Danny Maloney on AI’s Leap to Marketing Mastery

Ladies and gentlemen, boys and girls, and AI entities of all computational capacities, welcome to another riveting episode of the data driven podcast. Today, we embark on a journey through the digital landscape where data isn't just numbers. It's the very fabric of our digital existence. With the ever charming Frank steering the ship solo, Andy was off gallivanting at Busch Gardens when this was

recorded. We delve into the world of generative AI and marketing marvels with the illustrious Danny Maloney, the brain behind Tailwind. Picture this. A world where small businesses wield the power of giants, thanks to Tailwind's arsenal of automated marketing tools. From the nostalgic lanes of Google Street View to the cutting edge frontiers of AI driven marketing strategies, we're in for a treat. And since today is February 29th, leap day, we

figured, why not leap into another episode? So adjust your antennas, polish your circuits, and prepare for an electrifying discourse on how Tailwind is reshaping the marketing cosmos, one algorithm at a time. Buckle up. It's going to be a data driven ride. Hello and welcome to Data Driven, the podcast where we explore the emerging fields of data, artificial intelligence, and the ever present world of data engineering. But speaking of data engineering, my co host, Andy, is not

here today. He's having a fun family day at Busch Gardens. So, we wish him good weather and, short lines. So with me today is Danny Maloney, a successful Internet entrepreneur, CEO and cofounder of Tailwind, a software platform that provides small businesses with the marketing tools they need to compete. With over 1,000,000 users, Tailwind is a leader in generative AI and providing automated marketing plan creation, visual content design, copywriting, email campaign

building, ad optimization, and more. But before starting his own company, he worked at Google, YouTube, and AOL, and is part of it apparently had, worked on something I'm very fond of is, Street View and Google Maps. And, maybe we could talk a little bit more about that. So welcome to the show, Danny. Great. Thanks for having me, Frank. Glad to be here. Yeah. Good to have you. Good to have you. So generative AI, right, as probably since, you know, last November has been on everybody's

minds. What you've been how long ago did you start Tailwind? Yeah. So, Tailwind itself came to market in 2015. Okay. So we've been around for a while. You know, the way we tell our story is really in a couple of chapters. So there was kind of tailwind v 1, and there's tailwind 2 point o as we call it. And so for us, that was kinda 2015 to 2019 and then 2019 to the present

day. But going down the path of, generation, for clients and really thinking about how we do more of the work of marketing for them, was the real theme of this second chapter. So we started on that path back in 2019, believe it or not, even though, you know, the market is really kinda waking up and starting to pay attention to your generative AI today, there was a lot to learn and a lot to figure out in terms of how to go about it the right way. And so it's been a pretty fun

past few years for us going down that road. Interesting. So what does Tailwind generate specifically? Is it NLP? Is it LLM? Like, what is, image generation? Yeah. So there's actually multiple components. So like you said, we're trying to give small businesses the tools that they need to compete as marketers. So for us, that's not just one use case, but it's actually thinking about the entire marketing life cycle. And so we look at, today, 4 core components of what we help generate.

One piece is actually the marketing plan itself. And that so that's our own IP, our own technology that we built that builds and extends and evolves marketing plans based on the specifics of the business. Once you have that plan, then you've gotta actually execute it. So now we get into the next couple components. So one is a tool called Tailwind

Create, which is about visual generation. So we actually create the images that someone needs and, you know, don't think necessarily about a tool like a Canva or a PicMonkey in that sense of, you know, I'm just going through templates, and I'm designing it myself. What we do is we actually take in the assets and the inputs from a brand, and then we generate a large array of designs that they can scroll through and choose from instead of

having to do the design work. So it kinda takes the design process from 45 minutes to about 2 minutes, typically, and we generate it in all the formats they need for their different marketing assets. So visual design is the second part. The third part is copywriting. And so there, it's a mix of our IP and also leveraging third party tools that are out there. So leveraging LMS,

for example. But that's about, you know, being able to help people write the copy that's gonna be convincing, that's gonna help communicate with their audience. And then the 4th component is ads. So we actually acquired a company last year called Nectar 9, who they themselves had been building out an AI driven ad management platform for about 5 years prior to that. And, we're now integrating that into Tailwind so that our users will be able to mostly automate

the process of paid advertising also. But we look at that as a complete cycle. Right? So I've got a plan. I can create the content for the plan, then I can distribute it and get it out to my audience. And the data from that cycle should inform my plan so I can get better and get smarter into the future. Interesting. That's one of the things that I I I wonder about these these generative tools. Like, do they take the feedback

and that, like, you know so eventually, it would get better. Ideally, it would eventually get better. And it sounds like you do include that in your feedback loop. There are some. I mean, honestly, I think we're in early days of

that. Right? If you look at some of the more prevalent models that are out there, you know, the the various OpenAI models, for instance, I think there is an inherent feedback loop in how those operate as they continue to train on more data. Right? When you talk about things from a marketing context in particular, the feedback loop for us has been around things like, how do we train, you know, our models, our products, or third party models

around specific use cases. Right? So, looking at the corpus of data we have around what works, what doesn't work, enabling it to be more tailored to the use case that the user is trying to solve. So, you know, when you're designing a pin for Pinterest, that should be different than a, you know, feed post on Instagram. Right? A feed post on Instagram is different from

writing a script for a reels, right, or a TikTok. So, for each of those use cases, we found you can start generating much stronger results with more specific training. Interesting. Yeah. I I I wonder, like, what what the future holds for this type of work. You know, there's definitely a lot of there's a lot of, you know, grinding and gnashing of teeth and, you know, prog you know, prognostic that's that's my fault for trying to use a a big word on, basically, what is a kind of a holiday

week. But, you know, but I think that it's interesting because it's it's probably opened up the opportunity for a company like you, to step in and kinda build these tools that really empower the smaller businesses because that seems like it's a pretty large, audience of folks. Yep. Yeah. It definitely is. And, honestly, I think it's the audience that

needs it most. Right? Because when you look at very large enterprises out there, and they've got world class marketing teams and world class agencies that they're working with, tons of data, tons of resources. They've got access to the best tools. And a lot of small business owners don't realize that in today's world of marketing, when you start a small business, you're actually competing with the big guys from very early on. Right? There's only so much

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

to compete for. And small businesses are drastically under resourced and under tooled compared to large enterprises. And then on top of that, it's usually the founder trying to figure out the marketing on their own, or maybe they can hire a 1 person team or a 2 person team in the early days. But I think, you know, you you look at this type of generative AI

evolution that's happening right now. I think it's that smaller, business, and I think it's, people who have been on the fringes of really having access to having their voice heard that this potentially helps the most. So just an example of that, a pocket of users we've seen on our ghostwriter capability, which is the the copy generation, part of Tailwind, a pocket of users we've seen who are really passionate about it are people who are

non native English speakers. Right? Right. And you go through user research, and you're watching some of the interviews, and we've literally had people in tears of, you know, how happy they were to be able to clear some really big emotional blockers for them around anxiety and fear of having to communicate in a non native language and knowing now that they can produce work at a higher

level is is life changing. Right? So, yeah, I I think of this, and I look back to the evolution of the Internet and think of, you know, I was like you said, I was at YouTube before. And we had similar stories at YouTube of, you know, the teenager in Africa who teaches himself calculus through YouTube videos. Right? And Right. Right. Right. Yeah,

or software development. Right? Who otherwise would not have had access to that level of education and couldn't have dreamt of it in some cases, but those were very real stories. And and it's a reminder for me of the power of technology to democratize access. Right? And so I think this next wave is gonna do that as well. And hopefully, in the long term, it just leads to, you know, the best ideas, the best content, the best product winning out.

Interesting. So what what did the and, you know, what did the first version of Alwin look like before the generative AI? Right? Were you you know, what did that touch AI in any way, or was it kind of sort of not? Yeah. It really didn't, but there was a common underpinning in a way. So one of the things we heard very early when we were starting and building up the company from especially the small business audience was, you know, everyone's giving us tools,

and these tools give us data and analytics. And we're supposed to have time and energy to go through that data and figure out what it's telling us. But, frankly, I don't wanna do that. Right? I I didn't start my business to dig through reports. I wanna spend time designing products or engaging with my customers or creating content, not doing data analytics. Right? And so what we heard was this common theme of, I wish I wish someone would just give me the tool that tells me what to do instead

of making me figure it out on myself. Like, pushing bits from point a to point b is fine. Right? There's value in that. There's value in things like scheduling content and email automation. Sure. That helps scale. But the real part people were struggling with was knowing what to do in the first place. And so as we built the first version of Tailwind, which was largely focused on social media scheduling and publishing, What we found was certain features that we built that were more predictive

in nature. So for example, the best time to post, for your audience as an example, or which you know, we have a hashtag finder feature from the first version. You know, which hashtag should I use

on this post? Those were some of the features that people got most excited about and that really generated a lot of energy, a lot of curiosity around. And so I see a common thread there. Right? Because it was taking away those psychological barriers and helping people clear that question of, am I doing the right thing, right, by giving the recommendation. Okay. I can follow the recommendation, follow the doctor's orders, so to speak, and

now I am unblocked. And I'm gonna move faster, and I'm gonna do more marketing, which is gonna help me build my business. And so, yeah, I think this generative AI wave now is the next level up of that concept of being able to guide people in an opinionated but data informed fashion on what they should be doing in far more points of their marketing journey in a way where beginners can get up to speed, you know, much faster than they otherwise would. Interesting.

Are people comfortable with being told what to do, or it depends on the the audience. Right? Because that was the first thing that's the first thing that you said that kinda made me, like, I wonder how people feel about that. Yeah. It definitely depends on the audience. I I think the if I had to observe the divide there that I've seen over time, experts are less comfortable being told what to do because they're experts. They've earned that

expertise. They've taken years of learning and labor to get there. Beginners are thrilled to be told what to do in the vast majority of cases. And and, you know, we we usually don't frame it that way. It's more of, like, guiding and recommending for that,

then, you know you don't want it to sound overbearing. But but the reality is when you're at that early stage and someone's there to help you out and you don't have to drop, you know, $1500 a month on a marketing consultant, you know, who's working part time for you and 10 other brands. Right? Like, that's that's a breakthrough for a small business owner. So they tend to be thrilled, and then you kinda have the messy middle, so to speak, of people who are kind of becoming experts

not quite there. Or maybe they're expert in a given area, and they don't feel like they need the advice there, but they don't feel as expert in the next area that they're trying to learn or expand into. And so, you know, I think that's that audience is where we kind of stretch up to serving. Right? And so we've had to build the interface and the system in a way where people can opt in or opt out of the advice at each

individual point. Right? You don't have to follow it. So we don't fully do it for you because, you know, I I think even from an ethical perspective, that that crosses the line, especially with AI. Right. But you don't wanna be like Clippy either, where it's like, hey, it looks like you're hey, it looks like you're writing a letter. Hey, it looks like you're writing a marketing plan. Like, that you know? And you want there to be a cycle of learning and

oversight there because, you know, every brand should be unique. Every voice should be unique. You do get into industries where facts really matter. Right? Like, you you need the marketing to be factual, and, you know, there are certainly been observed cases where LLMs don't always do so well at, you know, finding the factual information versus filling in the gaps themselves. But in the vast majority of cases, they do a pretty good job.

So, you know, I think that's where you get into some AI ethics conversations, and there's a line we don't wanna cross. Right? There should still be a human involved, but we can empower that human to do a lot more and to do it in less time. Well, I like that. I like that you're you're thinking about the ethical concerns here because a lot of a lot of businesses don't really think about that upfront. Yeah.

And so you you you mentioned the line. You don't wanna cross it. Do you do you have you found yourself in situation where you're kinda getting close to the line? Yeah. I think that's a really interesting

question. It's something we debate, and and we're actually, you know, Greg Starling, who's been leading up the AI initiatives for us from early on, he's actually starting an internal kind of, like, lunch talk series with the team to dive into those questions collaboratively. Right? Like, where are people struggling with this individually? Where are they seeing potential conflicts in their work? Or where are they running up to barriers that they don't know what the answer should be?

You know, I I think, the reality is there's not a big situation I can point to today where I can say, like, yeah. There's this really compelling example, but we know it's out there on the horizon. Right? And so the things that we worry about are, ways that AI can be misused, right, to spread misinformation, to do harm to society, to, do harm to other people.

And we think a lot about protecting against that type of abuse and trying to figure out, you know, how do we even detect and observe that type of abuse in the first place if it is happening so that we can then help protect against it. Interesting. Interesting. What do you see what do the experts think of this? Do they view this as a threat or are they just kind of because you're you're smart by going after the people who are not below the messy

middle. Right? I think that's smart because if someone wants to start a burger stand, they don't get into the burger stand business because they wanna rock social media. Right. Right? They don't do it because they wanna make marketing plans. They want it because they wanna cook burgers. Right? Like Yeah. In in the virtual green room, we were talking about Sonic and how they're headquartered. You're in Oklahoma City and how Sonic is headquartered. So now I'm thinking

about, like, fast food. But, I mean, like so, I mean, is it sounds like you found a receptive audience there. But what do the experts say? Do you like they kind of, you know, are they they they probably brush it off, but are they brushing it off in a legitimate way, or is it a little bit of, you know, jealousy or fear? I think there's a mix. I mean, realistically, just over that large of a population. Of what I observe. Yeah. In the grand scheme of things, I'd say we're in the

early adopter phase of generative AI. Right? So we are nowhere near mass market adoption yet as fast as it seems like certain tools have taken off. Right? Like, we're we're nowhere near mass market adoption yet. That's gonna be years down the road.

And so, you know, you're gonna get a variety of responses. I'd say they range from, you know, some experts who have leaned in whole hog and are experimenting as much as they possibly can with all the different platforms and are publicly publishing those experiments on LinkedIn or Twitter or wherever they might be doing it, so others can learn from it as well. You've got, folks at the opposite end of the spectrum who I think are often reacting with denial or fear.

Right? And I think you also have some just very correct observations around things that AI is not yet going to do well. Right? So, yeah, I I observed this especially within the community of kinda, like, professional SEO folks. Right? And, yeah, there are systems and philosophies of SEO and processes that have been developed that are highly specialized that a chat gpt is not designed for today. Right? And so you'll see

these threads where someone goes to chat gpt, and they say, hey. Give me an idea for 10 keywords that I can target on this blog post that I'm writing, and it spits out an answer. Right? And the professional SEO will look at that and say, well, are you using data to analyze whether or not these are actually the right terms to be targeting based on where people are searching and, what you can actually win and so forth. And that's a very fair criticism.

I think we're not far from the point where some tool, and maybe it's the tools who are already deep in the SEO space, is going to marry that data to generative AI. Right? And so now you will have the response being informed by data. Right? And I think that's where it becomes a lot a lot more frightening because then people start asking, like, okay. What is my job now? And that's really the deeper conversation I think needs to happen because what should result

here is that people are leveled up. Right? People are leveled up to more strategic thinking and more strategic work, And they don't have to do as much of the rote processing that happens in a lot of jobs today. But when you're spending a lot of time on that today, it's a really scary thought. Right? So, as a society, we're gonna have to navigate that. And we're gonna have to figure out training paths for people to find their way to that next, next definition of

their job. But, you know, I I think it's gonna play out over years. It's not gonna play out over, you know, months or quarters. Okay. Interesting. What, what do you think the next step is in this? I know you kinda mentioned it, but, like, you you did you touched on some of that. It it's interesting. It's interesting to me how that how this is gonna unfold. And I know it's hard to predict the future, but what are your thoughts on kind of, like, of this? So, I mean, we

are early in the phase. I mean, I think a lot of people I think chat gpt got so much wind behind it because I I don't think anyone in the field expected that chat g p t would be as good as it was. I I I knew that we would come up with something like it, but I thought we were still, you know, 3 to 5 years out from something that good. Right. And I think that because it kinda leapfrog people's expectations, I think that really boosted it.

Yeah. You know, I think has really ignited people's imaginations both in good ways and bad ways. Yeah. I I think you're dead on there. And I think we're in the experiments the experimentation early adopter phase. Right? So if I think back to other ecosystems, like, you know, the early Facebook API and Twitter API or the early mobile app ecosystems, right, What we saw was this almost gold rush mentality of tons of experimentation, a lot of independent developers coming in and just

building things to see what is possible. And then you fast forward 3 years, and 90% of those projects are dead. Right? Right.

Not Because it didn't pan out or it wasn't quite impactful enough or the developer lost interest. So I think we're in that phase right now. You know, the the good news is the 10% that survive that phase can end up being really impactful applications and really impactful companies. What's interesting and maybe different this time around is I see a lot of incumbents and established companies jumping in the game early. Right? Right. And I consider this still relatively

early. You know, even though we've been at it for a while on our road map, That was maybe too early or, you know, super early. But I think a lot of companies right now are asking themselves, what does this mean for us and what does this mean for our user? Because a lot of software that's been built will need to be rebuilt

and rethought. A lot of processes will need to be rethought. So, you know, a good, example I like is what HubSpot put out where they said, you know, with the simple example of using a chatbot to do things like update a CRM record. Right? Right. And it's like, right, you have to you know, before it was input, output, input, output, input, output, input, output, and then eventually, the record was updated. Right? Right. And the user had to click all those buttons and provide all those

inputs. And now it could just be a one line chat input, and it's updated. Right? So it's rethinking interfaces. It's rethinking what software can actually do in someone's life. And and I think that's gonna be a multiyear cycle, because companies are gonna have to experiment. Some are gonna hit on big innovations. Some are gonna fail. But I think that's the next chapter. Right? Can can we take chat chat gpt and similar concepts, similar models from novelty to

broad utility in a way that makes sense for people. No. That's a great point. And if you you kinda watch I grew up watching, you know, Star Trek the next generation and d space 9. And if you watch those shows, there's always this this thing where the computer becomes a character in the story in the sense that they say, computer, extrapolate all possibilities of the warp drive going, like, whatever. Yep. You know? And you even saw this in The Expanse, which is Andy and I's probably

our favorite show. And there was a there's a scene where one of the characters is interacting, trying to find an ideal orbit around, getting around all these warships and the whole thing. But he goes, you know, what if I did it with minimal thrust? And he was asking you all these questions, and it was basically computing all of these kind of parameters. When I use chat GPT, I kinda feel like I'm doing that.

Yeah. You know? You know, why one of the things that that that that I have for for my blog is I have a tool called Dingo, and it kind of helps me produce content and all that. And I originally wrote it in in c sharp, but I ported it over to Python with the help of chat gpt. I basically asked it, What if I wanted a pro I described the program. How would I write that in Python? And, you know, I was able and the code wasn't

perfect to your point that, you know, they're not always factual. Right? The code, if I copied and pasted it, had issues, but those were not insurmountable issues. So I I I I poked around with it. Long story short, original version of Dango took about 3, 4 weeks. The Python ported of Dingo took about a day and a half to get feature parity Interesting. Which isn't yeah. And obviously pretty revolutionary. And and you can just skeptic in me. It's like, yeah. But you already

wrote it. Right? So you kinda already thought through a lot of these problems. However, see you know, it just seems like it's a much faster process. Because I also think too, it's also a pretty patient mentor. You know what I mean? Where you can ask it dumb questions. And as long as the server is still running and it's not overloaded, it's always happy to answer your

questions. Right. Which which I think I think is really kind of the I saw a video the other day how it's gonna change language learning and this guy was talking about, you know, hey, you know, I'm intermediate Spanish speaker and I wanna to go to the next level. I can ask chat gpt to create this material that is custom tailored to me. And I think that is that's a fascinating use case because I don't think anyone thought that. That's not a use case that

you ordinarily would do it. So I find it interesting that I never would have thought marketing campaigns either, you know. Although I will admit, I have written, like, a YouTube video description, and I'm, like, make it more exciting, make it more, you know, dynamic. Right? Yeah. And it does. It does. It's it it, you know, it's not always something I would say, but that's that's for me, the human to go in and kinda edit it. Yeah. I gotta hook

you up with a ghostwriter account so you can give us feedback on that. Oh, sure, man. That'd be awesome. Because we've got things like, you know, YouTube description generation and and Right. Summarizing and cleaning up my

copy. But but it's interesting because what you hit on there, which is you had already spent the time thinking through the issues and architecting it and and figuring out what the challenges were, I see a direct parallel to what we've been working on in applying this tech and marketing. Because I I'm taking Ghost Rider as an example, Where a lot of the work has come is actually in prompt engineering, testing, quality control, and iteration.

And it's not a once and done process. Like, we're actually tracking success of various prompts and various use cases and going back and improving them over time because, I mean, first, the underlying models are changing. Right? And so, you know, that's a continuous force of change. But, also, more users are now using them, and so we have more data. But but I think part of the key there is you look at this field of prompt engineering that has now exploded all of a sudden. And while there are

a lot of early adopters diving in there and getting really excited about it, the reality is the average person should never have to learn prompt engineering as a new skill. Right? Like, that is that's not where we end up from a user interface perspective here. And so we're kind of baking that into our

solution where we say, okay. Part of the value we bring is that we have a series of expert created and groomed and tested prompts, that are trained on real data that, you know, helps perform an improvement or it helps improve performance. And that makes the technology now accessible to people who are not going to spend time on learning prompt engineering. And so,

I I think we'll see those types of evolutions here. And maybe, eventually, prompt engineering won't be as necessary as it is at the moment as the models get better, But that might be a longer time line until you can really get to that point where you don't need that type of work at least going on in the background. Right. I I do wonder, like, is prompt engineering the next hot job title? Yeah. Or is it gonna be more as these models improve, like

you say, it'll be more prompt optimization. Right? Or and I'm sure I'm sure we'll come up with a fancy acronym for that because we always do. But probably start using AI to optimize the prompts also, right, to to monitor and measure and and No no joke. I've done that. I've done that with DALL E with Dolly. So so last year was an interesting year in AI. Right? Because Dolly alone and the

work that was done in image generation would have been the headline story. But at the, you know, the last minute, you know, chat gpt kinda took all the oxygen out of the room. I don't think people realize that. So there's actually I've actually, like, done it where I if I I I give it a prompt. Yep. That I wanna generate an image with.

And I tested this, and I'll let me do a blog post on this, where I say, give me a picture or painting of a doctor in the style of Rembrandt. Right? And then I asked chatty Pete, hey. How would you write this as a prompt to get the best output for DALL E 2? And it came up with a paragraph. Mhmm. You know, use this type of lighting, this type of paint. I mean, it

was just stuff I never would have thought of. And then just for grins, I put pasted that in and it the the the obviously, art is subjective, but I would say that the quality of that improved pump, the output was an order of magnitude better. That's really it. And that's cool because anyone can kind of experiment with that. Right? That's like a simple thing anyone can do, you know. You give it a basic prompt and you ask it to get make it make it more for, I think you're probably doing it

now. But, but, like, it's fascinating. Like, it comes up with a much better quality. And and I I think that that's interesting for a number of reasons. One, we're using AI to talk to AI. Right? Which is kind of a, I don't know, like, that sounds like the start of every bad sci fi movie. And the other thing is is that that potential to create that better image was always in that model. Yeah. We're just using the prompt to draw it out. I I I I think there's something very

curious and interesting about that. Right? That that model is they the model is maybe more capable than we're aware of Yep. Which blows my mind. I was looking up a thread that you know, pretty recent. If you look up Nick Floats on Twitter, at Nick Floats, so Midjourney came out with their new described

endpoint. And so he's basically doing that where he's he's generating an image, asking it to describe the image as a prompt, and then rerunning that prompt to see what the output looks like after rerunning the prompt that the tool actually generates for an existing output. And it's fascinating to see, both how Midjourney actually describes, right, in the in the first place, Right. The piece of content, but then also, the difference in in the two outputs. Right?

Yeah. I'm looking at his I'm looking at his Twitter feed now, and, like, I see the pictures and, you know, not that long ago, I would have said, oh, wow. You must have an artist friend or he must be an artist himself. Yeah. But now it's like and it's funny. I don't know. Maybe it's my imagination, but I can look at an image. I'm like, oh, that looks like a stable diffusion. That looks like a DALL E. That looks like a, Midjourney. Like, it's interesting how

those models have a certain style. Yeah. I did that for fun last year with DALL E. I, you know, generated a virtual piece of artwork and then posted it on my Instagram. And, I think I put the caption something like, you know, so can I say I can paint now or something? But I didn't include any other context. And I got comments back from some of my friends like, you made that. Oh my goodness. Right? But Yeah. And and and what a shift there's been. Because if you did that

today, people would be like, oh, you're using AI for that. Yeah. And You know? That couldn't have been more than 6 months ago that that happened. So It's really it's really gone fast. And you think about, like, GPT 4 is out and Yeah. GPT,

5 is in the works. And if nothing else, obviously, they have smart people working there, but the people who do the marketing at OpenAI are top notch because they already had GPT 4 kind of waiting in the wings when they released g chat GPT. Mhmm. And then once that once everybody got all crazy over that, then they released that. And apparently, 4 was being worked on. I mean, 5 was being worked on even as that was happening. So it's just it's fascinating to see

how this is going and it you're right, though. It's I haven't seen people kinda go this crazy since the beginning of the Internet. Yep. It's just in terms of, you know, we are in a not an ideal economic environment. There are banks collapsing all that, but the investment is not dried up in specifically AI. Big tech tech has obviously taken a hit, but, you know and you're right. Yeah. Even big players are jumping in with both

feet, You know? Although, I don't know if you played with Bard. I I haven't yet, personally. I I I don't wanna I know that they're working feverishly on it, but I was not impressed because I asked it. So if you ask if you ask chat g p t, you know, hey. Write a script that goes infectious weather data. Right? That's, like, my hello world. Right? Just to test it out. Right? Check CPT will happily give you a whole thing, talk about it, give you code,

You can copy and paste it. It mostly works. If you ask Bard, Bard actually says, I'm I'm a language model. I I can't do that. Interesting. Like, now, again, that was a week ago. This is a fast moving field. Yeah. But it it's kind of funny how I don't know, I think if you can kinda sense the style that's different in visual mediums, like, you know, the mid journeys and the, the dollies and, the stable diffusions. Is there going to be kind of a similar style difference,

you know, in the text generation ones. I suspect there will be. And it's interesting what g p t 35 knows versus g p t 4. Like Yeah. It it knows about the articles I've written. Right? So I can ask it to write an article in the style of Frank Lavinia. Right? And and and and it and it did. And I I read it and I'm like, yeah, it does look something like I would write. You know? Yeah. And I when I was writing for MSDN Magazine, well, I coulda I I could wholly coulda

used that. But it's interesting. When I asked g p t 4, GPT 4 has no idea who I am, which I'm not sure if I should be happy with that or a little upset at that. It just hasn't found you. Yes. Right. It hasn't found me yet. But it it it's it's also telling that there's a lot of motion here of people taking this offline. Right? So you wanna train your own model and, like, you know, the the amount of I think you're right.

We're only at the beginning of the innovation curve on this one. Yeah. You know, like, when the Internet first came out, who would have thought of something like Uber or Lyft. Right? Or DoorDash. Right? Like that or, you know, or I don't think we can we're so early in it. We can't really predict the future beyond the next couple of weeks. Yeah. Yeah. And and it's interesting you bring up that example because that's something we're working on in real time in our context. Right.

We have a a large user base. And, again, there's the the experts down to the beginners and all different levels of experience in between. And so within our member base, we have people who have incredibly well defined brand voices and styles where they do have enough you know, 1, have enough seed content to train personalized versions of the model on their voice, and to want that. Right? Like, they they wanna maintain their

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

the other other end of the spectrum who might need help even being introduced to the concept of developing your voice and working through what your voice should be and testing iterations, off of

different sample datasets. And so, we're basically working through from a, you know, how does this technology come to market perspective, solving that exact problem now of, you know, for a large diverse user base, how can we give people the ability to tailor outputs to their voice and their style if they know what that is and simultaneously help people develop that if they don't know what it is. So that's yeah. Hopefully, we'll

have something there in market pretty soon. I I think we're not that far away, but I'm sure that's something we'll need to, you know, iterate on and learn learn about over time too. Well, cool. So now we'll switch over to the pre canned questions. Okay. Which I pasted in the chat. Not they're not, real brain teasers. They're just kind of, general stuff. Yeah. How did you find your way into data and AI? Did did data find you, or did you find data?

I always loved math. Always, always, always loved math. If I had to really answer this, I'd say, you know, the 2 earliest examples that come to mind, I was nuts about baseball growing up and baseball statistics. Oh, cool. That was probably one of the first ways that data found me. And then a little bit later than that, the the first company I ever tried starting when I was in college was, essentially arbitraging the collectibles

market. So think about things like Magic the Gathering cards. Right? And so, yeah, I I began tracking prices that different collectibles were selling at on eBay and other platforms. Yeah. This is going back, over 20 years at this point. But Right. Right. Tracking prices to learn what was a good price and what was a bad price before we had that information readily accessible and then buying and selling against it. Those are the or 2 of the examples,

I think. But, yeah, data just always spoke to me. I I've always loved math, and so it was symbiotic. Yeah. That's funny. I was also a huge baseball fan growing up, and it's one of those I mean, if you're if you're if you're a baseball fan, statistics is a natural field for you to study because you've already Mhmm. You've already done a lot of it. Right? So it's it's that's it's it's interesting. We'll have to figure out what who who which team you root for.

But, I don't know if I wanna admit that. Well, my my Xbox gamer tank was Frankie Bronx, so you could probably figure out that I'm a Yankee fan. That I grew up mostly. I'm sorry. No. I expect the hate mail to come in. Yeah. I I grew up mostly in South Florida, and the Marlins came about. So when I was very young, we were a mess household. Oh, okay. And then, the Marlins came

about, and I became a Marlins fan. So yeah. Cool. Being a Marlins fan, it's been a few years that were really fun and a whole bunch of misery. I I I've noticed that. I'm kinda surprised at that, but I guess that's what it that's, you know although you went from being a Mets fan. I know Mets Mets have good years and bad years. Some would say good decades and bad decades, but Yep. The nice thing about baseball is that there's always another game and another season, you

know, you can you can always hold out hope. Yeah. Alright. So on to the next question. What's the favorite part of your current gig? Yeah. I think it's it's honestly this period of innovation we're in. It's fun. It's new. The answers are unknown. There are so many different paths it could take, and I think there's a lot of good that can be created. So it it reminds me in a lot of ways of the early ideological days of the

Internet. And, you know, I think we need to learn from that chapter in terms of things that weren't regulated or managed well as the Internet grew and make sure we don't make those same mistakes again. But that that excitement's back for me personally. I think we're experiencing a pace of innovation all of a sudden in software that we really haven't seen, you know, in 10 or 20 years. Yeah. That is a really

I that is a that is an excellent point. I think the fact that they kept pushing out models every few weeks publicly and being able to keep up with the met the demand more or less, as as does take me back to those days where people were, like, I'll never forget. It was an ad I saw in a magazine, which one of them one of the millions of web development magazines that came out in 96.

Right? Yep. And, and it was, like, you know, they show a picture of somebody with a with a sleeping, bag under their desk and somebody checking their email right away, like like, half in the bag, half well, that sounds bad. Half in the sleeping bag and half on the computer. And they were saying, you know, something like and the caption was, what did you miss? Like, it was like it was like really that type of mentality that you're right. I haven't seen this in a while.

So the we have 3 complete this sentence, questions. The first one is, when I'm not working, I enjoy blank. Being a dad. That's that's it for me. You know, between founder life, that's about all there is time for, honestly. Yeah. But, our daughter is at a really fun age. She's gonna turn 8 pretty soon here, and, I just enjoy being able to be goofy and have fun and play. And, yeah, she's wonderful. That's cool. That is a fun age. My youngest is 8. So

Yeah. The, the next question is, complete the sentence. I think the coolest thing in technology today is blank. Well, I guess I gotta say a tailwind. Right? What we're doing here. Right. I really do think a lot of what we're doing is cool, but but more broadly, I'd say, yeah, I think the coolest thing is that our perceptions of what technology is capable of are changing very quickly, and, that's fun. Yeah. Absolutely. I mean, one of the things you would hear was that

creative jobs were safe for a long time. And I think that, if anything, we've learned in the last 3 to 6 months, that's not necessarily the case. Yeah. So I think that's also a part of, you know it's it's probably not

a coincidence that, you know, Chat GPT upset people more than the image ones because the pack media, the people who write those articles, their jobs are, I think, are in I wouldn't say imminent jeopardy, but they are definitely on the firing line. Yeah. Whereas, you know, a year ago, oh, no. No one AI can never be creative. And yet, here we are. Mhmm. Alright. The last, complete the sentence. I look forward to the day technology I can use technology to blank. Teleport. Yes. How's I like that. I

like that answer. Yeah. That's even better than self driving cars because you wouldn't waste time in the car at all. Exactly. I love seeing new places. I hate getting there. Yeah. And we should probably ask chat g p t if there's a way we can make air travel less awful. We'll have the gpt airlines soon. Too. GPT Airlines. That's funny. Alright. So share something different about yourself, but, we like our clean Itunes, rating. So keep it keep it within those parameters.

Oh, this is a tough one. I I never know how to answer this, honestly. Something different about myself. Like, you know, I I don't know if it's super different, but I'll just say I'm I'm a huge strategy game nerd. Oh, interesting. And so, yeah, I mentioned with the magic cards before, it's yeah. I still love magic. I love settlers, risk, chess. Throw any strategy game at me, and, I could lose myself for days. That's cool. That's cool. So, Audible sponsors data driven.

Can you recommend a good audiobook if you do audiobooks? If you don't do audiobooks, just recommend a good, regular old dead tree book. Yeah. So, I'll recommend oh, I'm, like, staring at my bookshelf here. I think BoomTown is a really interesting one. So, before I moved to Oklahoma, I knew nothing about Oklahoma. Uh-huh. And, its history is absolutely fascinating. So, I think that's a really cool one, for people who have never been here before and just wanna learn about a new place

and a new place and time. It might challenge a lot of perceptions, but, it's a really good read. K. I'll have to check that out. And where can folks find out more about you and what you're up to? Yeah. Absolutely. So, for Tailwind, we are tailwindapp.com. That's our website. And you can follow us and and find us pretty easily on all the different social platforms and blog. For me personally, you know, probably just look me up on LinkedIn. I'm Daniel Maloney.

I think Daniel p Maloney is my handle. But that's probably one of the platforms I'm more active on these days, in terms of wanting to connect. Well, that's awesome. Well, thanks for joining us, and, I'll let Bailey finish the show. And just

like that, we've reached the end of today's digital odyssey. A huge thanks to our phenomenal guest, Danny Maloney, for sharing his insights and to Tailwind for redefining the marketing landscape. To our listeners, your curiosity fuels this journey, and we're immensely grateful for your companionship. If today's episode sparked a bit of that data driven wonder in you, why not share the love? Like, share, and subscribe to keep this conversation going and to ensure you never miss an

episode. Until next time, keep those circuits buzzing and your data flowing. Cheerio.