Technology, Connected - AI Stole The Music. This Is How We Take It Back

Episode Date: December 18, 2025

Making music used to require heartbreak, bleeding fingers, and a thousand late nights. Now, with SUNO, you can write AI songs in 30 seconds.This changes everything about taste, credit, and what it means to be a musician.Nicholas Ponari—guitarist, investor, COO at Overtune—explains how musicians get paid when AI generates the music.The old model is dead. You used to need:- A guitarist- A bass player- A drummer- A producer- A recording studio- Years of practiceNow you need a laptop. But someone still created the guitar riffs AI learned from. Someone played the drums that trained the model. Someone wrote the chord progressions.So who gets paid?Overtune solved this with vector mathematics. Here's how it works:They convert music into high-dimensional vectors. When AI generates a song, they measure the "distance" between the output and every input in the training data. The closest matches get credit. And payment.Bass player's groove gets used? They get paid.Drummer's pattern shows up? They get paid.Producer's mixing style? They get paid.It's automatic. It's fair. It's the only way AI music doesn't become theft at scale.We also talk about:- Why Suno and Udio's approach creates legal nightmares- Whether AI musicians can coexist with human musicians- Why taste matters more than ever (anyone can make music now)- The 10,000 hours that separate making music from being a musician- Why every Mars mission needs a guitarist (seriously—group survival research)Nicholas's take: AI should lower the barrier to entry. If you outgrow Overtune and start hiring real producers, they've succeeded. You've graduated.The question isn't whether AI can make music. It's whether we build tools that empower musicians—or replace them.---Guest: Nicholas Ponari, COO, Overtune | Investor, GuitaristCompany: Overtune.comTopics: AI music, copyright, attribution, royalties, music creation, licensing, vector mathComparison: Suno, Udio (scraping approach) vs Overtune (licensed approach)Please enjoy the show.And remember: Stay curious. Be disruptive. Keep Thinking on Paper.Cheers, Mark & JeremyPS: Please subscribe. It’s the best way you can help other curious minds find our channel.--Take your Technology thinking beyond.⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [e-mail] On YouTube: TIMESTAMPS:(00:00) Trailer(00:59) Why music feels like “magic”(04:51) Overtune’s real customer: vocalists who can’t produce(07:51) The hard problem: attribution, not “make a song”(08:05) Why the easy button fails(12:49) Training on licensed music and where the ethics line sits(16:08) Who gets paid: splits, volume, and realistic expectations(18:32) How attribution actually works: vectors, thresholds, and cutoffs(20:44) Can scraped music ever be fixed after the fact(27:07) Interactive music, live coding, and the future of performance(29:14) The Kevin Kelly question: what do we want humans to be?

The amazing thing is that with audio generating systems, the process is kind of very similar, where all of the musical stems get converted into what we call audio latent vectors. But ultimately, what can happen is that we can measure the distance between all of the origin vectors to the new song creation, figure out which artists contributed the most to it. If a generation is made up of primarily four artists, then if that song goes commercial, that slice of that stem in terms of the royalties, it's going to get divided by those four artists. for that's them. Everything is measured again mathematically to ensure that proportionally everything is fairly distributed. But the answer is yes, everybody would get their fair remuneration for their

work. We took a hard stance against Suno and Urio, especially as we were learning what they were doing. I don't believe music is a one-shot creation process with a prompt. I was one of the first investors into Overtune. This happened at the very beginning of the pandemic. And what I had felt that, you know, was missing was connection. People were really desperate to connect. And for myself throughout my entire life, music was always a method of not just connecting with other people at a concert. You know, the euphoria and the feeling of being in a live music event with other people is magic.

It's you feel so connected to your fellow humans, also connecting with yourself. I discovered at a young age, and for me it was through Pink Floyd's comfortably numb. Where, you know, you hear that ending the outro solo, and I just felt this deep, deep sadness. And that's when it clicked in my brain where it was like, music is the language of emotion. There's no words, but I know the pain that this person was feeling because I can feel it in the guitar, the way it was played, the musicality of how that comes through, even in a recording. It's, it's so different. So overtune had come out with a way to help people create and share music. And, you know, I made this investment based on nothing, but this is what the world needs

more of. It needs more people being in touch with themselves, sharing themselves, getting themselves out there. And that's what put me on the same path as Overtune. As the company grew, you know, they started going through their seed round and through their CERRise and so on. They needed somebody with more business experience to help them achieve their next levels of growth. And that's when I joined in full time and I took some of the experience that had from my previous jobs into building something really unique for Overtune. So Overtune is a beatmaker. I know you're doing more things with it, which I want to unpack. But I got on it. I tried the demo, the beatmaker. The shading of the of the dots are really

interesting how as a mechanism instead of dragging and dropping if you don't have experience in a DA or you haven't ever programmed beat yourself out in, you know, analog world. It's a very accessible means to, means to jump in. There are a few other people doing things like that, right? Splice and, you know, even garage band to a certain extent. With those things already being out there, what did you want to take this core functionality of making insurance? music and making that accessible, where are you headed with it? He wants to turn you into Dave Gilmore. I would love to do that, by the way.

I want to turn myself into Dave Gilmore. Like, that's what I'm going for. It's a very interesting question. And I think the answer really falls onto all of the customer interviews that we've been doing and really figuring out who's been resonating the most with our product. In our case, it's non-musical vocalists. We found out this is the cohort of people that resonates most with our product. who wants more features, who stays the longest, pays the most.

And it's really about giving that platform to, you know, how does like, so we've conducted, you know, dozens of customer interviews. And like, what does that process look like for somebody who wants to get something from their heart into the world? And usually it sets with an idea while you're driving. And what happens is, like, those lyrics hit. You pull out your phone, you do a voice recording, you sing into your phone. Then you go home, you write the rest of your lyrics.

Maybe you have, you know, a semi-professional microphone. You sing into it. And then the problem comes with, you know, well, now I need a backing tracker. I need to have music to accompany this. And it's like, well, I can reach out to my artist friends. You know, who can I afford? Who can't I? Who wants to collaborate?

And then once you get that done, mixing and mastering. And then how do you publish to Spotify? How do you publish to Soundline or whatever it is? So to answer your question in terms of, you know, this cohort specifically, we help those vocalists create that backing track very easily. we help them mix and master the entire thing. We have tools in the back end that handle all that. And we have the tools for exporting directly to Spotify, SoundOn, SoundCloud, etc., etc., right?

Now, what is the next step to that? It's great that we can work with beats, and this is where we're going to go into AI attribution and how we're handling it, and I don't mind giving away that secrets loss. Think about what was said earlier on, right, where somebody with a musical background can get better results from an LLM. There's a difference between having the system generate a stem with, I want an Afrobeats backing track or give me melancholic jazz piano.

And then there's a difference between putting in, like give me a pentatonic minor piano, jazz and whatever, right? We wanted to create an AI that really sort of responded to that need. A response to the need of somebody who understands the music that they're singing and wants to create an adequate backing track for it. And then the question on top of that, which we can get into afterwards, is how do we do that ethically? How do you do that without scraping all of Spotify or Apple music, stealing all of this information, and then creating a model that kind of like generates this music? Let me go back to your David Gilmore comment because I want to, I want to, this has been stewing in the back of my brain a little bit.

The idea of making someone a David Gilmore. If we keep easy buttoning, our music theory people are just going to kind of wean away. How do you ease my mind with that? So I'm going to take a very strong personal position on this podcast right now where I think human in the loop and human effort are paramount to wall. And I think in the future, that easy button is going to be the big differentiator between someone who's legitimately impressive and someone who is not. Now, you know, given that, so I'm almost 40. So I grew up in a world before all of this stuff, right? My guitar playing skills were built in the basement of my parents' home with, you know, tens of hours a week, building up the calluses and just going at it, right?

No TikTok, no YouTube defined the short circuit. Nothing, right? It was ultimate guitar and learning the guitar tabs. Like, you start with that, right? Same. There is, there is a human satisfaction in actually picking up a skill. I think a big, a big, sorry, a person I'm a big fan of actually is Lindsay Sterling. you know, she does the dancing, violin, stuff.

Like, that's absurdly difficult. Like, you don't just pick that. There's no easy button to learning how to do that. So to go back to your point, AI is not going to make anybody David Gilmore. It's going to be hours and hours and hours of practice with the guitar, fumbling, figuring out why your pinky is muting your sixth string and just getting fresh rate over and over and figuring out your hand positioning. You know, like it's going to be the hours of the mistakes and failures until you get to a point where you're actually good. Now, OverTune is not that.

Okay. We are a platform to help people unlock their creativity. And the way I've always seen it, and the way I've always pitched it to investors is that one day somebody might outgrow over tune. In which case, you know what? You want to start hiring real producers. You want to start getting real musicians. But we congratulate you. We applaud you.

You have graduated from us. Go on and do amazing great things. We're proud of you. We love you. Go make your way. We really want to, we see ourselves at the entry level. Using AI to build the interest. That's awesome.

I know a lot of people listening right now are probably going. That all sounds great. I love that idea. But they're hearing about how AI and music, how things are being built. And you referenced, you know, scraping and stealing and all of that stuff. I understand that your product, like your core product was actually trained on licensed music to start off with. Is that accurate? That's accurate.

Yes. Can you talk us through it? Yeah. So we took a hard standard. against Suno and Urio, especially as, you know, we were learning what they were doing. I don't believe music is a one-shot creation process with a prompt. I downloaded it over last Christmas. It was fun with my cousins and my sister where, you know, hey, create a song about our grandfather who escaped Italy,

came to Montreal, married somebody, whatever. I'm like, yeah, we're laughing, but the fun is over after a month. I thought it got boring fairly quickly, and it's because of that lack of human in the loop, right? Okay, so our model, our training model. Yes, we have around 20,000 loops all created by our in-house producers, but we figure out a way that, you know, we think we want to try to disrupt the industry in another way, where any producer who creates beats can submit their stems or their loop pack to our model. And it goes back to the mathematics of how these LMs work.

And I'm going to get a little bit technical here, but it's because I want people to understand, like, you know, this stuff is not impossible, that we have the mechanics to do it. Language can be converted into math, which we call vectors using deep embedding system. Once there's an output, you can match the output to the distance from the original vectors to figure out which vectors had the biggest impact on the output. The amazing thing is that with audio generating systems, the process is kind of very similar, where all of the musical stems get converted into what we call audio latent vectors. Now, when you ask it for it with a prompt and it converts everything into, you know, a wave form ultimately,

and I can go into the details of that if you're interested, but I'll avoid it for now. But ultimately, what can happen is that we can measure the distance between all of the origin vectors to the new song creation, figure out which artists contributed the most to it. Now, I want you to imagine what that world looks like, right? Where let's say you really enjoy making Afrobeat soundtracks, right? and you're a producer who thinks, hey, in the future, Afrobeat is going to be really, really hot. So you can kind of get ahead of the curve and start submitting stuff to our model.

And as more people are jumping on the trend, well, you have a new revenue stream for you, right? Where people are going to be using your stems and your chords for new generations. But here's where it gets even cooler. If we can match all of those artists to any songs that you're producing, imagine if we can say, hey, you've used many stems that were primarily influenced by this artist. Why don't you get in touch with them and do a co-lab, since like your styles are so similar? Why don't you guys do a live event? And then we have like a live event

platform that can get that set up. How do we, you know, take things from the music platform to the rest of the world? How do we really encourage what music is really meant to do? Bring people together, shared experiences and all that. So bring it into the live space, connecting artists together, creating new creations together. How accurate is that? I would say there's no more accurate system in the entire world. We're talking about vector mathematics. We're able to ultra-precisely to infinite flotation points know how close the origin is to a new generation. So I make the new arm and break. I don't have to worry about my work being ripped stolen or used without my permission. That's exactly right. And I can collect all the realties from those

number one hits which come. That's exactly right. So if a generation is made up a primarily for artists, then if that song goes commercial, that slice of that stem in terms of the royalties, it's going to get divided by those four artists for that stem across all the stems that are used for the song in terms of like their time length and all that. So everything is measured again mathematically to ensure that proportionally everything is fairly distributed. But the answer is yes, everybody would get their fair remuneration for their work. Yes. All right. So we're famous for silly thought experiments. So let's Let's try to break this down a little bit. So the three of us are on your platform.

I've got a baseline that I put out there. You've got a drum line that you put out there. And Mark has this nice little vocal stem. So these things are kind of out there for people to use and come together. All right. So say there's a fourth person that is out there kind of looking, all right, I want to create this beat. So they grab my baseline.

They grab your drum beat. They grab Mark's vocal stem and they add a few other things to it. And they put out a song. This thing goes to distribution and it goes to Spotify and gets a million plays on Spotify. What does it look like for all four of us? So ultimately, okay, wait, let's just talk about like in the real world as well, right? I know it's a funny thought experiment, but we all know Spotify pays very little. Right.

A million. A billion. Billion lessons. Yeah, okay. So, I mean, because I want to make sure that the expectation is set correctly here. That from our perspective, the musician, let's say, you know, the total payout is a hundred bucks. They'd probably get 50, and then the rest of the 50 would be divided between us three, right? So it's, you know, tens of $12, $13, $14, whatever maybe.

Is that split sheet automatically on the back end locked into that arrangement of pieces and parts? No, no, no. To be frank with you, it's something that we're still toying with. So even like I have numbers now, it's not going to be probably what it ends up being at the end, right? But for this thought experiment, we're just going to go with easy numbers. But ultimately, for us three, it's a volume play. a million people using our music for their stuff. Suddenly we have $10 per commercially available song. And suddenly it's just a trickling and then suddenly it's like a giant wave depending on

how good or how trending or whatever factors, the more people are using your stems, the more it's going to make you some money. But ultimately that's what it's going to come down to, right? So it's realistically a volume play. What does the final automation of attribution look like in this world. The prompt itself is going to dictate from which vectors it draws on. And typically, it's going to find the middle of several that are similar. So in that respect, it's not going to just be my good, like in a world where, you know, this AI model just has our tracks, yes, but in the real world, it's actually closer to like, you know, dozens of contributing factors, right? But I also want you to imagine all of the infinite points in between all those vectors and the infinite

arrangements of sounds you can get. So in one aspect, no one is going to get the same sound, which is kind of magical about it, right? But when we're talking about the simplicity and distilling it to its vectors, it's because we're imagining sometimes a vector in 2D space, but the reality is that these vectors exist in like almost 8,000 dimensions. So it's tracked across every type of property you can possibly imagine. That being said, every generation is going to be different. The proportionality of it is going to change. There's always going to be one In fact, that's closest because you're going to say, like, hey, give me melancholic afrobeats with whatever it is. And there's going to be somebody up there.

And it's going to land closer to someone than someone else. And then we sort of have to, like, cut it down where it's like, hey, well, everybody between, you know, 70 and 90 percent, those are the secondaries in from 10 to 100. Like, it's like gravity, right? Where Pluto exerts a gravitational force on the earth, but it's so small you wouldn't really count it, right? So like the last 10% are influencing it a little bit, but not so much to the point where it makes a big impact. It's like in our model right now, those are cut off from the commercial attribution. So it's really about crediting the primaries and secondaries first and then. Because even like a soulful jazz drumbeat is going to have an impact on somebody asking for like a hardcore gangster rap song, right?

So it's like, where do you make that cut off? What's the thresholds? How do you make these decisions? And these are questions we tackle every day. You chop up the pie so much that it becomes really. So like you say, maybe it's going back to the volume play, like you said. Correct. Yeah.

And that's the only way like mathematically, again, and I'm a math guy. I'm at origins. That's why I talk about it a lot. It's the only way it can make sense. However, however, if you lowered the barrier for entry downstream, the volume play can make sense. It just, you have to have a tool that works so well. And it's so easy to use that everybody just jumps on board. It really requires you to catch lightning in a bottle twice.

But no one said this was going to be easy, so we're going to go for it. Is it possible to backdate this vector technology? Is it possible to solve the current problem of music being ripped and stolen to create? Do you see a way to solve for what's already happened? I think that's more of a culture question in the United States than it is anything else. this culture in the states where, you know, let's break it first and then ask for permission later. Uber did it. Airbnb did it.

And, you know, these companies eventually go on to become like these mega unicorns, public companies and all that, right? So I think, like, I'm trying to figure out what to say because I don't want to throw any companies under the bus in a way that can put me in a weird position. Well, do you think that does the technology exist? Will it ever exist to solve for that problem? No, honestly, what I think has to happen is that they're going to have to rebuild their model from the ground up using new stems and craft everything at the stem level instead of like this one shot, large generative model where you crunch all this data in one place and then, you know, hope for the best. My answer to that is no. I don't think there is an easy or elegant way to do it. I really think it requires a complete reconstruction of the technology to consider attribution, to consider at the stem level, to consider all of the instruments that individually make up an entire song and then having that fit into like a four bar loop. It's pretty complex and technical and these models are just not there yet. I use the analogy a couple of weeks ago is like trying to pull the drops of tea out or drops of cream after you put it in your tea and stir it up. It's really hard to unmix that.

I'll say it's impossible. Well, not impossible. Nothing's impossible. But yeah, it's exactly akin to that. What a research project that would be to get like a group of some of the smartest engineers, your music-based focus engineers to see if that's possible to put the genie back in the bottle. Like that, to me, that's a killer problem to try and solve. But it's like, like you said, man, it's like, holy smart. Mox. Can you give us any numbers? How many people are using it? What are your projections?

Right now we have 300,000 downloads between our web app and our Apple App Store. Users recurring, it can fluctuate anywhere between. You know, some months we're at 7K users, somewhere at 15. It often changes. We're trying to track where people are taking our music. Oftentimes people are going to output a song that they make and overturned. They're going to bring it to audacity. We have a pretty strong partnership with audacity as well. But ultimately the projection is that we're working on

building a more robust set of voice recording features. We wanted to include some auto tune in there. The idea here is that so by the way, quick addendum before this I had a video game studio where I was also the first video game programmer and the first game

designer at that company. And I look around, Nicholas. It's an interesting backstory you've got there. Yeah, and that could be an episode on its own, actually, because that was a fascinating story. We were doing athletic virtual reality. So games that, you know, force people to get up and move because I wanted people to stop being so lazy.

But the takeaway from that is that every time I design a product, I wanted to impart a feeling, which is what you get in game design. In game design, you always want somebody to come away with a feeling. And the feeling that I want to impart to our overtune product is that I want people to walk away saying,

I can make music. So part of that is, you know, our voice recording software having an auto tune feature where you can like auto tune your voice a little bit. But we want people to walk away saying, hey, that thing I made sounded really good and I'm really impressed with myself and I want to keep doing it. All right. Let me throw this out there. I'm just going to, I'm just going to riff a little bit and I want you guys to react.

Imagine it's, let's just say it's like 2045. We're moving in a direction where all music is an interactive experience, right? In part of that world, is there eventually going to be. something that is like our composer in a pocket that lets us, that basically scores our experience in the world? That already exists, by the way. I don't know if we've ever used Endel. Endel is like this procedurally generated music app and I was using it for my anxiety at some point and actually it worked very well. But on the paid tier of that, they have like this workout mode where the music was adjusting to the beat of my footsteps.

So as I took a step, as I was walking to work, like from the metro to my office, it would play the song in tune with my steps. And it felt like my life was being scored and like the music was being generated as I was walking. And I think I ran across the street to like catch it to not miss a yellow or red light. And like the pace picked up as I started running. It was really interesting.

To answer your question, no, I don't think that's going to happen at all. I saw the future very, very differently. And I'm going to iterate my vision through the story of my son. So my son right now is six years old, and I imagine when he's 17, right? Maybe one day he's going to say, I want to make a movie. And he's going to go to a specialized LLM that's going to generate a script. I want a script about a war in the afterlife with something or whatever. Okay. And then he's going to be able to input that script into a video generator.

It's like generate me a one hour and a half film in the style of Stanley Kubrick mixed with Quentin Tarantino and, you know, hit these plot points. And here's the script and yada. And then he's going to have a movie, right? And then there's going to be a button, like publish this to Netflix. What I'm trying to get across by this is that anybody and everybody will be able to be a content creator in any medium they want, whether it be music, video, writing, literature, poetry, whatever. maybe. What does that mean? It means that we are going to see a surer, like this resurgence of content that we've never experienced before. It's going to be literally impossible for anybody to consume all the content that's going to be being produced by other humans. What does that mean? It means that nobody wants to consume all the content. People just want to watch and listen to stuff they like. So I imagine actually AI in the future being the largest curator of content that you can ever

imagine where hey you liked ab and c titles in netflix or you like ab and c and music well here's the next ten songs you should be listening to and i think it's just going to be like everybody's just going to be continually fed content that is like really suited to their their unique tastes that's kind of how i saw i don't know maybe you guys see it differently i'd like to hear your takes jeremy who's painted a hellscape for me the hyper personalization of music wherever you go there's an a i i in your pocket that is soundtracking your life for you, which means that everybody is walking around in their own hyper-personalised musical bubble. And hey, did you hear that? And you said, oh, no, sorry, you didn't because nobody else in the universe has heard it except me. Oh, did you see that new movie?

Oh, no, sorry, I made it in my head. Nobody else has seen it except me. There would be no shared culture, no music, no movies, no art, because everything is hyper-personalised. And in that world, Like some people, kids, on the whole, have terrible music taste, right? But there's too many adults in the world who hold on to that bad... You're paralyzed in their adult life. So there are people who have no taste in music, and they will listen to whatever the hell they're told to listen to. But that, thankfully, that's a small minority.

What you describe is not happening, not on my watch, no way. I think both can exist. I think both can exist. for that is because it's going to actually solidify the importance of shared experiences. So everybody can have their own hyper-personal bubble of music while you're walking with your headphones, fair, in the world that I show that you can still share music because it's not being generated on the fly. However, there is still a magic to the human experience of going to live music concert. Like, you don't need hyper-personalization to enjoy that experience. Actually,

it's going to make that experience much more valuable. And the artists who can do interesting things in that venue are going to be the ones who are going to be the superstars of the future. That applies to everything from film to retail. It's not going to be good enough to just have a retail front. Yeah, here's my plant store. No, no, no, no, no. At this plant store, there's also classes where we teach you how to properly fertilize a repot a plant. And, you know, then we have like the plant meeting greets where you can like, hey, find, you know, your partner who's also interested in plants. Or it could be that for video games or it's not just a video game store where you buy video games, you also compete. And there's contests and prizes,

so on and so forth. Everything is going to be based on experiences. Like the days of a brick and mortar store just being a place where you go and buy something and pay for them leave, that's going to be gone. At least in like 2045. What if, all right, so Mark, Nicholas and I say in 2040, hey, Mark, come see a show. There's this, there's this creator. We're going to call them a creator. We're not going to call them a musician for, for reasons why I'll say in a minute. So it's a Creators on stage, picture minority report, you know, Tom Cruise throwing things around like this, and there's audio output of that. What is your reaction seeing someone up there doing this, moving things around that's an AI, that's basically directing an AI model versus what we know as playing music right now. How do you feel about that?

You just described an AI Jean-Moiselle-Jard. I don't totally understand the question. Would I be hearing the same thing as everybody else in the venue? Yes. would I go and listen to AI generated music? I think that's really the heart of your question. And my answer to that is the same as would I read an AI generated book? Would I watch an AI generated movie?

Would I go to a museum and look at AI generated art on the wall? My initial answer is no, I need to see the struggle. I need to have seen the pain. I need to have seen the joy. There's a human doing it though. That went into that. No, I wouldn't come with you. I'd stay at home and I'd listen to Dark Side of the Moon.

Always a good choice. Would you have some thoughts, Nicholas, on that? Absolutely, I do. Okay, before I start this, to properly contextualize this, have any of you ever heard of DJ Dave? DJ Dave, no. DJ Dave is an American-based DJ who produces her beats live on set while live coding them. Like she vibe codes the beats live. Check her out.

It's called, actually, it's creating a new genre of show called an algorithm. rave. So live, yeah, as you're there, as everybody is dancing to the music, she's programming beats and she's using mathematics to generate like an algorithmic EDM rave type beat. And it's, it's awesome. So people, like her shows are already selling out. She is going to have two shows in LA. I wanted to go. That's why I'm following her. I just I won't be in LA during that time. But I would absolutely go to experience a live because like there's no set. She doesn't know what she's going to do.

It's going to be based on the feeling of the room. And I imagine like of the two shows, both are going to be entirely unique based on what the crowd is like, how the crowd is vibing. So that's the first step to your question because maybe one day it's going to be AI generated and they're going to just be prompting it as it goes.

It's like this is an indication that there is an appetite for that kind of thing. What Nicholas has described isn't too different from house music and the rave movement of the 90s and every DJ now who will change what they play based on the feeling of the roof. She's mathematically coding it. She's mathematically coding it. Yeah, she's vibe coding. It's, you have to see, you have to watch your videos. I live in the wrong town, Nicholas. I need to move to a place where the first thing happened because where I live, there is nobody vibe coding the music scene where I live. DJ Dave. DJ Dave.

DJ Dave. Check her out. It's going to blow your mind. Awesome. Well, let's land the plane. This has been fun, but Mark, I know you got a roll. We end every show, Nicholas, with the same question from the Wired founder, Kevin Kelly. What do we want humans to be and how does technology help us get there? We want everybody to experience the totality of this very beautiful human experience. And I mean the totality of it. It means everything from love to heartbreak, creation, failure and success, relationships, solitude, and everything, you know, we have to offer each other.

Everything, like I said, from dance to music, to film, to literature, to poetry. The world is an incredibly large and beautiful place, and the amazing thing about it all is that this life gives us enough time to actually experience it all. Not, of course, all the content, but we get to experience all those aspects of it. And if technology can serve to connect,

us to these experiences and help us get through experiencing them and documenting them and, you know, allowing us to look back on all of that. And then at the end of your life, you can look back on a life well lived, you know? And I think ultimately that's the real objective of technology to help us just get more out of everything. That's how I see it. And that's how I live my life. And that's how I kind of want everybody to live their own life. That, you know, this life is a beautiful thing. And we have one shot at it and to just get the most out of it. Wise words. Friends. and neighbors, I would agree with everything you just said. That's amazing. And the work you're doing, the work you're doing that as well, which is super cool. So thanks for joining us. Keep us posted

on how you guys are evolving. Keep us posted on this Roblox experience you guys got coming up. And maybe we'll have you back on down the road. The pleasure was all mine. Thank you for the time. Brilliant. Turn it up to 11. Jeremy. Turn it up to 11. World needs a soundtrack. Sounds like your plan. Hey, be curious. Stay disruptive. Keep thinking on page. Wow, that was an emotional episode of Thinking on Paper. If you're still with us, and of course you are after that, if you have any questions about that show, email us at Hello at Thinkingonpaper. X, Y, Z. Subscribe on the platform you're listening and please, as a curious mind, share this with just one person in your life who wants needs would like to hear these conversations. Maybe two. Share it with two.

Maybe two. Share it with two.