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Danny Maloney on AI’s Leap to Marketing Mastery
Episode 3029th February 2024 • Data Driven • Data Driven
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In this episode, Danny Maloney is going to lead you on a journey to explore how artificial intelligence is not just a fleeting novelty but a tool of immense utility that's changing the playing field for individuals and small businesses alike. Danny brings his passion for algorithmic innovation from his experiences as a data-loving youth to his leadership role at Tailwind, where they leverage AI to level the marketing playing field for small businesses.

Show Notes

06:16 Early days of prevalent AI models, feedback loop.

08:37 Small businesses struggle with limited resources for marketing.

12:31 AI guides marketing decisions for faster success.

16:34 AI leader initiating internal discussions on AI's impact.

19:18 Experts experimenting, varying responses to AI capabilities.

23:43 Early phase of tech development and impact.

26:35 Tool Dingo ported from C# to Python.

29:47 Making prompt engineering unnecessary for average users.

31:39 Requested a specific image prompt and tested.

35:03 OpenAI developing GPT-5, creating internet frenzy.

38:58 Helping users personalize and develop voice technology.

43:24 Retro tech culture and its work ethic.

45:47 Chat GPT upsets media writers, AI creativity.

48:57 Digital journey ends with gratitude and encouragement.

Transcripts

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Ladies and gentlemen, boys and girls, and AI entities of

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all computational capacities, welcome to another riveting

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episode of the data driven podcast. Today,

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we embark on a journey through the digital landscape where data

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isn't just numbers. It's the very fabric of our

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digital existence. With the ever charming Frank steering

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the ship solo, Andy was off gallivanting at Busch Gardens when this was

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recorded. We delve into the world of generative AI and

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marketing marvels with the illustrious Danny Maloney, the brain behind

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Tailwind. Picture this. A

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world where small businesses wield the power of giants, thanks to

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Tailwind's arsenal of automated marketing tools.

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From the nostalgic lanes of Google Street View to the cutting edge frontiers of

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AI driven marketing strategies, we're in for a treat.

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And since today is February 29th, leap day, we

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figured, why not leap into another episode? So

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adjust your antennas, polish your circuits, and prepare for an

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electrifying discourse on how Tailwind is reshaping the marketing

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cosmos, one algorithm at a time. Buckle

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up. It's going to be a data driven ride.

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Hello and welcome to Data Driven, the podcast where we explore the emerging fields

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of data, artificial intelligence, and the ever present world of data engineering.

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But speaking of data engineering, my co host, Andy, is not

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here today. He's having a fun family day at Busch Gardens.

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So, we wish him good weather and,

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short lines. So with me today

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is Danny Maloney, a successful Internet entrepreneur,

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CEO and cofounder of Tailwind, a software platform that

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provides small businesses with the marketing tools they need to compete. With

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over 1,000,000 users, Tailwind is a leader in

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generative AI and providing automated marketing plan

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creation, visual content design, copywriting, email campaign

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building, ad optimization, and more. But before starting his

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own company, he worked at Google, YouTube, and AOL,

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and is part of it apparently had, worked on something I'm very

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fond of is, Street View and Google Maps. And, maybe we

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could talk a little bit more about that. So welcome to the show, Danny.

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Great. Thanks for having me, Frank. Glad to be here. Yeah. Good to have you.

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Good to have you. So generative AI, right, as

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probably since, you know,

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last November has been on everybody's

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minds. What you've been how long

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ago did you start Tailwind? Yeah. So,

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Tailwind itself came to market in 2015. Okay. So we've

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been around for a while. You know, the way we tell our story is

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really in a couple of chapters. So there was kind of tailwind v 1,

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and there's tailwind 2 point o as we call it. And so for

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us, that was kinda 2015 to 2019 and then 2019 to the present

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day. But going down the path of,

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generation, for clients and really thinking about how we

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do more of the work of marketing for them, was

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the real theme of this second chapter. So we started on that path

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back in 2019, believe it or not, even though, you know, the market is

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really kinda waking up and starting to pay attention to your generative AI today,

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there was a lot to learn and a lot to figure out in terms of

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how to go about it the right way. And so it's been a pretty fun

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past few years for us going down that road. Interesting. So what

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does Tailwind generate specifically? Is it NLP? Is it

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LLM? Like, what is, image generation?

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Yeah. So there's actually multiple components. So like you said, we're trying to

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give small businesses the tools that they need to compete as marketers.

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So for us, that's not just one use case, but it's actually thinking

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about the entire marketing life cycle. And so we look at,

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today, 4 core components of what we help generate.

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One piece is actually the marketing plan itself. And that so that's our own

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IP, our own technology that we built that builds and

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extends and evolves marketing plans based on the specifics of the

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business. Once you have that plan, then you've gotta actually execute it. So

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now we get into the next couple components. So one is a tool called Tailwind

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Create, which is about visual generation. So we actually

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create the images that someone needs and, you know, don't think

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necessarily about a tool like a Canva or a PicMonkey in that

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sense of, you know, I'm just going through templates, and I'm designing it

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myself. What we do is we actually take in the assets and the

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inputs from a brand, and then we generate a large array of

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designs that they can scroll through and choose from instead of

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having to do the design work. So it kinda takes the design process from

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45 minutes to about 2 minutes, typically, and we generate it

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in all the formats they need for their different marketing assets. So

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visual design is the second part. The third part is copywriting.

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And so there, it's a mix of our IP and also

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leveraging third party tools that are out there. So leveraging LMS,

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for example. But that's about, you know, being

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able to help people write the copy that's gonna be convincing, that's gonna

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help communicate with their audience. And then the 4th component

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is ads. So we actually acquired a company last year

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called Nectar 9, who they themselves had been building out

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an AI driven ad management platform for about 5 years prior

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to that. And, we're now integrating that into

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Tailwind so that our users will be able to mostly automate

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the process of paid advertising also. But we look at that as a

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complete cycle. Right? So I've got a plan. I can create the content for the

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plan, then I can distribute it and get it out to my audience.

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And the data from that cycle should inform my

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plan so I can get better and get smarter into the future.

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Interesting. That's one of the things that I I I

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wonder about these these generative tools. Like, do they take the feedback

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and that, like, you know so eventually, it would get better. Ideally, it

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would eventually get better. And it sounds like you do include that in your feedback

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loop. There are some. I mean, honestly, I think we're in early days of

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that. Right? If you look at some of the more prevalent models that are

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out there, you know, the the various OpenAI models, for

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instance, I think there is an inherent feedback loop in how

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those operate as they continue to train on more data.

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Right? When you talk about things from a marketing context in

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particular, the feedback loop for us has been around

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things like, how do we train, you know,

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our models, our products, or third party models

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around specific use cases. Right? So, looking at the

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corpus of data we have around what works, what doesn't work,

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enabling it to be more tailored to the use case that the user

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is trying to solve. So, you know, when you're designing a

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pin for Pinterest, that should be different than a, you know,

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feed post on Instagram. Right? A feed post on Instagram is different from

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writing a script for a reels, right, or a TikTok. So,

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for each of those use cases, we found you can start generating much stronger

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results with more specific training.

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Interesting. Yeah. I I I wonder, like, what

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what the future holds for this type of work. You know, there's definitely a lot

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of there's a lot of, you know,

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grinding and gnashing of teeth and, you know, prog you know,

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prognostic that's that's my fault for trying to use a

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a big word on, basically, what is a kind of a holiday

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week. But, you know, but I

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think that it's interesting because it's it's probably opened up the opportunity for a

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company like you, to step in and kinda build these

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tools that really empower the smaller businesses because that seems like it's a

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pretty large, audience of folks.

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Yep. Yeah. It definitely is. And, honestly, I think it's the audience that

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needs it most. Right? Because when you look at very large

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enterprises out there, and they've got world class marketing teams and world

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class agencies that they're working with, tons of data, tons of

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resources. They've got access to the best tools. And a

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lot of small business owners don't realize that in today's world of

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marketing, when you start a small business, you're actually competing with the big

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guys from very early on. Right? There's only so much

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consumer attention out there. There are only so many eyeballs, so many minutes

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to compete for. And small businesses are

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drastically under resourced and under tooled compared to

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large enterprises. And then on top of that, it's usually the

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founder trying to figure out the marketing on their own, or maybe they can hire

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a 1 person team or a 2 person team in the early days.

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But I think, you know, you you look at this type of generative AI

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evolution that's happening right now. I think it's that

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smaller, business, and I think it's, people

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who have been on the fringes of really having access to having

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their voice heard that this potentially helps the most. So

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just an example of that, a pocket of users we've seen

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on our ghostwriter capability, which is the the copy

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generation, part of Tailwind, a

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pocket of users we've seen who are really passionate about it are people who are

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non native English speakers. Right? Right. And you

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go through user research, and you're watching some of

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the interviews, and we've literally had people in tears of,

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you know, how happy they were to be able to clear some

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really big emotional blockers for them around

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anxiety and fear of having to communicate in a non native language and

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knowing now that they can produce work at a higher

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level is is life changing. Right? So, yeah, I I think of this, and

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I look back to the evolution of the Internet and think of,

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you know, I was like you said, I was at YouTube before. And we

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had similar stories at YouTube of,

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you know, the teenager in Africa who teaches himself calculus

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through YouTube videos. Right? And Right. Right. Right. Yeah,

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or software development. Right? Who otherwise would not

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have had access to that level of education and

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couldn't have dreamt of it in some cases, but those were very real stories.

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And and it's a reminder for me of the power of technology to

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democratize access. Right? And so I think this next wave is gonna

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do that as well. And hopefully, in the long term, it just leads to, you

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know, the best ideas, the best content, the best product winning out.

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Interesting. So what what did the and, you know, what did the first

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version of Alwin look like before the generative AI? Right? Were

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you you know, what did that touch AI in any way, or

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was it kind of sort of not? Yeah. It really

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didn't, but there was a common underpinning in a

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way. So one of the things we heard very early when we

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were starting and building up the company from especially the small business

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audience was, you know, everyone's giving us tools,

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and these tools give us data and analytics. And

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we're supposed to have time and energy to go through that data and

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figure out what it's telling us. But, frankly, I don't wanna do that. Right? I

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I didn't start my business to dig through reports. I wanna spend

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time designing products or engaging with my customers or creating content,

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not doing data analytics. Right? And so what we heard was this common

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theme of, I wish I wish someone would just give me the

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tool that tells me what to do instead

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of making me figure it out on myself. Like, pushing

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bits from point a to point b is fine. Right? There's value in that. There's

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value in things like scheduling content and

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email automation. Sure. That helps scale.

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But the real part people were struggling with was knowing what to do in the

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first place. And so as we built the first version of

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Tailwind, which was largely focused on social media scheduling and

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publishing, What we found was

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certain features that we built that were more predictive

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in nature. So for example, the best time to post,

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for your audience as an example, or which you know, we have a

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hashtag finder feature from the first version. You know, which hashtag should I use

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on this post? Those were some of the features that people

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got most excited about and that really generated a lot of energy, a

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lot of curiosity around. And so I see a common thread there.

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Right? Because it was taking away those psychological

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barriers and helping people clear that question of, am I

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doing the right thing, right, by giving the recommendation. Okay. I can

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follow the recommendation, follow the doctor's orders, so to speak, and

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now I am unblocked. And I'm gonna move faster, and I'm gonna do more

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marketing, which is gonna help me build my business. And so, yeah, I

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think this generative AI wave now is the

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next level up of that concept of being

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able to guide people in an

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opinionated but data informed fashion on

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what they should be doing in far more points of their marketing journey

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in a way where beginners can get up to speed, you know, much faster than

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they otherwise would. Interesting.

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Are people comfortable with being told what

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to do, or it depends on the the audience. Right? Because that was the first

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thing that's the first thing that you said that kinda made me, like, I wonder

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how people feel about that. Yeah. It definitely depends on the

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audience. I I think the if I had to observe the divide

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there that I've seen over time, experts are less comfortable being

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told what to do because they're experts. They've earned that

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expertise. They've taken years of learning and labor to get

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there. Beginners are thrilled to be told what to

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do in the vast majority of cases. And and, you know, we we usually don't

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frame it that way. It's more of, like, guiding and recommending for that,

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then, you know you don't want it to sound overbearing. But but the

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reality is when you're at that early stage and someone's there to help

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you out and you don't have to drop, you know, $1500 a month on a

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marketing consultant, you know, who's working part time for you and 10 other

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brands. Right? Like, that's that's a breakthrough for a small business

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owner. So they tend to be thrilled, and then you kinda have

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the messy middle, so to speak, of people who are kind of becoming experts

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not quite there. Or maybe they're expert in a given area,

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and they don't feel like they need the advice there, but they don't feel as

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expert in the next area that they're trying to learn or expand into.

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And so, you know, I think that's that audience is where

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we kind of stretch up to serving. Right? And so

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we've had to build the interface and the system in a way

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where people can opt in or opt out of the advice at each

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individual point. Right? You don't have to follow it. So we

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don't fully do it for you because, you

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know, I I think even from an ethical perspective, that that crosses the line,

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especially with AI. Right. But you don't wanna be like

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Clippy either, where it's like, hey, it looks like you're hey, it looks like you're

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writing a letter. Hey, it looks like you're writing a marketing plan. Like, that you

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know? And you want there to be a cycle of learning and

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oversight there because, you know, every brand should be unique. Every

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voice should be unique. You do get into

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industries where facts really matter. Right? Like, you you need the

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marketing to be factual, and, you know, there are certainly been observed cases

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where LLMs don't always do so well at, you know,

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finding the factual information versus filling in the gaps themselves.

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But in the vast majority of cases, they do a pretty good job.

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So, you know, I think that's where you get into some AI ethics

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conversations, and there's a line we don't wanna cross. Right?

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There should still be a human involved, but we can

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empower that human to do a lot more and to do it in less time.

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Well, I like that. I like that you're you're thinking about the ethical concerns here

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because a lot of a lot of businesses

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don't really think about that upfront. Yeah.

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And so you you you mentioned the line. You don't wanna cross it. Do

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you do you have you found yourself in situation where you're kinda getting close to

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the line? Yeah. I think that's a really interesting

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question. It's something we debate, and and we're

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actually, you know, Greg Starling, who's been leading up the

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AI initiatives for us from early on, he's actually

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starting an internal kind of, like, lunch talk

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series with the team to dive into those questions

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collaboratively. Right? Like, where are people struggling with this individually?

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Where are they seeing potential conflicts in their work? Or where are they running

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up to barriers that they don't know what the answer should be?

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You know, I I think, the reality is there's

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not a big situation I can point to today where I

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can say, like, yeah. There's this really compelling example, but we know it's

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out there on the horizon. Right? And so the things that we

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worry about are, ways that AI can

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be misused, right, to spread misinformation, to do

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harm to society, to, do harm to other people.

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And we think a lot about protecting against that

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type of abuse and trying to figure out, you know, how do we even

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detect and observe that type of abuse in the first place if it is

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happening so that we can then help protect against it.

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Interesting. Interesting. What

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do you see what do the experts think of this? Do they view

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this as a threat or are they just kind of because you're you're smart

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by going after the people who are not below the messy

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middle. Right? I think that's smart because if someone wants to

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start a burger stand, they don't get into the burger stand

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business because they wanna rock social media. Right. Right? They don't

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do it because they wanna make marketing plans. They want it because they wanna cook

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burgers. Right? Like Yeah.

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In in the virtual green room, we were talking about Sonic and how they're headquartered.

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You're in Oklahoma City and how Sonic is headquartered. So now I'm thinking

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about, like, fast food. But,

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I mean, like so, I mean, is it sounds like you found a

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receptive audience there. But what do the experts say? Do you like

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they kind of, you know, are they they

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they probably brush it off, but are they brushing it off in a

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legitimate way, or is it a little bit of, you know, jealousy or

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fear? I think there's a mix. I mean, realistically, just over that large

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of a population. Of what I observe.

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Yeah. In the grand scheme of things, I'd say we're in the

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early adopter phase of generative AI. Right? So we are nowhere

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near mass market adoption yet as fast as it seems like

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certain tools have taken off. Right? Like, we're we're nowhere near mass

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market adoption yet. That's gonna be years down the road.

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And so, you know, you're gonna get a variety

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of responses. I'd say they range from, you know, some

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experts who have leaned in whole hog and are experimenting

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as much as they possibly can with all the different platforms and are publicly

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publishing those experiments on LinkedIn or Twitter or wherever they might

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be doing it, so others can learn from it as well.

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You've got, folks at the opposite end of the spectrum who I think

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are often reacting with denial or fear.

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Right? And I think you also have some just

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very correct observations around things

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that AI is not yet going to do well. Right?

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So, yeah, I I observed this especially

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within the community of kinda, like, professional SEO

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folks. Right? And, yeah, there are systems and

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philosophies of SEO and processes that have been developed that are highly

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specialized that a chat gpt is

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not designed for today. Right? And so you'll see

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these threads where someone goes to chat gpt, and they say, hey. Give me an

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idea for 10 keywords that I can target on this blog post that I'm

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writing, and it spits out an answer. Right? And the professional SEO will look

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at that and say, well, are you using data to analyze

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whether or not these are actually the right terms to be targeting based on

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where people are searching and, what you can actually win

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and so forth. And that's a very fair criticism.

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I think we're not far from the point where some tool, and

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maybe it's the tools who are already deep in the SEO space, is

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going to marry that data to generative AI. Right?

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And so now you will have the response being informed

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by data. Right? And I think that's where it becomes a lot

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a lot more frightening because then people start asking, like, okay.

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What is my job now? And that's really the deeper

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conversation I think needs to happen because what should result

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here is that people are leveled up. Right? People are leveled up to

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more strategic thinking and more strategic work, And they

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don't have to do as much of the rote processing that happens in

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a lot of jobs today. But when you're spending a lot of time on

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that today, it's a really scary thought. Right? So,

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as a society, we're gonna have to navigate that. And we're gonna have to

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figure out training paths for people to

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find their way to that next, next definition of

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their job. But, you know, I I think it's gonna

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play out over years. It's not gonna play out over, you know, months or quarters.

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Okay. Interesting.

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What, what do you think the next step is

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in this? I know you kinda mentioned it, but, like, you you did you touched

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on some of that. It

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it's interesting. It's interesting to me how that

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how this is gonna unfold. And I know it's hard to predict the future, but

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what are your thoughts on kind of, like, of this? So, I mean, we

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are early in the phase. I mean, I think a lot of people I think

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chat gpt got so much wind behind it

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because I I don't think anyone in the field expected that chat g p

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t would be as good as it was. I I I knew that we would

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come up with something like it, but I thought we were still, you know, 3

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to 5 years out from something that good. Right. And I think

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that because it kinda leapfrog people's expectations, I think that

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really boosted it.

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Yeah. You know,

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I think has really ignited people's imaginations both in good ways and

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bad ways. Yeah. I I think you're dead on there.

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And I think we're in the experiments the experimentation early

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adopter phase. Right? So if I think back to other ecosystems,

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like, you know, the early Facebook API and Twitter API

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or the early mobile app ecosystems, right, What we saw

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was this almost gold rush mentality of

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tons of experimentation, a lot of independent developers coming in and just

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building things to see what is possible. And then you fast forward 3

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years, and 90% of those projects are dead. Right? Right.

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Not Because it didn't pan out or it wasn't quite

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impactful enough or the developer lost interest. So I think we're

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in that phase right now. You know, the the good news is the 10%

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that survive that phase can end up being really impactful

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applications and really impactful companies.

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What's interesting and maybe different this time around

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is I see a lot of incumbents and established companies jumping in

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the game early. Right? Right. And I consider this still relatively

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early. You know, even though we've been at it for a while on our road

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map, That was maybe too early or, you know, super

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early. But I think a lot of companies right now

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are asking themselves, what does this mean for us and what does

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this mean for our user? Because a lot of software

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that's been built will need to be rebuilt

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and rethought. A lot of processes will need to be rethought. So, you know, a

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good, example I like is what HubSpot put out where they

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said, you know, with the simple example of using a chatbot to do things

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like update a CRM record. Right? Right. And it's

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like, right, you have to you know, before it was input, output, input, output, input,

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output, input, output, and then eventually, the record was updated.

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Right? Right. And the user had to click all those buttons and provide all those

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inputs. And now it could just be a one line

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chat input, and it's updated. Right?

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So it's rethinking interfaces. It's rethinking

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what software can actually do in someone's life. And and I think that's

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gonna be a multiyear cycle, because companies are

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gonna have to experiment. Some are gonna hit on big

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innovations. Some are gonna fail.

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But I think that's the next chapter. Right? Can can we

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take chat chat gpt and similar concepts, similar

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models from novelty to

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broad utility in a way that makes sense for people. No. That's

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a great point. And if you you kinda watch I grew up watching, you

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know, Star Trek the next generation and d space 9. And if

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you watch those shows, there's always this this

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thing where the computer becomes a character in the story in the sense that

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they say, computer, extrapolate all possibilities of the warp drive

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going, like, whatever. Yep. You know? And

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you even saw this in The Expanse, which is Andy and I's probably

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our favorite show. And there was a there's a scene where one

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of the characters is interacting, trying to find an

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ideal orbit around, getting around all these warships

Speaker:

and the whole thing. But he goes, you know, what

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if I did it with minimal thrust? And he was asking you all these questions,

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and it was basically computing all of these kind of parameters. When I

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use chat GPT, I kinda feel like I'm doing that.

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Yeah. You know? You know, why one of the things that that that that

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I have for for my blog is I have a tool called Dingo,

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and it kind of helps me produce content and all that.

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And I originally wrote it in in c sharp, but I ported

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it over to Python with the

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help of chat gpt. I basically asked it, What if I wanted a pro

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I described the program. How would I write that in Python?

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And, you know, I was able and the code wasn't

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perfect to your point that, you know, they're not always factual. Right? The

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code, if I copied and pasted it, had issues, but those were

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not insurmountable issues. So I I I I poked around with

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it. Long story short, original version of Dango took about 3,

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4 weeks. The Python ported of Dingo

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took about a day and a half to get feature parity

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Interesting. Which isn't yeah. And obviously pretty

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revolutionary. And and you can just skeptic in me. It's like, yeah. But you already

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wrote it. Right? So you kinda already thought through a lot of these problems. However,

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see you know, it just seems like it's a much faster process. Because I also

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think too, it's also a pretty patient mentor. You know what I mean? Where you

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can ask it dumb questions. And as long as the server is still

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running and it's not overloaded, it's always happy to answer your

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questions. Right. Which which I think I think is really kind of the

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I saw a video the other day how it's gonna change language learning and this

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guy was talking about, you know, hey, you know, I'm intermediate Spanish speaker

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and I wanna to go to the next level. I can ask chat

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gpt to create this material that is

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custom tailored to me. And I think that is that's a fascinating use case

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because I don't think anyone thought that. That's not a use case that

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you ordinarily would do it. So I find it interesting that I never would have

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thought marketing campaigns either, you know. Although I will admit, I have written, like, a

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YouTube video description, and I'm, like, make it more exciting, make it more, you

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know, dynamic. Right? Yeah. And it does. It does. It's it

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it, you know, it's not always something I would say, but that's that's

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for me, the human to go in and kinda edit it. Yeah. I gotta hook

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you up with a ghostwriter account so you can give us feedback on that. Oh,

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sure, man. That'd be awesome. Because we've got things like, you know, YouTube

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description generation and and Right. Summarizing and cleaning up my

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copy. But but it's interesting because what you hit on there, which is you

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had already spent the time thinking through the issues and architecting it and

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and figuring out what the challenges were, I see

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a direct parallel to what we've been working on in applying this tech and marketing.

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Because I I'm taking Ghost Rider as an example, Where a

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lot of the work has come is actually in prompt

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engineering, testing, quality control, and iteration.

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And it's not a once and done process. Like, we're actually

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tracking success of various prompts and various use cases and

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going back and improving them over time because,

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I mean, first, the underlying models are changing. Right? And so, you know, that's a

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continuous force of change. But, also, more users are now

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using them, and so we have more data. But

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but I think part of the key there is you look at this field of

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prompt engineering that has now exploded all of a sudden. And while there are

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a lot of early adopters diving in there and getting really excited about

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it, the reality is the average person should never have

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to learn prompt engineering as a new skill. Right?

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Like, that is that's not where we end up from a user interface

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perspective here. And so we're kind of baking that into our

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solution where we say, okay. Part of the value we bring is that

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we have a series of

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expert created and groomed and tested

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prompts, that are trained on real data that, you

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know, helps perform an improvement or it helps improve performance.

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And that makes the technology now accessible to people who

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are not going to spend time on learning prompt engineering. And so,

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I I think we'll see those types of evolutions here. And maybe,

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eventually, prompt engineering won't be as necessary as it is at

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the moment as the models get better, But that might be a longer time line

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until you can really get to that point where you don't need

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that type of work at least going on in the background. Right. I I do

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wonder, like, is prompt engineering the next hot job title?

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Yeah. Or is it gonna be more as these models improve, like

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you say, it'll be more prompt optimization. Right? Or and I'm

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sure I'm sure we'll come up with a fancy acronym for that because we always

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do. But probably start using AI to optimize the prompts

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also, right, to to monitor and measure and and

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No no joke. I've done that. I've done that with DALL E with Dolly. So

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so last year was an interesting year in AI. Right? Because Dolly alone and the

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work that was done in image generation would have been the headline story. But at

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the, you know, the last minute, you know, chat

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gpt kinda took all the oxygen out of the room. I don't think people realize

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that. So there's actually I've actually, like, done it where I if I

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I I give it a prompt. Yep. That I wanna generate an image with.

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And I tested this, and I'll let me do a blog post on this, where

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I say, give me a picture or painting of a doctor in the style of

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Rembrandt. Right? And then I asked

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chatty Pete, hey. How would you write this as a prompt to get the

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best output for DALL E 2? And it came up with a paragraph.

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Mhmm. You know, use this type of lighting, this type of paint. I mean, it

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was just stuff I never would have thought of. And then just for grins, I

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put pasted that in and it the the the

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obviously, art is subjective, but I would say that the

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quality of that improved pump, the output was an order of

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magnitude better. That's really it. And

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that's cool because anyone can kind of experiment with that. Right? That's like a simple

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thing anyone can do, you know. You give it a basic prompt and you ask

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it to get make it make it more for, I think you're probably doing it

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now. But, but, like, it's

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fascinating. Like, it comes up with a much better quality. And and I I think

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that that's interesting for a number of reasons. One, we're using AI to talk to

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AI. Right? Which is kind of a, I don't know, like, that sounds like the

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start of every bad sci fi movie. And the

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other thing is is that that potential to create that

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better image was always in that model.

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Yeah. We're just using the prompt to draw it out. I

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I I I think there's something very

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curious and interesting about that. Right? That that model is they the model

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is maybe more capable than we're aware of Yep.

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Which blows my mind. I was looking up a

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thread that you know, pretty recent. If you look up

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Nick Floats on Twitter, at Nick Floats,

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so Midjourney came out with their new described

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endpoint. And so he's basically doing that where he's he's

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generating an image, asking it to describe

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the image as a prompt, and then rerunning that prompt to see

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what the output looks like after rerunning the prompt that the tool actually generates

Speaker:

for an existing output. And it's fascinating to see,

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both how Midjourney actually describes, right, in the in the

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first place, Right. The piece of content, but then also,

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the difference in in the two outputs. Right?

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Yeah. I'm looking at his I'm looking at his Twitter feed now, and, like, I

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see the pictures and, you know, not that long ago, I would have said,

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oh, wow. You must have an artist friend or he must be an artist himself.

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Yeah. But now it's like and it's funny. I don't know. Maybe it's my

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imagination, but I can look at an image. I'm like, oh, that looks like a

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stable diffusion. That looks like a DALL E. That looks like a,

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Midjourney. Like, it's interesting how

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those models have a certain style. Yeah. I did that

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for fun last year with DALL E. I, you

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know, generated a virtual piece of artwork and then posted it on my

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Instagram. And, I think I put the caption something like, you

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know, so can I say I can paint now or something? But I didn't include

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any other context. And I got comments back from some of my friends

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like, you made that. Oh my goodness. Right?

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But Yeah. And and and what a shift there's been. Because if you did that

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today, people would be like, oh, you're using AI for that. Yeah. And You

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know? That couldn't have been more than 6 months ago that that happened. So

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It's really it's really gone fast. And you think about, like, GPT 4 is

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out and Yeah. GPT,

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5 is in the works. And

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if nothing else, obviously, they have smart people working there, but the people who do

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the marketing at OpenAI are top notch because

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they already had GPT 4 kind of waiting in the wings when

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they released g chat GPT. Mhmm. And then once

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that once everybody got all crazy over that, then they released that. And apparently,

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4 was being worked on. I mean, 5 was being worked

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on even as that was happening. So it's just it's fascinating to see

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how this is going and it you're right, though. It's I

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haven't seen people kinda go this crazy since the beginning of the Internet.

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Yep. It's just in terms of, you

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know, we are in a not an ideal economic environment. There are banks

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collapsing all that, but the investment is not dried up in

Speaker:

specifically AI. Big tech tech has obviously taken a hit,

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but, you know and you're right. Yeah. Even big players are jumping in with both

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feet, You know? Although, I don't know if you played with Bard. I I haven't

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yet, personally. I I I don't wanna I know that they're

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working feverishly on it, but I was not impressed because I

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asked it. So if you ask if you ask chat g p t, you know,

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hey. Write a script that goes infectious weather data. Right? That's, like, my

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hello world. Right? Just to test it out. Right? Check CPT will

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happily give you a whole thing, talk about it, give you code,

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You can copy and paste it. It mostly works. If

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you ask Bard, Bard actually says, I'm I'm a language model.

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I I can't do that. Interesting. Like, now, again, that

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was a week ago. This is a fast moving field. Yeah. But it it's

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kind of funny how I don't know, I think if you

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can kinda sense the style that's different in visual mediums,

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like, you know, the mid journeys and the, the dollies and,

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the stable diffusions. Is there going to be kind of a

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similar style difference,

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you know, in the text generation ones. I suspect there will be.

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And it's interesting what g p t 35 knows versus g p

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t 4. Like Yeah. It it knows about the articles

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I've written. Right? So I can ask it to write an article in

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the style of Frank Lavinia. Right? And and and

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and it and it did. And I I read it and I'm like, yeah, it

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does look something like I would write. You know? Yeah. And I

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when I was writing for MSDN Magazine, well, I coulda I I could wholly coulda

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used that. But it's interesting. When I asked g p t 4, GPT

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4 has no idea who I am, which I'm not sure if I should

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be happy with that or a little upset at that.

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It just hasn't found you. Yes. Right. It hasn't found me yet. But it it

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it's it's also telling that there's a lot of motion here of

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people taking this offline. Right? So you wanna train your own

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model and, like, you know, the the amount of I think you're right.

Speaker:

We're only at the beginning of the innovation curve on this one. Yeah. You

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know, like, when the Internet first came out, who would have thought

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of something like Uber or Lyft. Right? Or DoorDash. Right?

Speaker:

Like that or, you know, or I don't think we can we're so

Speaker:

early in it. We can't really predict the future beyond the next couple of

Speaker:

weeks. Yeah. Yeah. And and it's interesting you bring up that

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example because that's something we're working on in real time in our context. Right.

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We have a a large user base. And, again, there's the the experts down to

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the beginners and all different levels of experience in between.

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And so within our member

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base, we have people who have incredibly well defined

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brand voices and styles where they do

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have enough you know, 1, have enough seed content to train

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personalized versions of the model on their voice,

Speaker:

and to want that. Right? Like, they they wanna maintain their

Speaker:

voice, and they don't wanna sound like everyone else. And then you have folks at

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the other other end of the spectrum who might need help even

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being introduced to the concept of developing your voice and working through what

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your voice should be and testing iterations, off of

Speaker:

different sample datasets. And so, we're

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basically working through from a, you know, how does this technology come to market

Speaker:

perspective, solving that exact problem now of, you know,

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for a large diverse user base, how can we give people

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the ability to tailor outputs to their voice and their style if they

Speaker:

know what that is and simultaneously help people develop that if they

Speaker:

don't know what it is. So that's yeah. Hopefully, we'll

Speaker:

have something there in market pretty soon. I I think we're not that far away,

Speaker:

but I'm sure that's something we'll need to, you know, iterate on and learn learn

Speaker:

about over time too. Well, cool. So now

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we'll switch over to the pre canned questions. Okay.

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Which I pasted in the chat. Not they're not, real brain teasers. They're

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just kind of, general stuff.

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Yeah. How did you find your way into data and AI?

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Did did data find you, or did you find data?

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I always loved math. Always, always, always loved math. If I had

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to really answer this, I'd say, you know, the 2 earliest examples that come to

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mind, I was nuts about baseball growing up and

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baseball statistics. Oh, cool. That was probably

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one of the first ways that data found me. And then a little bit later

Speaker:

than that, the the first company I ever tried starting when I was in

Speaker:

college was, essentially arbitraging the collectibles

Speaker:

market. So think about things like Magic the Gathering cards. Right?

Speaker:

And so, yeah, I I began tracking

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prices that different collectibles were selling at on eBay and other

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platforms. Yeah. This is going back,

Speaker:

over 20 years at this point. But Right. Right.

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Tracking prices to learn what was a good price and what was a bad price

Speaker:

before we had that information readily accessible and then buying and selling

Speaker:

against it. Those are the or 2 of the examples,

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I think. But, yeah, data just always spoke to me. I I've

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always loved math, and so it was symbiotic. Yeah.

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That's funny. I was also a huge baseball fan growing up, and

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it's one of those I mean, if you're if you're if you're a

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baseball fan, statistics is a natural

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field for you to study because you've already Mhmm. You've already done a lot

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of it. Right? So it's it's that's it's it's interesting.

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We'll have to figure out what who who which team you root for.

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But, I don't know if I wanna admit that.

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Well, my my Xbox gamer tank

Speaker:

was Frankie Bronx, so you could probably figure out that I'm a Yankee fan.

Speaker:

That I grew up mostly. I'm sorry. No. I expect the

Speaker:

hate mail to come in. Yeah. I I grew up mostly in South

Speaker:

Florida, and the Marlins came about. So when I was very young, we were a

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mess household. Oh, okay. And then, the Marlins came

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about, and I became a Marlins fan. So yeah. Cool. Being a Marlins fan, it's

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been a few years that were really fun and a whole bunch of misery.

Speaker:

I I I've noticed that. I'm kinda surprised at that, but I

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guess that's what it that's, you know

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although you went from being a Mets fan. I know Mets Mets have good years

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and bad years. Some would say good decades and bad decades,

Speaker:

but Yep.

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The nice thing about baseball is that there's always another game and another season, you

Speaker:

know, you can you can always hold out hope. Yeah. Alright.

Speaker:

So on to the next question. What's the favorite part of your current

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gig? Yeah. I think it's it's honestly this period of

Speaker:

innovation we're in. It's fun. It's new. The answers

Speaker:

are unknown. There are so many different paths it could take, and I

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think there's a lot of good that can be created. So it it reminds me

Speaker:

in a lot of ways of the early ideological days of the

Speaker:

Internet. And, you know, I think we need to learn

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from that chapter in terms of things that weren't

Speaker:

regulated or managed well as the Internet grew

Speaker:

and make sure we don't make those same mistakes again. But that that

Speaker:

excitement's back for me personally. I think we're experiencing a pace of

Speaker:

innovation all of a sudden in software that we really haven't seen,

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you know, in 10 or 20 years. Yeah. That is a really

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I that is a that is an excellent point. I think the fact that

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they kept pushing out models every few weeks publicly

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and being

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able to keep up with the met the demand more or less,

Speaker:

as as does take me back to those days where people were, like,

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I'll never forget. It was an ad I saw in a magazine, which one of

Speaker:

them one of the millions of web development magazines that came out in 96.

Speaker:

Right? Yep. And, and it was, like, you know,

Speaker:

they show a picture of somebody with a with a sleeping,

Speaker:

bag under their desk and somebody checking their email right away,

Speaker:

like like, half in the bag, half well, that sounds bad. Half in the sleeping

Speaker:

bag and half on the computer. And they were saying, you know, something

Speaker:

like and the caption was, what did you miss? Like, it was like it was

Speaker:

like really that type of mentality that you're right. I haven't

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seen this in a while.

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So the we have 3 complete this sentence, questions. The first

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one is, when I'm not working, I enjoy blank.

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Being a dad. That's that's it for me.

Speaker:

You know, between founder life, that's about all there is time for, honestly.

Speaker:

Yeah. But, our daughter is at a really fun

Speaker:

age. She's gonna turn 8 pretty soon here, and,

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I just enjoy being able to be goofy and have fun and

Speaker:

play. And, yeah, she's wonderful.

Speaker:

That's cool. That is a fun age. My youngest is 8. So

Speaker:

Yeah. The, the next

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question is, complete the sentence.

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I think the coolest thing in technology today is blank.

Speaker:

Well, I guess I gotta say a tailwind. Right? What we're doing here.

Speaker:

Right. I really do think a lot of what we're doing is cool, but but

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more broadly, I'd say, yeah, I think

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the coolest thing is that our

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perceptions of what technology is capable of are changing

Speaker:

very quickly, and, that's fun. Yeah.

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Absolutely. I mean, one of the things you would hear was that

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creative jobs were safe for a long time. And I think

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that, if anything, we've learned in the last 3 to 6 months,

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that's not necessarily the case.

Speaker:

Yeah. So I think that's also a part of, you know it's it's probably not

Speaker:

a coincidence that, you know, Chat GPT upset people more than the image

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ones because the pack media,

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the people who write those articles, their jobs are, I think, are in I wouldn't

Speaker:

say imminent jeopardy, but they are definitely on the firing line. Yeah.

Speaker:

Whereas, you know, a year ago, oh, no. No one AI can never be

Speaker:

creative. And yet, here we are. Mhmm.

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Alright. The last, complete the sentence. I look forward to the day

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technology I can use technology to blank. Teleport.

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Yes. How's I like that. I

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like that answer. Yeah. That's even better than self driving cars because

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you wouldn't waste time in the car at all. Exactly. I love

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seeing new places. I hate getting there. Yeah.

Speaker:

And we should probably ask chat g p t if there's a way we can

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make air travel less awful.

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We'll have the gpt airlines soon. Too. GPT

Speaker:

Airlines. That's funny.

Speaker:

Alright. So share something different about

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yourself, but, we like our clean Itunes, rating.

Speaker:

So keep it keep it within those parameters.

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Oh, this is a tough one. I I never know how to answer this, honestly.

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Something different about myself. Like, you know, I I don't know if it's super different,

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but I'll just say I'm I'm a huge strategy game nerd. Oh,

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interesting. And so, yeah, I mentioned with the magic cards before, it's

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yeah. I still love magic. I love settlers, risk,

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chess. Throw any strategy game at me, and, I

Speaker:

could lose myself for days. That's cool. That's cool.

Speaker:

So, Audible sponsors data driven.

Speaker:

Can you recommend a good audiobook if you do audiobooks? If you don't do audiobooks,

Speaker:

just recommend a good, regular old dead tree book.

Speaker:

Yeah. So, I'll recommend oh, I'm, like, staring at my

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bookshelf here. I think BoomTown

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is a really interesting one. So,

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before I moved to Oklahoma, I knew nothing about Oklahoma.

Speaker:

Uh-huh. And, its history is absolutely fascinating.

Speaker:

So, I think that's a really cool one, for people who

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have never been here before and just wanna learn about a new place

Speaker:

and a new place and time. It might challenge a lot of perceptions,

Speaker:

but, it's a really good read.

Speaker:

K. I'll have to check that out. And where can folks find

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out more about you and what you're up to? Yeah. Absolutely. So,

Speaker:

for Tailwind, we are tailwindapp.com.

Speaker:

That's our website. And you can follow us and and find us pretty easily on

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all the different social platforms and blog. For me personally,

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you know, probably just look me up on LinkedIn. I'm Daniel Maloney.

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I think Daniel p Maloney is my handle. But that's probably one of the

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platforms I'm more active on these days, in terms of

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wanting to connect. Well, that's awesome. Well, thanks for joining

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us, and, I'll let Bailey finish the show. And just

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like that, we've reached the end of today's digital odyssey.

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A huge thanks to our phenomenal guest, Danny Maloney, for

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sharing his insights and to Tailwind for redefining the marketing

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landscape. To our listeners, your curiosity

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fuels this journey, and we're immensely grateful for your companionship.

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If today's episode sparked a bit of that data driven wonder in you,

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why not share the love? Like, share, and

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subscribe to keep this conversation going and to ensure you never miss an

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episode. Until next time, keep those circuits

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buzzing and your data flowing. Cheerio.

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