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.

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.