Ep 124: Rethinking Content Discovery and Responsible Innovation with Daniel Sieberg

[00:00:09] Hey everyone, it's Bob. Welcome back to Elevate Your AIQ, your go-to source for insightful conversations on human-centric AI readiness, responsible innovation, and the future of work. My guest today is Daniel Sieberg. He's the co-founder and CEO of Screen Genius, the media discovery as a service platform using AI to help people cut through the noise and find content that actually matches what they're in the mood for across streaming, books, retail, and beyond.

[00:00:34] Daniel and I met recently in New York City as co-panelists for a human-centric AI event, and I was really intrigued by both his background and his new venture, so of course I wanted to get him in the studio. Daniel's varied experiences include journalism as a science and technology correspondent at CNN, CBS, and ABC, to product management, spending nearly a decade at Google building the Google News Lab,

[00:00:57] So now running an early-stage startup that is putting responsible human-centric AI to work on one of the most relatable problems out there, paradox of choice on our screens. We get into how Screen Genius works, why most recommendation engines have under-delivered despite years of data, what it takes to build AI that respects the user rather than manipulates them, and why Daniel sees this moment not as the AI generation but as generation transition.

[00:01:22] If you've ever stared at a streaming menu for 20 minutes and thought there must be a way for AI to help, this one's for you. Thanks for listening. Let's go talk to Daniel. Hey everyone, welcome back to another episode of Elevate Your AIQ. I am your host, Bob Pulver, and with me today, I have the pleasure of speaking to Mr. Daniel Sieberg. How are you today, Daniel? I am doing nothing but blessed, Bob. Thank you for having me on as a guest. I enjoyed our recent opportunity to connect and meet at an event not too long ago.

[00:01:50] Yeah, yeah, that was a fun networking event and panel. Human centricity is certainly a hot and important topic these days that I know we'll get into, but yeah, it was a pleasure to be on the panel with you. Yeah, it feels like a rebellious act to fight for humanity these days, but here we are. It's crazy, right?

[00:02:07] Right. So yeah, so Daniel, before we get into some of the topics, and I want to hear all about the work that you're doing, but you've got some amazing experience under your belt, and so I just thought you could give my listeners a little bit about your background from, I guess, journalism to today, but you can go back further than that if you want. Oh, well, thank you, Bob. My first job, I mean, my first job technically was at McDonald's, but that's when I was in high school, and even before that I worked on a horse farm and shoveled horse manure.

[00:02:37] So I have worked in a lot of different jobs. I would say the professional career side kicked off with journalism. I was a daily reporter at the Vancouver Sun, and then I ended up working at CNN, at CBS, and ABC as science and technology correspondent,

[00:02:54] and got an opportunity to meet so many people who were building things and sharing what they knew and traveled the world and reported on just an extraordinary range of subjects and innovation and development over that roughly 12 years of my life. I left behind being a journalist. I haven't been a journalist for longer than I was one.

[00:03:16] When I went to work at Google in 2011, that was entirely unexpected, and I came in with a lot of imposter syndrome and felt way out of my depth and managed to push through all of that and meet some of the most extraordinary people who are still contemporaries and friends in my life. And went from journalism to marketing. I actually started with the geo team at Google, so working on Google Earth, Google Maps, data product called Fusion Tables.

[00:03:46] And eventually found an opportunity to collaborate with some others there, building something called the Google News Lab, which still continues to this day. To some degree, it was incorporated into the Google News Initiative at a certain point. And I went on to become a number of things at Google, including a Google spokesperson and all kinds of stuff. I was a heady, heady time in my life from 2011 to 2017.

[00:04:09] And I left Google six years to the day after I started to create kind of a bittersweet venture off into the unknown of entrepreneurship moment and leaving, you know, money on the table, so to speak. And that was about nine years ago. And so in the past nine years, I have held a couple of other job jobs, if you will. I've worked at Moody's Analytics as part of the innovation team for a little more than a year.

[00:04:36] And I was at Huawei, USA, the Chinese tech company, for a little more than a year. Some of this during the pandemic. And then I had the opportunity to co-found a couple of other companies before Screen Genius. One was called Civil. It was all about a decentralized platform to tackle misinformation that doesn't exist anymore today. And that was a big learning opportunity experience.

[00:04:59] And then Good Trust, which continues today as a started as a digital legacy way to help people during the pandemic to take care of their digital life. So what happens to everything you've ever created in the digital world after you pass away? And then we pivoted that into traditional estate planning and then pivoted a B2C platform into more of a B2B strategy. And I wound up as a CMO.

[00:05:25] And then I had the idea for Screen Genius around the time that Good Trust was going through its own, I would say, shift, change. And I have been pursuing Screen Genius for the better part of three years. And I would say that we are a discovery as a service, someday to be platform. So we're early stage in the way that we're prototyping and piloting and learning.

[00:05:49] But I have two technical co-founders and a really incredible group of people who keep me honest and know more than me about all sorts of things. So it's been the journey of a lifetime. Yeah, that is quite a tour and quite a career. I see a lot of sort of connection points that I think we'll certainly dig into in some of your journalism and media experiences.

[00:06:10] But also, it sounds like with Good Trust and some of the other aspects, I mean, you've really had opportunities to think deeply about privacy and digital content. And, you know, what that means to people, how important it is to people. And then, you know, I think we'll get into some of this with Screen Geniuses specifically around, you know, how do people make better use of their time?

[00:06:38] If like you are going to sit down and consume media for entertainment, for learning, et cetera, how do we do a better job of that? And so I'm sure you'll hear me interject some personal frustrations and experiences with some of the platforms that I have. Overall, I would say a lot of disappointing experiences, you know, being here in 2026 and people still don't seem to understand what my preferences are. I don't see that it's possible. And yet here we are.

[00:07:08] So I'm hoping Screen Genius has a lot of success to, you know, disrupt this space, which should have matured and innovated better than it has in my opinion. And, you know, I mean, I founded this company as a frustrated dad who couldn't figure out what to watch. So and I even before I got into any kind of a career or jobs, I was always the curious kid who had lots of questions.

[00:07:34] And, you know, my mom is a single mom growing up in Western Canada. I'm a dual citizen. But when I was a kid, she bought two books from National Geographic. One was called Our World and the other was called Our Universe. And there are these big, glossy, hardcover books that sat on our coffee table. We didn't have a computer in the house or anything back then. And so that was my entry point into just sort of seeing the world and thinking about what's out there.

[00:08:00] And for me, the value of what Screen Genius and certainly AI's utility can provide is this kind of helping us to make sense of all of the digital stuff that we've ever created as a species. And there are so many sites and apps that are almost like an entire web or LLM on their own. There are thousands, hundreds of thousands, millions of product SKUs and stuff to discover.

[00:08:29] And we've built all this up over the last 25, 30 years. And now we need, in our opinion, better ways to map that to whatever it is that we're in the mood for. So understanding our intent and then returning the results that feel most relevant. I guess I'm curious as you collect that data. Well, maybe we could just unpack the sounds like we get the, you know, the impetus for it. And everyone can relate to that for sure. The paradox of choice. Yeah, exactly. Yeah.

[00:08:59] I mean, you could be saving me a lot of time and frustration on Friday nights. I can tell you that. But yeah, I mean, talk about like, you know, I guess what are some of the sort of differentiating sort of pieces of this platform? Like where do you think you can succeed where others have, as I alluded, have sort of fallen a little bit flat? Yeah. And, you know, we started, when I founded this company, we were called the Streaming Guide. Okay?

[00:09:28] Because we were building a B2C app that was going to index as much as we could possibly find to do with news, entertainment, and sports. And that you could use your smartphone as a new kind of smart remote to sell it when you were in the mood for and then cast it to your TV if you wanted it to or look at it on your phone. And we built out an MVP experience. It got some traction and people seemed to see the value of it. And certainly we could all relate to the problem statement of feeling overwhelmed by what to watch.

[00:09:56] But what became clear was that the recommendation engine or algorithm that we built for streaming would potentially have more value across a number of different verticals. So while we started with streaming and saw that that was something, of course, people do almost on a daily basis, how can AI help us to make those decisions? There are other things that we consume on our screen. That's why the name is Screen Genius. We want to help people be smarter about what's on their screen. We're not the Screen Geniuses so much.

[00:10:25] But in this way, we looked at different verticals where this would be potentially most relevant and started talking to companies. This is really what we've been doing over the last couple of years. From kind of a journalistic way of research and understanding the market and ensuring that we have some, you know, a real opportunity here, we started talking to retail companies. So we started looking at, you know, different products could be sweaters or, you know, just kind of clothes as a category, you know, thinking of the value there. Audio. So, you know, what you listen to.

[00:10:55] Certainly Spotify has it, you know, largely covered, but other companies have audio platforms. How might we integrate with that? Books. You know, the ways in which we decide what to read. You know, there's much more nuance across content these days. It's not categorized in the same way it used to be. So books is another one. Art, one of our customers that we're talking to, prospective customers, is about art. Another is about food. And these are all things that we consume on our screens that we need help with on an almost daily basis.

[00:11:23] And so we're now at a point where I think we've got, if not product market fit, pretty close to it. I mean, it's not like we invented this discovery as a service category. There are other companies that are in and around this. We pivoted into this. And I think what I love about this is that we were kind of pulled into this direction and saw the interest from these companies we were talking to and, you know, pushed away from something that, you know, a B2C guide that felt like it needed a lot of marketing money.

[00:11:52] It was a slow burn. And we saw this bigger, scalable opportunity as a platform. We have not built the platform, just to be clear. And the engineers in this remind me that we're not building a platform, is what they like to say to me. And this is where I feel like someone like you who has much more technical experience than I do could relate.

[00:12:15] But what we're doing, I would say, is we're understanding these technical integrations from a pattern recognition perspective. What is the value of an API integration to these data sets? How do we manage people's data? How do we do this in a way that's, you know, private, secure, minimizes latency, all these things that are a consideration. And then take those integrations across different types of stuff.

[00:12:42] So books, food, movies, art, whatever it is, and synthesize that into either a single API or a limited number of them. So it's like a single line of code kind of gets access to our algorithm, if you will. And that's a bit where we are. We're architecting this platform as we learn. We're prototyping and piloting. You know, some of these are leading to revenue. We've got some early revenue in this.

[00:13:07] And, you know, it's exciting because now that some of the, you know, the ways that like cloud code, for example, allow you to prototype much faster than, you know, once upon a time you want to prototype for another company, it'd take you months to finally come up with something that you wanted to show them or get them thinking because people don't know what they don't know, right? So just something to react to. And now you can do that in days and weeks as opposed to months. And so the cycles in this are much shorter.

[00:13:37] So we're feeling this momentum now. And there's a big company in our category called Algolia. And Algolia has been around for almost a decade or even more maybe. And they predate some of the more recent developments in AI. So we think we can compete in ways that were more nimble, more flexible, a little more aligned with today's kind of lightweight integrations. And the companies we're talking to are facing this kind of classic buyer bill. It's not that they couldn't do this on their own.

[00:14:05] It's that this is the, you know, this and this is where, I mean, what we see is this pushing away from the hype cycle of AI and the kind of slop and all the things, shiny objects that people have gotten carried away with for the last two or three years into something that feels like a utility. So if you think of a place you go to decide what to consume on your screen and you're used to seeing a search bar,

[00:14:31] imagine getting rid of that or at least augmenting it with more semantic style queries, conversational search, maybe a photo upload of your closet because you want to figure out if something matches your taste. These are all ways that AI powered modalities, we think, are going to become increasingly commonplace. And so, yeah, you know, we're not trying to become a household brand. You know, of course, it'd be cool if people found out what we were up to and liked it. But we're, you know, we're a middleware company.

[00:14:59] And this is the layer that we think is coming to more and more places that we already go. Yeah. So when you think about those different categories of content, some of those people may not even really think about when they go to sit on it. Couch, right? They think they assume that what they want is, you know, find me the best thing for the mood that I'm in or time of day or who's watching with you or, you know, all of these things.

[00:15:29] And so, I mean, that that filtering alone, I feel like is missing from pretty much every, you know, streaming device or, you know, you know, voice capable, you know, whether it's a Roku or what do I have? I have an NVIDIA Shield in my living room. Had that for, that's a pretty slick device, actually, just in terms of hardware, hardware, and it's been built on Android OS.

[00:15:58] But the guide itself, I mean, there's a lot of stuff that looks like a lot of visual. It's a good visual interface. The problem is it knows nothing about me. It's not even properly utilizing the data that it has. I'm sure, I'm sure I opted in to let it see what I watch and don't watch. Sure. And yet, here we are. You're like recommending the same stuff that I skipped over for the last decade. So why am I still looking at this?

[00:16:24] And, you know, I'll use, one of the customers we're starting to talk to is about books, and it happens to be about particular type of books, so romantic fantasy books. That's what they specialize in. And what they see and what we're offering with them is a way to understand their customer, like that personalization of the moment that they're in, like really fast, as opposed to trying to interpret over some amount of watch time or read time

[00:16:51] or listen time or buy time or whatever it is, what to recommend. But the onboarding experience can be much more personalized. The day-to-day interactions, you know, one of the retailers we're talking to has something like 68 million product SKUs and a lot of turnover, and they allowed their data into OpenAI and ChatGPT. So let's say you're looking for a new sweater.

[00:17:20] Their recommendations might come up inside that little, you know, experience inside ChatGPT. They allowed that to happen. They didn't love it. They didn't see necessarily a ton of traction, and they were competing with all these other recommendations. And then they also didn't love that the AI in that case was trying to recommend the perfect product for the person. They want to have a little bit of browsing, right?

[00:17:46] I mean, that's kind of the, I think, the opportunity in this is to, you know, elicit from people what they are in the mood for, whether it's about, you know, what you want to read, listen to, watch, art, buy, this, that. We don't always know exactly what we're in the mood for. We kind of have some idea. And this is where the kind of back and forth nature of a conversational experience can help to refine that and dynamically changing the recommendations in real time. You know, maybe about this, how about this, right?

[00:18:16] And that, I think, is almost, you know, I feel like the funny thing is, in having worked at Google and been part of a team that was actually helping to manage the Google Trends API and the Google Trends product, being really close to a lot of the data, I used to go on the Today Show and talk about the, you know, Google Trends and seeing what people were searching for. But by and large, we were all trained to search like a machine.

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[00:19:20] And putting in keywords. And now, when you try to expand that into more kind of sentences or semantic queries, a lot of these search bars, they break down, right? We're all familiar with, you put in something that feels like what you want to look for, and then it goes, no results found, don't understand, can't find anything. You think, well, gosh, you know, that's not how I want to search like a human. So ironically, I think what's happening in some ways is AI is now helping us to search more

[00:19:48] like a human, which I find fascinating in kind of the discovery evolution of where this is all going. So you've got, you're kind of tackling multiple challenges at once, I feel like, right? Like there's this stuff on the, that maybe your competitor and other services have been doing since way before, well, at least before generative AI entered our vernacular. But, so you've got what I refer to as, you know, sort of predictive AI looking, as you

[00:20:16] mentioned, like the, you know, pattern recognition and, you know, sort of maybe trending, you know, behaviors, like quantifiable sort of data that you have on what are, what do people like you, you know, typically like, you know, typical sort of recommendation engine. But you're right, you don't want to completely eliminate it. Like here's what you're going to watch now. It feels a bit weird. Yeah. Or pick from these two, two or three things. It's like, no, just, just get rid of the noise. Right.

[00:20:43] Just understand what kind of signal I might look for and then give me those options. Maybe it is two, two, maybe it is, you know, two options for a documentary and two options for, you know, this or whatever. And then, and then let me choose. It's almost like build your own sort of media adventure. Sure. Yeah. It's like, you know, taking, you know, the paradox of choice from a human psychology standpoint.

[00:21:08] My understanding is once you go beyond five things, you're asking the human brain to stretch. And that's why when you go into a bakery and they have 10 different donut options, we all go, I don't know. Right. And then you look at the other person and they go, yeah, I don't know. I mean, right. So the, the opportunity is to take these massive, you know, our ICPs, our ideal customer profiles are companies with large digital catalogs of some kind inventory.

[00:21:36] Again, they could be streams, books, art, food, this, that, you know, sweaters, tens of thousands, hundreds of thousands, millions. Understanding to the best of our ability, this is our layer. What is this person interested in? What is their intent? And bringing that number down to kind of as small as possible in that moment. And on a phone, right, we can only look at so many products, even on a screen at once anyway, but bringing it down to something that feels somewhat manageable and then going back and forth a little bit. Is this what you mean?

[00:22:05] Is this what you're thinking? Help me understand a bit more human. You know, is this the price point? Is this the size? Is this the thing? And, and then, you know, it refines and refines and refines until you get to a point where like, yeah, yeah. And you end up with something maybe that you didn't even know is what you wanted. And, you know, I think that's part of the magic of discovery in life in general, you know, that we, we wander into a bookstore. I was in a bookstore yesterday here in Brooklyn. It's called Books Are Magic. And I went in and I didn't know what I wanted to read.

[00:22:35] I knew I was in the mood for a new book, but I just started browsing around a little bit and looking here and there. That's, that is part of the joy of the experience. And, you know, yeah, I would, it would definitely feel odd for the AI to dictate. Like, we know exactly what you're thinking, Daniel. You must watch this now or you must read this now, right? That'd be a good Johnny Carson. Yeah. Or a Black Mirror. We're living. You are about to watch. Yeah. The car. Yeah.

[00:23:05] Exactly. Yeah. So I was thinking about the, the sort of second generation devices that people have in their homes, in their, you know, pseudo smart homes now, right? Instead of Alexa, you've got Alexa plus that has a lot more capability. Siri, I think Apple's announcing today some new Siri, you know, capabilities. I don't know if it's, of course my phone answers me when I'm saying that.

[00:23:32] And so I'm curious what, what they're doing, but you know, my, I opted in to have my Google home, you know, have an actual conversation with me. So it's more than just that first generation sort of conversational assistant now. And so I wonder, just curious if, if what you're working on with screen genius would think about those things as, as data sources. I mean, I don't know how the licensing works, but it seems like if you, if you want to help

[00:24:01] people discover things that they didn't know existed, right? There's a whole set of, you know, unknown unknowns, right? Like you don't know, you didn't know that a sequel to this, you know, movie is now on video as of, you know, two weeks ago. You didn't know that your favorite actor is in this show on a streaming service that you don't actually have, but you would like them. You'd like them so much that you would actually sign up for that new streaming service.

[00:24:26] So it doesn't, so it just seems like tools aren't today are thinking through actual human decision-making and the logic with which, with which, yeah. Right. And I think we have the opportunity to maybe break down some silos in this, you know, we all tend to gravitate towards things we think we would like or recommendations that are familiar to us somehow. You know, but this is where, you know, the, the value of AI, sometimes I think of our experience

[00:24:55] is like this, you know, if, if, if, you know, if, if data, sometimes I think of data as like a library, right? And, and, and I know even they get called libraries and, you know, but just that once upon a time, you know, we had the Dewey decimal system to help us to navigate a library. And then we went to a microfiche kind of, and then we had, you know, other, we had, you know, Google and keyword search.

[00:25:21] And now we need something that can help us to make decisions in a, across a vast, so much more information in the libraries are gigantic now. And I, I think of it like if you go to your favorite retailer website, that website is asking its customers to walk into an enormous warehouse with sort of nobody there to help you. Lots of products that you might be interested in to your point about the, like unknown unknowns.

[00:25:52] And, you know, the value of that relationship is so important and to be able to personalize it in the moment, we all want things when we want them, but to do it at, you know, the sort of the shortest distance and present these recommendations to people and to create a relationship that maybe feels valuable amidst all this and helps the brand to align with that customer over time. You know, these are all things that I think are, are useful in the utility of AI.

[00:26:18] I mean, I know it's a bit, you know, du jour to just come down on AI and my daughters don't love it and they love to thumbs down. There are lots of commencement speeches where people freak out about AI. You know, we can all see that there could be, of course, a dystopian future in all this. I hope that what we're doing is adding to a solution, not creating more of a problem.

[00:26:44] And this is, you know, and to me, this is one of the reasons why I don't mind that we're a bit of a slow burn. Of course, we'd love to, you know, raise the millions of dollars and go do this at scale instantly. Who doesn't start a startup and think that that would be fun. But, but at the same time, as we've kind of grown and moved through the noise, we've seen some of these other things kind of fall away as people have gotten a little carried away and, you know, oh, it can do this with video and it can all.

[00:27:11] And there's this oversimplification, I think, of what is possible with AI too. You know, I'll kind of give an example. I, you know, I ride the subway here in New York and there's an ad for a company I won't name. And it says, oh, it used to take, you know, two or three weeks or a month to create your deck. Now you just push a button and it's all done. Okay. Well, I can buy that a version of it will be done, but that does not mean that it's the one you want to put in front of your customer. It doesn't mean the one you're happy with.

[00:27:40] And what ends up happening in my experience is that you get a version of it and then to make simple syntax changes or grammar or change out of photo, it's costing you tokens. You got to redo it, cost you a thing. Sad now that it asks you to upgrade to a higher tier because you've already made 20 edits and you think I could do these edits myself, but I have to ask the AI to do it. And then, you know, it's not as simple as people think. And even these integrations that we're doing, you know, it's not, it's not as complicated

[00:28:08] maybe as it once was, but that doesn't mean that the CIOs and the CTOs and others don't have massive considerations about data management and privacy and latency, all the things. I mean, you know, these are no different in today's AI world. And I think that, you know, the discoverability part, I go back to, this is why I feel like I'm on planet Earth, Bob. I mean, I, you know, bore everybody to tears with the founder story. But this to me is the sum total of my life.

[00:28:36] This is not my life's work because my life's not over and this isn't over and, you know, but it feels like all roads have led to Screen Genius. And I get excited about, you know, some of the prototypes that we're building. You know, one of them is for a massive museum here in New York. One of them's for this bookseller. One of them's for a publisher that's thinking about bringing us archive of food reviews to life in a restaurant recommender.

[00:29:01] You know, these are, seem like practical ways to apply not just AI because it feels like kind of the front end way of interacting with us, but the machine learning, the making sense of data, you know, doing this in a way that feels like it adds value to people's lives. And that's, that's why I'm here. That's why we're here. And, you know, so it's been, the last three years have been, I do feel like we've been a little bit heads down because while all these things have played out here and there

[00:29:30] and the models are faster and better and we're all chasing AGI or whatever it is, doesn't change the simple fact that we've created all this digital stuff and we need a simpler, better way to navigate what we have all created as a species. So. No, I mean, the premise makes complete sense. And, and I mean, one of the reasons I wanted to have you on the show is because you're,

[00:29:54] you think about this in, in a human centric way and human centricity, as you and I discussed last time we talked, I mean, there's a lot of facets to that, but one of them is respecting people's time, right? You've got valuable time. Maybe it's family time. Maybe it's like, you've got, you know, you've got an hour. You're maybe you're a busy executive. You've got exactly one hour and you want to make, you want to make sure that that's a valuable use of your time.

[00:30:24] Again, maybe it is, you know, a document, however you choose to, to unwind or to learn or be entertained. You know, you, how many times have people just sort of finished watching a show or movie like, oh, that's an hour. I'll never get back. So here you are, Daniel, you're, you're giving it, you're giving it back to them. Well, that's right. And I, you know, and we have, so our future platform someday will offer companies that kind of experience for their customers.

[00:30:50] So, you know, if you're shopping for whatever it is, a more efficient kind of an experience and something we're also building is internally for the enterprise for, so for the employees, because there's a need to navigate data and archives and all kinds of information for knowledge workers internally at a company to just simply do their job. So there's kind of two sides of this AI powered discoverability platform that we're building and we've been piloting and testing it in different ways.

[00:31:18] And the crazy thing in my life, I think most founders, I hope that most founders go back and look at their own family history and ancestors and think about like sort of what led them here. And was there something else in your past that was relevant? One of the things that I've thought about and have learned about in my life, my great, great grandfather and his brother, Henry and Otto Seberg, were chemists at the University of Heidelberg back in the 19th century.

[00:31:47] And they patented a chemical compound that they moved from Germany to Scotland and sold this chemical compound called Seberg salts. My dad still has the marketing flyers, okay, from like 1893. And they were selling this chemical compound to the steam engines and the boilers of the day, which were kind of the industrial revolution of its time was the steam powered stuff. And the chemical compound, the Seberg salts they were selling was cleaning out the inside of

[00:32:16] these engines and making them more efficient. And so I think, well, a little bit, that's what we're doing. We're cleaning the data. We're making more sense. We're making it more efficient for people. There's kind of a 21st century way of what we're doing that ties back to that part of my family history. And I hope that after I'm gone and everybody I've ever known is gone and everything else, that we've added to something beneficial to humanity. I know that sounds like a lofty statement. We're a scrappy little early stage startup.

[00:32:45] But in the ways in which we can all see how AI can become a negative thing, I hope that will contribute to the other side of the scale, if you will. And that's something that we talk a lot about from a mission perspective. You know, it's one of the reasons I think we've been taking our time a bit. Not that we're not hustling and grinding, but we also see this as something that's built for the long term because, you know, search bars and websites and apps have been around for a long time.

[00:33:13] And now this is part of this sea change and where we want to be part of it. Yeah, I think the continued, certainly evolution of some of this technology as well as just the scale of, the scale and fragmentation, I think, of all the content that we're consuming is just out of control. And so I don't think we've built the right sort of mechanisms to keep up with all of that.

[00:33:41] I mean, yeah, someone could sort of, I guess, if they were so inclined and technically savvy, they could probably sort of string some of these things together, like you mentioned Cloud Code earlier. But you'd still need all that access. You'd still have to work out like the mission structures. That's right. You'd still have to realize that this is a time sensitive, you know, endeavor to basically help you decide how to use, you know, some of your downtime.

[00:34:09] And I just feel like the practicality of it makes complete sense. And I like when people are using AI to solve, you know, everyday practical problems. And so if you, somewhere between, you know, the snake oil and these people trying to, you know, put us into space and what have you are these everyday things that don't get, I can't even get my multiple calendars to synchronize.

[00:34:39] Like, why isn't anyone fixing some of these like everyday, you know, nuisances that are super frustrating? So, so I love it. And I, obviously I, my family and I are consumers of, of media, you know, quite a bit. And we've got three different types of streaming devices and they're all sort of doing things. And I've been disappointed with all of them. I mean, even just to pick on your old employer, Google, I mean, I, I love the convenience of YouTube TV.

[00:35:08] I can access it wherever I am in the world. I can access my programming. I can access my recordings and things like that. But every day, depending on what TV I turn on, I get different log on experience. And then I get shows presented. Some of the first channels that I see when I log on are channels that I have never watched in my life. Why, why have you learned nothing?

[00:35:35] I mean, I cut the, I cut the cord on cable TV, you know, five, it was like mid pandemic maybe. So like five, five years ago. And so five years ago, that's a lot of TV watching over the years. And you, you haven't figured out why am I, why shouldn't I only see the things that the channel that I typically watch or the shows that I typically watch. And that's just one type of content. Right. Right. Exactly.

[00:36:04] And this is where, you know, we like to call ourselves the universal navigation layer for human curiosity. And in that way, you know, we are curious as a species. We have questions, whether it's about what we want to watch and, you know, the watch one. I mean, I hope that we find a really compelling customer to work with on the streaming side. There are a couple of companies that we're talking to. We haven't done anything publicly with them, but, you know, to me, there's such a huge

[00:36:31] opportunity in that vertical, as you say, you know, when I came into this, one of the things that we thought about was just contextually who's in the room, right? So that the content you could talk about, you know, you have children a certain age and it starts to factor all these into the recommendations. How are you feeling, right? What are you in the mood for? This is something that we use to decide what we want to watch. How are we feeling?

[00:36:56] And that is hard to convey in a keyword search experience. And that's the other side of this is one of the things that we're seeing is that companies are in need of better tagging. So, you know, when people like to talk about metadata and all of that, what we're seeing is there's an increasing amount of a need for semantic tagging or just, you know, mapping

[00:37:20] more than just a description of the content, more of a holistic way of thinking about how you feel, the intent, you know, the kind of vibe, just things that are much more nuanced because whether you're talking about movies or books or audio or even clothes, I mean, they're just, there's endless categories now, right? You used to go in the record store and there'd be, and I even said record store. You go in the CD, you know, you used to go into a physical store and there were like maybe

[00:37:48] 10 or 12 different categories for music. Now it's hard to know where the categories stop. I mean, they're just all these different genres that get mixed together and, you know, it can be more about how it makes you feel when we can kind of maybe focus more on that as a species with music. So the discoverability has these multiple sides to it between what the companies do with their own data, how they help their customers to find whatever it is that they may or may not be in the mood for.

[00:38:18] And then we also are building something I think will, I hope will be valuable to our B2B customers. Hi there. I'm Peter Zollman. I'm a co-host of the Inside Job Boards and Recruitment Marketplaces podcast. And I'm Steven Rothberg. And I guess that makes me the other co-host. Every other week, we're joined by guests from the world's leading job sites. Together, we analyze news about general niche and aggregator job board and recruitment marketplaces sites.

[00:38:47] Make sure you sign up and subscribe today. Their internal discovery and a dashboard that will help them to see what their customers are in the mood for. And that's valuable data for recommendations, but also for ad targeting, for subscriptions, for different offers. You know, the museum that we're talking to, you know, they're not a for-profit company, but they also need to make money and they sell products.

[00:39:16] So, one of the things that we've been working on with them is understanding their customers' art identity. So, just a little bit of a profile. We do it with this dueling mechanism where we show different art and then they kind of choose, you know, which one they, which speaks to them more. Creates a bit of an art profile. And then, this museum can recommend the right tour for them in the museum, a personalized tour. Can recommend different exhibits that make sense from a marketing perspective.

[00:39:41] And it can connect them to products in their store that would ideally resonate more with them. This is all, you know, I, as a customer of most of the companies that we're talking to, I just happen to be or know something about them. These are things that I feel like would be valuable. And I think that's an important way to be as a founder. If it's not something you would use, then why on earth would you think anybody else would use it? So, we're dog fooding and testing and, you know, prototyping and all that stuff.

[00:40:10] But now when I go somewhere and I don't see something like what we've built, I think, what are you waiting for? But this is the innovator's dilemma, right? This is the classic Clayton Christensen innovator's dilemma. And even the big companies where, you know, people say to me, they're like, well, they could just go build this themselves. Of course they could. Anybody can go build whatever they want. It's the ROI, the time, you know, the value of it, et cetera. And this is how we think, you know, it's how SaaS markets come to be effectively. Yeah.

[00:40:40] You know, it's interesting when you say that I start thinking about some of the AI marketing that I see. And, you know, I know it's their job to sell you on, like, you can do anything. You can go into these biocoding tools and you can, you know, go here, go there and look at how easy it is. But, I mean, you can't discount, like, how challenging, it sounds so straightforward. Like, I can just build a custom, whatever.

[00:41:09] Like, it's not, it's really not that simple. It sort of ties to your example before. Like, look at the ad in the subway. It's just like, yeah. But, you know, you actually create, in some cases, you could actually create more, you know, rework. Yes. Yes. Or, or, or cost you more money. Yes. By trying to do it this way. So, it's not, yeah, I could, I could go out, you know. Yeah, I could, I could build a bar in my basement. Sure. I could. Yeah. But what are the chances that I'm going to?

[00:41:38] I'll probably hurt, probably wind up hurting myself. Yeah. Right. And doing a, doing a crappy job at it. Like, that's why it's not one of your core competencies. That's why we outsource things. Yes. Yes. And so, so it's just, some of the stuff is not that simple. If you're entrepreneurial and you have the time to commit to it and the resources, then by all means, you know, go for it. But we can't, we can't do everything.

[00:42:03] I mean, that's part of what the message here is, are we have a limited amount of time and how do you manage your time and attention? That's right. And you can, so you can certainly help to create something people can see, which I think has always been one of the big challenges, particularly with software is you have an idea for something and you're describing it to people and they're like, I can kind of picture what you mean. So now, of course you can take that, you know, light speed much faster. On the other hand, business is trust. Money is trust.

[00:42:33] Relationships are trust. Even the most amazing widget on planet earth that people think would be valuable to some company, you're going to need to talk to a human being about it. You're going to need to develop a relationship. You're going to need to maintain that relationship. You know, it's not AI talking to AI everywhere yet. So there's going to need to be that human interaction. And that is an entirely different skill set than vibe coding.

[00:43:00] You know, there's a reason why startups are a team, right? It's not just a person pushing a button on a demo and a thing. It's, you know, there are roles and responsibilities that will continue to exist even with all of the AI tools. And furthermore, you know, I think maybe if we can all agree, it's been like roughly three or four years, three or four years since we've been hearing about all these tools. Of course, AI goes back well before that, decades, maybe even more than a century.

[00:43:30] If you go back to the kind of concept of machine and Turing and this and that goes, okay, because we even back with Charles Babbage, right, thinking about just all of this. So as human beings, we've thought about this concept for a long time. And I think what tends to happen today is we think that AI can solve all these things that maybe it isn't prepared to do or won't do or can't do for some time. People joke about AI is going to solve cancer. Okay, well, when?

[00:44:00] I mean, everybody keeps talking about how powerful these models are. Now they can create themselves. They're doing all these things. Okay, so when is the big moment? Are we all going to know? Is it going to happen magically? Is it going to happen in a week, a month, a year, never? If you ask AI about solving cancer, the answers that come back are about what humans need to do. Well, humans are still figuring this out. No, no, no, no, right?

[00:44:22] And then the other end of the spectrum, we put all this, oh, it could go so south and sideways and, you know, we could start to tear away the fabric of society and turn. Okay, I was at an event last week as part of New York Tech Week, and it was a responsible AI event. And I don't remember the name of the woman who was on the panel, but she said that she's an optimist because, in her opinion,

[00:44:44] more people on planet Earth want AI to be something positive, want humanity to continue to, you know, evolve and to, of course, to, you know, be successful as a species. And thus, that's what will happen. Because the majority of people on planet Earth want it to be that way. So can we all come up with these kind of, you know, terrible outcomes? Of course, we could drive ourselves into the ground thinking of all the things that could happen. Could happen instantly over time. This, that. Yep, sure.

[00:45:14] But I think the, you know, one of, and the other term that I heard last week that I really love, I'm going to start using it more and more, and I also don't remember who to attribute it to. But the person used the term, you know, we all get caught up in Gen Z, you know, oh, Gen Z doesn't like AI now. Gen Alpha's like, oh, yuck. You know, Gen X brought this on, or the boomers, or millennials, and so on. Now there's all this, like, intergenerational stuff going on, okay? Everybody's fighting over who's responsible for the future of AI.

[00:45:40] The person framed it as Gen T, and that everybody alive today, if you're over the age of, let's say, 10, is part of Gen T. And Gen T is generation transition. And this is the AI transition from whatever it was before to whatever we want it to be. And we all have a shared responsibility in thinking that through. So rather than people wringing their hands and, you know, the sky is falling, let's think about the right ways to do it and do it responsibly.

[00:46:09] And that's where I hope Screen Genius lands in the annals of technology history, that we did our best to be part of the solution. You know, someday we could easily be acquired by a model or another company or whatever it is. But in the meantime, I hope that we are part of the solution.

[00:46:28] Daniel, I think even though you're not the technical, you know, co-founder, I think your experiences in the media industry and journalism, your understanding of and respect for, you know, user privacy, it just seems like you're building something that is responsible by design and that you, you know, think deeply about what this data means. These aren't just data points.

[00:46:58] These are attributes about you as a human being or about your family. And so there's a lot of sensitivity that needs to be considered as you're building these things. You're not just, you know, stitching a bunch of data together into this unified, you know, platform. There's people behind those profiles and, you know, things like that that you might be building. And so, I mean, I imagine we didn't get into this, but I imagine that, you know, as you're building this,

[00:47:26] there's an opportunity to use, you know, synthetic data to just see how these things are going to work and to evaluate the appropriateness of some of the solutions and maybe some red teaming and things like that. So I think that's what people want to see when you're all the people that are reluctant to, you know, use it, to have some of these devices and some of these capabilities within their own home. You know, is it constantly listening? Does it know too much about me? You know, all these things. I mean, it's almost inescapable.

[00:47:56] So what you do, and you brought this up before, is you make sure that the people that you are, you know, allowing to have access to this information are in fact, you know, trusted, you know, providers within the B2B, you know, ecosystem underneath and then how that translates to anything that interfaces on the consumer front. And so I think that those are really important points. I hope so.

[00:48:25] I mean, I want us to always go back to our mission of being this universal navigation layer for human curiosity. And, you know, it's, and someday, you know, I mean, we're not necessarily going to be a household brand. We're not going to, you know, be all over the media. You know, we're not, I don't know that we're going to become some multiple billion dollar company or something like that. That's not what we're aspiring to do.

[00:48:48] But I do hope that we champion this responsible AI flag for as long as we're in existence. And, you know, it, and the people who are in this with me, because I want to be clear that I would not be here without so many people who have contributed, helped me, always be learning. I could go on and on and on and sing praises of so many people. But to a person, they all carry a lot of the same ethos.

[00:49:14] And that's refreshing to be around that kind of problem solving and thinking about the value of AI rather than getting too carried away. And I have been personally and professionally enamored in thinking about AI since I read Isaac Asimov as a teenager. So certainly it's been rattling around in my head and then covered it as a subject as a journalist for a dozen years in different ways. Saw it in its infancy at various tech companies.

[00:49:40] And then I even had the opportunity to moderate a panel on artificial general intelligence back in 2019 that was, I would say, at the forefront of all of this thinking for the World Science Festival. So I feel privileged and feel like we carry a burden of responsibility to execute on this in a way that adheres to the future of humanity, such as that is when we could all die off tomorrow.

[00:50:07] But, you know, this is our mission and we've chosen to accept it. Yeah, yeah. No, I totally respect that. I agree. And yeah, we want our girls to grow up in a better environment and to think about these things. I mean, and your kids probably think about this, but my daughter knows what I do for a living and what's important to me and how to use it, you know, where you should, not wherever you can. That's right. And so far, so far, so good.

[00:50:36] We've made some progress. Your girls are a little younger than mine, but I'm glad she's going off to college, you know, knowing some of those core, you know, concepts. And she'll think about it as she takes on and, you know, spends her own money on some of these things. Yeah. Yeah. Well, Daniel, thank you so much for spending some time with me. It was great to see you and I think a lot of great insights from my listeners. So, and of course, best of luck to you. We'll put some links to Screen Genius

[00:51:03] and your Medium post or your Medium, you know, blog in the show notes. Thank you so much, Bob. It was a pleasure and a pleasure to be with you. My pleasure. All right. Thanks everyone for listening. We will see you next time.