1 00:00:00,240 --> 00:00:04,000 Ladies and gentlemen, boys and girls, and AI entities of 2 00:00:04,000 --> 00:00:07,379 all computational capacities, welcome to another riveting 3 00:00:07,440 --> 00:00:11,095 episode of the data driven podcast. Today, 4 00:00:11,095 --> 00:00:14,695 we embark on a journey through the digital landscape where data 5 00:00:14,695 --> 00:00:18,295 isn't just numbers. It's the very fabric of our 6 00:00:18,295 --> 00:00:22,060 digital existence. With the ever charming Frank steering 7 00:00:22,060 --> 00:00:25,740 the ship solo, Andy was off gallivanting at Busch Gardens when this was 8 00:00:25,740 --> 00:00:29,474 recorded. We delve into the world of generative AI and 9 00:00:29,474 --> 00:00:33,315 marketing marvels with the illustrious Danny Maloney, the brain behind 10 00:00:33,315 --> 00:00:36,860 Tailwind. Picture this. A 11 00:00:36,860 --> 00:00:40,300 world where small businesses wield the power of giants, thanks to 12 00:00:40,300 --> 00:00:43,120 Tailwind's arsenal of automated marketing tools. 13 00:00:43,975 --> 00:00:47,815 From the nostalgic lanes of Google Street View to the cutting edge frontiers of 14 00:00:47,815 --> 00:00:50,955 AI driven marketing strategies, we're in for a treat. 15 00:00:51,740 --> 00:00:55,420 And since today is February 29th, leap day, we 16 00:00:55,420 --> 00:00:59,235 figured, why not leap into another episode? So 17 00:00:59,235 --> 00:01:02,675 adjust your antennas, polish your circuits, and prepare for an 18 00:01:02,675 --> 00:01:06,135 electrifying discourse on how Tailwind is reshaping the marketing 19 00:01:06,195 --> 00:01:09,670 cosmos, one algorithm at a time. Buckle 20 00:01:09,670 --> 00:01:12,970 up. It's going to be a data driven ride. 21 00:01:17,725 --> 00:01:21,490 Hello and welcome to Data Driven, the podcast where we explore the emerging fields 22 00:01:21,490 --> 00:01:24,950 of data, artificial intelligence, and the ever present world of data engineering. 23 00:01:25,329 --> 00:01:29,170 But speaking of data engineering, my co host, Andy, is not 24 00:01:29,170 --> 00:01:32,895 here today. He's having a fun family day at Busch Gardens. 25 00:01:33,435 --> 00:01:36,095 So, we wish him good weather and, 26 00:01:37,435 --> 00:01:40,579 short lines. So with me today 27 00:01:41,920 --> 00:01:45,460 is Danny Maloney, a successful Internet entrepreneur, 28 00:01:46,159 --> 00:01:49,795 CEO and cofounder of Tailwind, a software platform that 29 00:01:49,795 --> 00:01:53,635 provides small businesses with the marketing tools they need to compete. With 30 00:01:53,635 --> 00:01:57,290 over 1,000,000 users, Tailwind is a leader in 31 00:01:57,290 --> 00:02:00,890 generative AI and providing automated marketing plan 32 00:02:00,890 --> 00:02:04,570 creation, visual content design, copywriting, email campaign 33 00:02:04,570 --> 00:02:08,315 building, ad optimization, and more. But before starting his 34 00:02:08,315 --> 00:02:12,095 own company, he worked at Google, YouTube, and AOL, 35 00:02:12,395 --> 00:02:16,180 and is part of it apparently had, worked on something I'm very 36 00:02:16,180 --> 00:02:19,940 fond of is, Street View and Google Maps. And, maybe we 37 00:02:19,940 --> 00:02:23,159 could talk a little bit more about that. So welcome to the show, Danny. 38 00:02:23,625 --> 00:02:27,065 Great. Thanks for having me, Frank. Glad to be here. Yeah. Good to have you. 39 00:02:27,065 --> 00:02:30,505 Good to have you. So generative AI, right, as 40 00:02:30,505 --> 00:02:33,450 probably since, you know, 41 00:02:34,070 --> 00:02:37,530 last November has been on everybody's 42 00:02:37,830 --> 00:02:41,645 minds. What you've been how long 43 00:02:41,645 --> 00:02:44,685 ago did you start Tailwind? Yeah. So, 44 00:02:45,725 --> 00:02:49,430 Tailwind itself came to market in 2015. Okay. So we've 45 00:02:49,430 --> 00:02:53,110 been around for a while. You know, the way we tell our story is 46 00:02:53,110 --> 00:02:56,925 really in a couple of chapters. So there was kind of tailwind v 1, 47 00:02:56,925 --> 00:03:00,765 and there's tailwind 2 point o as we call it. And so for 48 00:03:00,765 --> 00:03:04,605 us, that was kinda 2015 to 2019 and then 2019 to the present 49 00:03:04,605 --> 00:03:07,720 day. But going down the path of, 50 00:03:08,660 --> 00:03:12,420 generation, for clients and really thinking about how we 51 00:03:12,420 --> 00:03:16,135 do more of the work of marketing for them, was 52 00:03:16,135 --> 00:03:19,674 the real theme of this second chapter. So we started on that path 53 00:03:19,894 --> 00:03:23,254 back in 2019, believe it or not, even though, you know, the market is 54 00:03:23,254 --> 00:03:27,040 really kinda waking up and starting to pay attention to your generative AI today, 55 00:03:28,620 --> 00:03:31,580 there was a lot to learn and a lot to figure out in terms of 56 00:03:31,580 --> 00:03:34,225 how to go about it the right way. And so it's been a pretty fun 57 00:03:34,225 --> 00:03:37,985 past few years for us going down that road. Interesting. So what 58 00:03:37,985 --> 00:03:41,505 does Tailwind generate specifically? Is it NLP? Is it 59 00:03:41,505 --> 00:03:44,600 LLM? Like, what is, image generation? 60 00:03:45,700 --> 00:03:49,380 Yeah. So there's actually multiple components. So like you said, we're trying to 61 00:03:49,380 --> 00:03:52,735 give small businesses the tools that they need to compete as marketers. 62 00:03:53,515 --> 00:03:57,194 So for us, that's not just one use case, but it's actually thinking 63 00:03:57,194 --> 00:04:00,960 about the entire marketing life cycle. And so we look at, 64 00:04:00,960 --> 00:04:04,340 today, 4 core components of what we help generate. 65 00:04:04,800 --> 00:04:08,455 One piece is actually the marketing plan itself. And that so that's our own 66 00:04:08,515 --> 00:04:12,194 IP, our own technology that we built that builds and 67 00:04:12,194 --> 00:04:15,875 extends and evolves marketing plans based on the specifics of the 68 00:04:15,875 --> 00:04:19,529 business. Once you have that plan, then you've gotta actually execute it. So 69 00:04:19,529 --> 00:04:23,370 now we get into the next couple components. So one is a tool called Tailwind 70 00:04:23,370 --> 00:04:26,985 Create, which is about visual generation. So we actually 71 00:04:26,985 --> 00:04:30,505 create the images that someone needs and, you know, don't think 72 00:04:30,505 --> 00:04:34,280 necessarily about a tool like a Canva or a PicMonkey in that 73 00:04:34,280 --> 00:04:37,659 sense of, you know, I'm just going through templates, and I'm designing it 74 00:04:38,039 --> 00:04:41,879 myself. What we do is we actually take in the assets and the 75 00:04:41,879 --> 00:04:45,715 inputs from a brand, and then we generate a large array of 76 00:04:45,715 --> 00:04:49,395 designs that they can scroll through and choose from instead of 77 00:04:49,395 --> 00:04:52,935 having to do the design work. So it kinda takes the design process from 78 00:04:53,380 --> 00:04:57,140 45 minutes to about 2 minutes, typically, and we generate it 79 00:04:57,140 --> 00:05:00,795 in all the formats they need for their different marketing assets. So 80 00:05:00,795 --> 00:05:04,575 visual design is the second part. The third part is copywriting. 81 00:05:05,514 --> 00:05:09,110 And so there, it's a mix of our IP and also 82 00:05:09,110 --> 00:05:12,949 leveraging third party tools that are out there. So leveraging LMS, 83 00:05:12,949 --> 00:05:16,585 for example. But that's about, you know, being 84 00:05:16,585 --> 00:05:19,945 able to help people write the copy that's gonna be convincing, that's gonna 85 00:05:19,945 --> 00:05:23,725 help communicate with their audience. And then the 4th component 86 00:05:23,785 --> 00:05:27,290 is ads. So we actually acquired a company last year 87 00:05:27,510 --> 00:05:31,270 called Nectar 9, who they themselves had been building out 88 00:05:31,270 --> 00:05:35,115 an AI driven ad management platform for about 5 years prior 89 00:05:35,115 --> 00:05:38,555 to that. And, we're now integrating that into 90 00:05:38,555 --> 00:05:42,340 Tailwind so that our users will be able to mostly automate 91 00:05:42,340 --> 00:05:46,100 the process of paid advertising also. But we look at that as a 92 00:05:46,100 --> 00:05:49,514 complete cycle. Right? So I've got a plan. I can create the content for the 93 00:05:49,514 --> 00:05:52,495 plan, then I can distribute it and get it out to my audience. 94 00:05:52,955 --> 00:05:56,315 And the data from that cycle should inform my 95 00:05:56,315 --> 00:05:59,400 plan so I can get better and get smarter into the future. 96 00:06:01,380 --> 00:06:05,185 Interesting. That's one of the things that I I I 97 00:06:05,185 --> 00:06:08,944 wonder about these these generative tools. Like, do they take the feedback 98 00:06:08,944 --> 00:06:12,705 and that, like, you know so eventually, it would get better. Ideally, it 99 00:06:12,705 --> 00:06:15,827 would eventually get better. And it sounds like you do include that in your feedback 100 00:06:15,827 --> 00:06:19,510 loop. There are some. I mean, honestly, I think we're in early days of 101 00:06:19,510 --> 00:06:23,193 that. Right? If you look at some of the more prevalent models that are 102 00:06:23,193 --> 00:06:26,645 out there, you know, the the various OpenAI models, for 103 00:06:26,645 --> 00:06:30,165 instance, I think there is an inherent feedback loop in how 104 00:06:30,165 --> 00:06:33,650 those operate as they continue to train on more data. 105 00:06:33,650 --> 00:06:37,330 Right? When you talk about things from a marketing context in 106 00:06:37,330 --> 00:06:40,695 particular, the feedback loop for us has been around 107 00:06:40,695 --> 00:06:44,135 things like, how do we train, you know, 108 00:06:44,135 --> 00:06:46,875 our models, our products, or third party models 109 00:06:47,495 --> 00:06:51,319 around specific use cases. Right? So, looking at the 110 00:06:51,319 --> 00:06:54,539 corpus of data we have around what works, what doesn't work, 111 00:06:55,240 --> 00:06:58,965 enabling it to be more tailored to the use case that the user 112 00:06:58,965 --> 00:07:02,645 is trying to solve. So, you know, when you're designing a 113 00:07:02,645 --> 00:07:06,290 pin for Pinterest, that should be different than a, you know, 114 00:07:06,290 --> 00:07:09,990 feed post on Instagram. Right? A feed post on Instagram is different from 115 00:07:10,450 --> 00:07:14,050 writing a script for a reels, right, or a TikTok. So, 116 00:07:15,875 --> 00:07:19,715 for each of those use cases, we found you can start generating much stronger 117 00:07:19,715 --> 00:07:22,455 results with more specific training. 118 00:07:24,650 --> 00:07:27,230 Interesting. Yeah. I I I wonder, like, what 119 00:07:28,410 --> 00:07:31,930 what the future holds for this type of work. You know, there's definitely a lot 120 00:07:31,930 --> 00:07:35,175 of there's a lot of, you know, 121 00:07:35,555 --> 00:07:39,315 grinding and gnashing of teeth and, you know, prog you know, 122 00:07:39,315 --> 00:07:42,800 prognostic that's that's my fault for trying to use a 123 00:07:44,380 --> 00:07:47,740 a big word on, basically, what is a kind of a holiday 124 00:07:47,740 --> 00:07:51,455 week. But, you know, but I 125 00:07:51,455 --> 00:07:55,215 think that it's interesting because it's it's probably opened up the opportunity for a 126 00:07:55,215 --> 00:07:58,830 company like you, to step in and kinda build these 127 00:07:58,830 --> 00:08:02,449 tools that really empower the smaller businesses because that seems like it's a 128 00:08:02,750 --> 00:08:05,775 pretty large, audience of folks. 129 00:08:06,975 --> 00:08:10,815 Yep. Yeah. It definitely is. And, honestly, I think it's the audience that 130 00:08:10,815 --> 00:08:14,419 needs it most. Right? Because when you look at very large 131 00:08:14,419 --> 00:08:18,180 enterprises out there, and they've got world class marketing teams and world 132 00:08:18,180 --> 00:08:21,835 class agencies that they're working with, tons of data, tons of 133 00:08:21,835 --> 00:08:25,675 resources. They've got access to the best tools. And a 134 00:08:25,675 --> 00:08:29,195 lot of small business owners don't realize that in today's world of 135 00:08:29,195 --> 00:08:32,770 marketing, when you start a small business, you're actually competing with the big 136 00:08:32,770 --> 00:08:36,289 guys from very early on. Right? There's only so much 137 00:08:36,289 --> 00:08:39,909 consumer attention out there. There are only so many eyeballs, so many minutes 138 00:08:40,355 --> 00:08:43,795 to compete for. And small businesses are 139 00:08:43,795 --> 00:08:47,415 drastically under resourced and under tooled compared to 140 00:08:48,089 --> 00:08:51,850 large enterprises. And then on top of that, it's usually the 141 00:08:51,850 --> 00:08:55,290 founder trying to figure out the marketing on their own, or maybe they can hire 142 00:08:55,290 --> 00:08:59,055 a 1 person team or a 2 person team in the early days. 143 00:08:59,755 --> 00:09:03,455 But I think, you know, you you look at this type of generative AI 144 00:09:03,915 --> 00:09:07,140 evolution that's happening right now. I think it's that 145 00:09:07,360 --> 00:09:11,120 smaller, business, and I think it's, people 146 00:09:11,120 --> 00:09:14,904 who have been on the fringes of really having access to having 147 00:09:14,904 --> 00:09:18,264 their voice heard that this potentially helps the most. So 148 00:09:18,264 --> 00:09:21,725 just an example of that, a pocket of users we've seen 149 00:09:22,230 --> 00:09:25,670 on our ghostwriter capability, which is the the copy 150 00:09:25,670 --> 00:09:29,485 generation, part of Tailwind, a 151 00:09:29,485 --> 00:09:32,845 pocket of users we've seen who are really passionate about it are people who are 152 00:09:32,845 --> 00:09:36,605 non native English speakers. Right? Right. And you 153 00:09:36,605 --> 00:09:40,370 go through user research, and you're watching some of 154 00:09:40,370 --> 00:09:44,050 the interviews, and we've literally had people in tears of, 155 00:09:44,050 --> 00:09:47,654 you know, how happy they were to be able to clear some 156 00:09:47,654 --> 00:09:51,274 really big emotional blockers for them around 157 00:09:51,495 --> 00:09:55,120 anxiety and fear of having to communicate in a non native language and 158 00:09:55,120 --> 00:09:58,720 knowing now that they can produce work at a higher 159 00:09:58,720 --> 00:10:02,480 level is is life changing. Right? So, yeah, I I think of this, and 160 00:10:02,480 --> 00:10:06,295 I look back to the evolution of the Internet and think of, 161 00:10:06,295 --> 00:10:10,135 you know, I was like you said, I was at YouTube before. And we 162 00:10:10,135 --> 00:10:13,100 had similar stories at YouTube of, 163 00:10:13,640 --> 00:10:17,320 you know, the teenager in Africa who teaches himself calculus 164 00:10:17,320 --> 00:10:21,000 through YouTube videos. Right? And Right. Right. Right. Yeah, 165 00:10:21,000 --> 00:10:24,595 or software development. Right? Who otherwise would not 166 00:10:24,595 --> 00:10:28,195 have had access to that level of education and 167 00:10:28,195 --> 00:10:32,029 couldn't have dreamt of it in some cases, but those were very real stories. 168 00:10:32,810 --> 00:10:36,250 And and it's a reminder for me of the power of technology to 169 00:10:36,250 --> 00:10:40,010 democratize access. Right? And so I think this next wave is gonna 170 00:10:40,010 --> 00:10:43,595 do that as well. And hopefully, in the long term, it just leads to, you 171 00:10:43,595 --> 00:10:47,214 know, the best ideas, the best content, the best product winning out. 172 00:10:48,315 --> 00:10:52,110 Interesting. So what what did the and, you know, what did the first 173 00:10:52,110 --> 00:10:55,950 version of Alwin look like before the generative AI? Right? Were 174 00:10:55,950 --> 00:10:59,545 you you know, what did that touch AI in any way, or 175 00:10:59,545 --> 00:11:03,305 was it kind of sort of not? Yeah. It really 176 00:11:03,305 --> 00:11:06,665 didn't, but there was a common underpinning in a 177 00:11:06,665 --> 00:11:10,490 way. So one of the things we heard very early when we 178 00:11:10,490 --> 00:11:14,329 were starting and building up the company from especially the small business 179 00:11:14,329 --> 00:11:17,945 audience was, you know, everyone's giving us tools, 180 00:11:18,245 --> 00:11:22,084 and these tools give us data and analytics. And 181 00:11:22,084 --> 00:11:25,830 we're supposed to have time and energy to go through that data and 182 00:11:25,830 --> 00:11:28,950 figure out what it's telling us. But, frankly, I don't wanna do that. Right? I 183 00:11:28,950 --> 00:11:32,685 I didn't start my business to dig through reports. I wanna spend 184 00:11:32,685 --> 00:11:36,385 time designing products or engaging with my customers or creating content, 185 00:11:36,925 --> 00:11:40,685 not doing data analytics. Right? And so what we heard was this common 186 00:11:40,685 --> 00:11:44,110 theme of, I wish I wish someone would just give me the 187 00:11:44,110 --> 00:11:47,870 tool that tells me what to do instead 188 00:11:47,870 --> 00:11:51,035 of making me figure it out on myself. Like, pushing 189 00:11:51,495 --> 00:11:55,255 bits from point a to point b is fine. Right? There's value in that. There's 190 00:11:55,255 --> 00:11:58,839 value in things like scheduling content and 191 00:11:58,839 --> 00:12:02,060 email automation. Sure. That helps scale. 192 00:12:02,680 --> 00:12:05,959 But the real part people were struggling with was knowing what to do in the 193 00:12:05,959 --> 00:12:09,695 first place. And so as we built the first version of 194 00:12:09,695 --> 00:12:13,055 Tailwind, which was largely focused on social media scheduling and 195 00:12:13,055 --> 00:12:16,829 publishing, What we found was 196 00:12:16,829 --> 00:12:20,449 certain features that we built that were more predictive 197 00:12:20,510 --> 00:12:24,345 in nature. So for example, the best time to post, 198 00:12:25,045 --> 00:12:28,805 for your audience as an example, or which you know, we have a 199 00:12:28,805 --> 00:12:32,570 hashtag finder feature from the first version. You know, which hashtag should I use 200 00:12:32,570 --> 00:12:36,410 on this post? Those were some of the features that people 201 00:12:36,410 --> 00:12:40,175 got most excited about and that really generated a lot of energy, a 202 00:12:40,175 --> 00:12:44,015 lot of curiosity around. And so I see a common thread there. 203 00:12:44,015 --> 00:12:47,649 Right? Because it was taking away those psychological 204 00:12:48,029 --> 00:12:51,630 barriers and helping people clear that question of, am I 205 00:12:51,630 --> 00:12:55,435 doing the right thing, right, by giving the recommendation. Okay. I can 206 00:12:55,435 --> 00:12:59,115 follow the recommendation, follow the doctor's orders, so to speak, and 207 00:12:59,115 --> 00:13:02,715 now I am unblocked. And I'm gonna move faster, and I'm gonna do more 208 00:13:02,715 --> 00:13:06,440 marketing, which is gonna help me build my business. And so, yeah, I 209 00:13:06,440 --> 00:13:10,200 think this generative AI wave now is the 210 00:13:10,200 --> 00:13:13,845 next level up of that concept of being 211 00:13:13,845 --> 00:13:17,205 able to guide people in an 212 00:13:17,205 --> 00:13:20,810 opinionated but data informed fashion on 213 00:13:20,870 --> 00:13:24,410 what they should be doing in far more points of their marketing journey 214 00:13:24,790 --> 00:13:28,415 in a way where beginners can get up to speed, you know, much faster than 215 00:13:28,415 --> 00:13:30,595 they otherwise would. Interesting. 216 00:13:32,175 --> 00:13:36,019 Are people comfortable with being told what 217 00:13:36,019 --> 00:13:38,899 to do, or it depends on the the audience. Right? Because that was the first 218 00:13:38,899 --> 00:13:42,180 thing that's the first thing that you said that kinda made me, like, I wonder 219 00:13:42,180 --> 00:13:45,585 how people feel about that. Yeah. It definitely depends on the 220 00:13:45,585 --> 00:13:49,265 audience. I I think the if I had to observe the divide 221 00:13:49,265 --> 00:13:53,089 there that I've seen over time, experts are less comfortable being 222 00:13:53,089 --> 00:13:56,690 told what to do because they're experts. They've earned that 223 00:13:56,690 --> 00:14:00,355 expertise. They've taken years of learning and labor to get 224 00:14:00,355 --> 00:14:04,035 there. Beginners are thrilled to be told what to 225 00:14:04,035 --> 00:14:07,635 do in the vast majority of cases. And and, you know, we we usually don't 226 00:14:07,635 --> 00:14:11,150 frame it that way. It's more of, like, guiding and recommending for that, 227 00:14:12,010 --> 00:14:15,850 then, you know you don't want it to sound overbearing. But but the 228 00:14:15,850 --> 00:14:19,055 reality is when you're at that early stage and someone's there to help 229 00:14:19,774 --> 00:14:23,214 you out and you don't have to drop, you know, $1500 a month on a 230 00:14:23,214 --> 00:14:26,895 marketing consultant, you know, who's working part time for you and 10 other 231 00:14:26,895 --> 00:14:30,250 brands. Right? Like, that's that's a breakthrough for a small business 232 00:14:30,250 --> 00:14:34,010 owner. So they tend to be thrilled, and then you kinda have 233 00:14:34,010 --> 00:14:37,725 the messy middle, so to speak, of people who are kind of becoming experts 234 00:14:38,345 --> 00:14:41,805 not quite there. Or maybe they're expert in a given area, 235 00:14:42,185 --> 00:14:45,900 and they don't feel like they need the advice there, but they don't feel as 236 00:14:45,900 --> 00:14:49,440 expert in the next area that they're trying to learn or expand into. 237 00:14:50,460 --> 00:14:54,000 And so, you know, I think that's that audience is where 238 00:14:54,685 --> 00:14:57,985 we kind of stretch up to serving. Right? And so 239 00:14:58,365 --> 00:15:01,985 we've had to build the interface and the system in a way 240 00:15:02,230 --> 00:15:05,990 where people can opt in or opt out of the advice at each 241 00:15:05,990 --> 00:15:09,725 individual point. Right? You don't have to follow it. So we 242 00:15:09,725 --> 00:15:13,485 don't fully do it for you because, you 243 00:15:13,485 --> 00:15:17,250 know, I I think even from an ethical perspective, that that crosses the line, 244 00:15:17,410 --> 00:15:21,250 especially with AI. Right. But you don't wanna be like 245 00:15:21,250 --> 00:15:24,770 Clippy either, where it's like, hey, it looks like you're hey, it looks like you're 246 00:15:24,770 --> 00:15:28,435 writing a letter. Hey, it looks like you're writing a marketing plan. Like, that you 247 00:15:28,435 --> 00:15:31,954 know? And you want there to be a cycle of learning and 248 00:15:31,954 --> 00:15:35,660 oversight there because, you know, every brand should be unique. Every 249 00:15:35,660 --> 00:15:39,100 voice should be unique. You do get into 250 00:15:39,100 --> 00:15:42,835 industries where facts really matter. Right? Like, you you need the 251 00:15:42,835 --> 00:15:46,675 marketing to be factual, and, you know, there are certainly been observed cases 252 00:15:46,675 --> 00:15:50,455 where LLMs don't always do so well at, you know, 253 00:15:51,150 --> 00:15:54,450 finding the factual information versus filling in the gaps themselves. 254 00:15:55,470 --> 00:15:58,055 But in the vast majority of cases, they do a pretty good job. 255 00:15:59,095 --> 00:16:02,855 So, you know, I think that's where you get into some AI ethics 256 00:16:02,855 --> 00:16:06,315 conversations, and there's a line we don't wanna cross. Right? 257 00:16:06,519 --> 00:16:10,360 There should still be a human involved, but we can 258 00:16:10,360 --> 00:16:14,060 empower that human to do a lot more and to do it in less time. 259 00:16:14,584 --> 00:16:18,425 Well, I like that. I like that you're you're thinking about the ethical concerns here 260 00:16:18,425 --> 00:16:22,125 because a lot of a lot of businesses 261 00:16:22,345 --> 00:16:25,180 don't really think about that upfront. Yeah. 262 00:16:25,820 --> 00:16:29,580 And so you you you mentioned the line. You don't wanna cross it. Do 263 00:16:29,580 --> 00:16:32,735 you do you have you found yourself in situation where you're kinda getting close to 264 00:16:32,735 --> 00:16:36,255 the line? Yeah. I think that's a really interesting 265 00:16:36,255 --> 00:16:39,775 question. It's something we debate, and and we're 266 00:16:39,775 --> 00:16:43,530 actually, you know, Greg Starling, who's been leading up the 267 00:16:43,530 --> 00:16:47,210 AI initiatives for us from early on, he's actually 268 00:16:47,210 --> 00:16:50,595 starting an internal kind of, like, lunch talk 269 00:16:50,595 --> 00:16:54,355 series with the team to dive into those questions 270 00:16:54,355 --> 00:16:58,200 collaboratively. Right? Like, where are people struggling with this individually? 271 00:16:58,420 --> 00:17:02,180 Where are they seeing potential conflicts in their work? Or where are they running 272 00:17:02,180 --> 00:17:04,839 up to barriers that they don't know what the answer should be? 273 00:17:06,385 --> 00:17:09,984 You know, I I think, the reality is there's 274 00:17:09,984 --> 00:17:13,779 not a big situation I can point to today where I 275 00:17:13,779 --> 00:17:17,539 can say, like, yeah. There's this really compelling example, but we know it's 276 00:17:17,539 --> 00:17:21,220 out there on the horizon. Right? And so the things that we 277 00:17:21,220 --> 00:17:24,974 worry about are, ways that AI can 278 00:17:24,974 --> 00:17:28,654 be misused, right, to spread misinformation, to do 279 00:17:28,654 --> 00:17:32,490 harm to society, to, do harm to other people. 280 00:17:32,490 --> 00:17:36,330 And we think a lot about protecting against that 281 00:17:36,330 --> 00:17:39,309 type of abuse and trying to figure out, you know, how do we even 282 00:17:39,745 --> 00:17:43,185 detect and observe that type of abuse in the first place if it is 283 00:17:43,185 --> 00:17:46,325 happening so that we can then help protect against it. 284 00:17:46,840 --> 00:17:50,679 Interesting. Interesting. What 285 00:17:50,679 --> 00:17:54,385 do you see what do the experts think of this? Do they view 286 00:17:54,385 --> 00:17:58,225 this as a threat or are they just kind of because you're you're smart 287 00:17:58,225 --> 00:18:01,985 by going after the people who are not below the messy 288 00:18:01,985 --> 00:18:05,460 middle. Right? I think that's smart because if someone wants to 289 00:18:05,460 --> 00:18:08,900 start a burger stand, they don't get into the burger stand 290 00:18:08,900 --> 00:18:12,674 business because they wanna rock social media. Right. Right? They don't 291 00:18:12,674 --> 00:18:16,115 do it because they wanna make marketing plans. They want it because they wanna cook 292 00:18:16,115 --> 00:18:17,895 burgers. Right? Like Yeah. 293 00:18:21,110 --> 00:18:24,549 In in the virtual green room, we were talking about Sonic and how they're headquartered. 294 00:18:24,549 --> 00:18:28,230 You're in Oklahoma City and how Sonic is headquartered. So now I'm thinking 295 00:18:28,230 --> 00:18:29,855 about, like, fast food. But, 296 00:18:31,934 --> 00:18:35,294 I mean, like so, I mean, is it sounds like you found a 297 00:18:35,294 --> 00:18:39,120 receptive audience there. But what do the experts say? Do you like 298 00:18:39,200 --> 00:18:42,960 they kind of, you know, are they they 299 00:18:42,960 --> 00:18:46,419 they probably brush it off, but are they brushing it off in a 300 00:18:46,904 --> 00:18:50,745 legitimate way, or is it a little bit of, you know, jealousy or 301 00:18:50,745 --> 00:18:54,585 fear? I think there's a mix. I mean, realistically, just over that large 302 00:18:54,585 --> 00:18:58,000 of a population. Of what I observe. 303 00:18:58,539 --> 00:19:02,139 Yeah. In the grand scheme of things, I'd say we're in the 304 00:19:02,139 --> 00:19:05,815 early adopter phase of generative AI. Right? So we are nowhere 305 00:19:05,815 --> 00:19:09,654 near mass market adoption yet as fast as it seems like 306 00:19:09,654 --> 00:19:13,400 certain tools have taken off. Right? Like, we're we're nowhere near mass 307 00:19:13,400 --> 00:19:16,540 market adoption yet. That's gonna be years down the road. 308 00:19:18,120 --> 00:19:21,885 And so, you know, you're gonna get a variety 309 00:19:21,945 --> 00:19:25,625 of responses. I'd say they range from, you know, some 310 00:19:25,625 --> 00:19:29,325 experts who have leaned in whole hog and are experimenting 311 00:19:30,264 --> 00:19:34,090 as much as they possibly can with all the different platforms and are publicly 312 00:19:34,090 --> 00:19:37,930 publishing those experiments on LinkedIn or Twitter or wherever they might 313 00:19:37,930 --> 00:19:41,115 be doing it, so others can learn from it as well. 314 00:19:41,894 --> 00:19:45,654 You've got, folks at the opposite end of the spectrum who I think 315 00:19:45,654 --> 00:19:49,309 are often reacting with denial or fear. 316 00:19:49,850 --> 00:19:53,549 Right? And I think you also have some just 317 00:19:53,690 --> 00:19:57,424 very correct observations around things 318 00:19:57,424 --> 00:20:01,025 that AI is not yet going to do well. Right? 319 00:20:01,025 --> 00:20:04,690 So, yeah, I I observed this especially 320 00:20:04,750 --> 00:20:07,970 within the community of kinda, like, professional SEO 321 00:20:08,110 --> 00:20:11,745 folks. Right? And, yeah, there are systems and 322 00:20:11,745 --> 00:20:15,505 philosophies of SEO and processes that have been developed that are highly 323 00:20:15,505 --> 00:20:19,059 specialized that a chat gpt is 324 00:20:19,059 --> 00:20:22,899 not designed for today. Right? And so you'll see 325 00:20:22,899 --> 00:20:26,705 these threads where someone goes to chat gpt, and they say, hey. Give me an 326 00:20:26,705 --> 00:20:30,384 idea for 10 keywords that I can target on this blog post that I'm 327 00:20:30,384 --> 00:20:34,225 writing, and it spits out an answer. Right? And the professional SEO will look 328 00:20:34,225 --> 00:20:37,830 at that and say, well, are you using data to analyze 329 00:20:37,890 --> 00:20:41,730 whether or not these are actually the right terms to be targeting based on 330 00:20:41,730 --> 00:20:45,345 where people are searching and, what you can actually win 331 00:20:45,345 --> 00:20:47,685 and so forth. And that's a very fair criticism. 332 00:20:49,265 --> 00:20:53,070 I think we're not far from the point where some tool, and 333 00:20:53,070 --> 00:20:56,830 maybe it's the tools who are already deep in the SEO space, is 334 00:20:56,830 --> 00:21:00,525 going to marry that data to generative AI. Right? 335 00:21:00,525 --> 00:21:04,285 And so now you will have the response being informed 336 00:21:04,285 --> 00:21:08,040 by data. Right? And I think that's where it becomes a lot 337 00:21:08,040 --> 00:21:11,260 a lot more frightening because then people start asking, like, okay. 338 00:21:11,800 --> 00:21:15,560 What is my job now? And that's really the deeper 339 00:21:15,560 --> 00:21:19,404 conversation I think needs to happen because what should result 340 00:21:19,404 --> 00:21:22,945 here is that people are leveled up. Right? People are leveled up to 341 00:21:23,404 --> 00:21:27,080 more strategic thinking and more strategic work, And they 342 00:21:27,080 --> 00:21:30,920 don't have to do as much of the rote processing that happens in 343 00:21:30,920 --> 00:21:34,679 a lot of jobs today. But when you're spending a lot of time on 344 00:21:34,679 --> 00:21:37,325 that today, it's a really scary thought. Right? So, 345 00:21:38,745 --> 00:21:42,424 as a society, we're gonna have to navigate that. And we're gonna have to 346 00:21:42,424 --> 00:21:45,965 figure out training paths for people to 347 00:21:46,570 --> 00:21:50,250 find their way to that next, next definition of 348 00:21:50,250 --> 00:21:54,090 their job. But, you know, I I think it's gonna 349 00:21:54,090 --> 00:21:57,615 play out over years. It's not gonna play out over, you know, months or quarters. 350 00:21:59,995 --> 00:22:01,855 Okay. Interesting. 351 00:22:04,529 --> 00:22:08,370 What, what do you think the next step is 352 00:22:08,370 --> 00:22:11,490 in this? I know you kinda mentioned it, but, like, you you did you touched 353 00:22:11,490 --> 00:22:13,815 on some of that. It 354 00:22:18,115 --> 00:22:20,914 it's interesting. It's interesting to me how that 355 00:22:23,540 --> 00:22:26,260 how this is gonna unfold. And I know it's hard to predict the future, but 356 00:22:26,260 --> 00:22:29,940 what are your thoughts on kind of, like, of this? So, I mean, we 357 00:22:29,940 --> 00:22:32,420 are early in the phase. I mean, I think a lot of people I think 358 00:22:32,420 --> 00:22:35,955 chat gpt got so much wind behind it 359 00:22:39,455 --> 00:22:43,130 because I I don't think anyone in the field expected that chat g p 360 00:22:43,130 --> 00:22:46,410 t would be as good as it was. I I I knew that we would 361 00:22:46,410 --> 00:22:50,250 come up with something like it, but I thought we were still, you know, 3 362 00:22:50,250 --> 00:22:53,995 to 5 years out from something that good. Right. And I think 363 00:22:53,995 --> 00:22:57,675 that because it kinda leapfrog people's expectations, I think that 364 00:22:57,675 --> 00:22:58,815 really boosted it. 365 00:23:01,460 --> 00:23:02,840 Yeah. You know, 366 00:23:05,460 --> 00:23:09,140 I think has really ignited people's imaginations both in good ways and 367 00:23:09,140 --> 00:23:12,355 bad ways. Yeah. I I think you're dead on there. 368 00:23:13,615 --> 00:23:17,455 And I think we're in the experiments the experimentation early 369 00:23:17,455 --> 00:23:20,140 adopter phase. Right? So if I think back to other ecosystems, 370 00:23:21,080 --> 00:23:24,760 like, you know, the early Facebook API and Twitter API 371 00:23:24,760 --> 00:23:28,544 or the early mobile app ecosystems, right, What we saw 372 00:23:28,544 --> 00:23:31,684 was this almost gold rush mentality of 373 00:23:32,304 --> 00:23:36,010 tons of experimentation, a lot of independent developers coming in and just 374 00:23:36,010 --> 00:23:39,770 building things to see what is possible. And then you fast forward 3 375 00:23:39,770 --> 00:23:43,290 years, and 90% of those projects are dead. Right? Right. 376 00:23:43,290 --> 00:23:46,925 Not Because it didn't pan out or it wasn't quite 377 00:23:47,145 --> 00:23:50,905 impactful enough or the developer lost interest. So I think we're 378 00:23:50,905 --> 00:23:54,410 in that phase right now. You know, the the good news is the 10% 379 00:23:54,549 --> 00:23:58,010 that survive that phase can end up being really impactful 380 00:23:58,150 --> 00:24:00,895 applications and really impactful companies. 381 00:24:02,475 --> 00:24:06,175 What's interesting and maybe different this time around 382 00:24:06,875 --> 00:24:10,650 is I see a lot of incumbents and established companies jumping in 383 00:24:10,650 --> 00:24:14,250 the game early. Right? Right. And I consider this still relatively 384 00:24:14,250 --> 00:24:17,530 early. You know, even though we've been at it for a while on our road 385 00:24:17,530 --> 00:24:21,085 map, That was maybe too early or, you know, super 386 00:24:21,085 --> 00:24:24,765 early. But I think a lot of companies right now 387 00:24:24,765 --> 00:24:28,420 are asking themselves, what does this mean for us and what does 388 00:24:28,420 --> 00:24:31,880 this mean for our user? Because a lot of software 389 00:24:32,740 --> 00:24:36,485 that's been built will need to be rebuilt 390 00:24:36,545 --> 00:24:40,385 and rethought. A lot of processes will need to be rethought. So, you know, a 391 00:24:40,385 --> 00:24:44,190 good, example I like is what HubSpot put out where they 392 00:24:44,190 --> 00:24:47,950 said, you know, with the simple example of using a chatbot to do things 393 00:24:47,950 --> 00:24:51,795 like update a CRM record. Right? Right. And it's 394 00:24:51,795 --> 00:24:55,635 like, right, you have to you know, before it was input, output, input, output, input, 395 00:24:55,635 --> 00:24:59,170 output, input, output, and then eventually, the record was updated. 396 00:24:59,170 --> 00:25:02,690 Right? Right. And the user had to click all those buttons and provide all those 397 00:25:02,690 --> 00:25:06,070 inputs. And now it could just be a one line 398 00:25:06,530 --> 00:25:09,945 chat input, and it's updated. Right? 399 00:25:09,945 --> 00:25:13,405 So it's rethinking interfaces. It's rethinking 400 00:25:15,120 --> 00:25:18,720 what software can actually do in someone's life. And and I think that's 401 00:25:18,720 --> 00:25:22,400 gonna be a multiyear cycle, because companies are 402 00:25:22,400 --> 00:25:25,755 gonna have to experiment. Some are gonna hit on big 403 00:25:25,755 --> 00:25:27,535 innovations. Some are gonna fail. 404 00:25:29,275 --> 00:25:33,059 But I think that's the next chapter. Right? Can can we 405 00:25:33,059 --> 00:25:36,580 take chat chat gpt and similar concepts, similar 406 00:25:36,580 --> 00:25:40,200 models from novelty to 407 00:25:41,165 --> 00:25:45,005 broad utility in a way that makes sense for people. No. That's 408 00:25:45,005 --> 00:25:48,605 a great point. And if you you kinda watch I grew up watching, you 409 00:25:48,605 --> 00:25:52,429 know, Star Trek the next generation and d space 9. And if 410 00:25:52,429 --> 00:25:56,029 you watch those shows, there's always this this 411 00:25:56,029 --> 00:25:59,825 thing where the computer becomes a character in the story in the sense that 412 00:25:59,825 --> 00:26:03,265 they say, computer, extrapolate all possibilities of the warp drive 413 00:26:03,265 --> 00:26:06,565 going, like, whatever. Yep. You know? And 414 00:26:06,880 --> 00:26:10,640 you even saw this in The Expanse, which is Andy and I's probably 415 00:26:10,640 --> 00:26:14,480 our favorite show. And there was a there's a scene where one 416 00:26:14,480 --> 00:26:17,965 of the characters is interacting, trying to find an 417 00:26:17,965 --> 00:26:21,405 ideal orbit around, getting around all these warships 418 00:26:21,405 --> 00:26:25,110 and the whole thing. But he goes, you know, what 419 00:26:25,110 --> 00:26:28,230 if I did it with minimal thrust? And he was asking you all these questions, 420 00:26:28,230 --> 00:26:31,805 and it was basically computing all of these kind of parameters. When I 421 00:26:31,805 --> 00:26:35,265 use chat GPT, I kinda feel like I'm doing that. 422 00:26:35,405 --> 00:26:39,165 Yeah. You know? You know, why one of the things that that that that 423 00:26:39,165 --> 00:26:42,750 I have for for my blog is I have a tool called Dingo, 424 00:26:43,530 --> 00:26:46,830 and it kind of helps me produce content and all that. 425 00:26:47,050 --> 00:26:50,485 And I originally wrote it in in c sharp, but I ported 426 00:26:50,485 --> 00:26:54,245 it over to Python with the 427 00:26:54,245 --> 00:26:58,010 help of chat gpt. I basically asked it, What if I wanted a pro 428 00:26:58,090 --> 00:27:00,990 I described the program. How would I write that in Python? 429 00:27:01,530 --> 00:27:05,290 And, you know, I was able and the code wasn't 430 00:27:05,290 --> 00:27:08,845 perfect to your point that, you know, they're not always factual. Right? The 431 00:27:08,845 --> 00:27:12,605 code, if I copied and pasted it, had issues, but those were 432 00:27:12,605 --> 00:27:16,289 not insurmountable issues. So I I I I poked around with 433 00:27:16,289 --> 00:27:20,130 it. Long story short, original version of Dango took about 3, 434 00:27:20,130 --> 00:27:23,350 4 weeks. The Python ported of Dingo 435 00:27:24,245 --> 00:27:27,304 took about a day and a half to get feature parity 436 00:27:28,085 --> 00:27:31,684 Interesting. Which isn't yeah. And obviously pretty 437 00:27:31,684 --> 00:27:34,750 revolutionary. And and you can just skeptic in me. It's like, yeah. But you already 438 00:27:34,750 --> 00:27:38,530 wrote it. Right? So you kinda already thought through a lot of these problems. However, 439 00:27:38,590 --> 00:27:42,375 see you know, it just seems like it's a much faster process. Because I also 440 00:27:42,375 --> 00:27:45,575 think too, it's also a pretty patient mentor. You know what I mean? Where you 441 00:27:45,575 --> 00:27:49,255 can ask it dumb questions. And as long as the server is still 442 00:27:49,255 --> 00:27:52,700 running and it's not overloaded, it's always happy to answer your 443 00:27:52,700 --> 00:27:56,480 questions. Right. Which which I think I think is really kind of the 444 00:27:56,940 --> 00:28:00,415 I saw a video the other day how it's gonna change language learning and this 445 00:28:00,415 --> 00:28:04,175 guy was talking about, you know, hey, you know, I'm intermediate Spanish speaker 446 00:28:04,175 --> 00:28:07,535 and I wanna to go to the next level. I can ask chat 447 00:28:07,535 --> 00:28:11,030 gpt to create this material that is 448 00:28:11,030 --> 00:28:14,790 custom tailored to me. And I think that is that's a fascinating use case 449 00:28:14,790 --> 00:28:18,495 because I don't think anyone thought that. That's not a use case that 450 00:28:18,495 --> 00:28:22,095 you ordinarily would do it. So I find it interesting that I never would have 451 00:28:22,095 --> 00:28:25,780 thought marketing campaigns either, you know. Although I will admit, I have written, like, a 452 00:28:25,780 --> 00:28:29,320 YouTube video description, and I'm, like, make it more exciting, make it more, you 453 00:28:29,860 --> 00:28:33,540 know, dynamic. Right? Yeah. And it does. It does. It's it 454 00:28:33,540 --> 00:28:37,355 it, you know, it's not always something I would say, but that's that's 455 00:28:37,355 --> 00:28:41,195 for me, the human to go in and kinda edit it. Yeah. I gotta hook 456 00:28:41,195 --> 00:28:44,210 you up with a ghostwriter account so you can give us feedback on that. Oh, 457 00:28:44,210 --> 00:28:47,649 sure, man. That'd be awesome. Because we've got things like, you know, YouTube 458 00:28:47,649 --> 00:28:51,330 description generation and and Right. Summarizing and cleaning up my 459 00:28:51,330 --> 00:28:54,995 copy. But but it's interesting because what you hit on there, which is you 460 00:28:54,995 --> 00:28:58,835 had already spent the time thinking through the issues and architecting it and 461 00:28:58,835 --> 00:29:02,550 and figuring out what the challenges were, I see 462 00:29:02,550 --> 00:29:06,310 a direct parallel to what we've been working on in applying this tech and marketing. 463 00:29:06,310 --> 00:29:10,044 Because I I'm taking Ghost Rider as an example, Where a 464 00:29:10,044 --> 00:29:13,725 lot of the work has come is actually in prompt 465 00:29:13,725 --> 00:29:17,424 engineering, testing, quality control, and iteration. 466 00:29:18,140 --> 00:29:21,360 And it's not a once and done process. Like, we're actually 467 00:29:21,900 --> 00:29:25,740 tracking success of various prompts and various use cases and 468 00:29:25,740 --> 00:29:28,915 going back and improving them over time because, 469 00:29:29,775 --> 00:29:33,375 I mean, first, the underlying models are changing. Right? And so, you know, that's a 470 00:29:33,375 --> 00:29:37,159 continuous force of change. But, also, more users are now 471 00:29:37,159 --> 00:29:41,000 using them, and so we have more data. But 472 00:29:41,000 --> 00:29:44,765 but I think part of the key there is you look at this field of 473 00:29:44,765 --> 00:29:48,605 prompt engineering that has now exploded all of a sudden. And while there are 474 00:29:48,605 --> 00:29:52,445 a lot of early adopters diving in there and getting really excited about 475 00:29:52,445 --> 00:29:56,140 it, the reality is the average person should never have 476 00:29:56,140 --> 00:29:59,980 to learn prompt engineering as a new skill. Right? 477 00:29:59,980 --> 00:30:03,655 Like, that is that's not where we end up from a user interface 478 00:30:03,655 --> 00:30:07,495 perspective here. And so we're kind of baking that into our 479 00:30:07,495 --> 00:30:11,020 solution where we say, okay. Part of the value we bring is that 480 00:30:11,020 --> 00:30:14,159 we have a series of 481 00:30:14,380 --> 00:30:17,919 expert created and groomed and tested 482 00:30:17,980 --> 00:30:21,755 prompts, that are trained on real data that, you 483 00:30:21,755 --> 00:30:24,815 know, helps perform an improvement or it helps improve performance. 484 00:30:26,130 --> 00:30:29,970 And that makes the technology now accessible to people who 485 00:30:29,970 --> 00:30:33,510 are not going to spend time on learning prompt engineering. And so, 486 00:30:35,055 --> 00:30:38,575 I I think we'll see those types of evolutions here. And maybe, 487 00:30:38,575 --> 00:30:42,415 eventually, prompt engineering won't be as necessary as it is at 488 00:30:42,415 --> 00:30:46,260 the moment as the models get better, But that might be a longer time line 489 00:30:46,260 --> 00:30:50,039 until you can really get to that point where you don't need 490 00:30:50,340 --> 00:30:54,045 that type of work at least going on in the background. Right. I I do 491 00:30:54,045 --> 00:30:57,345 wonder, like, is prompt engineering the next hot job title? 492 00:30:57,885 --> 00:31:01,700 Yeah. Or is it gonna be more as these models improve, like 493 00:31:01,700 --> 00:31:05,460 you say, it'll be more prompt optimization. Right? Or and I'm 494 00:31:05,460 --> 00:31:08,660 sure I'm sure we'll come up with a fancy acronym for that because we always 495 00:31:08,660 --> 00:31:12,455 do. But probably start using AI to optimize the prompts 496 00:31:12,455 --> 00:31:16,095 also, right, to to monitor and measure and and 497 00:31:16,515 --> 00:31:20,120 No no joke. I've done that. I've done that with DALL E with Dolly. So 498 00:31:20,120 --> 00:31:23,559 so last year was an interesting year in AI. Right? Because Dolly alone and the 499 00:31:23,559 --> 00:31:27,400 work that was done in image generation would have been the headline story. But at 500 00:31:27,400 --> 00:31:30,955 the, you know, the last minute, you know, chat 501 00:31:30,955 --> 00:31:33,835 gpt kinda took all the oxygen out of the room. I don't think people realize 502 00:31:33,835 --> 00:31:37,434 that. So there's actually I've actually, like, done it where I if I 503 00:31:37,434 --> 00:31:41,150 I I give it a prompt. Yep. That I wanna generate an image with. 504 00:31:41,150 --> 00:31:44,350 And I tested this, and I'll let me do a blog post on this, where 505 00:31:44,350 --> 00:31:47,470 I say, give me a picture or painting of a doctor in the style of 506 00:31:47,470 --> 00:31:51,085 Rembrandt. Right? And then I asked 507 00:31:51,085 --> 00:31:54,845 chatty Pete, hey. How would you write this as a prompt to get the 508 00:31:54,845 --> 00:31:58,260 best output for DALL E 2? And it came up with a paragraph. 509 00:31:58,880 --> 00:32:02,240 Mhmm. You know, use this type of lighting, this type of paint. I mean, it 510 00:32:02,240 --> 00:32:05,445 was just stuff I never would have thought of. And then just for grins, I 511 00:32:05,445 --> 00:32:08,645 put pasted that in and it the the the 512 00:32:08,805 --> 00:32:12,485 obviously, art is subjective, but I would say that the 513 00:32:12,485 --> 00:32:16,330 quality of that improved pump, the output was an order of 514 00:32:16,330 --> 00:32:20,169 magnitude better. That's really it. And 515 00:32:20,169 --> 00:32:23,135 that's cool because anyone can kind of experiment with that. Right? That's like a simple 516 00:32:23,135 --> 00:32:26,575 thing anyone can do, you know. You give it a basic prompt and you ask 517 00:32:26,575 --> 00:32:29,775 it to get make it make it more for, I think you're probably doing it 518 00:32:29,775 --> 00:32:33,190 now. But, but, like, it's 519 00:32:33,190 --> 00:32:36,630 fascinating. Like, it comes up with a much better quality. And and I I think 520 00:32:36,630 --> 00:32:40,390 that that's interesting for a number of reasons. One, we're using AI to talk to 521 00:32:40,390 --> 00:32:43,605 AI. Right? Which is kind of a, I don't know, like, that sounds like the 522 00:32:43,605 --> 00:32:47,365 start of every bad sci fi movie. And the 523 00:32:47,365 --> 00:32:51,210 other thing is is that that potential to create that 524 00:32:51,210 --> 00:32:54,430 better image was always in that model. 525 00:32:54,809 --> 00:32:58,585 Yeah. We're just using the prompt to draw it out. I 526 00:32:58,585 --> 00:33:00,525 I I I think there's something very 527 00:33:03,065 --> 00:33:06,745 curious and interesting about that. Right? That that model is they the model 528 00:33:06,745 --> 00:33:10,290 is maybe more capable than we're aware of Yep. 529 00:33:10,650 --> 00:33:14,010 Which blows my mind. I was looking up a 530 00:33:14,010 --> 00:33:17,465 thread that you know, pretty recent. If you look up 531 00:33:17,465 --> 00:33:19,965 Nick Floats on Twitter, at Nick Floats, 532 00:33:21,065 --> 00:33:24,205 so Midjourney came out with their new described 533 00:33:24,850 --> 00:33:28,289 endpoint. And so he's basically doing that where he's he's 534 00:33:28,289 --> 00:33:32,070 generating an image, asking it to describe 535 00:33:32,495 --> 00:33:36,335 the image as a prompt, and then rerunning that prompt to see 536 00:33:36,335 --> 00:33:40,174 what the output looks like after rerunning the prompt that the tool actually generates 537 00:33:40,174 --> 00:33:43,590 for an existing output. And it's fascinating to see, 538 00:33:44,610 --> 00:33:48,370 both how Midjourney actually describes, right, in the in the 539 00:33:48,370 --> 00:33:51,884 first place, Right. The piece of content, but then also, 540 00:33:53,144 --> 00:33:56,764 the difference in in the two outputs. Right? 541 00:33:58,200 --> 00:34:01,640 Yeah. I'm looking at his I'm looking at his Twitter feed now, and, like, I 542 00:34:01,640 --> 00:34:05,400 see the pictures and, you know, not that long ago, I would have said, 543 00:34:05,400 --> 00:34:08,315 oh, wow. You must have an artist friend or he must be an artist himself. 544 00:34:08,315 --> 00:34:12,155 Yeah. But now it's like and it's funny. I don't know. Maybe it's my 545 00:34:12,155 --> 00:34:14,875 imagination, but I can look at an image. I'm like, oh, that looks like a 546 00:34:14,875 --> 00:34:18,139 stable diffusion. That looks like a DALL E. That looks like a, 547 00:34:18,460 --> 00:34:22,000 Midjourney. Like, it's interesting how 548 00:34:22,219 --> 00:34:26,054 those models have a certain style. Yeah. I did that 549 00:34:26,054 --> 00:34:29,815 for fun last year with DALL E. I, you 550 00:34:29,815 --> 00:34:33,335 know, generated a virtual piece of artwork and then posted it on my 551 00:34:33,335 --> 00:34:36,900 Instagram. And, I think I put the caption something like, you 552 00:34:36,900 --> 00:34:40,100 know, so can I say I can paint now or something? But I didn't include 553 00:34:40,100 --> 00:34:43,705 any other context. And I got comments back from some of my friends 554 00:34:43,705 --> 00:34:46,205 like, you made that. Oh my goodness. Right? 555 00:34:47,705 --> 00:34:50,505 But Yeah. And and and what a shift there's been. Because if you did that 556 00:34:50,505 --> 00:34:54,247 today, people would be like, oh, you're using AI for that. Yeah. And You 557 00:34:54,247 --> 00:34:57,879 know? That couldn't have been more than 6 months ago that that happened. So 558 00:34:58,121 --> 00:35:01,905 It's really it's really gone fast. And you think about, like, GPT 4 is 559 00:35:01,905 --> 00:35:03,765 out and Yeah. GPT, 560 00:35:05,744 --> 00:35:07,525 5 is in the works. And 561 00:35:09,620 --> 00:35:12,820 if nothing else, obviously, they have smart people working there, but the people who do 562 00:35:12,820 --> 00:35:15,480 the marketing at OpenAI are top notch because 563 00:35:16,215 --> 00:35:20,055 they already had GPT 4 kind of waiting in the wings when 564 00:35:20,055 --> 00:35:23,789 they released g chat GPT. Mhmm. And then once 565 00:35:23,789 --> 00:35:27,490 that once everybody got all crazy over that, then they released that. And apparently, 566 00:35:28,030 --> 00:35:31,765 4 was being worked on. I mean, 5 was being worked 567 00:35:31,765 --> 00:35:35,445 on even as that was happening. So it's just it's fascinating to see 568 00:35:35,445 --> 00:35:39,230 how this is going and it you're right, though. It's I 569 00:35:39,230 --> 00:35:42,770 haven't seen people kinda go this crazy since the beginning of the Internet. 570 00:35:43,310 --> 00:35:46,805 Yep. It's just in terms of, you 571 00:35:46,805 --> 00:35:50,645 know, we are in a not an ideal economic environment. There are banks 572 00:35:50,645 --> 00:35:54,120 collapsing all that, but the investment is not dried up in 573 00:35:54,360 --> 00:35:58,200 specifically AI. Big tech tech has obviously taken a hit, 574 00:35:58,200 --> 00:36:02,040 but, you know and you're right. Yeah. Even big players are jumping in with both 575 00:36:02,040 --> 00:36:05,775 feet, You know? Although, I don't know if you played with Bard. I I haven't 576 00:36:05,775 --> 00:36:09,295 yet, personally. I I I don't wanna I know that they're 577 00:36:09,295 --> 00:36:12,960 working feverishly on it, but I was not impressed because I 578 00:36:12,960 --> 00:36:16,240 asked it. So if you ask if you ask chat g p t, you know, 579 00:36:16,240 --> 00:36:19,725 hey. Write a script that goes infectious weather data. Right? That's, like, my 580 00:36:19,805 --> 00:36:23,325 hello world. Right? Just to test it out. Right? Check CPT will 581 00:36:23,325 --> 00:36:26,865 happily give you a whole thing, talk about it, give you code, 582 00:36:27,370 --> 00:36:31,210 You can copy and paste it. It mostly works. If 583 00:36:31,210 --> 00:36:35,050 you ask Bard, Bard actually says, I'm I'm a language model. 584 00:36:35,050 --> 00:36:38,695 I I can't do that. Interesting. Like, now, again, that 585 00:36:38,695 --> 00:36:42,375 was a week ago. This is a fast moving field. Yeah. But it it's 586 00:36:42,375 --> 00:36:46,050 kind of funny how I don't know, I think if you 587 00:36:46,050 --> 00:36:49,590 can kinda sense the style that's different in visual mediums, 588 00:36:50,450 --> 00:36:54,005 like, you know, the mid journeys and the, the dollies and, 589 00:36:54,325 --> 00:36:57,845 the stable diffusions. Is there going to be kind of a 590 00:36:57,845 --> 00:37:00,745 similar style difference, 591 00:37:01,849 --> 00:37:04,829 you know, in the text generation ones. I suspect there will be. 592 00:37:06,329 --> 00:37:10,170 And it's interesting what g p t 35 knows versus g p 593 00:37:10,170 --> 00:37:13,795 t 4. Like Yeah. It it knows about the articles 594 00:37:13,795 --> 00:37:17,635 I've written. Right? So I can ask it to write an article in 595 00:37:17,635 --> 00:37:21,410 the style of Frank Lavinia. Right? And and and 596 00:37:21,410 --> 00:37:24,370 and it and it did. And I I read it and I'm like, yeah, it 597 00:37:24,370 --> 00:37:28,095 does look something like I would write. You know? Yeah. And I 598 00:37:28,175 --> 00:37:31,695 when I was writing for MSDN Magazine, well, I coulda I I could wholly coulda 599 00:37:31,695 --> 00:37:35,470 used that. But it's interesting. When I asked g p t 4, GPT 600 00:37:35,470 --> 00:37:38,830 4 has no idea who I am, which I'm not sure if I should 601 00:37:38,830 --> 00:37:41,650 be happy with that or a little upset at that. 602 00:37:42,990 --> 00:37:46,365 It just hasn't found you. Yes. Right. It hasn't found me yet. But it it 603 00:37:46,445 --> 00:37:50,125 it's it's also telling that there's a lot of motion here of 604 00:37:50,125 --> 00:37:53,770 people taking this offline. Right? So you wanna train your own 605 00:37:53,770 --> 00:37:57,450 model and, like, you know, the the amount of I think you're right. 606 00:37:57,450 --> 00:38:01,095 We're only at the beginning of the innovation curve on this one. Yeah. You 607 00:38:01,095 --> 00:38:03,994 know, like, when the Internet first came out, who would have thought 608 00:38:05,095 --> 00:38:08,855 of something like Uber or Lyft. Right? Or DoorDash. Right? 609 00:38:08,855 --> 00:38:12,510 Like that or, you know, or I don't think we can we're so 610 00:38:12,510 --> 00:38:16,270 early in it. We can't really predict the future beyond the next couple of 611 00:38:16,270 --> 00:38:20,085 weeks. Yeah. Yeah. And and it's interesting you bring up that 612 00:38:20,085 --> 00:38:23,625 example because that's something we're working on in real time in our context. Right. 613 00:38:24,710 --> 00:38:28,390 We have a a large user base. And, again, there's the the experts down to 614 00:38:28,390 --> 00:38:31,289 the beginners and all different levels of experience in between. 615 00:38:32,150 --> 00:38:35,625 And so within our member 616 00:38:35,625 --> 00:38:39,385 base, we have people who have incredibly well defined 617 00:38:39,385 --> 00:38:43,069 brand voices and styles where they do 618 00:38:43,069 --> 00:38:46,130 have enough you know, 1, have enough seed content to train 619 00:38:46,670 --> 00:38:49,895 personalized versions of the model on their voice, 620 00:38:51,875 --> 00:38:55,715 and to want that. Right? Like, they they wanna maintain their 621 00:38:55,715 --> 00:38:59,220 voice, and they don't wanna sound like everyone else. And then you have folks at 622 00:38:59,220 --> 00:39:02,760 the other other end of the spectrum who might need help even 623 00:39:02,820 --> 00:39:06,555 being introduced to the concept of developing your voice and working through what 624 00:39:06,555 --> 00:39:10,315 your voice should be and testing iterations, off of 625 00:39:10,315 --> 00:39:13,930 different sample datasets. And so, we're 626 00:39:13,930 --> 00:39:17,530 basically working through from a, you know, how does this technology come to market 627 00:39:17,530 --> 00:39:21,290 perspective, solving that exact problem now of, you know, 628 00:39:21,290 --> 00:39:25,105 for a large diverse user base, how can we give people 629 00:39:25,105 --> 00:39:28,945 the ability to tailor outputs to their voice and their style if they 630 00:39:28,945 --> 00:39:32,705 know what that is and simultaneously help people develop that if they 631 00:39:32,705 --> 00:39:36,550 don't know what it is. So that's yeah. Hopefully, we'll 632 00:39:36,550 --> 00:39:39,750 have something there in market pretty soon. I I think we're not that far away, 633 00:39:39,750 --> 00:39:43,595 but I'm sure that's something we'll need to, you know, iterate on and learn learn 634 00:39:43,595 --> 00:39:47,355 about over time too. Well, cool. So now 635 00:39:47,355 --> 00:39:50,910 we'll switch over to the pre canned questions. Okay. 636 00:39:50,910 --> 00:39:54,590 Which I pasted in the chat. Not they're not, real brain teasers. They're 637 00:39:54,590 --> 00:39:57,810 just kind of, general stuff. 638 00:39:58,345 --> 00:40:01,724 Yeah. How did you find your way into data and AI? 639 00:40:01,785 --> 00:40:05,085 Did did data find you, or did you find data? 640 00:40:05,840 --> 00:40:09,600 I always loved math. Always, always, always loved math. If I had 641 00:40:09,600 --> 00:40:13,040 to really answer this, I'd say, you know, the 2 earliest examples that come to 642 00:40:13,040 --> 00:40:16,674 mind, I was nuts about baseball growing up and 643 00:40:16,674 --> 00:40:20,434 baseball statistics. Oh, cool. That was probably 644 00:40:20,434 --> 00:40:24,079 one of the first ways that data found me. And then a little bit later 645 00:40:24,079 --> 00:40:27,760 than that, the the first company I ever tried starting when I was in 646 00:40:27,760 --> 00:40:31,059 college was, essentially arbitraging the collectibles 647 00:40:31,200 --> 00:40:34,775 market. So think about things like Magic the Gathering cards. Right? 648 00:40:36,115 --> 00:40:39,960 And so, yeah, I I began tracking 649 00:40:40,260 --> 00:40:44,099 prices that different collectibles were selling at on eBay and other 650 00:40:44,099 --> 00:40:46,040 platforms. Yeah. This is going back, 651 00:40:47,675 --> 00:40:51,515 over 20 years at this point. But Right. Right. 652 00:40:51,595 --> 00:40:55,420 Tracking prices to learn what was a good price and what was a bad price 653 00:40:55,420 --> 00:40:59,260 before we had that information readily accessible and then buying and selling 654 00:40:59,260 --> 00:41:02,480 against it. Those are the or 2 of the examples, 655 00:41:03,275 --> 00:41:06,955 I think. But, yeah, data just always spoke to me. I I've 656 00:41:06,955 --> 00:41:10,770 always loved math, and so it was symbiotic. Yeah. 657 00:41:10,770 --> 00:41:14,450 That's funny. I was also a huge baseball fan growing up, and 658 00:41:14,450 --> 00:41:18,130 it's one of those I mean, if you're if you're if you're a 659 00:41:18,130 --> 00:41:21,845 baseball fan, statistics is a natural 660 00:41:22,065 --> 00:41:25,905 field for you to study because you've already Mhmm. You've already done a lot 661 00:41:25,905 --> 00:41:29,125 of it. Right? So it's it's that's it's it's interesting. 662 00:41:29,550 --> 00:41:32,530 We'll have to figure out what who who which team you root for. 663 00:41:34,349 --> 00:41:37,490 But, I don't know if I wanna admit that. 664 00:41:39,615 --> 00:41:43,455 Well, my my Xbox gamer tank 665 00:41:43,455 --> 00:41:47,215 was Frankie Bronx, so you could probably figure out that I'm a Yankee fan. 666 00:41:47,215 --> 00:41:51,000 That I grew up mostly. I'm sorry. No. I expect the 667 00:41:51,000 --> 00:41:54,520 hate mail to come in. Yeah. I I grew up mostly in South 668 00:41:54,520 --> 00:41:57,720 Florida, and the Marlins came about. So when I was very young, we were a 669 00:41:57,720 --> 00:42:01,385 mess household. Oh, okay. And then, the Marlins came 670 00:42:01,385 --> 00:42:05,224 about, and I became a Marlins fan. So yeah. Cool. Being a Marlins fan, it's 671 00:42:05,224 --> 00:42:08,525 been a few years that were really fun and a whole bunch of misery. 672 00:42:09,589 --> 00:42:13,430 I I I've noticed that. I'm kinda surprised at that, but I 673 00:42:13,430 --> 00:42:16,655 guess that's what it that's, you know 674 00:42:17,115 --> 00:42:20,815 although you went from being a Mets fan. I know Mets Mets have good years 675 00:42:21,355 --> 00:42:24,735 and bad years. Some would say good decades and bad decades, 676 00:42:24,795 --> 00:42:27,359 but Yep. 677 00:42:28,940 --> 00:42:32,780 The nice thing about baseball is that there's always another game and another season, you 678 00:42:32,780 --> 00:42:36,555 know, you can you can always hold out hope. Yeah. Alright. 679 00:42:36,555 --> 00:42:40,075 So on to the next question. What's the favorite part of your current 680 00:42:40,075 --> 00:42:43,675 gig? Yeah. I think it's it's honestly this period of 681 00:42:43,675 --> 00:42:47,330 innovation we're in. It's fun. It's new. The answers 682 00:42:47,330 --> 00:42:51,170 are unknown. There are so many different paths it could take, and I 683 00:42:51,170 --> 00:42:54,905 think there's a lot of good that can be created. So it it reminds me 684 00:42:54,905 --> 00:42:58,744 in a lot of ways of the early ideological days of the 685 00:42:58,744 --> 00:43:02,450 Internet. And, you know, I think we need to learn 686 00:43:02,450 --> 00:43:05,890 from that chapter in terms of things that weren't 687 00:43:05,890 --> 00:43:08,630 regulated or managed well as the Internet grew 688 00:43:10,175 --> 00:43:14,015 and make sure we don't make those same mistakes again. But that that 689 00:43:14,015 --> 00:43:17,535 excitement's back for me personally. I think we're experiencing a pace of 690 00:43:17,535 --> 00:43:21,360 innovation all of a sudden in software that we really haven't seen, 691 00:43:21,360 --> 00:43:25,120 you know, in 10 or 20 years. Yeah. That is a really 692 00:43:25,200 --> 00:43:28,855 I that is a that is an excellent point. I think the fact that 693 00:43:28,855 --> 00:43:32,295 they kept pushing out models every few weeks publicly 694 00:43:32,295 --> 00:43:35,870 and being 695 00:43:35,870 --> 00:43:39,010 able to keep up with the met the demand more or less, 696 00:43:41,390 --> 00:43:45,175 as as does take me back to those days where people were, like, 697 00:43:45,234 --> 00:43:48,435 I'll never forget. It was an ad I saw in a magazine, which one of 698 00:43:48,435 --> 00:43:51,815 them one of the millions of web development magazines that came out in 96. 699 00:43:52,210 --> 00:43:56,049 Right? Yep. And, and it was, like, you know, 700 00:43:56,049 --> 00:43:59,430 they show a picture of somebody with a with a sleeping, 701 00:43:59,890 --> 00:44:03,515 bag under their desk and somebody checking their email right away, 702 00:44:03,974 --> 00:44:07,494 like like, half in the bag, half well, that sounds bad. Half in the sleeping 703 00:44:07,494 --> 00:44:11,190 bag and half on the computer. And they were saying, you know, something 704 00:44:11,190 --> 00:44:14,390 like and the caption was, what did you miss? Like, it was like it was 705 00:44:14,390 --> 00:44:18,069 like really that type of mentality that you're right. I haven't 706 00:44:18,069 --> 00:44:19,235 seen this in a while. 707 00:44:21,875 --> 00:44:25,715 So the we have 3 complete this sentence, questions. The first 708 00:44:25,715 --> 00:44:28,375 one is, when I'm not working, I enjoy blank. 709 00:44:29,760 --> 00:44:33,600 Being a dad. That's that's it for me. 710 00:44:34,400 --> 00:44:37,575 You know, between founder life, that's about all there is time for, honestly. 711 00:44:38,115 --> 00:44:41,955 Yeah. But, our daughter is at a really fun 712 00:44:41,955 --> 00:44:45,255 age. She's gonna turn 8 pretty soon here, and, 713 00:44:46,380 --> 00:44:49,900 I just enjoy being able to be goofy and have fun and 714 00:44:49,900 --> 00:44:53,440 play. And, yeah, she's wonderful. 715 00:44:54,055 --> 00:44:57,095 That's cool. That is a fun age. My youngest is 8. So 716 00:44:57,415 --> 00:45:00,855 Yeah. The, the next 717 00:45:00,855 --> 00:45:03,195 question is, complete the sentence. 718 00:45:04,839 --> 00:45:08,460 I think the coolest thing in technology today is blank. 719 00:45:09,799 --> 00:45:13,475 Well, I guess I gotta say a tailwind. Right? What we're doing here. 720 00:45:13,795 --> 00:45:16,915 Right. I really do think a lot of what we're doing is cool, but but 721 00:45:16,915 --> 00:45:20,755 more broadly, I'd say, yeah, I think 722 00:45:20,755 --> 00:45:24,400 the coolest thing is that our 723 00:45:24,400 --> 00:45:28,080 perceptions of what technology is capable of are changing 724 00:45:28,080 --> 00:45:31,545 very quickly, and, that's fun. Yeah. 725 00:45:31,545 --> 00:45:35,385 Absolutely. I mean, one of the things you would hear was that 726 00:45:35,385 --> 00:45:39,010 creative jobs were safe for a long time. And I think 727 00:45:39,010 --> 00:45:42,710 that, if anything, we've learned in the last 3 to 6 months, 728 00:45:43,329 --> 00:45:45,109 that's not necessarily the case. 729 00:45:46,905 --> 00:45:50,425 Yeah. So I think that's also a part of, you know it's it's probably not 730 00:45:50,425 --> 00:45:54,105 a coincidence that, you know, Chat GPT upset people more than the image 731 00:45:54,105 --> 00:45:57,470 ones because the pack media, 732 00:45:59,130 --> 00:46:02,730 the people who write those articles, their jobs are, I think, are in I wouldn't 733 00:46:02,730 --> 00:46:06,135 say imminent jeopardy, but they are definitely on the firing line. Yeah. 734 00:46:06,135 --> 00:46:09,655 Whereas, you know, a year ago, oh, no. No one AI can never be 735 00:46:09,655 --> 00:46:13,340 creative. And yet, here we are. Mhmm. 736 00:46:13,960 --> 00:46:17,560 Alright. The last, complete the sentence. I look forward to the day 737 00:46:17,560 --> 00:46:21,180 technology I can use technology to blank. Teleport. 738 00:46:22,075 --> 00:46:25,835 Yes. How's I like that. I 739 00:46:25,835 --> 00:46:29,595 like that answer. Yeah. That's even better than self driving cars because 740 00:46:29,595 --> 00:46:33,130 you wouldn't waste time in the car at all. Exactly. I love 741 00:46:33,130 --> 00:46:36,910 seeing new places. I hate getting there. Yeah. 742 00:46:37,610 --> 00:46:40,235 And we should probably ask chat g p t if there's a way we can 743 00:46:40,235 --> 00:46:41,775 make air travel less awful. 744 00:46:44,635 --> 00:46:48,430 We'll have the gpt airlines soon. Too. GPT 745 00:46:48,430 --> 00:46:49,810 Airlines. That's funny. 746 00:46:52,109 --> 00:46:55,765 Alright. So share something different about 747 00:46:55,765 --> 00:46:59,305 yourself, but, we like our clean Itunes, rating. 748 00:46:59,525 --> 00:47:02,505 So keep it keep it within those parameters. 749 00:47:03,339 --> 00:47:06,319 Oh, this is a tough one. I I never know how to answer this, honestly. 750 00:47:07,660 --> 00:47:10,540 Something different about myself. Like, you know, I I don't know if it's super different, 751 00:47:10,540 --> 00:47:14,105 but I'll just say I'm I'm a huge strategy game nerd. Oh, 752 00:47:14,105 --> 00:47:17,944 interesting. And so, yeah, I mentioned with the magic cards before, it's 753 00:47:18,105 --> 00:47:21,790 yeah. I still love magic. I love settlers, risk, 754 00:47:22,490 --> 00:47:26,250 chess. Throw any strategy game at me, and, I 755 00:47:26,250 --> 00:47:29,870 could lose myself for days. That's cool. That's cool. 756 00:47:32,315 --> 00:47:35,935 So, Audible sponsors data driven. 757 00:47:36,315 --> 00:47:39,775 Can you recommend a good audiobook if you do audiobooks? If you don't do audiobooks, 758 00:47:39,930 --> 00:47:43,710 just recommend a good, regular old dead tree book. 759 00:47:44,410 --> 00:47:48,010 Yeah. So, I'll recommend oh, I'm, like, staring at my 760 00:47:48,010 --> 00:47:51,125 bookshelf here. I think BoomTown 761 00:47:51,744 --> 00:47:54,645 is a really interesting one. So, 762 00:47:55,744 --> 00:47:59,130 before I moved to Oklahoma, I knew nothing about Oklahoma. 763 00:47:59,510 --> 00:48:03,130 Uh-huh. And, its history is absolutely fascinating. 764 00:48:03,350 --> 00:48:07,115 So, I think that's a really cool one, for people who 765 00:48:07,115 --> 00:48:10,955 have never been here before and just wanna learn about a new place 766 00:48:10,955 --> 00:48:14,735 and a new place and time. It might challenge a lot of perceptions, 767 00:48:14,955 --> 00:48:18,030 but, it's a really good read. 768 00:48:18,570 --> 00:48:22,330 K. I'll have to check that out. And where can folks find 769 00:48:22,330 --> 00:48:26,145 out more about you and what you're up to? Yeah. Absolutely. So, 770 00:48:26,444 --> 00:48:28,545 for Tailwind, we are tailwindapp.com. 771 00:48:30,765 --> 00:48:34,460 That's our website. And you can follow us and and find us pretty easily on 772 00:48:34,460 --> 00:48:38,160 all the different social platforms and blog. For me personally, 773 00:48:38,779 --> 00:48:42,115 you know, probably just look me up on LinkedIn. I'm Daniel Maloney. 774 00:48:43,375 --> 00:48:46,895 I think Daniel p Maloney is my handle. But that's probably one of the 775 00:48:46,895 --> 00:48:50,430 platforms I'm more active on these days, in terms of 776 00:48:50,430 --> 00:48:54,190 wanting to connect. Well, that's awesome. Well, thanks for joining 777 00:48:54,190 --> 00:48:57,955 us, and, I'll let Bailey finish the show. And just 778 00:48:57,955 --> 00:49:01,095 like that, we've reached the end of today's digital odyssey. 779 00:49:02,035 --> 00:49:05,770 A huge thanks to our phenomenal guest, Danny Maloney, for 780 00:49:05,770 --> 00:49:09,150 sharing his insights and to Tailwind for redefining the marketing 781 00:49:09,210 --> 00:49:12,589 landscape. To our listeners, your curiosity 782 00:49:12,810 --> 00:49:16,165 fuels this journey, and we're immensely grateful for your companionship. 783 00:49:17,345 --> 00:49:21,105 If today's episode sparked a bit of that data driven wonder in you, 784 00:49:21,105 --> 00:49:24,630 why not share the love? Like, share, and 785 00:49:24,630 --> 00:49:28,150 subscribe to keep this conversation going and to ensure you never miss an 786 00:49:28,150 --> 00:49:31,927 episode. Until next time, keep those circuits 787 00:49:31,927 --> 00:49:34,827 buzzing and your data flowing. Cheerio.