16:40,966 And here's the reasons and here are some alternatives. 348 00:16:40,966 --> 00:16:44,500 And I thought, wow, that's like that's like that's like implicating for brands. 349 00:16:44,600 --> 00:16:46,266 Yeah. That's like a whole different level. 350 00:16:46,266 --> 00:16:48,766 So I just thought so that the light bulb went off. 351 00:16:48,766 --> 00:16:51,833 I thought, you know, maybe there's a business model in creating kind 352 00:16:51,833 --> 00:16:55,866 of a Nielsen ratings of AI search results, helping 353 00:16:55,866 --> 00:16:59,866 brand builders understand where they stand in this world. 354 00:17:00,233 --> 00:17:05,133 And then could you use the, I'm a really passion about digital marketing, right? 355 00:17:05,133 --> 00:17:08,133 I kind of missed the role of being chief digital officer. 356 00:17:08,166 --> 00:17:09,300 You know. You do that well. 357 00:17:09,300 --> 00:17:09,866 Yeah. Yeah. 358 00:17:09,866 --> 00:17:12,266 But but I think that's at the forefront of change. 359 00:17:12,266 --> 00:17:13,633 And what I've noticed 360 00:17:13,633 --> 00:17:17,566 is that a lot of brands have gotten really lazy about not lazy. 361 00:17:17,566 --> 00:17:20,966 It's just I don't think they are even aware that they're not 362 00:17:21,466 --> 00:17:25,233 serving consumers at the level that they should in this environment. 363 00:17:25,233 --> 00:17:27,966 So for example, like what? What is. Stagnant? 364 00:17:27,966 --> 00:17:29,066 What is what is generative? 365 00:17:29,066 --> 00:17:32,933 I really done it's raised the bar for giving you answers 366 00:17:33,600 --> 00:17:35,600 with minimal friction, correct. 367 00:17:35,600 --> 00:17:40,066 And with shocking levels of detail and a willingness to continue the dialog. 368 00:17:40,066 --> 00:17:41,900 Yeah, right. I mean, you can just keep going. 369 00:17:41,900 --> 00:17:42,466 You can. 370 00:17:42,466 --> 00:17:45,633 And it feels like you're talking to a really smart best friend. 371 00:17:46,300 --> 00:17:48,633 And brands are really bad at that. Yeah. 372 00:17:48,633 --> 00:17:53,500 I mean, you can go to most brands from you know, whether it's Unilever. 373 00:17:53,900 --> 00:17:56,266 I mean, there's a bunch of them. I get names. 374 00:17:56,266 --> 00:17:59,633 I don't piss anybody off locally, but but yeah, if you go into their, 375 00:18:00,133 --> 00:18:02,666 their FAQs or their brand sites, you know, you're 376 00:18:02,666 --> 00:18:04,766 not going to get a lot of information very, very little. 377 00:18:04,766 --> 00:18:08,433 So you're more likely say I'm going to learn about Pampers on generative AI. 378 00:18:08,433 --> 00:18:09,266 The problem with that 379 00:18:09,266 --> 00:18:13,666 is that that narrative often is counter to the brand's desired narrative. 380 00:18:13,700 --> 00:18:18,233 So what we're doing is we're kind of metering it and we've run thousands 381 00:18:18,233 --> 00:18:24,233 of searches, to really help brands understand, you know, how they stack up. 382 00:18:24,233 --> 00:18:26,833 And then we're benchmarking it versus competition, 383 00:18:26,833 --> 00:18:29,300 and then we're putting a lot of consulting on top of it. 384 00:18:29,300 --> 00:18:32,433 Like it's not enough to just say, we metered your brand. 385 00:18:32,433 --> 00:18:34,166 It's like, here's what you need to do. 386 00:18:34,166 --> 00:18:36,500 And this gets tricky. Like to really win. 387 00:18:36,500 --> 00:18:39,500 In a world of generative AI, you have to market to algorithms. 388 00:18:39,733 --> 00:18:41,733 You have to understand how. 389 00:18:41,733 --> 00:18:43,166 How do you market the algorithm. 390 00:18:43,166 --> 00:18:44,400 Well this is important. 391 00:18:44,400 --> 00:18:45,300 So we just put out 392 00:18:45,300 --> 00:18:49,200 a white paper called Brand Vulnerability in the age of AI search. 393 00:18:49,200 --> 00:18:52,066 And there's something that's called algorithmic anchors okay. 394 00:18:52,066 --> 00:18:55,033 And so has some similar areas of search 1.0. 395 00:18:55,033 --> 00:18:58,566 But in generative AI there are clear anchors 396 00:18:58,566 --> 00:19:01,966 that disproportionately influence what is being said about you. 397 00:19:01,966 --> 00:19:04,100 Okay. So I'm. Looking at Rob Richardson. Okay. 398 00:19:04,100 --> 00:19:04,933 It's probably going 399 00:19:04,933 --> 00:19:08,200 to be one of your algorithmic anchors is probably going to be you see content. 400 00:19:08,766 --> 00:19:09,400 Okay. Yeah. 401 00:19:09,400 --> 00:19:11,500 The legacy of leadership here. 402 00:19:11,500 --> 00:19:13,200 It's shaped your brand. 403 00:19:13,200 --> 00:19:16,466 And I would say what you see is on a scale of 404 00:19:16,466 --> 00:19:19,633 1 to 10, I probably give them a 7 or 8 in terms of marketing algorithms. 405 00:19:19,633 --> 00:19:23,633 So your content has a good chance of getting to those algorithms. 406 00:19:23,633 --> 00:19:24,366 Okay. 407 00:19:24,366 --> 00:19:27,166 Brand websites are very, very if they're done 408 00:19:27,166 --> 00:19:30,833 right, right are very important algorithmic anchors. 409 00:19:30,833 --> 00:19:33,500 But it's not just the websites, it's how you build it. 410 00:19:33,500 --> 00:19:35,833 So for example, I was meeting with someone from, 411 00:19:36,866 --> 00:19:38,000 Kroger the other day. 412 00:19:38,000 --> 00:19:38,466 Okay. 413 00:19:38,466 --> 00:19:41,366 And Kroger's actually doing some pretty good work on 414 00:19:41,366 --> 00:19:44,400 zero waste, zero hunger. 415 00:19:44,400 --> 00:19:47,633 I really I, I've read all their reports, but I noticed 416 00:19:47,633 --> 00:19:50,733 they weren't getting enough credit in like the AI algorithms. 417 00:19:50,733 --> 00:19:54,500 And I suddenly dawned on me that they put most of their best data in PDFs. 418 00:19:54,933 --> 00:19:55,766 Oh, well, that'll do it. 419 00:19:55,766 --> 00:19:56,400 Okay. 420 00:19:56,400 --> 00:19:56,966 And so, you know, 421 00:19:56,966 --> 00:20:00,500 it's not a, you know, like a fake engine to get it's like in a PDF. 422 00:20:00,500 --> 00:20:01,466 And I'm thinking, oh my gosh. 423 00:20:01,466 --> 00:20:04,766 They're like they're only getting a fraction of the ROI on all that work. 424 00:20:04,766 --> 00:20:05,966 Think of all that work. 425 00:20:05,966 --> 00:20:07,500 Think of all the Rodney's speeches. 426 00:20:07,500 --> 00:20:09,700 Think about all those hundreds of employees 427 00:20:09,700 --> 00:20:13,166 that have kind of toiled to kind of get those statistics on the table. 428 00:20:13,166 --> 00:20:16,966 And then when you got to the last mile of helping the consumer to understand it, 429 00:20:17,066 --> 00:20:20,333 you put it in a file that the algorithms don't understand. 430 00:20:20,333 --> 00:20:21,433 And so I think, 431 00:20:21,433 --> 00:20:23,066 you know, we're almost trying to figure out 432 00:20:23,066 --> 00:20:25,533 scorecards that make it easy for brands to see, 433 00:20:25,533 --> 00:20:28,033 this is what I need to do to get full credit. Okay. 434 00:20:28,033 --> 00:20:31,866 Now, in some cases, you won't get full credit because you're not sustainable. 435 00:20:32,200 --> 00:20:34,100 You might be dishonest, you know? 436 00:20:34,100 --> 00:20:37,566 And again, the algorithms will go to town on you and that respect. 437 00:20:37,566 --> 00:20:38,700 But I think generally. 438 00:20:38,700 --> 00:20:40,400 Speaking, how are they going to know you if you're dishonest? 439 00:20:40,400 --> 00:20:41,266 Like, how would they. 440 00:20:41,266 --> 00:20:43,500 Oh, well, because they're looking at a lot of sources. 441 00:20:43,500 --> 00:20:44,066 I mean, I'd say 442 00:20:44,066 --> 00:20:47,900 the brand websites are clearly the most the biggest cheerleaders for their. 443 00:20:47,966 --> 00:20:49,133 Well, they're. Biased. They say, okay. 444 00:20:49,133 --> 00:20:50,433 Right. Yeah. I mean. 445 00:20:50,433 --> 00:20:52,166 It's an advertising. Vehicle. Exactly. 446 00:20:52,166 --> 00:20:57,266 But I mean, why is the New York Times suing OpenAI for 20, $40 billion? 447 00:20:57,600 --> 00:20:57,866 Yeah. 448 00:20:57,866 --> 00:21:01,333 It's taken they know that they're source of truth. 449 00:21:01,500 --> 00:21:03,966 This is being thrown into the blender. 450 00:21:03,966 --> 00:21:07,200 It's very hard for people to see the attribution, but they know 451 00:21:07,800 --> 00:21:12,033 it is weaving in old New York Times articles are pretty harsh about brand. 452 00:21:12,033 --> 00:21:13,900 I mean, they just kind of speak the truth, right? Right. 453 00:21:13,900 --> 00:21:16,500 And there's other sites. It could be Wikipedia. 454 00:21:16,500 --> 00:21:20,066 Google just spent, what, $40 million to license read it. 455 00:21:20,366 --> 00:21:22,366 Yeah. So that stuff's getting weaved in. 456 00:21:22,366 --> 00:21:25,366 Ratings and reviews are creeping into it as well. So. 457 00:21:25,466 --> 00:21:29,800 So one thing that we're, working on, and I can't get too specific, 458 00:21:29,800 --> 00:21:32,766 but I will say that this institution is helping our thinking. 459 00:21:32,766 --> 00:21:33,666 Right. A lot 460 00:21:34,633 --> 00:21:36,066 is really 461 00:21:36,066 --> 00:21:39,866 trying to develop some science around those sources of attribution, 462 00:21:40,100 --> 00:21:43,300 like what are those anchors and how do you get really precise? 463 00:21:43,300 --> 00:21:47,033 And I would love for, Greater 464 00:21:47,033 --> 00:21:51,266 Cincinnati to be a true hub for search analytics, AI, search analytics. 465 00:21:51,266 --> 00:21:54,633 And it's going to take some work, but I think we can figure it out. 466 00:21:54,633 --> 00:21:57,133 And I think the universities are a big are a big part of that. 467 00:21:57,133 --> 00:21:59,666 But I think every brand needs to understand 468 00:21:59,666 --> 00:22:01,566 what is disproportionately impacting. 469 00:22:01,566 --> 00:22:03,100 Their their search results. 470 00:22:03,100 --> 00:22:07,033 What would you say is your top three advice just 1 to 1 of the top three things 471 00:22:07,033 --> 00:22:12,566 brands need to do in the age of algorithms to protect their brand identity. 472 00:22:12,566 --> 00:22:14,233 Some of it is old fashioned guidance. 473 00:22:14,233 --> 00:22:17,666 I mean this you got to listen and these are different signals. 474 00:22:18,266 --> 00:22:21,333 So in the same way that you would listen to a consumer in a focus group 475 00:22:21,333 --> 00:22:24,666 or listen to website data or listen to social media conversation, 476 00:22:24,666 --> 00:22:28,233 you got to listen to these new search dynamics. 477 00:22:28,266 --> 00:22:28,566 Yeah. 478 00:22:28,566 --> 00:22:31,633 And that's we're kind of providing a tool that helps them listen. 479 00:22:32,200 --> 00:22:34,500 And then they need to they need to act on it. 480 00:22:34,500 --> 00:22:36,033 I mean they need to disrupt. 481 00:22:36,033 --> 00:22:36,266 Yeah. 482 00:22:36,266 --> 00:22:39,566 And and I mean we're trying to provide a data set that makes it easier to disrupt. 483 00:22:39,900 --> 00:22:42,866 In most cases, data leads you to incrementalism. 484 00:22:42,866 --> 00:22:43,266 Exactly. 485 00:22:43,266 --> 00:22:45,566 We're kind of saying you've got a long way to go. 486 00:22:45,566 --> 00:22:47,033 So brands need to move faster. 487 00:22:47,033 --> 00:22:48,033 Your website sucks. 488 00:22:48,033 --> 00:22:50,433 It doesn't. You know, the websites don't pay attention. 489 00:22:50,433 --> 00:22:54,900 The algorithms don't pay attention to it or you know, and yeah, so we're trying to 490 00:22:55,266 --> 00:22:58,566 and then and then you got to follow up and you got to be really iterative. 491 00:22:58,566 --> 00:23:01,966 Like like I would say, you know, if you look at our web, our, 492 00:23:02,066 --> 00:23:07,066 our online site, we say today brands need to think and act like algorithms. 493 00:23:07,533 --> 00:23:11,233 It doesn't mean we're all going to become bots, but we have to understand 494 00:23:11,666 --> 00:23:14,933 how these our whole future is going to get dictated by this stuff. 495 00:23:15,166 --> 00:23:17,333 But we can get in front of it, we can partner with it. 496 00:23:17,333 --> 00:23:18,900 But we got to understand what it is. 497 00:23:18,900 --> 00:23:20,866 And I think that's critical for everyone. 498 00:23:22,066 --> 00:23:22,666 Tough question here. 499 00:23:22,666 --> 00:23:25,333 What happens is the algorithms know us better than we know ourselves. 500 00:23:25,333 --> 00:23:28,666 This is we're going to well they will they will I mean, you know 501 00:23:28,800 --> 00:23:32,666 next year you're probably going to be interviewing yourself as a joke, right? 502 00:23:32,900 --> 00:23:34,766 You know two faces of Rob. 503 00:23:34,766 --> 00:23:36,500 That'll be interesting. Now the interest. 504 00:23:36,500 --> 00:23:36,700 Yeah. 505 00:23:36,700 --> 00:23:39,700 That might be, so 506 00:23:39,866 --> 00:23:40,466 I don't know. 507 00:23:40,466 --> 00:23:41,966 It's going to get really tricky. 508 00:23:41,966 --> 00:23:46,433 And we're going to have to really reboot 509 00:23:48,266 --> 00:23:49,033 not only brands. 510 00:23:49,033 --> 00:23:51,266 We got to reboot training. Yep. 511 00:23:51,266 --> 00:23:52,266 We're going to have to. 512 00:23:52,266 --> 00:23:55,366 I personally think gearing up to shake up the universities to think differently. 513 00:23:55,366 --> 00:23:55,600 Okay. 514 00:23:55,600 --> 00:23:58,633 Through all of it, I mean, think about it like with generative, 515 00:23:58,666 --> 00:24:00,566 I got Socrates in my pocket. Yeah. 516 00:24:00,566 --> 00:24:02,900 I mean, it's like an incredible learning experience. 517 00:24:02,900 --> 00:24:04,033 It teaches math. 518 00:24:04,033 --> 00:24:05,800 It never forgets anything. Yeah. 519 00:24:05,800 --> 00:24:09,800 It's like and so the potential for personalized education is huge, 520 00:24:09,800 --> 00:24:12,233 but there's gonna have to be give and take at the university level, right? 521 00:24:12,233 --> 00:24:13,766 Yeah, absolutely. I mean. It's going to be. Tricky. 522 00:24:13,766 --> 00:24:15,566 It isn't like how. Will Neville Pinto lead? 523 00:24:15,566 --> 00:24:17,700 I mean, I you know, I've even wrote a letter to him once. 524 00:24:17,700 --> 00:24:22,000 I said, dude, like this is your this will shape your legacy more than anything. 525 00:24:22,000 --> 00:24:22,466 Full stop. 526 00:24:22,466 --> 00:24:26,400 I'd say the same thing that John Mueller at PNG or Rodney at Kroger. 527 00:24:26,400 --> 00:24:29,533 This is like how you manage this is critical I agree. 528 00:24:29,566 --> 00:24:30,066 You know. 529 00:24:30,066 --> 00:24:32,066 And if you know that there's going to be, 530 00:24:32,066 --> 00:24:34,600 you know, job shakeups, how do you get in front of it? 531 00:24:34,600 --> 00:24:36,166 How do you start thinking about the new jobs? 532 00:24:36,166 --> 00:24:38,033 How do you manage expectations? 533 00:24:38,033 --> 00:24:39,533 How do you turn it into a positive? 534 00:24:39,533 --> 00:24:42,533 And it sounds romantic, but it's going to be tricky. 535 00:24:42,633 --> 00:24:43,733 It's going to be really, really tricky. 536 00:24:43,733 --> 00:24:46,800 It's, you know, before we go on to, to our talk to our other points, there's 537 00:24:47,200 --> 00:24:51,300 one line I read from you about customer service, about customer satisfaction. 538 00:24:51,300 --> 00:24:54,100 It's very important, I think, even more with algorithms, 539 00:24:54,100 --> 00:24:57,566 because I think you said this is that if you have one successful customer, 540 00:24:57,566 --> 00:25:01,200 they tell three people, if you have one bad customer, they sell 3000. 541 00:25:01,666 --> 00:25:04,233 And now algorithm. That was. My book, right? 542 00:25:04,233 --> 00:25:05,133 Right. Yes. That's your book. 543 00:25:05,133 --> 00:25:08,066 Yeah I see and algorithms. Now you can add to that. 544 00:25:08,066 --> 00:25:10,233 You give one good customer. Maybe you'll get six. 545 00:25:10,233 --> 00:25:14,500 You give one bad customer and you get 3 million because information 546 00:25:14,500 --> 00:25:16,966 can really just iterate on itself much faster. 547 00:25:16,966 --> 00:25:20,166 Well, I think the key Rob, is it 548 00:25:21,166 --> 00:25:21,633 you need to 549 00:25:21,633 --> 00:25:24,633 understand how to balance automation and intimacy. 550 00:25:25,033 --> 00:25:25,700 oh. That's good. 551 00:25:25,700 --> 00:25:27,600 Okay. What the important. So I'll say more like. 552 00:25:27,600 --> 00:25:31,566 So I lead, when I was running digital at Nestlé, 553 00:25:31,633 --> 00:25:33,333 you know, one of my number one 554 00:25:33,333 --> 00:25:37,433 initiatives was implementing Service Cloud across all the markets. 555 00:25:37,666 --> 00:25:42,600 You know, and the market I most admired was in China, 556 00:25:43,200 --> 00:25:48,566 and it was the, I think it was the Wyeth nutrition brand. 557 00:25:48,566 --> 00:25:51,333 And I see when I go to China, I would just spend time in the call center. 558 00:25:51,333 --> 00:25:51,566 Yeah. 559 00:25:51,566 --> 00:25:55,300 And it was interesting because they were really good at 560 00:25:57,000 --> 00:25:59,366 balancing Artemis, automation and intimacy. 561 00:25:59,366 --> 00:25:59,666 In fact, 562 00:25:59,666 --> 00:26:03,766 they were very good at converting, like, moms to e-commerce opportunities. 563 00:26:03,766 --> 00:26:06,833 And I use WeChat, which is like their version of. 564 00:26:07,166 --> 00:26:07,966 Facebook. 565 00:26:07,966 --> 00:26:11,233 Instagram, and, and they would kind of 566 00:26:11,233 --> 00:26:14,900 do it at scale and they money and they wanted to automate a lot of it. 567 00:26:14,900 --> 00:26:17,666 But other times, if they knew there was an upsell opportunity, 568 00:26:17,666 --> 00:26:20,666 they would, you know, they would have folks, you know, 569 00:26:20,666 --> 00:26:24,433 doing the chat and kind of work it and then or if a mom had a complaint 570 00:26:24,466 --> 00:26:28,666 about their child, then you automatically defaulted to a human. 571 00:26:29,100 --> 00:26:29,866 And so I think there's 572 00:26:29,866 --> 00:26:33,366 just a balancing act on how do you get the benefits of automation. 573 00:26:33,666 --> 00:26:35,400 And I think some of that could get really good. 574 00:26:35,400 --> 00:26:38,766 I mean, if you can automate a friendly bot or a boy 575 00:26:38,766 --> 00:26:41,766 and they're just answering questions, but with the 576 00:26:41,833 --> 00:26:44,333 with the happy face, I think that can be a big benefit. 577 00:26:44,333 --> 00:26:46,133 But there's anything that's on, 578 00:26:46,133 --> 00:26:51,400 the negative side or the sensitive side or the product has poison or whatever, 579 00:26:51,400 --> 00:26:53,033 then you want to immediately go to a human. 580 00:26:53,033 --> 00:26:55,066 And I think it's going to be really important or. 581 00:26:55,066 --> 00:26:57,000 Helpful for lots of other stuff. Yep. Yeah. 582 00:26:57,000 --> 00:27:01,166 Like so and I so I, I don't think people should over romanticize 100% automation. 583 00:27:01,166 --> 00:27:05,066 I think you just need to find the right balance to do it right. 584 00:27:05,066 --> 00:27:06,533 And it's the same thing with the 585 00:27:06,533 --> 00:27:10,266 certain things we like on the airline, where we can kind of get automatic alerts 586 00:27:10,266 --> 00:27:13,500 and other cases just get on the damn phone, for crying out loud. 587 00:27:13,633 --> 00:27:14,300 And they know. 588 00:27:14,300 --> 00:27:15,533 And there's a calculus 589 00:27:15,533 --> 00:27:19,166 that says if they completely ignore Pete when he really needs us, 590 00:27:19,366 --> 00:27:21,600 he's going to go to he's going to go from Delta to American. 591 00:27:21,600 --> 00:27:23,366 Exactly. You know, so. 592 00:27:23,366 --> 00:27:27,433 Now, what would you tell young people nowadays in this in this state, 593 00:27:27,433 --> 00:27:30,466 there's been more tech layoffs, I think, than we've ever seen since the 594 00:27:31,133 --> 00:27:33,266 you and I went through the, the ourselves. 595 00:27:33,266 --> 00:27:38,433 But the.com, burst and bubble, there's, there's a lot of tech layoffs. 596 00:27:38,433 --> 00:27:40,233 It's a, it's a it's a very shaky time for people 597 00:27:40,233 --> 00:27:42,766 that are in tech, that have been in careers 598 00:27:42,766 --> 00:27:45,133 that have always been thought to be untouchable. 599 00:27:45,133 --> 00:27:47,200 Right. Yeah. Oh big time. 600 00:27:47,200 --> 00:27:51,000 What do you say to, to, to those emerging in their career at this time? 601 00:27:51,000 --> 00:27:53,333 Like, what advice would you give them at this moment? 602 00:27:53,333 --> 00:27:55,733 I mean, number one, just eyes wide open. Yeah. 603 00:27:55,733 --> 00:27:58,766 And it's funny, my daughters, an incredible 604 00:27:59,166 --> 00:28:00,866 one of my daughters is this incredible artist. 605 00:28:00,866 --> 00:28:02,333 And when you're talking about she's only a sophomore 606 00:28:02,333 --> 00:28:06,666 and we were talking about real college and, and I was actually rattling 607 00:28:06,666 --> 00:28:09,666 off a number of design schools, including, you see, staff. 608 00:28:10,033 --> 00:28:12,866 And it was funny, like she was like, 609 00:28:12,866 --> 00:28:15,266 you know, hey, is all that going to be real? 610 00:28:15,266 --> 00:28:16,300 It was very perceptive. 611 00:28:16,300 --> 00:28:18,766 It's sort of like, you know, isn't that going to get kind of 612 00:28:18,766 --> 00:28:22,600 automated by and you know, some of it well, but a lot of it won't. 613 00:28:22,600 --> 00:28:24,400 And I think we're going to have to 614 00:28:25,466 --> 00:28:26,433 remind our young 615 00:28:26,433 --> 00:28:29,433 people that we're also in a renaissance of creative confidence. 616 00:28:29,733 --> 00:28:32,000 Yeah. Like my I'm sure you feel the same thing. 617 00:28:32,000 --> 00:28:35,066 Like my creative confidence quotient has gone up dramatically. 618 00:28:35,066 --> 00:28:37,466 Absolutely. Because I've got all these help. Absolutely. 619 00:28:37,466 --> 00:28:41,466 I am you know I'm writing better with a little bit of help. 620 00:28:41,766 --> 00:28:46,166 I'm taking sometimes scatterbrained concepts and turning them 621 00:28:46,166 --> 00:28:49,566 into like beautiful concepts that are kind of like and. 622 00:28:49,566 --> 00:28:51,100 I definitely, I think concur with that. 623 00:28:51,100 --> 00:28:54,633 And I think everyone's got artistic qualities are probably severely 624 00:28:54,633 --> 00:28:55,666 under leveraged. Yeah. 625 00:28:55,666 --> 00:28:58,600 So we need to figure out like how do we 626 00:28:58,600 --> 00:29:01,833 almost like bring more creativity in the world using those tools. 627 00:29:01,833 --> 00:29:04,133 But we're going to have to lead that thinking. 628 00:29:04,133 --> 00:29:06,633 But at the same time, we may have to tell the programmer 629 00:29:06,633 --> 00:29:10,500 that programing as we knew it before is not going to be as critical. 630 00:29:10,633 --> 00:29:10,833 Yeah. 631 00:29:10,833 --> 00:29:16,133 I mean, you've got the CEO of Nvidia basically saying, 632 00:29:16,133 --> 00:29:19,900 all this obsession with teaching every kid to be a coder is nuts. 633 00:29:20,366 --> 00:29:22,033 And he's the head of Nvidia. Yeah. 634 00:29:22,033 --> 00:29:26,266 And so now prompts experimentation. 635 00:29:26,666 --> 00:29:28,866 How do you turn rocks over? 636 00:29:28,866 --> 00:29:30,000 How do you test and learn? 637 00:29:30,000 --> 00:29:31,866 I mean, I think we're in a renaissance, right? 638 00:29:31,866 --> 00:29:35,933 I mean, I have written, I kid you not, maybe I'm a bit of a freak, 639 00:29:36,333 --> 00:29:39,866 but since I discovered generative, I probably written 100 business concepts 640 00:29:40,066 --> 00:29:42,366 all the way down to, like, images. Yeah. 641 00:29:42,366 --> 00:29:46,333 I think my last team thought I was crazy, but I am like a real entrepreneur. 642 00:29:46,333 --> 00:29:47,600 And it's like. 643 00:29:47,600 --> 00:29:49,433 And like the things that when I was at P&G, 644 00:29:49,433 --> 00:29:51,133 that would take like months to kind of 645 00:29:51,133 --> 00:29:53,766 get I'm like literally like doing like overnight. 646 00:29:53,766 --> 00:29:54,266 Yeah. 647 00:29:54,266 --> 00:29:57,333 You know, I wrote a concept this morning before I came here for a new product. 648 00:29:58,166 --> 00:30:03,000 and so that it's to some extent like the ideas 649 00:30:03,000 --> 00:30:07,166 and the concepts that we have in our mind can be formalized and laid out. 650 00:30:07,166 --> 00:30:09,600 And I think we just got to teach the young people to do this. 651 00:30:09,600 --> 00:30:12,600 Like all the great businesses I know, I actually. 652 00:30:12,866 --> 00:30:14,566 I agree, I have a of a bit of a slight. 653 00:30:14,566 --> 00:30:16,900 Yeah. take on that a different take. 654 00:30:16,900 --> 00:30:20,233 I would say that you can do those things well 655 00:30:20,766 --> 00:30:24,466 because you also have some training experience like. 656 00:30:25,566 --> 00:30:25,966 Right. 657 00:30:25,966 --> 00:30:29,066 Like say like you, you have these so like, so I want to 658 00:30:29,633 --> 00:30:32,633 and I still think there are skill sets to learning how to program, 659 00:30:32,933 --> 00:30:35,933 but I do I can I completely agree with this. 660 00:30:35,966 --> 00:30:37,833 Creativity has to be a part of what you do. 661 00:30:37,833 --> 00:30:40,900 You have to can't be one dimensional in anything you do anymore. 662 00:30:41,066 --> 00:30:43,266 Like it is not. It is not going to be. 663 00:30:43,266 --> 00:30:46,133 So my advice would be you don't be one dimensional, right? 664 00:30:46,133 --> 00:30:50,033 Can't just say people want to go in sometimes say, I just want to do this. 665 00:30:50,466 --> 00:30:52,766 That world is done. Like you work. 666 00:30:52,766 --> 00:30:53,333 To your point. 667 00:30:53,333 --> 00:30:54,566 I mean, think about some of the things 668 00:30:54,566 --> 00:30:57,533 that you typically learning university like critical thinking, right. 669 00:30:57,533 --> 00:30:58,733 How to interrogate. 670 00:30:58,733 --> 00:31:00,100 Now that's as relevant. 671 00:31:00,100 --> 00:31:04,166 It's always you're going to be constantly interrogating the bots. Yep. 672 00:31:04,166 --> 00:31:08,100 You know and it's going to be a long time before the deep fakes go away or 673 00:31:08,133 --> 00:31:09,400 the hallucinations. 674 00:31:09,400 --> 00:31:14,533 And what is real, what is not, what is a logical sequence of dialog. 675 00:31:14,533 --> 00:31:18,500 And I think these, you know, we may find that the things we always wanted 676 00:31:18,500 --> 00:31:23,033 kids to learn at universities are going to be amplified in this world. 677 00:31:23,033 --> 00:31:23,866 Zach. Highly. 678 00:31:23,866 --> 00:31:28,000 I wait when I don't say like I've got Plato in my pocket loosely. 679 00:31:28,000 --> 00:31:31,266 I mean, this is a highly dialectical process, right? 680 00:31:31,566 --> 00:31:34,566 I mean, you're going back and forth like 20 times more than you would. 681 00:31:34,566 --> 00:31:36,433 But you have to know, but you got to know how to do that. 682 00:31:36,433 --> 00:31:38,766 Well, but that's a skill. And I think that's that's a skill. 683 00:31:38,766 --> 00:31:40,666 You might find that some of the universities 684 00:31:40,666 --> 00:31:42,566 go back to some of the basics, right. 685 00:31:42,566 --> 00:31:44,966 Just to kind of get. Something, but like, oh, communication. 686 00:31:44,966 --> 00:31:46,500 Everybody doesn't know how to communicate 687 00:31:46,500 --> 00:31:48,233 more people are going to have to really know how to communicate. 688 00:31:48,233 --> 00:31:49,333 It's very important. 689 00:31:49,333 --> 00:31:52,600 I also believe that creativity is going to be highly important still. 690 00:31:52,966 --> 00:31:55,966 you know, I think we're both at a 691 00:31:56,366 --> 00:31:57,966 more creative moment, but at the same time, 692 00:31:57,966 --> 00:32:00,966 I think we're, as a society less creative than we've ever been. 693 00:32:01,100 --> 00:32:01,666 Right? 694 00:32:01,666 --> 00:32:03,800 I think about like musicians, for example. 695 00:32:03,800 --> 00:32:05,300 Like, I just like it. 696 00:32:05,300 --> 00:32:09,233 And this doesn't make me feel old, but I look at the music now 697 00:32:09,866 --> 00:32:12,200 and there's nowhere near the, musical input. 698 00:32:12,200 --> 00:32:13,433 And I think this started a while ago. 699 00:32:13,433 --> 00:32:13,700 Right. 700 00:32:13,700 --> 00:32:17,700 But, like, think about what Michael Jackson even original rappers did. 701 00:32:17,800 --> 00:32:19,500 They told stories, they put things into him. 702 00:32:19,500 --> 00:32:23,466 Now you can automate things, but it's not as good quality. 703 00:32:23,833 --> 00:32:25,600 People still like originality. 704 00:32:25,600 --> 00:32:26,433 So I think 705 00:32:26,433 --> 00:32:30,733 the age of artificial intelligence, like, one of my friends, Lisa Fancy 706 00:32:30,733 --> 00:32:33,533 Flowers, he's actually in the crypto world and she's in the AI world. 707 00:32:33,533 --> 00:32:35,633 She said, in a world of artificial intelligence, 708 00:32:35,633 --> 00:32:37,433 you need authentic intelligence. 709 00:32:37,433 --> 00:32:38,300 We are one of one. 710 00:32:38,300 --> 00:32:41,266 You can only fake talent so far, right? Right. 711 00:32:41,266 --> 00:32:45,700 And I sometimes I felt artificially more musical than I really am. 712 00:32:45,700 --> 00:32:48,900 But, you know, you still have to have the foundation of talent. 713 00:32:48,933 --> 00:32:51,300 You do seem like. They help you amplify. It's not. 714 00:32:51,300 --> 00:32:52,000 That's my point. 715 00:32:52,000 --> 00:32:55,000 Like the tool should be seen that way. 716 00:32:55,233 --> 00:32:57,066 And I think people are still. 717 00:32:57,066 --> 00:32:59,366 To your point, you mentioned earlier when you talked about 718 00:32:59,366 --> 00:33:01,066 I never heard it's phrased this way, 719 00:33:01,066 --> 00:33:05,033 you know, making sure you had a mixture between automation and intimacy. 720 00:33:05,366 --> 00:33:08,933 I look at that same way, like authenticity and artificial Intel. 721 00:33:08,933 --> 00:33:11,033 You need to have authenticity. Absolutely. 722 00:33:11,033 --> 00:33:12,266 And that's what's going to differentiate. 723 00:33:12,266 --> 00:33:15,000 What I love about what Kendra and, and Helen are doing. 724 00:33:15,000 --> 00:33:15,333 Absolutely. 725 00:33:15,333 --> 00:33:17,900 I mean, that whole human in the loop, it's a big part of our model. 726 00:33:17,900 --> 00:33:19,500 Like it's interesting, like I am. 727 00:33:19,500 --> 00:33:21,166 I'll give you a good example. So we're, 728 00:33:22,500 --> 00:33:25,433 putting out reports that are very data based, 729 00:33:25,433 --> 00:33:29,666 but throughout the report, you can click a button and there's Pete in the studio 730 00:33:29,666 --> 00:33:33,466 explaining how to use it with all of Pete's passion and authority. 731 00:33:33,466 --> 00:33:34,966 And it's really called data theater. 732 00:33:34,966 --> 00:33:36,866 Yeah, I've got to get myself an avatar too. 733 00:33:36,866 --> 00:33:37,866 I got to do that. But, you know. 734 00:33:37,866 --> 00:33:38,600 But it's real. 735 00:33:38,600 --> 00:33:41,833 It's real me because I, you know, data alone is not good enough. 736 00:33:41,833 --> 00:33:42,366 Sometimes. 737 00:33:42,366 --> 00:33:45,366 I'm always trying to channel my, you know, guilt tripping 738 00:33:45,366 --> 00:33:49,000 Italian mother to kind of get people to, like, act on the data. 739 00:33:49,000 --> 00:33:50,500 Data alone. Won't get. 740 00:33:50,500 --> 00:33:50,866 Hacked. 741 00:33:50,866 --> 00:33:53,733 And again, it's, it's that balancing act that we need to perfect. 742 00:33:53,733 --> 00:33:54,600 And it's the human in. 743 00:33:54,600 --> 00:33:56,566 Yeah. Data is guidance is not determinative. 744 00:33:56,566 --> 00:33:58,766 You have to it's something you can use as a map. 745 00:33:58,766 --> 00:33:59,700 But it won't. It won't. 746 00:33:59,700 --> 00:34:01,433 It won't mean anything if you don't use it. 747 00:34:01,433 --> 00:34:01,833 Yeah. 748 00:34:01,833 --> 00:34:04,833 It's not the things where we've been told to do because rationally it's right. 749 00:34:04,866 --> 00:34:05,800 Yeah. Eat your peas. 750 00:34:05,800 --> 00:34:06,300 Yeah. 751 00:34:06,300 --> 00:34:08,700 It's like, you know, it's like every study will tell you 752 00:34:08,700 --> 00:34:09,933 it's right that you need, like. 753 00:34:09,933 --> 00:34:14,100 Yeah, mom to say goddamn Jesus or you're not, you know. 754 00:34:14,566 --> 00:34:16,766 Yeah. I mean, we're not we're not rational people, so it's. 755 00:34:16,766 --> 00:34:17,433 We're good to go. 756 00:34:19,300 --> 00:34:22,900 so, I want to get to a few kind of, like, questions that I ask people. 757 00:34:22,900 --> 00:34:26,533 So you have a committee of three to advise you on 758 00:34:26,866 --> 00:34:28,833 life, business, whatever you want. 759 00:34:28,833 --> 00:34:31,833 Tell me who these three people are and why. 760 00:34:34,500 --> 00:34:37,766 I really admire John Pepper, the former CEO of P&G. 761 00:34:38,066 --> 00:34:41,066 I think he is just, he continues 762 00:34:41,166 --> 00:34:44,500 emailing him this morning on me, and he's the type of person. 763 00:34:44,500 --> 00:34:47,566 If he asked me to do something, I'd say how high or jump. 764 00:34:47,566 --> 00:34:50,666 And he's consistently there and just always 765 00:34:50,666 --> 00:34:53,666 kind of wise, ethical, 766 00:34:54,500 --> 00:34:56,900 Kind of, 767 00:34:56,900 --> 00:34:57,700 stem. 768 00:34:57,700 --> 00:35:00,600 You know, there's some, 769 00:35:00,600 --> 00:35:01,466 you know, 770 00:35:01,466 --> 00:35:04,033 I want isolated to one individual, but there's some folks 771 00:35:04,033 --> 00:35:05,233 from my Nestlé experience. 772 00:35:05,233 --> 00:35:08,233 So I just really, like to bring to the table. 773 00:35:08,533 --> 00:35:09,033 And I think 774 00:35:10,100 --> 00:35:10,400 a lot 775 00:35:10,400 --> 00:35:13,400 of, you know, family members are always there. 776 00:35:13,500 --> 00:35:14,933 So many respects. 777 00:35:14,933 --> 00:35:16,233 so you got one. You play. You know what? 778 00:35:16,233 --> 00:35:17,600 You know what? It used. 779 00:35:17,600 --> 00:35:20,200 Oh, I need to think a little bit harder about, 780 00:35:20,200 --> 00:35:23,200 you know, just because you might say I'm leaning on. 781 00:35:23,200 --> 00:35:26,466 I'm for certain things. I'm fine on my kids. 782 00:35:26,900 --> 00:35:27,300 That's fine. 783 00:35:27,300 --> 00:35:30,333 And that's a good that my daughter Layla is like a source of incredible insight. 784 00:35:30,333 --> 00:35:33,600 So my daughter Sophia, my son Liam, and, you know, 785 00:35:33,600 --> 00:35:37,466 you have to, like, be humble to know what you don't know. 786 00:35:37,466 --> 00:35:39,400 And almost there's a certain innocence and. 787 00:35:39,400 --> 00:35:40,866 Yeah, world that they see. 788 00:35:40,866 --> 00:35:46,000 And so, but I, you know, I generally try to, you know, listen to a lot of signals. 789 00:35:46,533 --> 00:35:48,500 About what you don't know. That's great. 790 00:35:48,500 --> 00:35:50,966 May I ask you the opposite of that question? Yeah. 791 00:35:50,966 --> 00:35:52,966 What do you know for sure? The Oprah question. 792 00:35:56,400 --> 00:35:57,400 And I know that I don't 793 00:35:57,400 --> 00:36:00,866 know, I kind of still Plato or Aristotle, but, 794 00:36:01,266 --> 00:36:03,900 you know, I'm pretty humble about 795 00:36:03,900 --> 00:36:06,900 the fact that there's still a lot, 796 00:36:07,266 --> 00:36:09,900 and, 797 00:36:09,900 --> 00:36:13,566 and that, you know, knowledge is, real quest. 798 00:36:13,566 --> 00:36:18,233 It never entirely just kind of an end point 799 00:36:19,633 --> 00:36:23,666 is, I'm really fanatical about 800 00:36:25,166 --> 00:36:28,166 standing. 801 00:36:29,233 --> 00:36:30,666 Kind of guided everything I've done. 802 00:36:30,666 --> 00:36:33,666 And to some extent, a lot of that is in the 803 00:36:33,733 --> 00:36:36,733 and, say, trust your inner. 804 00:36:37,300 --> 00:36:38,733 Yeah, 805 00:36:38,733 --> 00:36:39,566 that you're probably going 806 00:36:39,566 --> 00:36:42,566 to find all sorts of great business ideas and the like. 807 00:36:42,566 --> 00:36:43,433 All right. 808 00:36:43,433 --> 00:36:46,533 What's an important truth you have that very few people agree with you on? 809 00:36:47,066 --> 00:36:50,066 It's always a fun one for me. 810 00:36:50,133 --> 00:36:52,900 And I would say goes back to what I said before, I think, you know, the 811 00:36:52,900 --> 00:36:55,766 trust your inner consumers. I gave a Ted talk on it. 812 00:36:55,766 --> 00:36:59,900 I think, I think we does that sometimes. 813 00:36:59,900 --> 00:37:02,900 And I think what specifically is that is the belief that people 814 00:37:03,166 --> 00:37:04,666 agree with you on any I don't understand. 815 00:37:04,666 --> 00:37:06,266 Tell me that something in what do you mean? 816 00:37:06,266 --> 00:37:09,400 Like what's an important truth you have that most people may disagree with. 817 00:37:10,200 --> 00:37:11,700 Some people may disagree with that. 818 00:37:11,700 --> 00:37:13,866 It's almost like because I'm I've always believed that 819 00:37:13,866 --> 00:37:17,033 if you if you trust your inner consumer, you're almost bringing more of an outside 820 00:37:17,033 --> 00:37:18,466 in activist perspective. 821 00:37:18,466 --> 00:37:20,100 Okay. I think in business sometimes. 822 00:37:20,100 --> 00:37:21,866 What does that mean to trust your inner consumer? 823 00:37:21,866 --> 00:37:25,133 So basically means, so I've always been, 824 00:37:25,966 --> 00:37:28,133 I give a Ted talk called the Work Life Advantage, 825 00:37:28,133 --> 00:37:31,766 and it was all about how the things we do in our personal lives 826 00:37:32,233 --> 00:37:36,533 and digital really inform how we become business leaders. 827 00:37:36,866 --> 00:37:39,300 So you've got to do social media yourself. 828 00:37:39,300 --> 00:37:41,866 It'll kind of teach you about the power of iteration. 829 00:37:41,866 --> 00:37:46,333 you need to walk the talk, need to, Yeah. 830 00:37:46,333 --> 00:37:50,766 You just, you know, you need to become a good e-commerce shopper to really speak 831 00:37:50,766 --> 00:37:54,200 with authority when you're managing a team about winning with Amazon. 832 00:37:54,200 --> 00:37:55,200 And so it's like 833 00:37:55,200 --> 00:37:58,500 try and you also, you know, as consumers are pretty damn critical. 834 00:37:58,500 --> 00:37:58,833 Right. 835 00:37:58,833 --> 00:38:00,333 And sometimes we let it out. 836 00:38:00,333 --> 00:38:02,933 We're kind of, you know, we get we get really angry. 837 00:38:02,933 --> 00:38:09,200 We tell 3000 and so but that journey is what makes us really sharp. 838 00:38:09,200 --> 00:38:13,166 Now why do I feel like I'm qualified to go on a startup 839 00:38:13,466 --> 00:38:15,500 if I a million startups do an I? 840 00:38:15,500 --> 00:38:18,866 Honestly, I've been I've been trusting my inner consumer. 841 00:38:18,866 --> 00:38:21,733 I'm using this stuff like 100 times a day. 842 00:38:21,733 --> 00:38:25,700 I see a future that others don't because I'm channeling my consumer side. 843 00:38:25,700 --> 00:38:31,166 And I think that's always going to be my source of truth, my source of motivation. 844 00:38:31,600 --> 00:38:35,566 And, and I would I don't think that that truth will ever go away. 845 00:38:35,900 --> 00:38:36,566 All right. 846 00:38:36,566 --> 00:38:37,166 Final question. 847 00:38:37,166 --> 00:38:40,866 What does success look like for you or your business or yourself in five years? 848 00:38:41,733 --> 00:38:45,300 And I want to make a big it's funny, I'm not I want 849 00:38:46,766 --> 00:38:50,100 monetary wealth, but I'm not motivated by it. 850 00:38:50,100 --> 00:38:53,100 I'm very motivated by 851 00:38:53,366 --> 00:38:56,366 feeling that I did something 852 00:38:57,100 --> 00:38:59,333 in me 853 00:38:59,333 --> 00:39:00,533 out of positive 854 00:39:00,533 --> 00:39:05,166 sounds like lofty and idealistic, but it's just where I am in my day. 855 00:39:05,166 --> 00:39:09,066 It's like I if people read my epitaph at their 856 00:39:09,066 --> 00:39:12,366 give speech at the funeral and they don't say that I really moved 857 00:39:14,100 --> 00:39:16,566 in it very thoughtfully. 858 00:39:16,566 --> 00:39:19,966 I'll be yeah, I'll be really disappointed wherever I am. 859 00:39:20,366 --> 00:39:22,166 And, yeah. 860 00:39:22,166 --> 00:39:25,400 So I just, I want I want to drive real impact. 861 00:39:25,900 --> 00:39:26,966 I've always been that. 862 00:39:26,966 --> 00:39:30,133 I mean, I, I, I grew up, I wanted to be governor of California, 863 00:39:31,000 --> 00:39:33,166 and I've, I started 864 00:39:33,166 --> 00:39:34,600 and to some extent I've almost. 865 00:39:34,600 --> 00:39:36,166 You and I have that in common. 866 00:39:36,166 --> 00:39:36,966 Yeah. Yeah. 867 00:39:36,966 --> 00:39:37,766 Planted feedback. 868 00:39:37,766 --> 00:39:40,733 My first business was a bit of a, stick it to the man. 869 00:39:40,733 --> 00:39:42,600 Yeah. It also had a business side to it. 870 00:39:42,600 --> 00:39:46,100 So I've always like blended that, you know, drive impact, 871 00:39:46,400 --> 00:39:47,366 build a great business. 872 00:39:47,366 --> 00:39:48,000 And to some extent 873 00:39:48,000 --> 00:39:51,666 I feel like I'm doing that now because I like the idea of accountable. 874 00:39:52,333 --> 00:39:55,333 Like, don't talk about sustainability and not do it right. 875 00:39:55,433 --> 00:39:55,766 You know. 876 00:39:55,766 --> 00:39:56,900 So I'm kind of blame it on the 877 00:39:56,900 --> 00:40:00,600 I bought say you're lying or you're greenwashing, but I like that. 878 00:40:00,600 --> 00:40:04,266 It's like, you know, because I think it also drives drives to better outcomes. 879 00:40:04,266 --> 00:40:07,333 It might speed up companies to actually meeting 880 00:40:07,333 --> 00:40:10,300 their green commitments or realizing they can't go slow. 881 00:40:10,300 --> 00:40:12,566 Right. You know, so those are the things I really like. 882 00:40:12,566 --> 00:40:14,266 Yeah. People stop being a pleasure, man. Yeah. 883 00:40:14,266 --> 00:40:16,566 Thanks for all your leadership. That's fine. Thank you. And.