MEDIASCAPE: Insights From Digital Changemakers
Join hosts Joseph Itaya and Anika Jackson as they dive into conversations with leaders and changemakers shaping the future of digital media. Each episode explores the frontier of multimedia, artificial intelligence, marketing, branding, and communication, spotlighting how emerging digital trends and technologies are transforming industries across the globe.
MEDIASCAPE is proudly sponsored by USC Annenberg’s Master of Science in Digital Media Management (MSDMM) program. This online master’s program is designed to prepare practitioners to understand the evolving media landscape, make data-driven and ethical decisions, and build a more equitable future by leading diverse teams with the technical, artistic, analytical, and production skills needed to create engaging content and technologies for the global marketplace. Learn more or apply today at https://dmm.usc.edu.
MEDIASCAPE: Insights From Digital Changemakers
The Digital Exhaust: Why Your AI Data Matters More Than the Tools with Dan Baird
The technological landscape is shifting beneath our feet, and Dan Baird stands confidently at the intersection of entrepreneurship and artificial intelligence. In this riveting conversation, the co-founder and product lead at Wrench.ai delivers both a wake-up call and an invitation to the AI revolution that's already transforming business.
Baird's journey from selling beanbag chairs at Lovesac (which began as a joke and later went public) to developing patented AI-driven segmentation processes reveals the mind of a true builder and innovator. His candid insights cut through the hype surrounding artificial intelligence, distinguishing between different modalities like machine learning ("statistics on steroids") and generative AI ("autocomplete on steroids"), while emphasizing that understanding these distinctions is crucial for future success.
What sets this discussion apart is Baird's practical approach to AI implementation. He introduces the concept of building personal "agent armies" – specialized AI assistants for different tasks that form an organizational chart beneath you. This approach allows professionals to automate repetitive work while focusing on novel problem-solving. Perhaps most valuable is his exploration of "digital exhaust" – the data created during AI interactions that reveals preferences and decision-making patterns, which he argues may ultimately be more valuable than the tools themselves.
"You won't be replaced by AI, you'll be replaced by someone using AI," Baird warns, noting that professionals who don't embrace these technologies now risk becoming irrelevant within five years. Yet his message remains optimistic: "You are not late, you can absolutely catch up, and it actually really is fun and interesting." For students, professionals, and business leaders alike, this episode offers not just a glimpse into the future of work, but a practical roadmap for navigating it successfully.
Ready to explore how AI can transform your work and create new opportunities? Connect with Dan on social media and discover how these emerging technologies might help you build your own future.
This podcast is proudly sponsored by USC Annenberg’s Master of Science in Digital Media Management (MSDMM) program. An online master’s designed to prepare practitioners to understand the evolving media landscape, make data-driven and ethical decisions, and build a more equitable future by leading diverse teams with the technical, artistic, analytical, and production skills needed to create engaging content and technologies for the global marketplace. Learn more or apply today at https://dmm.usc.edu.
Welcome to Mediascape insights from digital changemakers, a speaker series and podcast brought to you by USC Annenberg's Digital Media Management Program. Join us as we unlock the secrets to success in an increasingly digital world.
Speaker 2:This is going to be a fun one. I am so thrilled to have Dan Baird on the show today. Dan, you are co-founder and product lead at Wrenchai. You also have an upcoming podcast, burn the Map. Your assistant found me on LinkedIn and introduced us to start having conversations LinkedIn and introduced us to start having conversations, and it's been a lot of fun to learn from you and to be able to, like you know, talk about what I'm teaching and the AI involvement that I have and where I should go and where my students should also go. So thank you, first and foremost, for being here.
Speaker 3:Hey, my pleasure. This is fun, interesting stuff. I'm fascinated You're in a really, really cool space. I'm excited to be here.
Speaker 2:Well, you're in a very cool space, so likewise, so you have an MBA in global branding. I've done a lot of different work in strategy design. I want to talk about how you got from there to now, figuring out and designing tools and processes. You have patented AI-driven segmentation processes.
Speaker 3:I have always been a builder. It is one of those things that kind of runs in my blood. Third generation entrepreneur. My dad owns a foundry. I grew up working for him. He's a master craftsman. He built planes in our garage when I was a little kid like super cool stuff and not like we do not come from money or anything. This is poor people building a plane in the garage. So I was always surrounded by building things and I've always really had absolutely a passion and soft spot in my heart for the people that go from zero to one. I think it's one of the hardest things you can do and growing up I'm in Salt Lake City, utah, and happened to have one of my friends start a little company a beanbag chair company, as a joke called Lovesack.
Speaker 2:Oh, as a joke, yeah, as a joke.
Speaker 3:So we went to a drive-in movie theater, threw that thing out.
Speaker 2:It was a joke.
Speaker 3:But literally everybody went I want one of those things. So we started a company. Lo Sack went public in 2019. So you can actually take things literally as a joke, like we would do that job because the economy was terrible and we could give ourselves really cool job titles that no one else would give us. But at 19, I ended up having 30 employees and those beanbag chairs if you look around the country you'll see them in malls Travis, barker and other people are doing their commercials and it's just a really fun and kind of cool concept. But it was also born out of the rags to riches kind of like. We did guerrilla marketing before it was called guerrilla marketing. We bootstrap stuff before it was called bootstrapping. So that was kind of my first foray. I'd go to school until noon and then I'd go work in the lovesack factory until midnight just to get stuff out. We'd sell out multiple times a day. It was just a really fun concept.
Speaker 3:They tried to get me to quit school and I was like I don't know if I'm that guy. They're like will you please move to Tijuana and run our production facility? And I was going like I'm like a year away. Guys, I really wanted to. So I ended up opening. I said, instead of giving me founder stock, I want to open a bunch of franchises myself. I did that in California, arizona.
Speaker 3:I found Thunderbird. Thunderbird is my MBA school and a thing that I wanted to do there was I was going. What is the best way I would get you know, cause I learned how to make product. I learned how to make retail stores, but I was constantly putting out fires and I wanted to go okay, how do people that really do this globally do this? So I wanted to go to get an MBA so I could learn how the world was run. And that I went and did. Yeah, as you mentioned, branding and a dual capstone and product management was the closest thing I could find for entrepreneurship and product management. Those were the two things, because if you're doing product management, you're building a product inside of a corporation from zero to one. So I loved building that stuff and you know I had one of my professors invented otter pops and I thought that was the coolest thing ever and really always liked the people that were building things.
Speaker 3:So I went and did innovation strategy for ConAgra. I worked on Healthy Choice and Orville Redenbacher and stuff like that and our job a small team of 12 of us was go build a platform. It wasn't new flavors, they're like you need to launch an entire platform. You'll get three-ish a year and if they don't do 20 million in their first year, you're a failure. So it was a really fun, cool job. You'd fly around the country, do focus groups, do prototypes. You had really they really funded that group. So we got to have a lot of fun building with some really cool companies.
Speaker 3:And in food, which is super regulated and you have to be really, really careful. But if you go down to your frozen aisle today and you go look at the frozen lasagna, you can read my prose on top of your Marie calendars. You know it's that. So fun stuff, cool stuff, hard stuff. And after that I took a little bit of a trip around the world and came back was seeing a few things that I thought were really cool.
Speaker 3:Crowdfunding at the time was a big deal and I was seeing an explosion of entrepreneurship and it doesn't sound like a big deal, but crowdfunding and just crowd dynamic based businesses are really really cool Cause they're kind of like open source. They flip the script and they use like the population and, like it's a, when we were doing Love Sack and selling out literally like multiple times a day, you would have to go buy your product from China and you would have to wait at least 90 days for it to show up, so you would run out and then have three months of nothing on the shelves until it got here and the logistics actually killed us. We had a really profitable business in terms of margin, but our cashflow got murdered just because it would take 90 days to do so. So crowdfunding actually allowed for the first time a really big deal allowed everybody to predict and measure demand before they actually paid for inventory. That doesn't sound like a big deal. That is a really, really, really big deal. That's like breaking down the fourth wall of a lot of strategies, because you could actually understand exactly how many units do I need to buy. I thought that was really cool, started a company called Crack the Crowd and we opened that thing up and we ended up being kind of one of the premier agencies and very specifically for equity crowdfunding in the United States.
Speaker 3:We did some of the first multi hundred million dollar raises for real estate, stadiums and stuff like that Found out. A good like this was a lot of the precursors to tokenomics, right to a lot of the crypto technologies and things like that. So we were on the front end of that and it was really Wild West. There's a lot of stuff hey, we're raising money for a submarine. Hey, we're raising money for a submarine. Hey, we're raising money for a lot of things we're like is this legit? A lot of them were, a lot of them weren't. A lot of them were asking us to do things that were, ultimately, that people didn't know this, but illegal, just like hey, you can't. Little things like when you're raising money very specifically for stocks and equities, you can't say we will make money. You can't even say we will, right, you have to be really, really, really careful. So it was a little too Wild West for us, because people are going well, that guy's making money, I'm going, he's making money, he's also going to go to jail in five years.
Speaker 3:And we pivoted into wrench wrench, which is now what we do, where we took a ton of the segmentation and the work that we did there and understanding who is your audience, how do you find that audience and who, what, where, who do you talk to, what do you say, and how do you say it? We use a lot of AI, multimodal AI, to do natural language processing, we do, yes, generative, we do machine learning and we actually mix a bunch of different models to understand, again, who is my audience, who am I talking to? What can I do to enhance our conversation and their understanding of it? How can I streamline the conversation and how can you do it at scale? So we focus on a lot of personalization technology and we focus on a lot of measured content-driven strategy.
Speaker 3:What do we say? And again, why Not just hey? The LLM told me to, anybody can do that. We actually went hey. When dollars are on the line, the differentiator between those two is going to be who actually has the data to justify their actions. And when you're at really big companies, they demand that level of understanding. That's what we do at Wrench. So that's how I got there. I got here A lot of that type of work.
Speaker 2:Yeah Well, and I appreciate that you mentioned you mix a lot of different types of what falls under the umbrella of artificial intelligence. Because that was one thing I talked to my students about and they're like, oh, I didn't even know there was a difference between AI and machine learning and deep learning and then generative and the use cases for each. I'm like, yeah, there is a difference and we need to understand that. The nuances.
Speaker 3:It's a big deal. I mean like it's all ultimately math, but you're talking about arithmetic versus algebra, versus calculus, versus string theory, versus. They're different types, yes, but they accomplish different things and a lot of them are really good at one thing and bad at another and for what it's worth, we're going through. You know, people talk about like the hype cycle and things of AI. Like we've been doing wrench for seven, eight years now, right, so like that's the joke. We were doing it before. It was cool is kind of a joke. I'm a pioneer dinosaur and it's eight years, like not even old, but it's a really big deal.
Speaker 3:A lot of people are making a lot of mistakes and that's a really really probably the biggest single mistake that they make is not understanding the differences between the two. Right, machine learning is pretty close to like statistics on steroids, right, it's mathematical based, large data sets, tabular data sets, meaning table-based data sets, and then generative. It's like well, they took those tabulized data sets and they're just predicting the next word, right, I mean, they really are fundamentally autocomplete on steroids. Most of the decisioning is brand new. They't been doing that and the amount of mistakes that they make is like way more like you're like it looks really good. I'm like that's only because you didn't check the answer.
Speaker 3:You have to check the answer yeah and most of the time when people don't, they're like, hey, it looks good and I go. Well, here's the other thing, the uh, by definition like uh. If you go through and you look machine learning, you move into generative ai, llm, right, gbt's being generative pre-trained models. They read the entire internet. They put it all into a table where they said, all right, what word do I have? Now it's like the word from, and they go okay, after reading the entire internet, what's the next most common word? That gets placed right after from. And that's what they're doing, one word at a time all the way down, which means most of the time, after reading it, they they spit out average answers, right, they summarize really really well. They're good at that. But that also means again that they give out average answers. That means most of the time.
Speaker 3:If you're impressed by what the LLM did, you're probably actually like a little bit below average. Or it's like, hey, look, it got me an average answer really quickly. But if you're really good at something and you look at what it gives you, you're like that's not right. Right, it's, it's. You can almost tell that it's fantastic, because that rising tide raises all boats. It does mean that we should actually see at least just average content and output from most of the product that comes out of it, which means below average is going to get higher and higher and higher. We're going to raise the stakes for what average is acceptable.
Speaker 3:But simultaneously, a ton of people are making a mistake for like hey, I use generative. It's like cool, you have no defensive moat, you are totally replaceable. Anybody could have done this with that amount of effort. And again, it is one of those tools where it's like you need to be using it, but simultaneously, like again they're right where they say hey, you won't be replaced by AI, you'll be replaced by someone using AI. And that's largely one of the reasons is they're going to get to the average answer, but then they're going to be doing additional research to get an above average answer. Yeah, yeah.
Speaker 3:Yeah, and that's largely what if they're just using AI. They're replaceable, like that. If you're not, if you're not using AI. Sadly, right now, like I mean, we just reached one of the first times in my company's history where we actually had people that we were hiring and we had someone that came up, good, worked in the space, highly recommended, and uh, we ultimately didn't hire them because they weren't using AI. It was one of the first times where we just literally went and looked.
Speaker 3:You probably have one, two years left before you are wholly irrelevant Unless you start now. You need to go look so you might be good. You're not going to be good for long. It's too easy to catch up now. So for any of the people watching this, it is one of those, like I just we hired some new graduates as well who are jumping in and they're neck deep, but I can't stress enough. They told me that at school they were discouraged, even like it was threatened as as though it was plagiarism to use LLMs.
Speaker 3:Screw that, that is stupid. I don't care if USC tells you guys to do that, use it anyways. I'm not telling you to have it write your papers. You need to know how it works. You need to know when. You need to know quickly. If you don't, in five years you will not be employable. So go play with it. There are different tools that really accomplish and excel at different things. You can go and use, like you know, google deep research to go write nice empirically based research papers. Still, you need to check all of the data sources, gamma to do presentations. There's any number of different tools for different kind of accomplished goals, images and stuff. Chatgpt is really good at now. They're really good at raising kids.
Speaker 3:My kids play with them at home and talk to it because it's fantastic at that type of work. But for what it's worth, dig in, jump in neck deep. I can't tell you it really does make me nervous. My job is wholly replaceable as a CEO. I can, and that is actually kind of the work I do. I can actually even show you I have AI agents. I have a Dan bot. They will, and they have been programmed with about three plus years. I'm on calls like this about 30 hours a week. So I have transcripts, all types of video. I plug them in that like there isn't a huge need to hire additional customer support anymore because we can totally replicate everything that I tell and do them. They won't want to just ask dan like we'll ask my bot and then you don't have to, but I can be in five places at once now yeah, have you used your bot to replace yourself in meetings yet?
Speaker 2:because we've talked about this, I have delphi. I have a delphi profile for myself. I haven't turned on the video component, but I can have her go in to meetings. I can connect her to Zoom or to my meeting schedule and she can go in and act as me.
Speaker 3:So I have not tried that one. That's an interesting one. What I will do is not attend the meeting but send my Read AI bot to it and read and summarize the transcripts and the action items after the fact so that I can go hey, I missed it, but I'll follow up with you like 20 minutes after the meeting. I got all the high points, and that way, oftentimes too, I can actually pay more attention than I can, and you know, sometimes it's like an hour, it's like I need to be there for five minutes.
Speaker 2:You know Right, right yeah.
Speaker 3:So I have sent it. In that sense I haven't sent it in my place yet, but again, I have messed with. Hey, jen is another one too where we have we've taken and programmed like scripts where they recorded me takes about two minutes and then now they have basically a live deep fake that they can feed a script to and I can sit and like read off a here's what this new feature does, type of stuff, without actually recording it. So we've used that one as well.
Speaker 2:So it is absolutely kind of that's the next thing I'm going to be adding is AI for my videos, so that, yeah, I'll be showing it for class, I'll be showing for podcasts, but there are other instances where I might not need to. If I want to create, like you said, promotional videos or project related videos for work, that I do. That's going to save so much time.
Speaker 3:Oh, yeah, and it's actually better for the student experience as well. Right, I don't know if you've seen it, but you should go check it out. There was a study. There's been some people that have been following a school in Africa where they've supplemented the learning experience with artificial intelligence and personalized tutors and they are doing like orders of magnitude, more like kind of adopted learning and internalized learning, like they basically said that the advancement from kind of that personalized experience was orders of magnitude of just the classroom experience alone.
Speaker 3:So at the very least, it isn't saying the classroom experience goes away, but it absolutely does say that there are personalized styles of learning, totally reflected, like we do a lot of this personalization where we'll like I can look at your LinkedIn profile and see if you're a data-driven person or if you're more socially driven and you'll take and learn and make decisions based off social cues. And it doesn't mean that one is better than the other, but it does usually mean that you prefer information that way, which means same thing for students. It's like if I give the one on the left data and I give the one on the right social proof, they'll actually believe me both ways and it's like, hey, this is just how they perceive to ingest that information and you're going wow, so we can potentially get twice as much done just by removing that psychological friction which I didn't have it turned on, I let people do like 10 anonymous questions.
Speaker 2:I was like, no, no, no, no, I need it because I can go in and see all the questions people asked. And I realized, oh, some people are just using it as the way that they should use chat or cloud or you know one of these other tools, or say, oh, can you just wordsmith this for me? I'm like that's not the best use case. The best use case is ask me about branding, public relations, podcasting, things that are in my knowledge base, right, and ask me those questions. And it saved so much time from having to figure out when can I schedule another meeting in between all my other meetings. And intro, you know like I'm like just go talk to her because she is me. And the great thing is that then even people can.
Speaker 2:Somebody said, oh, I didn't screenshot or save the information. I said well, it knows your IP address. Go back in, it's going to pick up the conversation with you, even if you call her instead of text or voice text her. When you call that number, she's going to say, oh, have you thought more about blah, blah, blah, whatever was the last thing that you texted? And so it's so amazing and this is something I'm really excited about. You mentioned, you know I'm building having your own agents, and that's something that I'm learning, starting tomorrow, is how to build my own agents and I to your point, like things are moving so fast. Some people might say, why are you getting your MBA with specialty in AI? But it's giving me that fundamental knowledge base. I'm getting to play with tools for business use cases in class, so it actually is expanding the knowledge way beyond what I would have done on my own, because it's really easy to delay learning when you have meetings, when you have a life, when you have all these other things you're trying to fit into your day.
Speaker 3:Well, if you think about it, so like we had to hypothesize what the world would look like, we literally sat down, so like we were in a spot where we were going hey, we're going to leave the tokenomics and the Coinbase kind of space right now, we have enough money to actually do this. We can make a decision. We're at an inflection point where we go we're not going to be poor, so let's have some fun. Our goal is to die with smile wrinkles on our face. We're going cool, let's put a dent in the universe and let's die with smile wrinkles. And a big part of that was going okay, well, what can we go into? What do we want to go into? And we talked about AI, we talked about drones, we talked about 3D printing, with all exponential technologies, with an exponential growth curve, when half of success is just showing up in the right place. Right, we ended up choosing AI because we had some fundamental skills in it. We knew some people in it and things like that. Again, it was early enough that people were like hey, granted, it's been around for 20, 30 years, but it was still early enough that it was like hey, which one of these do we really want to do and you think about it.
Speaker 3:The organization that you typically deal with, these large Fortune 100 companies and stuff like that. You have the bottom and the top of the organizational chart. The bottom is basically collecting data almost directly. They hand that to their boss, who takes a lot of predictable types of data and information. They look at that data, they make a decision and then they actually do the same thing. They hand it to their boss, who goes all right, how's this division doing? Okay, cool, how's this region doing? And they all just basically it's turtles all the way down.
Speaker 3:We're going in the future, you're going to largely be sitting around with novel data problems. Your job will be to generate new digital exhaust, for lack of a better term. The repetitive jobs are the ones that are going away. So when you do the AI stuff, it's like going back and I'm going like hey, we used to use a rotary dialer where it would take forever just to even call. You hated people that had like the. The zero was the joke. If you had a zero in your phone number, it took so long to even just spin it. That friction of how long it took just to prepare that data to make that novel decision was like 70 and 80% of your day Data silos, even four or five years ago, were substantial Marketing couldn't talk to sales, couldn't talk to product, couldn't talk to support. It still is kind of that problem. You're seeing the barriers almost break down now where largely like the trick is, with all of these AI tools and stuff like that, it's like well, wait, my job is to be the novel problem solver. So if they can aggregate that data, no-transcript, well, I got into this business so I could bake pies, but I'm telling everyone else how to bake pies. I haven't baked a pie.
Speaker 3:Through that job, you automate everything that is replicable, everything that is predictable, and then you move up one level in the org chart. You're doing that same thing with agents, where you go, okay, move up one level in the org chart, all right, now I've got to write manuals for the people below me. I'm going to write a bunch of manuals. Now I've packaged up this job and I'm going to move one step up the org chart. Where you're basically going, I'm going to build not even just one agent. You're going to build an organizational chart.
Speaker 3:Beneath you. You will have one person for communication, one sort of org chart I've got you know I call him Prof D but Professor G, right, scott Galloway. So I've got Prof D who does a lot of my copywriting and research and stuff, because I like the early smarmy swear a little bit type of copywriting. And I've got other ones that are doing finance and proposals and things like that. But like you're going to build an org chart, that just, and I actually think it'll follow you regardless of where you work, and your goal will be how much novel new decision-making can I kind of internalize? How much can I build underneath me so that when people send me that work I can hopefully send it through a completely automated workflow and other things? I've already answered that question. So your goal will be the most productive kind of org chart of you're going to have that swarm of agents that serve you. So it'll be nifty, it's fun stuff, yeah.
Speaker 2:Some people might hear this and go. I'm going to have a swarm of agents that are working for me, like my own agent army. But you know what if one goes rogue? You know there are people who are going to be really scared of that. But then there are those like you and me who I'm, like, so excited about that phase and the humanity it's bringing back into, like me actually being able to do my best work and live in that space where, you know, I'm still the decision maker, I'm still the one who's programming. Maybe, maybe I'll let some decision making, you know, come down with agents, but but where it's, it still has to be my unique knowledge base.
Speaker 3:Yes, it will, and like I mean, if you notice you follow these same patterns in normal society anyways, right, there are, like there's always tech and AI as a tool, just like anything else in the kitchen. You can make dinner with it, you can cut yourself with it, right, and just like that. So, and what we found when we're building agents, we do enterprise agents, so we have one and like they're fun and like our goal was we're not going to necessarily just build the fastest or the most convenient ones, we want to build the smartest ones. And we've had clients that have like built theirs and said I'm not going to show my clients this, because if they knew this was how smart they could be, they wouldn't hire us again. We pulled them back in so their clients wouldn't see them.
Speaker 3:But what you find out is just like humans, you get kind of a saturation of memory over capacity. You can only pack so much knowledge into one. It slows it down. And so what you do is you really you have, okay, now I've got one agent who's an orchestrator and they basically their job is to take a task and then to divvy it up into one of the sub agents beneath it. You specialize in finance. I'm going to send it to you because this agent over here has memory just dedicated to finance and they're a specialist in it. So you actually start to find out that even the agents that you build have an org chart that's relatively similar to your organizations and, just like your organization, there's governance. So you'll actually have a security agent. That security agent looks for stuff like wait, is this one someone trying to jailbreak our agent? Are they saying that they're looking for access to databases? So like in terms of going rogue, just like in real life, you actually have one person who's going hey, wait, before we publish anything, does this look like this has any nefarious steps in it, or anything like that. So you actually see it kind of follows some of the same dynamics we have in society. And again you go yep, I've got a security agent, I've got a marketing agent, I've got a sales agent, I've got a finance agent and they're all working collaboratively together. Final checks go through my security agent before they actually get shown to the public or anything like that.
Speaker 3:So, for what it's worth, it's like yeah, will they, could they? Yes, is there going to be bad actors? For sure there's people that use knives and every other tool for bad purposes, but it's not. You know it's. It's manageable and it's. One of those things was like there's not a monetary incentive on doing it nefariously. So 90% of them like we absolutely have to be careful. It still scares me how good they are and you absolutely have to watch that ethics and everything else in terms of data, data integrity. But overall, I think it's, it's, this is the new normal. This is not like pie in the sky stuff. This is now, this is next year. That's fast and it really. The thing that really really scares me more is what will happen to the people that don't adopt it soon enough, because it's going to be harder and harder and harder to catch up in many ways. So it's worth jumping in and jumping in neck deep right now.
Speaker 2:Yeah, well, and to that point, I have a lot of students who do work at large enterprise organizations. I have many who work at very small organizations that are starting their own businesses. You work with enterprise solutions. There are companies out there that will create agents for you.
Speaker 3:I'm trying to pull up my email and see which how many this week.
Speaker 2:Well, just yeah, there's a couple that I get emails from, but I've also been told oh, that one, you know, it's the one with all the cutesy cartoon like, well, it'll be all your agents for different parts of your workflow. But I've also been told they're just working off of chat GPT. They're not really probably going to do the whole thing A little more.
Speaker 2:Which could be good, right, but they might not be as good as they say they are, but that's an option for people who are smaller businesses. So I'd ask you, what are the best ways for people with smaller businesses to adapt technology, still be considered like maybe not the earliest adapters, but still on the forefront of the small business AI integration? Should they learn how to build their own agents? Should they look at some of these tools and how do they evaluate them appropriately to know that these are the best tools for them? Because there's also, as you said. I mean, even just looking at the MarTech landscape a couple of weeks ago for a class, we were like, yeah, this year there are 15,000 MarTech solutions and there are a lot more AI ones, and that's the small percentage increase from the year before, but it's still. There are a lot of small companies that say that they're the best and they simply aren't.
Speaker 3:And they're probably going to go away, right? So, oh yeah, and the the in many. Much of the good news for your students and everything else too is, quite frankly, that goes for actually a lot of the large ones too. We're still figuring it out. The joke is, is that like ai agents and agentic ai and everything else is, and people will say ai employees now, because they're going. It's one level above agentic, it's not just workflows. The joke is that it's like teenage sex Everyone says they're having it, everyone thinks everyone else is doing it and most people haven't figured it out at all and they're totally lying, pretending they know what they're doing. That is really the truth.
Speaker 3:So in 2024, I was talking to a senior solutions architect with AWS and I was making the observation like he said something to the tune of like hey, 40,000 new startups have showed up in this space. And I went, yeah, and as a joke, just because you know that, maybe a month prior, a study came out of Europe that said that something like only 25% of the AI startups out of Europe actually had AI at all. And so, as a joke, I'm like yeah, maybe 40% of them actually have AI. So 40,000 startups are like maybe 40% of them actually have AI. As a joke, he stops, he gets really serious. I'd be surprised if 40 of them had AI. So this is someone who sees behind the scenes and has. So there is a ton of them that do that, and it includes really large companies as well. So, for what it's worth, llm ops have existed, llm operations, and what you're talking about is like where we actually ingest, we do this, but the only a handful of companies really do right now.
Speaker 3:Where you're going. This is a big deal, because a lot of the interface that you're getting to in the web is going to be very personalized. It's going to be be like hey, I reckon to your point earlier. I recognize your IP address, so I know I'm going to show you these products and I'm going to say it this way and everything else. You're going to be like literally, the, the web of the future, is personalized to your preferences, which is really really cool. But the way that and what that means is, is that that interface, even the chat interface that you see, is actually probably now where some of the most important data is going to be exchanged. You know they say data is the new oil. They don't mean it's valuable. Data is the new oil. Oil is valuable as a commodity, yes, but it's valuable as a commodity because it's a fuel, because you can turn it into plastic and rubber and all types of different things.
Speaker 3:When you start to use those LLM interfaces with those agents, when you're typing to them, you're actually revealing a lot about yourself what you're looking for, what you're thinking of, what worries you, what way in which you present the same type of thing. I can use the word choice and frequency of the words that you choose to put into them to understand how you would prefer to receive information. And so a lot of those agentic tools don't understand that. Actually, one of the biggest tools that comes with, like, when you use, go build an agent, go build it with chat, gpt, it's 20 bucks, right, really, really cheap. But no, like that. Hey, you're not paying for it just because it's 20 bucks. Most of those accounts and the cost that it costs them, even to run the GPUs that answer your questions, is well over $20 per month. They're doing it because they're trying to buy your business in perpetuity, right, they're trying to build market share. They're trying to do it because, even though you're paying for that much the words that you type in are allowing them to build a corpus, a body of text in Latin, surrounding you. They're basically learning what exactly should I be saying to actually win the hearts and minds of these people? And they're building a customer for life.
Speaker 3:Anybody who is building an AI agent right now if you're really smart, you should actually be looking into the LLM operations behind the scenes. So I'd go okay, you built an agent. Can you actually store and look at the inputs and outputs from that agent? Can you say how many times it had issues where it froze? Can you tell me how many times you asked a question versus made a statement, versus provide any semantic feedback to it? That digital exhaust that just comes from interacting with the agent is worth its weight in gold.
Speaker 3:When you start to look at those companies, or if you're running one yourself, I would say, hey, start paying attention to where you can get those. The technology has existed for a long time. But when you start to think, if I build an LLM enterprise agent and I go and I give it to a fortune 100 company, the yes, there is the tool and the fact that I can get those workflows back, but I said I can also come back to the employer and go hey, as a heads up, I know a lot about what your workforce is thinking, what they're curious about, how much time they're spending on spending on specific tasks, and so when you go look at them, are there a lot that are going to be a flash in the pan? Yes, I would say it's those that don't realize how valuable that digital exhaust actually is, and the ones that do and again, the same thing when you're talking about students and how valuable this information is the ones that start to realize that, like, whether you're doing graphic design right, graphic design with the number of image and generative AI tools is low. If you understand that, I'm going to actually take and understand how people interacted with that image to turn that into an even better image. All right, now you've got a defensible moat going forward.
Speaker 3:Same with, like business and MBA students as well, I would be looking into the different multimodal type of AI that I have available to me and I'd be looking at all right, well, where do I get the data sources that actually helped me make those decisions and how can I basically build a model where I'm going okay, we're going to make and do this workflow with ML. We're going to do this workflow with generative. I'm going to become kind of a little bit of a master of what widgets do we have or what tools are in the toolbox and how do we solve these problems so that we can say, hey, we actually do know what we're doing there. We do know how to get better. We can actually measure our progress. Whether it's the small startups or, again, the really big companies, a ton of them still don't realize this. It's like hey, if you can't measure it, you don't know how good you are at it. You don't know how much better you could be at it. The people that are going to be really surviving and thriving in the near future are going to be tracking their progress. Again, llm operations is the name for it. I would look into those and the companies that are doing that. They're going to be the ones that stick around, I think, because they'll have a much better idea.
Speaker 3:Like it's too easy to switch from. Like, hey, I want to use deep seek today and I want to use anthropic tomorrow and I want to use switching cost is zero. They can go burn all the cash in the universe from their investors to try to build market share, but I don't know that it gained them any sort of like. I don't feel any. I don't feel that I own any responsibility to one or the other. If I switch, I can switch and there's zero concern for me to do so. What's really all said and done at the end is who do you own and who do you have relationships with? Right, not who do you own, but what relationships do you own and whom do you have those relationships with? That stuff is a really big deal in the future and that's what I think those agents will monetize.
Speaker 2:So Amazing, no, but really interesting and engaging information. I want to go back a little bit to talking about your brands. Right, so you had Crack the Code, crowd, crack the Crowd, and then you moved to Wrenchai. What was that transition like? Were you able to bring some of your previous clients along? Did you have to start fresh? How hard was it to rebrand the work that you're doing, along with having this organization?
Speaker 3:It was hard. I mean, one of the things that we found at Crack the Crowd that was interesting for us is that we did a lot of analysis, always been a really data-driven individual, like I always liked working in highly regulated industries for that reason, and one of the things that we found that the mom-and-pop shops like even the little Kickstarter things when we'd go and just observe them, but when we'd watch like just little Kickstarter campaigns do well or poorly, and then we'd go watch really big fortune 100 company backed companies and we'd watch they had the same issues and it was largely in their launch process and again how they kind of they took their crowd and how they organized the launch process. So there's actually like there's like there's like called crossing the chasm. But it's this phenomenon which really fun for MBAs. If you've never read crossing the chasm everybody watching you should go read it. Malcolm Gladwell said it's one of the most important concepts in business. You should go check it out.
Speaker 3:But the premise is there's like innovators, early adopters, late adopters, but there's actually kind of a timeline of adoption of a new product or technology and if you follow that kind of chronological rollout process you can actually help your products go big. Google, use this ring, use this, like some of the biggest companies you've ever heard of in the history of the world use this premise. And we would use that premise. So we would go and we found out the biggest thing that everyone made a mistake of as they launched and they treated everybody and they said the same thing to everybody all at once. And, uh, they just went hey, we launched and I went, you shouldn't do that. You should get kind of an inner circle and you should go tell them hey, we're going to launch and I need you guys very specifically to help me. And then you take your influencers and you go hey, I'm about to launch, I'm going to do can you do a product review and are exclusive or some sort of thing to help me promote it. And then, after you basically got those two people or those two segments on board, you go to the early adopters. They're the people that lean in. They stand in line for these types of products. They're less price sensitive.
Speaker 3:What we noticed is that, like, even though everybody learned this in business school at the time and everyone knows what an early adopter is, as soon as you get out into the field, it goes, disappears. Glitter, no one goes, wait, where's my early adopters? And you go, well, they're there. And we realized, like when we were at Con conagra, this is what they would pay us to do is to figure out where those little tiny focus groups of early adopters was at wrench. One of the first things we figured out how to do was actually digitize that process and recognize that if I analyze parts of speech or other things from authored content, I could recognize someone based on the language and how they people that know a category well are more likely to use its slang and its jargon. No rocket science there. What they don't and didn't think about is that if you use that to your advantage, you could go do things like go in and create pay-per-click campaigns and search where they'd go hey, if you don't have 12,000 searches nationally, your campaign doesn't have enough volume. So they would tell everybody like hey, if you're going to build PPC campaigns, make sure you have that search volume. We're like well, those like innovator populations and those really early adopters are very small, very tight but very fanatical populations and they use their own slang and jargon. They have their own language. So if we go bid on those keywords that are really low volume that people aren't even looking for. We had PPC campaigns that would convert at 25%. You're going, hey, like just not even supposed to be possible. So the first thing we did was basically go, hey, let's actually try to build that. That's what one of the patents is for, is basically hey, you can do that for any category, and if you can do that, you can actually take, instead of doing personas, where most people go, hey, we generated our personas and then we threw them in the filing cabinet and we never looked at them again. We don't know, you use personas because I can actually tell you who someone is as they walk through the door. The first time we can actually label them and go hey, this one is the early adopter, that one's a late adopter. One is not better than the other, but they take different styles and if you adapt to those, you can literally quintuple your conversions. And so the transition was actually relatively simple. But based on that just because I mean it seems like a joke We've been doing segmentation since the 50s, of 1950, right, rapid prototyping and stuff.
Speaker 3:People thought that, like, this was all new and digital. No, no, no, no, no, no, no, no, no, no. But until very recently, people would say, like my segment is males, 18 to 24. When you're on the internet, your gender and age matters less than it almost ever did, right, like. And so these are things where people are on the web who they want to be, they search for and they switch between products with zero cost of switching. And so, for that matter, I was going hey look, the future is going to be personalized content. Personalized content, and if we can jump on that bandwagon and understand early who should we do? We can build go-to-market strategies with this. We can double conversion rates of existing agencies, blah, blah, blah, blah, blah.
Speaker 3:So the transition for us from that perspective was actually a lot easier than we kind of thought it would be, just because that need for anyone that was going. It still happens. The marketing teams you go all right, we're going to. What's our new strategy? They look around who has the blackest turtleneck in the room? That person gets to make all the cool decisions because they look the part. Right, they have those Martin Scorsese glasses, those huge producer glasses, right, it wasn't and still isn't largely a space where a ton of data is actually really looked at. And in the future and I'm talking next 12 months, it's going to start being the place where it's like did you bring data In? God, we trust all others bring data, yeah. So I think that transition is what actually made our lives kind of easier. It allowed us to self-fund. We're profitable. We're one of the only AI companies that was able to do that.
Speaker 3:Thank you. That's huge. That was not easy. I've got the blood, sweat, tears and scars to prove know professors and PhDs in data science and stuff like that. You don't need to know it and do it. People confuse me with a data scientist all the time I have to crack them like no, no, no, no, no no, no, I'm not.
Speaker 3:But it absolutely is something you can learn. And a big part again. I wouldn't tell someone to go actually learn to code. I would tell them to go learn object-oriented programming, meaning you need to understand how the building blocks work at an abstract level. That's very doable, it's not very hard, that's a very good use of time.
Speaker 3:I would maybe learn some Python and things like that, but I would argue like, look just, your fingers aren't going to be on a keyboard in the near future. So do you want to understand how they work? Very much so. Do you need to be the person that's putting ones and zeros together? No, I don't think you need to be that person. But if you can and you're kind of continuing your study, I would absolutely focus on knowing those bits of information. They're around, they're free, right, they're oftentimes well, not always free, but like you have MOOCs and you have Coursera and things like that and things like that, you're in a world, too, where it's like, whether you have a degree or not, the person who is the most up to date on the newest information and is the most into it is going to be probably the most pragmatic hire.
Speaker 2:So yeah, again, so much great information here. This is such a fully packed episode. I do want to turn to your upcoming podcast. Yeah, talk about Burn the Map. What's behind the name?
Speaker 3:So you actually just heard some of it. So this is good. So crossing the chasm in that premise is the most interesting thing for me, because I used to love that space. I still love that space when you get into talk to like those innovators. So imagine a bell curve. That's what those, those segment groups, look like. People imagine their target market and they imagine a bullseye. It's like if you took your bullseye and you rotated it sideways so you could see that the innovators were the bullseye. Sure, they're the people that actually work on it. They have almost no price sensitivity. They like this problem so much that they work on it in their free time. And then you get to influencers and you get to early adopters people. You're a couple rings out now late adopters. These are the people that want discounts. They're not into the space. You really have to adapt your communication to get them interested because they're just totally not into it. Those innovators are fascinating to me. They're the people that do insane things.
Speaker 3:There's an old quote from never underestimate the ability of a small group of people to change the world. Indeed, it's the only thing that really has right. It's so true when you go find out there are very, very small groups of people that are actually the ones that put a dent in the universe, and most the rest of us are just kind of along for the ride. Earn the map is the name of the podcast, and it's because I wanted to actually go talk to those people not a not even necessarily just the people that were the visionaries and the pioneers that had accomplished it, but I wanted to talk to the people that were in the middle of the thick of it, because they're actually in a spot, not necessarily where a map is irrelevant, but where they've studied it so hard they realize they're no longer on the chartered map, right. So so it wasn't anything to say we disrespect the map, we love the map. It's just we've learned everything the map can teach us at this point, and we're starting to move into a new space, and so the team is using even some of our technology to start to highlight some people.
Speaker 3:You basically go into a skill set and we look for and we analyze their online behaviors and we look for people that have a specialty, almost to like kind of the fanatical level. So we can actually we did this yesterday with my assistant, where I pulled up her map like imagine a little scatter plot bubble chart and it has like people from zero to 100 that have an affinity for you, whatever else you want. But I was explaining to her I was like, hey, look, so I put in your bio, I measured everyone else's bio and how many times they use words, phrases and abstract concept language the same way you did. And this is a ranking of everybody that's in your social chart of how much they relate and have an affinity for you and like, yeah, whoever this is is probably a good match. It ended up being her boyfriend, like out of literally hundreds of dots on the screen. She's like, is that Colby? That's my boyfriend. I'm like, yeah, there you go. So we're doing that to analyze and basically have conversations with those people because they are delightful Like.
Speaker 3:One of our first episodes is with Bridger Jensen, who has been on the forefront of studying. He helped develop one of our personality models with us. This guy knows the difference between like Myers-Briggs and disc profiles and all the pros and cons of those. He ended up being on the forefront of therapy. He wanted to actually build a non-psychopharmacological I'm not even sure if I'm saying that word correctly. He wanted to see if he could actually build and measure the utility of like, say, going to CrossFit instead of like taking a pill. Here's one. There has to be solutions to some of these problems that don't require medicine. He went into the medicine medicinal space and he ended up being like one of the people that's leading. He's the first federally licensed psilocybin therapist and he's in Utah.
Speaker 3:They've rewritten and they're rewriting the definition of the terms, like religion, because of him and this guy had, I mean, phenomenal fun, interesting story, really genuine Like, hey, this this is a really responsible use of medicines. And the police raided his facility. They then found out that they raided it and that was actually harassment. They had to give him back psilocybin. It is a fascinating story. He's like, why did you do this man? And he was just absolutely, just absolutely positively sure, even though he had no data points surrounding him. He's like, no, this is the right thing to do and the right way to go about it. So it's one of the most fascinating just kind of you get to talk to these people and they're just absolutely enthralling. They're super, super fun to talk to. So we're making a space where we can actually talk to them and learn how they made those decisions, how they navigated them and how they live, to tell the tale. So that is, bring the man.
Speaker 2:Amazing. And when is that launching officially?
Speaker 3:That is amidst launch now, so it should be releasing in the next like week or so, or the first episode. So yeah, you're on one. So yes, I am, thank you, I am excited. You're absolutely fascinating, fun conversation, so I'm looking forward to it yeah, but much like this one.
Speaker 2:I'm really enjoying this and, uh, I am really wanting to know because, of course, we'll put the socials for the podcast for ranchai but what is one key takeaway that you want anybody listening, whether they are a grad student, an undergrad, an alum or just somebody out in the world of business who has stumbled upon mediascape insights from digital change makers one of the biggest insights I would say is just, that's never.
Speaker 3:You're still very much on the front end of this stuff. So, in terms of like the adoption of AI, the coolest thing about it is that a lot of people think they're going to be leaving behind their creative side, and I would. I would go. I actually think you're probably closer to being able to unlock it. Any generation has been subject to the tools at its disposal and you're looking at a different set of tools and I would just go look.
Speaker 3:The sooner and the more that you get involved, I think, the more fun you're going to have doing it, cause there is a ton of this stuff that is absolutely fascinating, super fun stuff.
Speaker 3:It does absolutely like the test of a vocation is the love, the love of its drudgery, and I go cool.
Speaker 3:If you jump into the space, you start learning kind of different modalities, different AI methodologies, and you understand what they can and cannot do, you'll be able to insulate yourself, to make yourself as as a non replicable, as humanly possible, and, uh, I think you'll be able to get more done in less time, and I actually do really think it's. Actually I'm very optimistic about the future because of it, but it is one of those where I go. I think the people that don't they look at it as a chore, that are going to feel it as an ever and ever chore just because there'll be further and further behind, feeling like it's harder and harder to catch up, and I think there's the others that jump in that deep and I think they'll be going. This is actually a really fun ride and ride the wave. So get started. You are not late, you can absolutely catch up and it actually really is fun and interesting and I think you'll actually be pretty amazed with what you find.
Speaker 2:Fantastic Thank you. This was such a fun conversation, as always. I love learning things. I've written down some resources and, while we've been on this interview, this call this Zoom. So danbairdwrenchai Awesome. Thank you so much. Thank you, Annika.
Speaker 3:Appreciate it Likewise. Thanks everybody. Awesome, thank you so much. Thank you, annika. Appreciate it Likewise. Thanks everybody.
Speaker 2:Yeah, thank you. Everybody who's watching this episode, listening to it, connect with Dan on socials. He's really open to talking to people about all of this wonderful journey that we're all on and, you know, use him as a resource.
Speaker 3:Yes, happy to chat. Thank you, appreciate it.
Speaker 1:Thank you, appreciate it. To learn more about the Master of Science in Digital Media Management program, visit us on the web at dmmuscedu.
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