Speaker 1: (00:06)
Hello, and welcome to another episode of the Duct Tape Marketing Podcast. This is John janz. My guest today is Kate Bradley ish, a former rock and roll dj, turn founder, and CEO of lately, AI tool that uses proprietary language models to craft personalized social media messaging. Lately, AI ensures data privacy by not relying on public data sets. Kate's also been a guest speaker at numerous industry events and organizations, including Walmart, Ericsson, and Harvard University. And I don't know, maybe third time back here on the Duct Tape Marketing podcast as well. Welcome back, Kate.
Speaker 2: (00:46)
Hey, John. So great to lay eyes on you. I feel like it's been a little while. It's been, you know, so
Speaker 1: (00:52)
I'm sure, I know you get tired of telling this story because it's the first thing everybody always asks you, but I, but I know people are, go, wait a minute. Rock and roll, DJ now founder of a company. Like how do you do that
Speaker 2: (01:01)
? Yeah. You know, I've got gotten better at telling the story too, which is, which is, you know, important I think. Um, and, and my, my co-founder teases me. I do often bury, you know, bury the lead, but, so yes, guilty is charge. I was broadcasting to 20 million listeners a day with, uh, XM Satellite Radio. I was the first music director for a channel called The Loft. But what was interesting to me about radio was the Theater of the Mind, which, you know, a lot about being in podcasting. Um, but to clarify for everybody else, so the theater of the Mind is the act of the imagination playing a role either when you're listening or reading, not when you're watching tv, for example. Um, and what you're doing is your imagination is filling in the blanks that you can't see, right. You're imagining what the characters look like or what they're doing. It's why, the reason, you know, when you see a movie and you've already read the book, you're kind of mad because it's never as good as what you imagined. Right? Well,
Speaker 1: (02:01)
Or, or like a lot of people, I love to listen to baseball as opposed to watch it, uh, because baseball announcers are so much better at describing what's going on because they have to.
Speaker 2: (02:11)
They have to, and they're so crafty. I mean, they're just, it's, that's a real sport in itself, you know? Exactly. Great point. So I was, um, and, and when I was in radio, I'm old enough so that there wasn't social media when I first started and or the internet, and you couldn't look people up. And so we would kind of mess around and play tricks on the listeners and, you know, make up these scenarios. Right. It was fun. , and I had written, um, hundreds of commercials because I learned quickly that was how you made money in radio. And I was a fiction writing major, and I saw these, these parallels between wielding the mic and wielding the pen and listening and reading and what I, my, my boss, I was number one in, in our format, which was very rare because, um, it was called AAA or Adult Album Alternative. There's only a handful of stations in the country, but, um, we were like, usually 2021
Speaker 1: (03:07)
And even, and even fewer women, um,
Speaker 2: (03:09)
and even fewer women. Yeah, yeah. It was just like totally random thing I fell into. Um, but like country and rock, those stations are number one, you know? Yeah. And so my bosses were like, what are you doing? And I'm like, well, I did know what I was doing. I threw out their playlists and I was, um, I was running the whole show because all the content was produced by me, all the commercials, all the drops, everything during, during my time, but I looked into it more. This is a long story. I hope it's interesting. , um, . And, um, I read this book called, this Is Your Brain on Music. You guys remember, I think Daniel Leviton did that. Yeah. Yeah. Hard read. It's a thick read, but it's about the neuroscience of, of music and music listening. And I learned something interesting about the parallel of music listening and theater of the mind. All of this relates to lately, somehow, but I'll share. So when you, when your brain listens to a new song, John, what, what songs do you like, by the way? Are you, are you classic rock guy? Like I'm,
Speaker 1: (04:09)
Yeah, yeah. I mean, I, you know, I listen to the Loft and, uh, occasionally jump over and listen to Earl Bailey, um, ,
Speaker 2: (04:16)
That was my station. So . Yeah. I love Earl. He's so great. Yeah, so great. Oh my God, you're you're making me, um, reminisce. So, um, great quality music, you know, rock and roll. That's for I think I'd say intelligent rock and roll, let's say. Yeah. Yeah. So what, what I was, what I was learning from Daniel was that when your brain listens to a new song, it must in instantly access every other song you've ever heard before. And it's trying to index that new song in the library of the memory of your brain. Yeah. This is happening like in a moment. Right? Makes sense. And of course, in order to access all that memory, it's pulling on nostalgia and emotion, obviously, memory, all those things that create trust and trust is why we buy now. Guess what? When the theater of the mind kicks in, same thing happens.
Speaker 2: (05:10)
Nostalgia, memory, emotion, yeah. Trust. And when you're doing a good job on the mic, John, you actually make your listeners feel as though they're talking, you're talking to them directly. Yeah. That this one way street is a two way street and they have ownership in the conversation. Yeah. And writing is the same thing, and it's a complicated feat to do it well, because, you know, you're talking to nobody , but, but also somebody specific. That's the, the magic. So I took these ideas to a little company, you know, called Walmart, and I got them 130% ROI year over year for three years with what became the prototype for lately.
Speaker 1: (05:53)
Screw it. We're not gonna talk about lately. Let's just talk about the Jay Hawk .
Speaker 2: (05:59)
Oh my God, you're so funny. I, I took out their, their last, well, I dunno if it was their last record, but the one with, um, cloud something cloud, it was their last, like, really poppy record from the nineties. And I was, I had that in my, I still have a CD player in my car. Oh,
Speaker 1: (06:16)
.
Speaker 2: (06:16)
Nice. . And I was rocking to that record. 'cause I love it so much. Any other Jayhawks fans listening to us? I wonder
Speaker 1: (06:24)
of a certain age, but
Speaker 2: (06:27)
Of a certain age. Yeah, yeah, for sure. Um, but I love that record and I, it got panned for being too poppy, but I think it's a real lot of gold in there.
Speaker 1: (06:36)
Absolutely. So let's talk about content marketing. Um, okay. AI seems like daily is changing. I mean, content marketing has changed dramatically over the last decade or so, but certainly AI seems to be changing it every day. What's your take on, you know, how it's changing, really, the whole landscape of content marketing?
Speaker 2: (06:55)
Well, I mean, thanks a lot chat GBT, because now everybody can make more garbage than ever before . And so like, they made our jobs, you know, a lot harder. The, the task for marketers, the challenge has always been how do we cut through the noise, right? And now there's just, you know, noise, noise, noise, noise. So that certainly has changed the landscape. Um, one thing that I'm seeing, and I wonder if you are, which is astonishing to me, what, what's the laziness is not changing, right? So it's sort of like, you know, specifically regenerative AI and, and text generative ai, which is where I live. Like people still hate writing, they don't wanna do it, but also their value behind it is save time as opposed to be more effective. Yeah. Yeah. That's shocking. Like, and honestly, I'll ask our own clients, this customer this question all the time, and like the, the CEO or the CRO, they wanna make more money, but the actual users are thinking of save time and getting them to be aligned is, uh, a challenge.
Speaker 1: (08:01)
Um, you know, my take on this a little bit, I, I agree with you at least we're th in this phase right now of more noise. Um, but I think eventually, like all things people are gonna go, it's pretty easy to separate noise from signal , you know, maybe even more so now, right? Because, you know, writing quality content still takes strategic thinking. Um, and that's right. I think that, I think it makes people who do strategic thinking even more valued, even though right now there a lot of 'em are feeling sort of undervalued.
Speaker 2: (08:32)
Yeah. So on parallel with that. And so, so there's this symbiotic relationship between AI and, and humans who can think strategically and analytically. Absolutely. And they rely upon each other, right? Um, and it's called collaborative ai, that this is the year of collaborative ai, in my opinion. We built collaborative AI into lately from the beginning, which you, you know, but it's the idea of a human analyzing and course correcting what the ai, uh, generates so that it can, you know, boost the learning. Here's what's fascinating about what we're, what you said, which is the number one vacuum of skills across the globe is guess what? The ability to, to analyze.
Speaker 1: (09:14)
Yeah. Yeah,
Speaker 2: (09:15)
Yeah. And the reason that is, is because we have, and this is back to laziness too, we've become a culture unable to identify problems because for so long, especially in corporate life, it was like, don't bring me a problem, bring me a solution. Yeah,
Speaker 1: (09:31)
Yeah.
Speaker 2: (09:32)
Right? So even when, um, like I have a friend who has some teenage daughters and when they need to go, they know they can Google the answer to anything, but they don't know what to type in .
Speaker 1: (09:46)
Yeah, yeah, yeah.
Speaker 2: (09:48)
Right.
Speaker 1: (09:49)
Well, and that's, so many things brought up there. Um, the, um, you know, what I tell people all the time is what we're left to provide is context. That context cannot be provided by chat GPT. Um, and, and so that's to your point of the search, I mean, a lot of it is the right context produces the right answer, but these machines are basically just going into a database and saying, here's whatever is, um, whereas we are saying, well, no, here's what, here's the real problems the customer is telling us they're struggling with. Um, and you know, why our solution or whatever it is we're selling, um, is you know, the answer for them. And I think short of having that understanding, you know, you who know, it's a crapshoot what you're gonna get back.
Speaker 2: (10:35)
Yeah. I mean that, thank you. I'm gonna steal that the context, because it is so true. Someone was just asking me the other day, well, should I, should I second guess everything that late lately generates for me? And I said, yes, you are still the king, the humans, we are still the king of the food chains . Yeah, of course. You know, and he's like, well, won't, won't, won't AI know better than me? And I'm like, no, never. No. Like all AI is good at doing is now is is synthesizing scale really,
Speaker 1: (11:07)
Right? Yeah. In fact, I've been for a long time, you know, because I don't really think it's AI yet to be truthful, right? It's not really artificial intelligence, turn it around. It's more informed assistance. is That's right. Is kind of how I, you know, talk about it. All right. So I'm gonna ask you a really hard lately question.
Speaker 1: (11:25)
Okay. , uh, you and I started talking about lately three or four years ago at least. Um, and at that point, three or four years ago, what lately was doing was very cutting edge. Um, people didn't necessarily understand it, but it definitely produced a result that was very cutting edge. Um, fast forward to today, um, uh, you know, everything you buy now has AI in it. Uh, supposedly like your detergent now has AI in it. I think if you buy it , so you know what's what, what's the differentiator or your stay ahead, you know, cutting edge, um, play.
Speaker 2: (12:00)
Yeah, yeah. So that's a question I told you it be hard question. Well, a couple of things. Yeah. It's a hard one. Um, and there's, there's, there's like functional answers and then there's, um, kind of aesthetic answers I'm gonna call them. But so functionally we're the only generative AI that, that I know of where, um, we have a continuous performance learning loop plugged in so that the results that we generate for you are always tied to your personal analytics or the analytics of your company, which is to say it's never out of thin air. So all other generative AI doesn't know you in any way and can never give you results that are essentially really meaningful. Um, the other component there is the collaborative ai. So because we built that into the product, the whole product originally, we have kind of pole vaulted over everybody else.
Speaker 2: (12:51)
Um, Harvard Business Review just released an article about collaborative AI citing lately as a leader. And one of the studies they did show that collaborative AI outperforms AI alone two to seven x every time. But the, on the sort of aesthetic side, what's, again, what's really interesting to me is that save time piece, right? So of course we save time like everywhere else, but that is not our cutting edge kind of leg up. The leg up is we show you why, right? Why it's effective, what, what's the DNA of the messaging that will get you the highest response. Um, and I think what we've done a poor job of is actually leveraging how well we do that and what that value is. So I've called upon my, uh, engineering team for this year to actually do, do a better job of getting people to understand this information and how to use it.
Speaker 2: (13:52)
That's what's fascinating to me, is like I can show you these words, John, I can show you the ideas, the send, this structures all the things that will get you the most engagement, but people then don't know what to do with it, which is like, to me, duh. But, but that is the, that is the crux, right? The last answer to your question is going even deeper here. So I'm planning an integration with my friend David Allison, who owns the value graphics database. And value graphics are identifying how to group people by what they care about as opposed to demographics, which is more insightful, radically more insightful, and they consult the United Nations. Um, yeah. And so, like some characteristics would be like, if I care about the environment and you're selling me lipstick, you wanna sell me lipstick that talks about how great it is to, for the environment, right? Or if you're selling me, um, lipstick and I care about family, you wanna mention that the family company has been around for a hundred years, passed on by to daughter by daughter, whatever. Um, and so we're working on a way of integrating these values inside lately, so you can get more of that why and understand who your target audience is and, and why are they, why are they responding to the content we're generating for you? Kind of nerdy, I don't know. I'm excited about this. No,
Speaker 1: (15:15)
No, no, I, but I think you're absolutely right. I mean, I've been saying for years, I'd rather, I mean, my target market is based on behavior, not on, you know, how old somebody is. Um, it's what they value, right? It's, it's do they invest in the community? I mean, do they invest how we actually identify some of those behaviors? And I feel like that's a, that's a way to actually niche down, um, is to focus on behaviors. So having obviously tools that, and I'm guessing that you're gonna go into personalization, , um, you know, at some point with, uh, that level of segmentation as well. Let me ask you, I said in the intro, in your, in your bio, um, I mentioned the idea of data privacy, um, how, I know, you know, it's a big deal. How much is the market perceive this idea of public data sets versus privacy versus, I don't know what you're talking about.
Speaker 2: (16:06)
Yeah, pretty huge. And it's another AR arena of AI where everybody thinks they know all about it, but they don't, which is kind of the whole trend of the last year. Um, so some companies come to us with like an AI task force, and that has to be part of one of the initial calls where they're, we're checking the boxes for them, you know, the safety boxes with their legal and IT teams, which we check a lot of those boxes. Um, you know, the, then there's other companies like P WC who've gone full on into LA into ai, and they don't seem to really care, you know, which is, and then there's a lot of companies where they know people are using it even though there's a maybe a ban on Chachi PT throughout the company, but people are using it anyways. Yeah. So there, there's nothing consistent for sure. Um, you know, my husband actually just bought chat BT for his phone because he didn't wanna put on on his work computer, but he wants to be able to use it for work to help him do things faster, smarter, better, you know? Of course. Yeah. I think there's a lot of that going on. Um, yeah,
Speaker 1: (17:16)
Yeah, yeah.
Speaker 2: (17:17)
What I understand, you know, I, I mean it's, how about you? It's stupid,
Speaker 1: (17:20)
It's stupid. I mean, I, it is like I can write a formula in Excel or I can just dump all this in and say, gimme the answer. Um, you know, so I use it all the time for stuff like that, right? , but, um, mm-hmm, , maybe we better back up just a minute because I, I asked that question assuming a lot and assuming that people knew what, you know, that really meant, so when I go to chat, GPT put in there is helping teach the, the entire language model and you know, everything from my, you know, customer statistics to my Google Analytics that I'm getting analyzed. I mean, that's all just being fed, right? And, and in theoretically in some fashion, anybody has access to it that, I mean, not specifically to it, but it's feeding the machine that then is going to produce something. Whereas the private data set, you know, that is if I come to lately and I put that same, only going to be used to build my personal model, is that the way to sort of explain it?
Speaker 2: (18:14)
That's right. That's correct. Yeah. We don't take any of your information and muddy it with anybody else's. And like the, the, the one thing we can see is the patterns that if things are working well for you and they're working well for another customer, we can see those patterns, but we don't share them with you individually. We would take the knowledge and share it at large, you know? Um, and so that's been a real win for us, by the way, is because, I mean, I've been asked, I've been asked to actually give courses on AI to educate companies on why that exact thing matters. Yeah, yeah. Um, I think David Alice, not David Allison, David Meerman Scott, who was, um, an investor and friend, he put it succinctly where to help people understand, you know, he was like, listen, there's only two questions that matter whose data and whose math with chat, CBT, it's the world's, it's, it's your data, it's, you know, the world's data, everybody's data and general math, like a general gen generic math with lately it's your private data and then our math on top of it, you know?
Speaker 1: (19:26)
So I need to, two more questions. I'm gonna ask you the first one we Sure. Just because we haven't, you know, you and I have talked a number of times, it's like, you know, that idea of like, oh, oh, oh yeah, we have listeners too. Um, but if somebody came to you and said, lately, I kind of heard of that, what does lately do? How would you describe what lately does?
Speaker 2: (19:45)
Oh, I'm so bad at this. It's like the shoemaker has no shoes. Um, but I'm evolving. So lately, um, learns the patterns of when you write well, what helps you do that. And it also learns the patterns of what your unique audience will actually reply to on social media. And then we help you evolve that model by repurposing long form content and identifying what part of that content will actually get you the highest possible engagement on social. Awesome. How did that go? So that
Speaker 1: (20:15)
Was great. So, so the output could be a blog post? No, that was very good. It was still maybe a little philosophical. Um, so it's long.
Speaker 2: (20:24)
I know.
Speaker 1: (20:25)
So the output, the end output is a blog post or is a LinkedIn post or is a, is a, uh, x uh, post, right?
Speaker 2: (20:32)
Yeah. The output is a social media post. Um, but, and so much more, I mean it's really the insights to know this is why we have investors like you and David Merman, Scott and others, is like, there's so much potential in what we've identified. How can we evolve the product to really give you more, you know,
Speaker 1: (20:51)
So, uh, an entrepreneurial question to send us out. Um, does, do you think, 'cause I know you haven't done this a hundred times, do you think that working in an industry that is evolving so quickly is make it, makes it even harder to evolve a business?
Speaker 2: (21:10)
Oh, I mean there, I, the challenges are yes, for sure. Um, but there's so many other smaller challenges that I didn't expect that seem to me to eclipse that. Um, some of it's being a female entrepreneur, let's be honest. Yeah. Um, some of it's working through a pandemic . Um, you know, I think the way that we come at this from radio, from this totally unfathomable background gives us a huge insight as a company, not just me, at how we go at AI and we go at it very humanly because that's just how we, we did it from the beginning. Um, so I love that what we're able to see about the benefits of it are often inside out of what, what everybody else is seeing. And I feel really proud about that.
Speaker 1: (22:04)
Yeah, that's really interesting because I do think a lot of people approach this as what can the machine do? Um, and I think that, that you're actually saying though, our point of view is, you know, how do we get the output that's gonna, uh, have the most impact from a neuroscience, um, point of view? And I think that that's, uh, I think that's a harder one to explain probably, but it's uh, certainly more impactful than a machine view for sure.
Speaker 2: (22:30)
I just got a Kennedy chill, so like, not what can machine do for you, but what can you do for your machine? . .
Speaker 1: (22:36)
I like it. I like it. Okay. T-shirts. Let's start printing T-shirts right now. , we have to. Alright. Kate, I start catching up with you. Um, you want to, uh, obviously we've mentioned, uh, lately AI numerous times. Is there anywhere else somebody, uh, should connect with you?
Speaker 2: (22:53)
They can find me in all the places, uh, LinkedIn. I'm just playing Kate Bradley. And tell me that you met me with, with John and uh, we can be friends.
Speaker 1: (23:02)
Okay. Awesome. Well it was great catch up with you again. Hopefully we'll run into you soon out there on the road.
Speaker 2: (23:07)
For sure. Thanks.
