Danny Maloney on AI’s Leap to Marketing Mastery
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.
Transcript
Ladies and gentlemen, boys and girls, and AI entities of
Speaker:all computational capacities, welcome to another riveting
Speaker:episode of the data driven podcast. Today,
Speaker:we embark on a journey through the digital landscape where data
Speaker:isn't just numbers. It's the very fabric of our
Speaker:digital existence. With the ever charming Frank steering
Speaker:the ship solo, Andy was off gallivanting at Busch Gardens when this was
Speaker:recorded. We delve into the world of generative AI and
Speaker:marketing marvels with the illustrious Danny Maloney, the brain behind
Speaker:Tailwind. Picture this. A
Speaker:world where small businesses wield the power of giants, thanks to
Speaker:Tailwind's arsenal of automated marketing tools.
Speaker:From the nostalgic lanes of Google Street View to the cutting edge frontiers of
Speaker:AI driven marketing strategies, we're in for a treat.
Speaker:And since today is February 29th, leap day, we
Speaker:figured, why not leap into another episode? So
Speaker:adjust your antennas, polish your circuits, and prepare for an
Speaker:electrifying discourse on how Tailwind is reshaping the marketing
Speaker:cosmos, one algorithm at a time. Buckle
Speaker:up. It's going to be a data driven ride.
Speaker:Hello and welcome to Data Driven, the podcast where we explore the emerging fields
Speaker:of data, artificial intelligence, and the ever present world of data engineering.
Speaker:But speaking of data engineering, my co host, Andy, is not
Speaker:here today. He's having a fun family day at Busch Gardens.
Speaker:So, we wish him good weather and,
Speaker:short lines. So with me today
Speaker:is Danny Maloney, a successful Internet entrepreneur,
Speaker:CEO and cofounder of Tailwind, a software platform that
Speaker:provides small businesses with the marketing tools they need to compete. With
Speaker:over 1,000,000 users, Tailwind is a leader in
Speaker:generative AI and providing automated marketing plan
Speaker:creation, visual content design, copywriting, email campaign
Speaker:building, ad optimization, and more. But before starting his
Speaker:own company, he worked at Google, YouTube, and AOL,
Speaker:and is part of it apparently had, worked on something I'm very
Speaker:fond of is, Street View and Google Maps. And, maybe we
Speaker:could talk a little bit more about that. So welcome to the show, Danny.
Speaker:Great. Thanks for having me, Frank. Glad to be here. Yeah. Good to have you.
Speaker:Good to have you. So generative AI, right, as
Speaker:probably since, you know,
Speaker:last November has been on everybody's
Speaker:minds. What you've been how long
Speaker:ago did you start Tailwind? Yeah. So,
Speaker:Tailwind itself came to market in 2015. Okay. So we've
Speaker:been around for a while. You know, the way we tell our story is
Speaker:really in a couple of chapters. So there was kind of tailwind v 1,
Speaker:and there's tailwind 2 point o as we call it. And so for
Speaker:us, that was kinda 2015 to 2019 and then 2019 to the present
Speaker:day. But going down the path of,
Speaker:generation, for clients and really thinking about how we
Speaker:do more of the work of marketing for them, was
Speaker:the real theme of this second chapter. So we started on that path
Speaker:back in 2019, believe it or not, even though, you know, the market is
Speaker:really kinda waking up and starting to pay attention to your generative AI today,
Speaker:there was a lot to learn and a lot to figure out in terms of
Speaker:how to go about it the right way. And so it's been a pretty fun
Speaker:past few years for us going down that road. Interesting. So what
Speaker:does Tailwind generate specifically? Is it NLP? Is it
Speaker:LLM? Like, what is, image generation?
Speaker:Yeah. So there's actually multiple components. So like you said, we're trying to
Speaker:give small businesses the tools that they need to compete as marketers.
Speaker:So for us, that's not just one use case, but it's actually thinking
Speaker:about the entire marketing life cycle. And so we look at,
Speaker:today, 4 core components of what we help generate.
Speaker:One piece is actually the marketing plan itself. And that so that's our own
Speaker:IP, our own technology that we built that builds and
Speaker:extends and evolves marketing plans based on the specifics of the
Speaker:business. Once you have that plan, then you've gotta actually execute it. So
Speaker:now we get into the next couple components. So one is a tool called Tailwind
Speaker:Create, which is about visual generation. So we actually
Speaker:create the images that someone needs and, you know, don't think
Speaker:necessarily about a tool like a Canva or a PicMonkey in that
Speaker:sense of, you know, I'm just going through templates, and I'm designing it
Speaker:myself. What we do is we actually take in the assets and the
Speaker:inputs from a brand, and then we generate a large array of
Speaker:designs that they can scroll through and choose from instead of
Speaker:having to do the design work. So it kinda takes the design process from
Speaker:45 minutes to about 2 minutes, typically, and we generate it
Speaker:in all the formats they need for their different marketing assets. So
Speaker:visual design is the second part. The third part is copywriting.
Speaker:And so there, it's a mix of our IP and also
Speaker:leveraging third party tools that are out there. So leveraging LMS,
Speaker:for example. But that's about, you know, being
Speaker:able to help people write the copy that's gonna be convincing, that's gonna
Speaker:help communicate with their audience. And then the 4th component
Speaker:is ads. So we actually acquired a company last year
Speaker:called Nectar 9, who they themselves had been building out
Speaker:an AI driven ad management platform for about 5 years prior
Speaker:to that. And, we're now integrating that into
Speaker:Tailwind so that our users will be able to mostly automate
Speaker:the process of paid advertising also. But we look at that as a
Speaker:complete cycle. Right? So I've got a plan. I can create the content for the
Speaker:plan, then I can distribute it and get it out to my audience.
Speaker:And the data from that cycle should inform my
Speaker:plan so I can get better and get smarter into the future.
Speaker:Interesting. That's one of the things that I I I
Speaker:wonder about these these generative tools. Like, do they take the feedback
Speaker:and that, like, you know so eventually, it would get better. Ideally, it
Speaker:would eventually get better. And it sounds like you do include that in your feedback
Speaker:loop. There are some. I mean, honestly, I think we're in early days of
Speaker:that. Right? If you look at some of the more prevalent models that are
Speaker:out there, you know, the the various OpenAI models, for
Speaker:instance, I think there is an inherent feedback loop in how
Speaker:those operate as they continue to train on more data.
Speaker:Right? When you talk about things from a marketing context in
Speaker:particular, the feedback loop for us has been around
Speaker:things like, how do we train, you know,
Speaker:our models, our products, or third party models
Speaker:around specific use cases. Right? So, looking at the
Speaker:corpus of data we have around what works, what doesn't work,
Speaker:enabling it to be more tailored to the use case that the user
Speaker:is trying to solve. So, you know, when you're designing a
Speaker:pin for Pinterest, that should be different than a, you know,
Speaker:feed post on Instagram. Right? A feed post on Instagram is different from
Speaker:writing a script for a reels, right, or a TikTok. So,
Speaker:for each of those use cases, we found you can start generating much stronger
Speaker:results with more specific training.
Speaker:Interesting. Yeah. I I I wonder, like, what
Speaker:what the future holds for this type of work. You know, there's definitely a lot
Speaker:of there's a lot of, you know,
Speaker:grinding and gnashing of teeth and, you know, prog you know,
Speaker:prognostic that's that's my fault for trying to use a
Speaker:a big word on, basically, what is a kind of a holiday
Speaker:week. But, you know, but I
Speaker:think that it's interesting because it's it's probably opened up the opportunity for a
Speaker:company like you, to step in and kinda build these
Speaker:tools that really empower the smaller businesses because that seems like it's a
Speaker:pretty large, audience of folks.
Speaker:Yep. Yeah. It definitely is. And, honestly, I think it's the audience that
Speaker:needs it most. Right? Because when you look at very large
Speaker:enterprises out there, and they've got world class marketing teams and world
Speaker:class agencies that they're working with, tons of data, tons of
Speaker:resources. They've got access to the best tools. And a
Speaker:lot of small business owners don't realize that in today's world of
Speaker:marketing, when you start a small business, you're actually competing with the big
Speaker:guys from very early on. Right? There's only so much
Speaker:consumer attention out there. There are only so many eyeballs, so many minutes
Speaker:to compete for. And small businesses are
Speaker:drastically under resourced and under tooled compared to
Speaker:large enterprises. And then on top of that, it's usually the
Speaker:founder trying to figure out the marketing on their own, or maybe they can hire
Speaker:a 1 person team or a 2 person team in the early days.
Speaker:But I think, you know, you you look at this type of generative AI
Speaker:evolution that's happening right now. I think it's that
Speaker:smaller, business, and I think it's, people
Speaker:who have been on the fringes of really having access to having
Speaker:their voice heard that this potentially helps the most. So
Speaker:just an example of that, a pocket of users we've seen
Speaker:on our ghostwriter capability, which is the the copy
Speaker:generation, part of Tailwind, a
Speaker:pocket of users we've seen who are really passionate about it are people who are
Speaker:non native English speakers. Right? Right. And you
Speaker:go through user research, and you're watching some of
Speaker:the interviews, and we've literally had people in tears of,
Speaker:you know, how happy they were to be able to clear some
Speaker:really big emotional blockers for them around
Speaker:anxiety and fear of having to communicate in a non native language and
Speaker:knowing now that they can produce work at a higher
Speaker:level is is life changing. Right? So, yeah, I I think of this, and
Speaker:I look back to the evolution of the Internet and think of,
Speaker:you know, I was like you said, I was at YouTube before. And we
Speaker:had similar stories at YouTube of,
Speaker:you know, the teenager in Africa who teaches himself calculus
Speaker:through YouTube videos. Right? And Right. Right. Right. Yeah,
Speaker:or software development. Right? Who otherwise would not
Speaker:have had access to that level of education and
Speaker:couldn't have dreamt of it in some cases, but those were very real stories.
Speaker:And and it's a reminder for me of the power of technology to
Speaker:democratize access. Right? And so I think this next wave is gonna
Speaker:do that as well. And hopefully, in the long term, it just leads to, you
Speaker:know, the best ideas, the best content, the best product winning out.
Speaker:Interesting. So what what did the and, you know, what did the first
Speaker:version of Alwin look like before the generative AI? Right? Were
Speaker:you you know, what did that touch AI in any way, or
Speaker:was it kind of sort of not? Yeah. It really
Speaker:didn't, but there was a common underpinning in a
Speaker:way. So one of the things we heard very early when we
Speaker:were starting and building up the company from especially the small business
Speaker:audience was, you know, everyone's giving us tools,
Speaker:and these tools give us data and analytics. And
Speaker:we're supposed to have time and energy to go through that data and
Speaker:figure out what it's telling us. But, frankly, I don't wanna do that. Right? I
Speaker:I didn't start my business to dig through reports. I wanna spend
Speaker:time designing products or engaging with my customers or creating content,
Speaker:not doing data analytics. Right? And so what we heard was this common
Speaker:theme of, I wish I wish someone would just give me the
Speaker:tool that tells me what to do instead
Speaker:of making me figure it out on myself. Like, pushing
Speaker:bits from point a to point b is fine. Right? There's value in that. There's
Speaker:value in things like scheduling content and
Speaker:email automation. Sure. That helps scale.
Speaker:But the real part people were struggling with was knowing what to do in the
Speaker:first place. And so as we built the first version of
Speaker:Tailwind, which was largely focused on social media scheduling and
Speaker:publishing, What we found was
Speaker:certain features that we built that were more predictive
Speaker:in nature. So for example, the best time to post,
Speaker:for your audience as an example, or which you know, we have a
Speaker:hashtag finder feature from the first version. You know, which hashtag should I use
Speaker:on this post? Those were some of the features that people
Speaker:got most excited about and that really generated a lot of energy, a
Speaker:lot of curiosity around. And so I see a common thread there.
Speaker:Right? Because it was taking away those psychological
Speaker:barriers and helping people clear that question of, am I
Speaker:doing the right thing, right, by giving the recommendation. Okay. I can
Speaker:follow the recommendation, follow the doctor's orders, so to speak, and
Speaker:now I am unblocked. And I'm gonna move faster, and I'm gonna do more
Speaker:marketing, which is gonna help me build my business. And so, yeah, I
Speaker:think this generative AI wave now is the
Speaker:next level up of that concept of being
Speaker:able to guide people in an
Speaker:opinionated but data informed fashion on
Speaker:what they should be doing in far more points of their marketing journey
Speaker:in a way where beginners can get up to speed, you know, much faster than
Speaker:they otherwise would. Interesting.
Speaker:Are people comfortable with being told what
Speaker:to do, or it depends on the the audience. Right? Because that was the first
Speaker:thing that's the first thing that you said that kinda made me, like, I wonder
Speaker:how people feel about that. Yeah. It definitely depends on the
Speaker:audience. I I think the if I had to observe the divide
Speaker:there that I've seen over time, experts are less comfortable being
Speaker:told what to do because they're experts. They've earned that
Speaker:expertise. They've taken years of learning and labor to get
Speaker:there. Beginners are thrilled to be told what to
Speaker:do in the vast majority of cases. And and, you know, we we usually don't
Speaker:frame it that way. It's more of, like, guiding and recommending for that,
Speaker:then, you know you don't want it to sound overbearing. But but the
Speaker:reality is when you're at that early stage and someone's there to help
Speaker:you out and you don't have to drop, you know, $1500 a month on a
Speaker:marketing consultant, you know, who's working part time for you and 10 other
Speaker:brands. Right? Like, that's that's a breakthrough for a small business
Speaker:owner. So they tend to be thrilled, and then you kinda have
Speaker:the messy middle, so to speak, of people who are kind of becoming experts
Speaker:not quite there. Or maybe they're expert in a given area,
Speaker:and they don't feel like they need the advice there, but they don't feel as
Speaker:expert in the next area that they're trying to learn or expand into.
Speaker:And so, you know, I think that's that audience is where
Speaker:we kind of stretch up to serving. Right? And so
Speaker:we've had to build the interface and the system in a way
Speaker:where people can opt in or opt out of the advice at each
Speaker:individual point. Right? You don't have to follow it. So we
Speaker:don't fully do it for you because, you
Speaker:know, I I think even from an ethical perspective, that that crosses the line,
Speaker:especially with AI. Right. But you don't wanna be like
Speaker:Clippy either, where it's like, hey, it looks like you're hey, it looks like you're
Speaker:writing a letter. Hey, it looks like you're writing a marketing plan. Like, that you
Speaker:know? And you want there to be a cycle of learning and
Speaker:oversight there because, you know, every brand should be unique. Every
Speaker:voice should be unique. You do get into
Speaker:industries where facts really matter. Right? Like, you you need the
Speaker:marketing to be factual, and, you know, there are certainly been observed cases
Speaker:where LLMs don't always do so well at, you know,
Speaker:finding the factual information versus filling in the gaps themselves.
Speaker:But in the vast majority of cases, they do a pretty good job.
Speaker:So, you know, I think that's where you get into some AI ethics
Speaker:conversations, and there's a line we don't wanna cross. Right?
Speaker:There should still be a human involved, but we can
Speaker:empower that human to do a lot more and to do it in less time.
Speaker:Well, I like that. I like that you're you're thinking about the ethical concerns here
Speaker:because a lot of a lot of businesses
Speaker:don't really think about that upfront. Yeah.
Speaker:And so you you you mentioned the line. You don't wanna cross it. Do
Speaker:you do you have you found yourself in situation where you're kinda getting close to
Speaker:the line? Yeah. I think that's a really interesting
Speaker:question. It's something we debate, and and we're
Speaker:actually, you know, Greg Starling, who's been leading up the
Speaker:AI initiatives for us from early on, he's actually
Speaker:starting an internal kind of, like, lunch talk
Speaker:series with the team to dive into those questions
Speaker:collaboratively. Right? Like, where are people struggling with this individually?
Speaker:Where are they seeing potential conflicts in their work? Or where are they running
Speaker:up to barriers that they don't know what the answer should be?
Speaker:You know, I I think, the reality is there's
Speaker:not a big situation I can point to today where I
Speaker:can say, like, yeah. There's this really compelling example, but we know it's
Speaker:out there on the horizon. Right? And so the things that we
Speaker:worry about are, ways that AI can
Speaker:be misused, right, to spread misinformation, to do
Speaker:harm to society, to, do harm to other people.
Speaker:And we think a lot about protecting against that
Speaker:type of abuse and trying to figure out, you know, how do we even
Speaker:detect and observe that type of abuse in the first place if it is
Speaker:happening so that we can then help protect against it.
Speaker:Interesting. Interesting. What
Speaker:do you see what do the experts think of this? Do they view
Speaker:this as a threat or are they just kind of because you're you're smart
Speaker:by going after the people who are not below the messy
Speaker:middle. Right? I think that's smart because if someone wants to
Speaker:start a burger stand, they don't get into the burger stand
Speaker:business because they wanna rock social media. Right. Right? They don't
Speaker:do it because they wanna make marketing plans. They want it because they wanna cook
Speaker:burgers. Right? Like Yeah.
Speaker:In in the virtual green room, we were talking about Sonic and how they're headquartered.
Speaker:You're in Oklahoma City and how Sonic is headquartered. So now I'm thinking
Speaker:about, like, fast food. But,
Speaker:I mean, like so, I mean, is it sounds like you found a
Speaker:receptive audience there. But what do the experts say? Do you like
Speaker:they kind of, you know, are they they
Speaker:they probably brush it off, but are they brushing it off in a
Speaker:legitimate way, or is it a little bit of, you know, jealousy or
Speaker:fear? I think there's a mix. I mean, realistically, just over that large
Speaker:of a population. Of what I observe.
Speaker:Yeah. In the grand scheme of things, I'd say we're in the
Speaker:early adopter phase of generative AI. Right? So we are nowhere
Speaker:near mass market adoption yet as fast as it seems like
Speaker:certain tools have taken off. Right? Like, we're we're nowhere near mass
Speaker:market adoption yet. That's gonna be years down the road.
Speaker:And so, you know, you're gonna get a variety
Speaker:of responses. I'd say they range from, you know, some
Speaker:experts who have leaned in whole hog and are experimenting
Speaker:as much as they possibly can with all the different platforms and are publicly
Speaker:publishing those experiments on LinkedIn or Twitter or wherever they might
Speaker:be doing it, so others can learn from it as well.
Speaker:You've got, folks at the opposite end of the spectrum who I think
Speaker:are often reacting with denial or fear.
Speaker:Right? And I think you also have some just
Speaker:very correct observations around things
Speaker:that AI is not yet going to do well. Right?
Speaker:So, yeah, I I observed this especially
Speaker:within the community of kinda, like, professional SEO
Speaker:folks. Right? And, yeah, there are systems and
Speaker:philosophies of SEO and processes that have been developed that are highly
Speaker:specialized that a chat gpt is
Speaker:not designed for today. Right? And so you'll see
Speaker:these threads where someone goes to chat gpt, and they say, hey. Give me an
Speaker:idea for 10 keywords that I can target on this blog post that I'm
Speaker:writing, and it spits out an answer. Right? And the professional SEO will look
Speaker:at that and say, well, are you using data to analyze
Speaker:whether or not these are actually the right terms to be targeting based on
Speaker:where people are searching and, what you can actually win
Speaker:and so forth. And that's a very fair criticism.
Speaker:I think we're not far from the point where some tool, and
Speaker:maybe it's the tools who are already deep in the SEO space, is
Speaker:going to marry that data to generative AI. Right?
Speaker:And so now you will have the response being informed
Speaker:by data. Right? And I think that's where it becomes a lot
Speaker:a lot more frightening because then people start asking, like, okay.
Speaker:What is my job now? And that's really the deeper
Speaker:conversation I think needs to happen because what should result
Speaker:here is that people are leveled up. Right? People are leveled up to
Speaker:more strategic thinking and more strategic work, And they
Speaker:don't have to do as much of the rote processing that happens in
Speaker:a lot of jobs today. But when you're spending a lot of time on
Speaker:that today, it's a really scary thought. Right? So,
Speaker:as a society, we're gonna have to navigate that. And we're gonna have to
Speaker:figure out training paths for people to
Speaker:find their way to that next, next definition of
Speaker:their job. But, you know, I I think it's gonna
Speaker:play out over years. It's not gonna play out over, you know, months or quarters.
Speaker:Okay. Interesting.
Speaker:What, what do you think the next step is
Speaker:in this? I know you kinda mentioned it, but, like, you you did you touched
Speaker:on some of that. It
Speaker:it's interesting. It's interesting to me how that
Speaker:how this is gonna unfold. And I know it's hard to predict the future, but
Speaker:what are your thoughts on kind of, like, of this? So, I mean, we
Speaker:are early in the phase. I mean, I think a lot of people I think
Speaker:chat gpt got so much wind behind it
Speaker:because I I don't think anyone in the field expected that chat g p
Speaker:t would be as good as it was. I I I knew that we would
Speaker:come up with something like it, but I thought we were still, you know, 3
Speaker:to 5 years out from something that good. Right. And I think
Speaker:that because it kinda leapfrog people's expectations, I think that
Speaker:really boosted it.
Speaker:Yeah. You know,
Speaker:I think has really ignited people's imaginations both in good ways and
Speaker:bad ways. Yeah. I I think you're dead on there.
Speaker:And I think we're in the experiments the experimentation early
Speaker:adopter phase. Right? So if I think back to other ecosystems,
Speaker:like, you know, the early Facebook API and Twitter API
Speaker:or the early mobile app ecosystems, right, What we saw
Speaker:was this almost gold rush mentality of
Speaker:tons of experimentation, a lot of independent developers coming in and just
Speaker:building things to see what is possible. And then you fast forward 3
Speaker:years, and 90% of those projects are dead. Right? Right.
Speaker:Not Because it didn't pan out or it wasn't quite
Speaker:impactful enough or the developer lost interest. So I think we're
Speaker:in that phase right now. You know, the the good news is the 10%
Speaker:that survive that phase can end up being really impactful
Speaker:applications and really impactful companies.
Speaker:What's interesting and maybe different this time around
Speaker:is I see a lot of incumbents and established companies jumping in
Speaker:the game early. Right? Right. And I consider this still relatively
Speaker:early. You know, even though we've been at it for a while on our road
Speaker:map, That was maybe too early or, you know, super
Speaker:early. But I think a lot of companies right now
Speaker:are asking themselves, what does this mean for us and what does
Speaker:this mean for our user? Because a lot of software
Speaker:that's been built will need to be rebuilt
Speaker:and rethought. A lot of processes will need to be rethought. So, you know, a
Speaker:good, example I like is what HubSpot put out where they
Speaker:said, you know, with the simple example of using a chatbot to do things
Speaker:like update a CRM record. Right? Right. And it's
Speaker:like, right, you have to you know, before it was input, output, input, output, input,
Speaker:output, input, output, and then eventually, the record was updated.
Speaker:Right? Right. And the user had to click all those buttons and provide all those
Speaker:inputs. And now it could just be a one line
Speaker:chat input, and it's updated. Right?
Speaker:So it's rethinking interfaces. It's rethinking
Speaker:what software can actually do in someone's life. And and I think that's
Speaker:gonna be a multiyear cycle, because companies are
Speaker:gonna have to experiment. Some are gonna hit on big
Speaker:innovations. Some are gonna fail.
Speaker:But I think that's the next chapter. Right? Can can we
Speaker:take chat chat gpt and similar concepts, similar
Speaker:models from novelty to
Speaker:broad utility in a way that makes sense for people. No. That's
Speaker:a great point. And if you you kinda watch I grew up watching, you
Speaker:know, Star Trek the next generation and d space 9. And if
Speaker:you watch those shows, there's always this this
Speaker:thing where the computer becomes a character in the story in the sense that
Speaker:they say, computer, extrapolate all possibilities of the warp drive
Speaker:going, like, whatever. Yep. You know? And
Speaker:you even saw this in The Expanse, which is Andy and I's probably
Speaker:our favorite show. And there was a there's a scene where one
Speaker:of the characters is interacting, trying to find an
Speaker:ideal orbit around, getting around all these warships
Speaker:and the whole thing. But he goes, you know, what
Speaker:if I did it with minimal thrust? And he was asking you all these questions,
Speaker:and it was basically computing all of these kind of parameters. When I
Speaker:use chat GPT, I kinda feel like I'm doing that.
Speaker:Yeah. You know? You know, why one of the things that that that that
Speaker:I have for for my blog is I have a tool called Dingo,
Speaker:and it kind of helps me produce content and all that.
Speaker:And I originally wrote it in in c sharp, but I ported
Speaker:it over to Python with the
Speaker:help of chat gpt. I basically asked it, What if I wanted a pro
Speaker:I described the program. How would I write that in Python?
Speaker:And, you know, I was able and the code wasn't
Speaker:perfect to your point that, you know, they're not always factual. Right? The
Speaker:code, if I copied and pasted it, had issues, but those were
Speaker:not insurmountable issues. So I I I I poked around with
Speaker:it. Long story short, original version of Dango took about 3,
Speaker:4 weeks. The Python ported of Dingo
Speaker:took about a day and a half to get feature parity
Speaker:Interesting. Which isn't yeah. And obviously pretty
Speaker:revolutionary. And and you can just skeptic in me. It's like, yeah. But you already
Speaker:wrote it. Right? So you kinda already thought through a lot of these problems. However,
Speaker:see you know, it just seems like it's a much faster process. Because I also
Speaker:think too, it's also a pretty patient mentor. You know what I mean? Where you
Speaker:can ask it dumb questions. And as long as the server is still
Speaker:running and it's not overloaded, it's always happy to answer your
Speaker:questions. Right. Which which I think I think is really kind of the
Speaker:I saw a video the other day how it's gonna change language learning and this
Speaker:guy was talking about, you know, hey, you know, I'm intermediate Spanish speaker
Speaker:and I wanna to go to the next level. I can ask chat
Speaker:gpt to create this material that is
Speaker:custom tailored to me. And I think that is that's a fascinating use case
Speaker:because I don't think anyone thought that. That's not a use case that
Speaker:you ordinarily would do it. So I find it interesting that I never would have
Speaker:thought marketing campaigns either, you know. Although I will admit, I have written, like, a
Speaker:YouTube video description, and I'm, like, make it more exciting, make it more, you
Speaker:know, dynamic. Right? Yeah. And it does. It does. It's it
Speaker:it, you know, it's not always something I would say, but that's that's
Speaker:for me, the human to go in and kinda edit it. Yeah. I gotta hook
Speaker:you up with a ghostwriter account so you can give us feedback on that. Oh,
Speaker:sure, man. That'd be awesome. Because we've got things like, you know, YouTube
Speaker:description generation and and Right. Summarizing and cleaning up my
Speaker:copy. But but it's interesting because what you hit on there, which is you
Speaker:had already spent the time thinking through the issues and architecting it and
Speaker:and figuring out what the challenges were, I see
Speaker:a direct parallel to what we've been working on in applying this tech and marketing.
Speaker:Because I I'm taking Ghost Rider as an example, Where a
Speaker:lot of the work has come is actually in prompt
Speaker:engineering, testing, quality control, and iteration.
Speaker:And it's not a once and done process. Like, we're actually
Speaker:tracking success of various prompts and various use cases and
Speaker:going back and improving them over time because,
Speaker:I mean, first, the underlying models are changing. Right? And so, you know, that's a
Speaker:continuous force of change. But, also, more users are now
Speaker:using them, and so we have more data. But
Speaker:but I think part of the key there is you look at this field of
Speaker:prompt engineering that has now exploded all of a sudden. And while there are
Speaker:a lot of early adopters diving in there and getting really excited about
Speaker:it, the reality is the average person should never have
Speaker:to learn prompt engineering as a new skill. Right?
Speaker:Like, that is that's not where we end up from a user interface
Speaker:perspective here. And so we're kind of baking that into our
Speaker:solution where we say, okay. Part of the value we bring is that
Speaker:we have a series of
Speaker:expert created and groomed and tested
Speaker:prompts, that are trained on real data that, you
Speaker:know, helps perform an improvement or it helps improve performance.
Speaker:And that makes the technology now accessible to people who
Speaker:are not going to spend time on learning prompt engineering. And so,
Speaker:I I think we'll see those types of evolutions here. And maybe,
Speaker:eventually, prompt engineering won't be as necessary as it is at
Speaker:the moment as the models get better, But that might be a longer time line
Speaker:until you can really get to that point where you don't need
Speaker:that type of work at least going on in the background. Right. I I do
Speaker:wonder, like, is prompt engineering the next hot job title?
Speaker:Yeah. Or is it gonna be more as these models improve, like
Speaker:you say, it'll be more prompt optimization. Right? Or and I'm
Speaker:sure I'm sure we'll come up with a fancy acronym for that because we always
Speaker:do. But probably start using AI to optimize the prompts
Speaker:also, right, to to monitor and measure and and
Speaker:No no joke. I've done that. I've done that with DALL E with Dolly. So
Speaker:so last year was an interesting year in AI. Right? Because Dolly alone and the
Speaker:work that was done in image generation would have been the headline story. But at
Speaker:the, you know, the last minute, you know, chat
Speaker:gpt kinda took all the oxygen out of the room. I don't think people realize
Speaker:that. So there's actually I've actually, like, done it where I if I
Speaker:I I give it a prompt. Yep. That I wanna generate an image with.
Speaker:And I tested this, and I'll let me do a blog post on this, where
Speaker:I say, give me a picture or painting of a doctor in the style of
Speaker:Rembrandt. Right? And then I asked
Speaker:chatty Pete, hey. How would you write this as a prompt to get the
Speaker:best output for DALL E 2? And it came up with a paragraph.
Speaker:Mhmm. You know, use this type of lighting, this type of paint. I mean, it
Speaker:was just stuff I never would have thought of. And then just for grins, I
Speaker:put pasted that in and it the the the
Speaker:obviously, art is subjective, but I would say that the
Speaker:quality of that improved pump, the output was an order of
Speaker:magnitude better. That's really it. And
Speaker:that's cool because anyone can kind of experiment with that. Right? That's like a simple
Speaker:thing anyone can do, you know. You give it a basic prompt and you ask
Speaker:it to get make it make it more for, I think you're probably doing it
Speaker:now. But, but, like, it's
Speaker:fascinating. Like, it comes up with a much better quality. And and I I think
Speaker:that that's interesting for a number of reasons. One, we're using AI to talk to
Speaker:AI. Right? Which is kind of a, I don't know, like, that sounds like the
Speaker:start of every bad sci fi movie. And the
Speaker:other thing is is that that potential to create that
Speaker:better image was always in that model.
Speaker:Yeah. We're just using the prompt to draw it out. I
Speaker:I I I think there's something very
Speaker:curious and interesting about that. Right? That that model is they the model
Speaker:is maybe more capable than we're aware of Yep.
Speaker:Which blows my mind. I was looking up a
Speaker:thread that you know, pretty recent. If you look up
Speaker:Nick Floats on Twitter, at Nick Floats,
Speaker:so Midjourney came out with their new described
Speaker:endpoint. And so he's basically doing that where he's he's
Speaker:generating an image, asking it to describe
Speaker:the image as a prompt, and then rerunning that prompt to see
Speaker:what the output looks like after rerunning the prompt that the tool actually generates
Speaker:for an existing output. And it's fascinating to see,
Speaker:both how Midjourney actually describes, right, in the in the
Speaker:first place, Right. The piece of content, but then also,
Speaker:the difference in in the two outputs. Right?
Speaker:Yeah. I'm looking at his I'm looking at his Twitter feed now, and, like, I
Speaker:see the pictures and, you know, not that long ago, I would have said,
Speaker:oh, wow. You must have an artist friend or he must be an artist himself.
Speaker:Yeah. But now it's like and it's funny. I don't know. Maybe it's my
Speaker:imagination, but I can look at an image. I'm like, oh, that looks like a
Speaker:stable diffusion. That looks like a DALL E. That looks like a,
Speaker:Midjourney. Like, it's interesting how
Speaker:those models have a certain style. Yeah. I did that
Speaker:for fun last year with DALL E. I, you
Speaker:know, generated a virtual piece of artwork and then posted it on my
Speaker:Instagram. And, I think I put the caption something like, you
Speaker:know, so can I say I can paint now or something? But I didn't include
Speaker:any other context. And I got comments back from some of my friends
Speaker:like, you made that. Oh my goodness. Right?
Speaker:But Yeah. And and and what a shift there's been. Because if you did that
Speaker:today, people would be like, oh, you're using AI for that. Yeah. And You
Speaker:know? That couldn't have been more than 6 months ago that that happened. So
Speaker:It's really it's really gone fast. And you think about, like, GPT 4 is
Speaker:out and Yeah. GPT,
Speaker:5 is in the works. And
Speaker:if nothing else, obviously, they have smart people working there, but the people who do
Speaker:the marketing at OpenAI are top notch because
Speaker:they already had GPT 4 kind of waiting in the wings when
Speaker:they released g chat GPT. Mhmm. And then once
Speaker:that once everybody got all crazy over that, then they released that. And apparently,
Speaker:4 was being worked on. I mean, 5 was being worked
Speaker:on even as that was happening. So it's just it's fascinating to see
Speaker:how this is going and it you're right, though. It's I
Speaker:haven't seen people kinda go this crazy since the beginning of the Internet.
Speaker:Yep. It's just in terms of, you
Speaker:know, we are in a not an ideal economic environment. There are banks
Speaker:collapsing all that, but the investment is not dried up in
Speaker:specifically AI. Big tech tech has obviously taken a hit,
Speaker:but, you know and you're right. Yeah. Even big players are jumping in with both
Speaker:feet, You know? Although, I don't know if you played with Bard. I I haven't
Speaker:yet, personally. I I I don't wanna I know that they're
Speaker:working feverishly on it, but I was not impressed because I
Speaker:asked it. So if you ask if you ask chat g p t, you know,
Speaker:hey. Write a script that goes infectious weather data. Right? That's, like, my
Speaker:hello world. Right? Just to test it out. Right? Check CPT will
Speaker:happily give you a whole thing, talk about it, give you code,
Speaker:You can copy and paste it. It mostly works. If
Speaker:you ask Bard, Bard actually says, I'm I'm a language model.
Speaker:I I can't do that. Interesting. Like, now, again, that
Speaker:was a week ago. This is a fast moving field. Yeah. But it it's
Speaker:kind of funny how I don't know, I think if you
Speaker:can kinda sense the style that's different in visual mediums,
Speaker:like, you know, the mid journeys and the, the dollies and,
Speaker:the stable diffusions. Is there going to be kind of a
Speaker:similar style difference,
Speaker:you know, in the text generation ones. I suspect there will be.
Speaker:And it's interesting what g p t 35 knows versus g p
Speaker:t 4. Like Yeah. It it knows about the articles
Speaker:I've written. Right? So I can ask it to write an article in
Speaker:the style of Frank Lavinia. Right? And and and
Speaker:and it and it did. And I I read it and I'm like, yeah, it
Speaker:does look something like I would write. You know? Yeah. And I
Speaker:when I was writing for MSDN Magazine, well, I coulda I I could wholly coulda
Speaker:used that. But it's interesting. When I asked g p t 4, GPT
Speaker:4 has no idea who I am, which I'm not sure if I should
Speaker:be happy with that or a little upset at that.
Speaker:It just hasn't found you. Yes. Right. It hasn't found me yet. But it it
Speaker:it's it's also telling that there's a lot of motion here of
Speaker:people taking this offline. Right? So you wanna train your own
Speaker:model and, like, you know, the the amount of I think you're right.
Speaker:We're only at the beginning of the innovation curve on this one. Yeah. You
Speaker:know, like, when the Internet first came out, who would have thought
Speaker:of something like Uber or Lyft. Right? Or DoorDash. Right?
Speaker:Like that or, you know, or I don't think we can we're so
Speaker:early in it. We can't really predict the future beyond the next couple of
Speaker:weeks. Yeah. Yeah. And and it's interesting you bring up that
Speaker:example because that's something we're working on in real time in our context. Right.
Speaker:We have a a large user base. And, again, there's the the experts down to
Speaker:the beginners and all different levels of experience in between.
Speaker:And so within our member
Speaker:base, we have people who have incredibly well defined
Speaker:brand voices and styles where they do
Speaker:have enough you know, 1, have enough seed content to train
Speaker:personalized versions of the model on their voice,
Speaker:and to want that. Right? Like, they they wanna maintain their
Speaker:voice, and they don't wanna sound like everyone else. And then you have folks at
Speaker:the other other end of the spectrum who might need help even
Speaker:being introduced to the concept of developing your voice and working through what
Speaker:your voice should be and testing iterations, off of
Speaker:different sample datasets. And so, we're
Speaker:basically working through from a, you know, how does this technology come to market
Speaker:perspective, solving that exact problem now of, you know,
Speaker:for a large diverse user base, how can we give people
Speaker:the ability to tailor outputs to their voice and their style if they
Speaker:know what that is and simultaneously help people develop that if they
Speaker:don't know what it is. So that's yeah. Hopefully, we'll
Speaker:have something there in market pretty soon. I I think we're not that far away,
Speaker:but I'm sure that's something we'll need to, you know, iterate on and learn learn
Speaker:about over time too. Well, cool. So now
Speaker:we'll switch over to the pre canned questions. Okay.
Speaker:Which I pasted in the chat. Not they're not, real brain teasers. They're
Speaker:just kind of, general stuff.
Speaker:Yeah. How did you find your way into data and AI?
Speaker:Did did data find you, or did you find data?
Speaker:I always loved math. Always, always, always loved math. If I had
Speaker:to really answer this, I'd say, you know, the 2 earliest examples that come to
Speaker:mind, I was nuts about baseball growing up and
Speaker:baseball statistics. Oh, cool. That was probably
Speaker:one of the first ways that data found me. And then a little bit later
Speaker:than that, the the first company I ever tried starting when I was in
Speaker:college was, essentially arbitraging the collectibles
Speaker:market. So think about things like Magic the Gathering cards. Right?
Speaker:And so, yeah, I I began tracking
Speaker:prices that different collectibles were selling at on eBay and other
Speaker:platforms. Yeah. This is going back,
Speaker:over 20 years at this point. But Right. Right.
Speaker:Tracking prices to learn what was a good price and what was a bad price
Speaker:before we had that information readily accessible and then buying and selling
Speaker:against it. Those are the or 2 of the examples,
Speaker:I think. But, yeah, data just always spoke to me. I I've
Speaker:always loved math, and so it was symbiotic. Yeah.
Speaker:That's funny. I was also a huge baseball fan growing up, and
Speaker:it's one of those I mean, if you're if you're if you're a
Speaker:baseball fan, statistics is a natural
Speaker:field for you to study because you've already Mhmm. You've already done a lot
Speaker:of it. Right? So it's it's that's it's it's interesting.
Speaker:We'll have to figure out what who who which team you root for.
Speaker:But, I don't know if I wanna admit that.
Speaker:Well, my my Xbox gamer tank
Speaker:was Frankie Bronx, so you could probably figure out that I'm a Yankee fan.
Speaker:That I grew up mostly. I'm sorry. No. I expect the
Speaker:hate mail to come in. Yeah. I I grew up mostly in South
Speaker:Florida, and the Marlins came about. So when I was very young, we were a
Speaker:mess household. Oh, okay. And then, the Marlins came
Speaker:about, and I became a Marlins fan. So yeah. Cool. Being a Marlins fan, it's
Speaker:been a few years that were really fun and a whole bunch of misery.
Speaker:I I I've noticed that. I'm kinda surprised at that, but I
Speaker:guess that's what it that's, you know
Speaker:although you went from being a Mets fan. I know Mets Mets have good years
Speaker:and bad years. Some would say good decades and bad decades,
Speaker:but Yep.
Speaker:The nice thing about baseball is that there's always another game and another season, you
Speaker:know, you can you can always hold out hope. Yeah. Alright.
Speaker:So on to the next question. What's the favorite part of your current
Speaker:gig? Yeah. I think it's it's honestly this period of
Speaker:innovation we're in. It's fun. It's new. The answers
Speaker:are unknown. There are so many different paths it could take, and I
Speaker:think there's a lot of good that can be created. So it it reminds me
Speaker:in a lot of ways of the early ideological days of the
Speaker:Internet. And, you know, I think we need to learn
Speaker:from that chapter in terms of things that weren't
Speaker:regulated or managed well as the Internet grew
Speaker:and make sure we don't make those same mistakes again. But that that
Speaker:excitement's back for me personally. I think we're experiencing a pace of
Speaker:innovation all of a sudden in software that we really haven't seen,
Speaker:you know, in 10 or 20 years. Yeah. That is a really
Speaker:I that is a that is an excellent point. I think the fact that
Speaker:they kept pushing out models every few weeks publicly
Speaker:and being
Speaker:able to keep up with the met the demand more or less,
Speaker:as as does take me back to those days where people were, like,
Speaker:I'll never forget. It was an ad I saw in a magazine, which one of
Speaker:them one of the millions of web development magazines that came out in 96.
Speaker:Right? Yep. And, and it was, like, you know,
Speaker:they show a picture of somebody with a with a sleeping,
Speaker:bag under their desk and somebody checking their email right away,
Speaker:like like, half in the bag, half well, that sounds bad. Half in the sleeping
Speaker:bag and half on the computer. And they were saying, you know, something
Speaker:like and the caption was, what did you miss? Like, it was like it was
Speaker:like really that type of mentality that you're right. I haven't
Speaker:seen this in a while.
Speaker:So the we have 3 complete this sentence, questions. The first
Speaker:one is, when I'm not working, I enjoy blank.
Speaker:Being a dad. That's that's it for me.
Speaker:You know, between founder life, that's about all there is time for, honestly.
Speaker:Yeah. But, our daughter is at a really fun
Speaker:age. She's gonna turn 8 pretty soon here, and,
Speaker:I just enjoy being able to be goofy and have fun and
Speaker:play. And, yeah, she's wonderful.
Speaker:That's cool. That is a fun age. My youngest is 8. So
Speaker:Yeah. The, the next
Speaker:question is, complete the sentence.
Speaker:I think the coolest thing in technology today is blank.
Speaker:Well, I guess I gotta say a tailwind. Right? What we're doing here.
Speaker:Right. I really do think a lot of what we're doing is cool, but but
Speaker:more broadly, I'd say, yeah, I think
Speaker:the coolest thing is that our
Speaker:perceptions of what technology is capable of are changing
Speaker:very quickly, and, that's fun. Yeah.
Speaker:Absolutely. I mean, one of the things you would hear was that
Speaker:creative jobs were safe for a long time. And I think
Speaker:that, if anything, we've learned in the last 3 to 6 months,
Speaker:that's not necessarily the case.
Speaker:Yeah. So I think that's also a part of, you know it's it's probably not
Speaker:a coincidence that, you know, Chat GPT upset people more than the image
Speaker:ones because the pack media,
Speaker:the people who write those articles, their jobs are, I think, are in I wouldn't
Speaker:say imminent jeopardy, but they are definitely on the firing line. Yeah.
Speaker:Whereas, you know, a year ago, oh, no. No one AI can never be
Speaker:creative. And yet, here we are. Mhmm.
Speaker:Alright. The last, complete the sentence. I look forward to the day
Speaker:technology I can use technology to blank. Teleport.
Speaker:Yes. How's I like that. I
Speaker:like that answer. Yeah. That's even better than self driving cars because
Speaker:you wouldn't waste time in the car at all. Exactly. I love
Speaker:seeing new places. I hate getting there. Yeah.
Speaker:And we should probably ask chat g p t if there's a way we can
Speaker:make air travel less awful.
Speaker:We'll have the gpt airlines soon. Too. GPT
Speaker:Airlines. That's funny.
Speaker:Alright. So share something different about
Speaker:yourself, but, we like our clean Itunes, rating.
Speaker:So keep it keep it within those parameters.
Speaker:Oh, this is a tough one. I I never know how to answer this, honestly.
Speaker:Something different about myself. Like, you know, I I don't know if it's super different,
Speaker:but I'll just say I'm I'm a huge strategy game nerd. Oh,
Speaker:interesting. And so, yeah, I mentioned with the magic cards before, it's
Speaker:yeah. I still love magic. I love settlers, risk,
Speaker:chess. Throw any strategy game at me, and, I
Speaker:could lose myself for days. That's cool. That's cool.
Speaker:So, Audible sponsors data driven.
Speaker:Can you recommend a good audiobook if you do audiobooks? If you don't do audiobooks,
Speaker:just recommend a good, regular old dead tree book.
Speaker:Yeah. So, I'll recommend oh, I'm, like, staring at my
Speaker:bookshelf here. I think BoomTown
Speaker:is a really interesting one. So,
Speaker:before I moved to Oklahoma, I knew nothing about Oklahoma.
Speaker:Uh-huh. And, its history is absolutely fascinating.
Speaker:So, I think that's a really cool one, for people who
Speaker:have never been here before and just wanna learn about a new place
Speaker:and a new place and time. It might challenge a lot of perceptions,
Speaker:but, it's a really good read.
Speaker:K. I'll have to check that out. And where can folks find
Speaker:out more about you and what you're up to? Yeah. Absolutely. So,
Speaker:for Tailwind, we are tailwindapp.com.
Speaker:That's our website. And you can follow us and and find us pretty easily on
Speaker:all the different social platforms and blog. For me personally,
Speaker:you know, probably just look me up on LinkedIn. I'm Daniel Maloney.
Speaker:I think Daniel p Maloney is my handle. But that's probably one of the
Speaker:platforms I'm more active on these days, in terms of
Speaker:wanting to connect. Well, that's awesome. Well, thanks for joining
Speaker:us, and, I'll let Bailey finish the show. And just
Speaker:like that, we've reached the end of today's digital odyssey.
Speaker:A huge thanks to our phenomenal guest, Danny Maloney, for
Speaker:sharing his insights and to Tailwind for redefining the marketing
Speaker:landscape. To our listeners, your curiosity
Speaker:fuels this journey, and we're immensely grateful for your companionship.
Speaker:If today's episode sparked a bit of that data driven wonder in you,
Speaker:why not share the love? Like, share, and
Speaker:subscribe to keep this conversation going and to ensure you never miss an
Speaker:episode. Until next time, keep those circuits
Speaker:buzzing and your data flowing. Cheerio.