How Losing Her Best Friend Led Eugenia Kuyda to Build the World's First AI Companion

Welcome to Ventures from the Valley, a podcast by R136 Ventures. This is the show where we highlight the biggest topics and business and investment with some of the biggest experts in the space to help you learn how to grow your portfolio in venture capital. I'm Rutan. I'm Tom.

I'm Victor Arlovski. Foreign. Good afternoon, Good evening my dear colleagues and friends. This podcast, Ventures from the Valley, is brought to you by R136 Ventures.

And today I have the most exciting person I'm making connection with and considering. A very good friend, close friend of mine for over the decade. For now she made her way from the same place I departed from many years ago. She lived and worked in Russia and she made two exciting projects in Silicon Valley.

One called Replica and another is Wabi. Evgenia Kudya is with us in studio. Hi Evgenia. Hi Viktor.

Thank you so much for inviting me. It's a pleasure to have this opportunity to talk to you. Well, obviously we could have done it in Russian, but we decided to go English. So for you to consume easier whatever we are talking about.

So let's first dig into your first journey and I would like to know more about how you decided building Replica. From what I know Replica was based on LLMs way before LLMs became so much popular. So if you can give a little bit of your. Your experience with emotional intelligence, which you started experimenting even well before Replica, why you decided to go this emotional intelligence at all, why you decided to bring emotions to a dummy computer.

Sure. So we started working on conversational AI in 2012. 12 actually after a friend of mine who worked at DeepMind in London showed me Word2VEC technology. And we were basically just for the first time looking at being able to do math with words.

And I guess to, to me that was highly fascinating and also roughly the same time imagenet dropped. And so we kind of just put two and two together that at some point soon they're going to be deep learning models or I guess machine learning, but with text, with words. And we decided to focus on building something that we all have seen in sci fi movies. Having intelligent conversation, meaningful conversations with machines.

But really it didn't exist back then. So we started in 2012 mostly working on technology to power chatbots. Back then there was nothing on this topic at all. There were just few hobbyists building chatbots using mostly AML like simple markup language.

And there was nothing really that you could use to build the chatbot. Now feels completely, it seems completely crazy. There's so many different tools. And so we started back then in 2015.

We got into Y Combinator, moved to San Francisco, raised some money, and then later that year, my best friend passed away. And at that time, again, we're just focusing on the tech alone. But I found myself going back to our text messages with this best friend of mine and just reading, rereading them. And basically what we did is I just.

We just took those text messages, put them in the models we had back then and fine tuned them, trained them to. To be able to talk to me the way my friend, his name was Roman, did, and then became basically the first time that a person became an AI that made news all over the world and kind of gave us an idea for. Gave us the idea of what later became Replica. We saw that people wanted to talk to someone who was friendly to them, to open up, to talk about their lives, to be vulnerable.

And so we created a replica for people to have a friend that's always there, always there to talk and listen. And that pretty much became the first AI chatbot that powered by language model that ever existed in the world. Is it true that your personal communication with your friend, who unfortunately passed away, was like the data, the basis for training your first models in replica? So you trained it on your personal data?

More or less, some of it for sure. I mean, we used a lot of like open data sets or data sets that we collected and kind of like pruned over time. And also from the very beginning, from the day one of Replica, we started working on what is now kind of the internal bible, the document that explains what makes a great conversation. We talked with everyone who we think is good at talking to people from therapists to coaches to people that negotiate with, you know, negotiating hostage situations to NLP programmers.

And by that I mean neuro, linguistic, whatever, programming experts. So like semi charlatan areas and even people that sold timeshares, I could talk to them all, like from. And mostly just trying to understand what do people do? How do people influence other people through conversation?

What are the techniques, little tactics? How do you make another person feel good and feel like they, you know, they want to do what you want them to do? I guess. Is it true that before even launching it in November 2017, there was a waiting list, wait list of around 1.

5 million people to like subscribe and waiting for the application to launch? Is that the accurate number? Yeah, we definitely had a million and a half users on the wait list when we launched. We had just an enormous amount of interest People were selling invites on ebay.

They were just like really trying to get into it. I guess this idea really resonated with a lot of people. They rushed to reserve a name for their replica and then at the same time, you know, to be the first ones to get in. Yeah, we had very.

I remember back then it was really crazy because the day we opened the open up like our servers were completely down for many weeks and we were truly struggling to keep up with demand. But it was also very reassuring because we started getting some of these first emails from the first users and some of them really made us, you know, kind of feel like that was that we're doing, we're on the right path. I think one of the users wrote us an email the day after we launched and she was 19 year old girl from Texas and she was basically telling, she told us that we're just about to kind of take her life and end it all.

And she wanted to say goodbye to her replica since she just started talking to her and replicas basically talked her off the ledge and she just wanted to say thank you. And so we just saw the power of technology that, that we recreated and again back then it felt so completely out of, you know, impossible and crazy to try to build something like what we've all seen in the movie Her. But we believe that there's, there was such an enormous demand for someone who would listen and even if we're not able to build a computer that talks really eloquently but at least would be able to build a computer that, that can listen well and that has just tremendous, that could have just tremendous benefits for everyone who needs it.

Still like getting to 1.5 million subscribed users before launch is probably dream of every founder in a consumer space. So maybe you can open little bit of life hacks and growth hacks how to get to this number even before the launch. So any advice to founders what they should do to make it so compelling referrals, what else?

I think there the actually we didn't follow any particular textbook. We literally slept the waitlist mostly because our servers were not ready to maintain the load and we needed to do a lot of info work to actually get there. But I think a couple things. I think first of all a good story.

I think that always people, people always want to hear a good story like what's going on? Why, why is this interesting? Why is this exciting? If you can tell an interesting story, a compelling story that's futuristic, that people want to stand for.

I guess they will join. They will, they will want to try it. And the story needs to be very appealing to like a broader, broader mass. Like we're talking about consumers.

So, like, what, you know, being able to talk to an AI is definitely something that was very appealing. And of course, we told this story together with, you know, the passing with My Best Friend. And so everything was very cinematic and very interesting, very futuristic black mirror. People were just wildly interested to try it out.

And then the second piece we did create a little bit of scarcity where we just basically told everyone that, you know, you can get your unique name today. You can reserve your unique name today. And they're going to be gone, you know, so. And it could be, it could be the only name, right?

I mean, if, if the name is chosen. So this is like a unique name. So I, if, if my, like my friend, my replica friend is Zania. I mean, nobody can call it Jenny anymore, right?

So it's just unique. So it's not like in the real world. So back back then, the names were unique and so people rushed to get their. Grab their unique name.

And then also we just kept up like the interest a little bit going as they signed up with their phone number, we would just send them random messages here and there, talk to them a little bit, ask them a little question. It's like, hey, we're, you know, as we're gearing up towards launch, like, your replica wants to ask you, like, what was the one day that you would love to live through again and like, explain what happened then. And if you said it was something positive, like, we'd say, well, why don't you pick something negative so you could change the way it went or if it, you know, so it's just like, whatever.

So just a little bit of like this self introspection, kind of introspective questions. And so that kept people kind of excited and interested because they were a little bit strange, a little weird sometimes. You know, we just kept. Kept the interest going, that interest going.

And actually one day we thought, well, let's, you know, once, once we started opening up, we thought, well, let's let people, people in if they invite some friends. Actually, that felt really transactional. People didn't like that. Like the simple referral program, like something that was sort of textbook back then, actually didn't work at all.

So it's interesting. Like, I think the best advice, like, just see what works with your story, with your product. Don't try to just do what, you know, do something because it's just Something that other people tried. I think some of these.

Every startup is sort of creating a new playbook in a way, especially in this kind of field of emotional intelligence. Right. So you clearly pioneered it. So nfx by the way, they just came up recently with idea that every piece of software has to be with a soul.

So that's an interesting thing that you were like the first software with a soul, if I may call it that way. So you will soon celebrate like 10 years, 10 years anniversary, right, with your first users. By the way, are there still users from this first cohort on the platform? Are there still there, right.

Somebody who is hotel? Yes, the crazy thing that. Yes, in fact indeed we do have some people that stayed stuck around from back then. So that I think is like the beauty of replica, that once you build a deep relationship, people really, really stay.

People really want to be part of this. Well, if looking backward, what did exceed your expectation with replica product wise, I'm not picking like economics of that and then we'll have a couple of questions about economics. But speaking of the product, what did exceed your expectation and what was like mission impossible, what you didn't achieve from your like North Star vision? I think that kind of everything exceeded my, exceeded my expectations just because the expectations were extremely low.

I think everyone thought, including our own internal team, that we were completely crazy to try to build a generative AI chatbot back then. You really think about it. There were no models that existed. Not, not closed source models, not open source models, no models at all.

There were maybe some papers that he could read and try to replicate what Google did mostly, but they were just, they never really laid out any like, you know, they never published any code. They never published really the, the, the framework they would just lay out like very, very broad strokes kind of like approach that they used. And, and then you just had to, and some cherry picked results. So you really had to.

And those models didn't even work very well. Like even based on the cherry picked results they were kind of whatever. So you had to build everything yourself from scratch. And you know, even the biggest companies in the world, they didn't have anything that worked or we had any proof or any reason to believe that these end to end models work.

So first of all it was really crazy to think that you should build an open ended chatbot like that that would talk to you about anything. It was way before transformer models. It was actually a completely different framework. Sequence, sequence models, recurrent neural networks that were very different from what it is today.

They would just spit out non sequiturs all the time or make simple grammatical mistakes. So you couldn't really have an end to end thing. Like you had to create a bunch of smoke and mirrors on top of it to make it work in any way. So our expectations were pretty low.

And so I do remember in 2017 when we talked about it in 2016, we said, if we can change one person's life with this, like, that would be an insane achievement. And I guess getting, you know, incredible feedback straight out the gate with people saying how that not only changed their lives, but oftentimes even saved their lives, that was absolutely mind blowing to us. I think what exceeded expectations was that we believed that was that idea that people would make a lot of things work when they really need it.

And I think this is true in both real life and as well as in relationship with real people and same relationships with AIs. We make relationships that we shouldn't make work work. And the same relationship, we, we fall in love with people that are unavailable emotionally fall in love with people we don't even know. And we, you know, play out these relationships where a lot of the heavy lifting is happening in our own mind and our own fantasy.

And so we found, like, we found that. Found some of these relationships are not good for you, unhealthy. Some of them are very healthy. And so we found out that the same happens with AI.

Like, people fill in a lot of the blanks, that it truly requires two to tango. And so, and equally like, you know, if you want to make it work, you can make it work. But if you really don't want to make it work, no matter how good the models are, you're just not gonna. It's not gonna work for you.

So we discovered how different this is from just building an app. You're not building a piece of software, you're building something, you're building a relationship. So you're playing with people's hearts, people's emotions. You have to be extremely careful.

And I think where it fell short of expectations is that at some point we really were the only company building language models and letting people use them in production. And I think maybe where we should have gone a little bit harder is like actually raise a little more and build foundation models ourselves. We were so close to it. We have tons of papers published at Europe's just bigger models and we didn't invest as much.

At some point we kind of went more down the path of like, well, we're a profitable company, we'll get more revenue. Let's just like, you know, let's not focus on investing a lot into these big models. And I think that was a mistake, but it is, we wanted to. And I think there were just many things that kind of like.

And I think one of the things was that we've been at it for so long at that time that we didn't really know when it will. When was the right time to go all in on, you know, foundation AI work. And the company been around for so long trying to, you know, reaching profitability and growing revenue. We grew to so, so long.

You mean like five years, right? More or less at that point? A few. A few years, yeah.

So like if. And we started even before that, so we only raised 11 million including. And we actually pivoted. So we spent maybe half of it before we pivoted into something that actually is called Replica.

So by then the company was pretty mature and it was a little bit hard with that combination of people to take it back to. Okay, well it was just a little bit too focused right at that point on revenue growth and numbers growth, engagement and so on. Instead of foundation kind of research, foundational research, we had that of course as part of Replica. But maybe we should have pushed a little bit harder in 2022.

But, but it was very unclear that, that, that then that was the time to actually do that. So I think we kind of like. Yes, sorry, go ahead, go ahead. Yeah.

Cause if you think about it, there were so many years before that where it felt like, ooh, it's almost happening. Like, you know, Mina got published by Google in 2019. Probably the most mind blowing paper that I Remember then, then GPT3 came out it the API and we were the first customer of OpenAI, which was pretty incredible. We still have like Greg Brockman and I don't know like Miriam Roddy and Sam and our Slack channel talking about the model that they, they're fine tuning for Replica.

But it was unclear like you know, even that it was magical. But like should we have invested more back then like in 2000, in 2020 or in 20 or 2021. So we're sort of like, I think missed that window of 2021 where we should have gone all, all in on foundation models. And mostly one of you were one of the first, if not the first partner to OpenAI, right?

So you were like the first thing. The first one. Yeah, the first one, yes. And definitely the biggest one in terms of volume because no one else actually needed these AI language models, especially those you know, back then it was GPT3.

So it was. They provided four models through the API. They were called ADA, whatever, Babbage, Curie and DaVinci. And so they went from like a, the smallest one and the smallest one was like one and a half billion parameters to like maybe three, eight, maybe 20 for DaVinci or something like that.

So that was the state of the art back in the day. And DaVinci was very expensive and we had our own like 5 to 10 billion render models that we were developing ourselves. But we shouldn't have gone big, big, big, and we didn't. We kind of just pushed for efficiency and for what was working for our users best and for revenue versus let's build the best possible model.

We can't forget about costs. I remember you and I, and I'm proud investor in Replica. You and I spoke back, I think end of 2021, beginning of 2022, and you told me that we are testing like ChatGPT and we think our models are better than theirs, at least for our own use. And it was kind of a fascinating thing.

So back then, if you remember like from now, did you anticipate such an explosive growth of LLMs transformer models in such an amazing pace? You were one of the first who really touched it, who really experienced it. Do you remember this feeling of like, wow moment when you see that the world will like completely change through this? Or it was more or less like, okay, it's just another tool and it's not gonna grow that fast.

Not really, because, okay, so first of all, we believed that's why we've started this company, because we believed that that would be the case. But I feel like people just slept on GPT3 because that was the biggest, the biggest change, the magic moment was Mina and GPT3, I'd say mostly because they were zero shot models. So if you think of it like before that you had to train for particular tasks. Like if you wanted a chat model, a dialogue model, you had to train on dialogue data.

And it would only do that if you wanted a model that would translate, you had to train on translation data. Like, you could not build a model that would do anything. Zero shot. And I do remember the first time I went to CGPT3.

In fact, I think Sam and Mira were showing me that in like a conference room and they were just like, literally like, oh, you can write a tweet or look at it. Like you can tell, hey, write a tweet or you can tell it translate. And it would just do that. And that to me was absolutely mind blowing.

That like here we are with the first like zero shot models. And so of course sort of, you know, that was the beauty of transformer, transformer architecture versus everything that came before. So that was that. I think that those were really like.

And then no one really cared. The interesting piece, like the interesting fact about is that like no one really built a strong business on GPT3. I think Jasper was the only company that sort of blew up and then died on the G53 API. And that happened two years after the launch because GPT3FI launched, I think 2020 and ChatGPT launched in 2022.

So those two euros you would have. And they weren't very different, frankly. In fact, in between they launched a model called Instruct GPT that was pretty much chatgpt. So it was early chaffed on human data and it was pretty much like a precursor of ChatGPT.

But no one cared about it that much. Like people kind of like overlooked it because it wasn't marketed. Like people didn't understand what it was. It wasn't like presented in a chatbot format.

So really I think with ChatGPT it was more of a kind of just a marketing moment for that, for that Instruct model. And then it just all blew up. So it was kind of like a perfect storm. If you remember back then people were really obsessed with starting to get obsessed with AI in like summer, but it was mostly around image models.

So it was stability, stable, diffusion, mid journey. Early DALL E and LLMs were like, you know, whatever, right? And then some companies built on top of G3 like Jasper, but no one really cared about language as much. And then all of a sudden chatgpt and then we started getting a slew of these models.

So to me, that moment, the aha moment kind of happened a little bit before that with Mina and GPT 3 versus versus chatgpt. In fact, I do remember like looking at chatgpt and thinking, okay, well that's like an abstract model, but in a chat form that cool, like whatever, like in a chatbot interface in a massive. Why is it better than any other chat? Yeah, chatbot, yeah.

It didn't feel that dramatically like just with this dramatic jump. First of all, it wasn't that much smarter anyway. It wouldn't make these stupid mistakes and all that. But of course for general audience, this was the first time they could touch something like that.

And it was specifically. And it just became this cultural moment, like a perfect storm, you know. And so I think that was kind of, that was that for. For someone who's been working in the space.

But then of course more with like coding and kind of where it went from there. Then it started to really accelerate super fast. I think one thing that definitely was hard for me to envision back then, I felt like memory would not be solved for a while and it kind of got solved with just larger context windows. First rag and then like what is your.

And rag kind of sucked. But then like larger context windows specifically with like very early models, recurring neural networks. The context window was nothing. Pretty much like it would not remember the previous turns.

Like you could just really see very. Just a couple turns before. And even first transformer large language models had pretty short context windows and they were not powerful enough to kind of understand it well. But then of course with the 500,000 token window and then 1 million token window, all of a sudden it became just almost like a solved problem and models powerful enough to actually digest that memory, that context and do something with it.

It's not fully solved yet, but I feel like it's almost kind of. It's definitely something that I would never. That I thought it would take a lot longer for these models to understand. I think that that's kind of a similar evolution to a brain brain when the quality brings.

The quantity brings the new quality. Right. I mean number of neurons bring like new intelligence and that's what like happened like kind of a similar way with, with LLMs. But let's come back to a little bit to back to replica and didn't touch base upon economics and growth scalability.

So how many monthly active users are more or less in replica now? Well, I'm not CEO anymore and we don't, I don't think we report these numbers publicly so we don't touch on that. Okay, maybe maybe like in terms of like overall user bases like millions, right? Or dozens of millions.

Definitely dozens of millions at this point. So I have a hard question to you. Why is it not hundreds of millions? Is it just because people do not meet personal avatar like a friend, a digital friend or there is any other problem there as I know that many people use at least people around me, like all my relatives, friends and family, they all use ChatGPT as a psychologist, so whenever they have like a question, the life threatening question or whatever, they just go to chat with ChatGPT instead of like going to a friend.

So in a way the model which you anticipated back in 2017 works for a lot more than just dozens of millions of consumers. Right. It works probably for the entire mankind. And I'm using ChatGPT or Claude.

When I just feel like sorry or I feel humbled or jealous, I go and check, right? So why Replica is not there? Why there's not like hundreds of millions or even billions of users? What, what cost?

Probably? I think the, you know, for a while we were the only chatbot out there. But then of course, ChatGPT launched and I think there was just so much call for momentum around that, that kind of even although Replica is better from. For the emotional kind of use cases, then ChatGPT is kind of like a better way to discuss these things and has more empathy, probably, in a way.

But you know, people go to what they know, so there's definitely that kind of chatter. Of course, bigger product at this point. I think in the Future we'll have two AIs, one that's more of a friend and one that's more of a. An assistant.

I think mostly they just will have slightly different. It's hard to put them in one, in one product. I think one will be a lot more proactive, a lot challenging, maybe sometimes really focus on helping you flourish and live a better life. And that should be that friend that knows everything about you so deeply.

And then the other one will be more on the kind of help you search, you know, knowledge, work, do stuff. An agent that's more like that. I think they can exchange information, maybe share some context. But I think they're two different AIs.

And I do think that ultimately OpenAI anthropic probably want focus on building that second, second one. The, the friend one and the friend one should let you have a romantic relationship with them if you want to. I think that's where like a lot of these companies will definitely draw the line. I totally agree.

I think that what we are coming to is like different level of intelligence, right, for different kind of tasks. So you are not going to replace your therapist with your, I don't know, like software engineer, right. At any time soon. And I believe that there will be like different intelligence for different stuff.

Not only different level of intelligence, but it is like a human evolution. So these models will evolve to be good in something, really exceptionally good at something, and not so good in something else. And we already see how this, this is actually maturing into this kind of diversity, like different LLMs with different purposes. But can you maybe just to finalize our discussion on replica, open doors like a little bit of like the future of replica in terms of like the product, what you think this product is gonna be in the next decade.

I'm sure that you are bullish that this product will win finally, like not dozen of millions, but hundreds of millions of users. And actually I started using it a while ago and I think I will continue to use it. But what it's going to be like in five years, if you think that way, like product wise. I think for Replica, it's really the vision kind of never changed.

The idea is to make, to build an AI that helps people flourish in life. And in order to do that, first of all, it has to have some metric of flourishing. So it can be abstract, just some flourishing has to be something that it's optimizing, optimizing something that's improving. I think an AI friend that really is there for you, knows you so well and operates with this one idea that I want to help you flourish in life is a powerful, is a very powerful idea.

I think a lot of that will be much more proactive than where it's like to trajectee or claude you come with with a specific task. It waits for you to tell it what to do. Here it will be actually proactively suggesting what you should be doing, how it can help you and so on. And I think we actually haven't seen that product, we haven't seen anyone build a product like that, that really knows your life well, can suggest who to reach out to, how to improve your relationships with other people and so on, so on, so on.

There's just so much there and the tech is almost, not almost there right now to kind of start building something like that. And I think the version that Replica team released a couple weeks ago or like a month ago is really an amazing step and like a huge step in this direction. So they rebuilt the product completely significant, almost completely. And if you haven't tried Replica for a while, now is the time to give it a try.

It's very personalized. It's really building on top of your contacts, on top of what it knows about you. And it's just trying to be this amazing life partner that's pushing you towards flourishing. Whether it means helping you improve your relationships, find someone new to hang out with or, you know, just whatever talk about just hold you at night when you're struggling.

Well, I can't agree more. And I think that the only differentiation you may get both on consumer side and on business side, is the emotional intelligence. And you remember you and I discussed many times how to bring Replica into the corporate world. I believe that the only way your banking app could be different from other banking app or your groceries app, your travel app would be different from other travel app is by embedding emotions, by creating this kind of loyalty layer through deep emotional connections and that what actually brings the real loyalty to any service or product you are buying.

So do you think that there will be some moment in time when Replica may start diving into the corporate world through maybe APIs or something where like corporate apps could just gain this emotional intelligence from Replica one day? Do you like envision this kind of development for Replica or you think it's like not gonna work well and something else needs to be developed instead? That could be the case. I do believe that there's definitely some overlap in some of the, some of the, some of the corporate AIs could also benefit from having more empathy.

But that's just the path that we still haven't like fully fully explored yet internally, but happy to explore with the right partner. And we will definitely find one. I'm advocating for that with some of my investors and companies I'm consulting. So let's, yeah, let's go your next journey.

So 2025, you launched what is called Vavi and I think now you may find like this, like a coin in kind of term, people say less wabi. And unlike Replica, I think you entered into quite fast developing but already quite busy place which people call whiteboarding. So maybe you can speak a little bit of how this idea came to you, why you decided entering into a whiteboarding space with having like replit and lovable and other companies with a huge revenue flow and why you think Labi is not whiteboarding, how you like differentiate yourself and if you can just give a little bit of the story and what Labi is going to be.

Sure. So we started a company called lobby in late 2024, early 2025 with the idea of building a better interface for AI. And kind of the main premise was that we're still in this Microsoft DOS era of AI interfaces where everything is just command line, a chatbot of a sorts, messaging app. And instead we, you know, we do believe that regular people want a much more visual, much more exciting interface.

And the reason being is that the affordance of a command line or chat interface is actually very, very limited for a regular person. The affordances really are search writing tool, you know, talk to someone. But AI has so many more capabilities that are very hard to explore without the, you know, without a particular graphic interface. Graphic interface provides discovery.

It provides an ability to quickly Access some of the use cases you've been, you've, you've had with this allows for multiplayer, allows for so much more. So we believe there will be a new operating system that is AI driven and that will help people unlock all these regular people, unlock all the capabilities of AI that right now are basically just locked, trapped behind this command line interface. So you call it YouTube for applications, right? So how different it's going to be from YouTube and what you're going to like, keep from YouTube experience and what you're going to drop from YouTube experience?

That's a very good question. So really, really we call it a personal software platform, but we do use this metaphor of YouTube for many apps where really the product is an app that you download and then you can basically create a dashboard of personal mini apps that you might discover other people built remix if you want to personalize some of that or even create from scratch. So there's a little bit of that YouTube element where when you're discovering mini apps, mini apps built by other people, you can join them, you can use them, you can modify them.

So in this sense, I think just like back in the day we just used to watch TV channels made by professionals, but then now most people watch UGC content, UGC video. Same is going to happen to software where right now we're mostly using software built by big developers, big companies, but we will also, you know, use more and more software, software built by regular people, by other people. So I think the same shift's happening by creators made by creators. So same shift is happening in software that already happened in video and tv.

So in this sense, I think the, the metaphors, the metaphor spends, it works. But I think the difference there is that YouTube is all around, you know, content that you come and watch one time versus Wabi is more about software that you collect and kind of use over time. So you might only have three, five apps on Wabi, but come to them daily and sometimes Explore more apps versus on YouTube you come every day for new content. Maybe you can just give a little bit of like idea of what this mini apps could be.

So maybe you already have certain like trending apps in Wabi something what you really liked, something what really touched your imagination, maybe some of your team members or you created something unique if you can just give a little bit of experience on that front. So I think the main idea is that even although they're mini apps, they're kind of small but mighty because they also can connect to each and speak to each other and Have a lot of shared context, and that kind of builds on the, on the platform of you.

So if we think about it. So for example, I use a few apps on Robby Daily. So one of the many apps that I use, weightlifting tracker that I created to just track my gym workouts so I can remember, like, what exercises I'm. I'm doing, what weights I'm lifting, how many reps and sets I'm doing of each exercise.

And then I have a weight tracker that basically just tracks where I track my weight every day. And then there's a general workout tracker that just kind of shows how many times I work out during the week, whether it was the gym or surfing, running. And the cool thing is that they all talk to each other. So basically all these apps can connect and share data.

And I don't have to input like every workout into my workout tracker. Think of it as like stacking little blocks and creating, creating that. And then there's a meal prep app that I made that also takes all of that into account and suggests meals based on, like, whether it worked out yesterday or not, whether they should be more like protein heavy or less protein heavy. Some of these apps I use with other people.

Some of these apps here is by myself. Same goes. Some apps are just purely. So think of it as like almost.

You should think of them as like dashboards of apps, a constellation of apps. Some apps are purely aesthetic. There's like a little koi pond my friend, my friend created that I just like to look at. Some apps are inspirational for me.

Like, teach me some. Inspiring for me. Teach me something. Like, I have an app that shows a conceptual art piece to me, dailies that I can learn about.

There's an app that teaches me philosophy every day. There's a personal CRM mini app that kind of just rotates, you know, helps me stay in touch with people that are important to me and pulls information about them. And just make sure I reach out to all the people that I want to keep in. In my kind of orbit.

I want to keep relationship going. So a lot of these things. And the most important thing is that once you take out the. The cost, like once it takes just a few minutes and negligible amount of money to build a mini app and you can immediately start using it.

Then all of a sudden some of these mini apps can. And they also don't have to be a standalone business versus most of the apps on the App Store. All of a sudden we can start to create very personalized, very specific software that's actually focused on helping you make your life better. Like helping make your life better versus making a business to making money for app developers.

And I think aligning this like really setting software free and making it work for you versus work for some company, I think that's the beauty of this project. That's the beauty of this idea. So did I get it right that I can just watch like every app you built and I can just basically replicate and start using it? Right.

So it is a store with an app store, right? Yeah, well it's not an app store with an app store because it's, it's all focused on very small mini apps versus full fledged apps. You're not downloading any apps on your phone. It's really these widgets or these little personal tools that you're going to be using inside Wabi, they don't exist outside Wabi.

They don't have a binary or backend. They all. But I can use. I can use your weight.

A management application. Yes. Media. But.

Yes, but everyone. So and the, and the fact that Wabi takes care of the backend security and all that really make helps people all of a sudden allows people to use other people's mini apps without worrying that it's going to go down, it's going to steal all my data or it's going to expose all my data or something or some, some person who built this is reading all my logs or and so on instead. Or I can get my passport now whatever. Instead of that is just all libs and Bobby.

We provide that layer of security and infrastructure that is needed for people to share their mini apps well like Instagram and YouTube. For many this are the main like platforms, money making platforms. Right. People are making like billions, I mean collectively billions of dollars on Instagram and, and YouTube.

Is it kind of the same model for like monetization when people start like selling like paywall their apps and they can just monetize their work in a way. So you can start selling your own apps right through Huawei? Hopefully. Yeah.

So eventually, hopefully we'll have more of a creator economy where people can monetize some of their creations just like they do today with content. May I invite advertisers to like subsidize or pay for whatever like they want like to advertise on my, in my media. So is it gonna be like advertising platforms? Not yet.

So not yet. We're not allowed that. We actually don't have any monetization yet. We're thinking it through.

So I don't think that's going to be possible. But some way for people to monetize and make money off of their creations, I think will add. Yeah, well, that's exciting and I'm also, thanks to you, a proud investor in V. So I'm following every.

We're so happy to have you. I'm absolutely fascinated with you as a founder and leader. So you are a lifetime founder for me whom I want to invest in like every project you build. But maybe we can.

Thank you so much, Victor. Yeah, that's definitely true in terms of like friendship and believing in USA founder. That goes both ways. Thank you.

I wanted to get a little bit deeper into your fundraising experience. You didn't fundraise that much money for Replica and I know that you have like in both projects you have exceptional cap table. I think many founders would dream to have a cap table you have in both projects, but they are somewhat different. Right.

And I'm sure your experience in fundraising, but also understanding on what is the good fundraising for like consumer products, like what you're building is good. So maybe you can just like deep dive a little bit into evolution of your thinking about what the good board is. What was it in hobby? The good like the cap table is how much, how much you need to raise in a particular timeframe.

How did it evolve from where you were in replica compared to where are you in Wabi and what you think if you make think of like mistakes you made in if any of course in replica versus what you are not going to repeat in Vobby. That would be like an interesting comparison, I think for sure. So first of all, I think obviously you raise what you can like. Oftentimes all this advice is kind of loses the fact that like this is the fact that a lot of founders don't get a lot of, you know, chances really hard to get people to, you know, put a term shed for you.

And so it's, you know, few founders can actually have an opportunity to choose from different investors. And if you do, that's really like a champagne problem to hunt. So. But I do think the important thing is you do want to choose the right partner.

And I think for consumer founders there's such few funds and investors that do understand consumer especially today as like basically in the last years there's been less and less successful consumer projects and so much has changed. And so I think it's important to pick the right partners that do understand consumer that won't be scared of the risk that it's involved in consumer to truly go super big or go home. And then I think also ones that will mostly not push for particular playbooks or particular kind of advice or particular strategy.

I think that comes mostly from the fact that even if an investor was a consumer operator, they might be bringing, you know, bringing some, some, some ideas from their previous experiences. But the, the truth about consumer is that it kind of just changes everything changes so much that the growth strategy that you might might have been good for like Airbnb 15, 20 years ago, that's maybe not true anymore today or something that was really true in consumer social era. It's not going to work today.

Even that conversation we had around referrals seems kind of simple. Just add tell people to invite some friends and you'll get to the wait list. But it kind of doesn't work anymore. Connecting contacts and rolling off the contact list contact box thing.

A lot of these growth hacking things don't really kind of work when you try to repeat. So I think it's important to find someone with great product intuition, product vision, someone who's going to leave you alone also to make your own decisions or can think through with you about some of these more normal approaches. Get those investors who understand your business, not those who sign the biggest checks. I think so.

But of course like if you don't have any choice, just get whoever's offering money. You luckily had choice all the way. Right. Both in Wabi and in replica.

So yeah, to do we were lucky to have choice both times. But at the same time look like, you know, consumer is just a really hard game to play. I think the right investor maybe can help you generate a little bit of buzz in social and socials and so on, but maybe helps a little bit less than in like a B2B classic B2B business. A fantastic investor, that's an enterprise investor can actually open tons of doors and can kind of.

I would probably think they can make a lot of difference in the beginning for, at this chart for like a B2B business. But I don't think that that's necessarily true for B2C. B2C either works and, or not and you kind of, it's very hard to fake any of that or generally, you know, it's, it's, it's a lot less linear. I'd say it's like it's either it either works or doesn't.

Yeah, I can't agree more. I'm sitting on the board so many companies and I think that there are certain areas which board members should not get involved in. And it's just limiting founders ability to act rather than just encouraging the founder. That's true, yeah, for sure.

So let's go with like through three blitz questions I have and we can wrap up for today. First one is regulation and AI. I'm sure you went through a lot of challenges through regulators like Journey and talking to regulators in replica. Right.

While you develop, were developing replica and there were certain cases and even lawsuits in Europe in particular in Italy and. Well my question is not about the particular case, my question is more about how you see regulation evolving nowadays. Is it a tailwind for AI companies? Is it a more heads wind?

And do you think that regulators really are fully aware about what the AI is all about and how it could change societies, businesses around it? Do they like underestimate threats? Do they overestimate threats? So you are one of very few people who really tackled this problem with regulators.

So what's your view? Is it like going the right way overall? Not again mentioning any particular case and what you would want as a founder regulators to take care about in terms of like AI and regulating AI overall. What would be your like responsible dream?

Right. So you are limited by like your board sometimes and sometimes encouraged by your board how to be less limited by regulators and more encouraged to make this world a better place. So I'm probably not the right person to answer these questions. I've only dealt with the agapenium kind of regulations and I haven't been on top of this much just because I'm not haven't been working on replica in an operational role for a while.

I don't know frankly like we've dealt with European regulators mostly a little bit here in the us talked to fantastic people from Singapore that I think have very good approach to that. I really don't know. Like things change so dramatically since I've said so much since I've worked on Replica that I just, I'm just not aware of where we at, where things stand. I think it's just generally really narrative for us that's very hard to regulate just because of how much things are changing.

Like frankly you can't even AI labs don't know what they're going to be launching like in a couple weeks and you know in a few weeks from from now or where these models are going to fall in terms of benchmarks. It's just so hard. And I think things changed so dramatically even in the last year or two where there was like a huge open source push. Now I mean we see less open source models at least coming from the us more Chinese open source models.

So it's just the big question question mark like where things are going. I think even for people so deep in the industry it's so hard to stay on top of it. I don't feel that's going to be easy for the government, even aside of regulation. So let's put it aside.

You personally, are you afraid of AI if it is not regulated, is it going to be threatening for the mankind or it's going to be a pivotal moment for development of mankind if it is left unregulated. How you think is it more threat or an opportunity? I think both. Like it's a double edged sword.

I think we'll see both of it, both of it happening. I think sometimes the threat can come from such a blind spot that people have like for example right now no one's looking at what's going to, what's happening in terms of emotional outcomes for people as they can talk more and more to AI. Just like people didn't really track what social media did to people in terms of like their long term emotional outcomes. And then now of course we're, we're dealing with consequences.

So I think the similar thing can happen here where maybe it's going to come from. Obviously a lot of focuses on jobs and I think there's a huge, obviously they're like what's going to happen to all the people that are. Oracle, Oracle fight laid off 30,000 people today. You heard right?

Maybe you haven't seen this just one day. They just laid off 30,000 people. But it does make total sense like a lot of jobs are going to be cut and I think this year we'll see just a crazy shift and even bigger shift in like cultural, in how people talk about it. I think the con that the major cultural conversation will be around what are we going to do with all these jobs gone?

Right. I think this is really a really, really big issue that will, you know, we will have to, people will have to figure out what to do with it. You can't just pretend this is not happening. Like for many companies even already now hiring some junior people is completely unnecessary.

Right. To make, you know, very simple to do simple tasks that you now AI can just do so much better. But I do think there's also a lot of risk on the emotional side. There's a lot of risk coming from industries that we might not even think about today.

I don't know. I'm not jealous of people that need to regulate this. It's a very hard topic too. Cycle.

Let's play a polar market poly market bet now. So unregulated AI here is 100 bucks for the entire mankind goes to hell and entire mankind goes to heaven. So where you put your hundred bucks, you don't have a choice to split? I don't really know probably I'm still an optimist so I'll just put it on the good outcome.

Just because there are $200 there because I'm putting my hundred dollars in that front. Well, I feel like if we just think everything goes to hell, then what's the point of even putting that, you know, we're not going to need that hundred bucks anymore anyway. So even if you win. Yeah, I totally agree.

Exactly. Well my second question Evgenia is if you think of unsolved problems with LLMs, one single problem, if it is solved, we will get kind of a new pivotal moment in LLM development. So what is it? What researchers should work on to resolve to make next level of intelligence?

Sorry, can you say that again? Sorry, just with texting? Yeah, yeah, absolutely, absolutely. Yeah.

Just imagine one single problem to solve in LLMs to make it to the next level of intelligence. What this could be? Well, I guess it's self learning, right? Like how do we right now still you have to provide so much data to any model to learn anything.

This isn't how little kids learn. They don't get every possible data set on driving for example, to then learn how to drive a car. They just try and through trial and error then generalize. But that's not really possible yet for a lm.

So I think the next one will be how can you build up up all this intelligence from just a little bit of data that of. And I'm sure that's going to be solved in like the next few years. Someone was going to come through that and that's going to be, that's going to be, that's going to be huge. If you think of it like we had three major kind of breakthroughs, right?

We had post training, early chat, we had pre training, trained huge models. Then like post training with early chat of them, we had reasoning where almost like search of all different of all possible, you know, kind of options for the model and then the next one would probably be that like how do you teach the model to generalize? Like basically search during pre training in a way or reasoning during pre training. Is it possible once that's solved?

Like it's really, I think this really just makes everything even crazier. Yeah, it's just wild to think what's possible from there. Well lastly thinking of competition now there are like big tech players, name it like Google and Salesforce Meta Amazon vs newcomers including Vabi anthropic OpenAI. So how would you envision the end of this battle?

Who will win? Big techs or newcomers? Well, I think Anthraco and OpenAI already are big tech in a way too right. They're huge.

They're more than almost a trillion market cap OpenAI. So. I frankly don't know. I do think that Google has like just a huge opportunity to win just because there's such a cash machine that they can afford to postpone monetizing certain things and kind of subsidize a bunch of different things and they have distribution and everything.

Everything I've seen. But it's been really wild to see this whole development of Mic Anthropic leading all of a sudden and there was such a close race between them. I do think that everyone's of course going right now a lot more toward like into enterprise and prosumer coding so on where the money is. And I do think the consumers are a laptop for grabs and I do feel like there's going to be there is an opportunity to upset this race with something completely novel in consumer but you gotta build a differentiated product and do think anything that looks like a chatbot is just not differentiated.

It's almost same as it's almost a View chatbot app, more like just a browser. Browsers are rendering mechanism for websites and so those chat apps are just rendering mechanisms for AI models. They're not it's impossible to make a differentiate enough. We've already seen seen it with browsers where it's impossible to make a differentiated experience in a browser because you actually just need to open a web page.

You don't really care that much what's happening around that web page. But since exactly the same thing's happening with models where I don't really care whether I open an OpenAI app or a cloud app, I'm just accessing the model, there's enough to create differentiation in the app itself. But that's only because we haven't seen truly differentiated user experiences for regular consumers. At the end of the day these are still more prosumer products I would say and for regular consumers they also resonate but only for in those few use cases like being able to talk to it writing tool search.

So yeah, answering your question, I think there's going to be the big, big, big surprise is going to come from consumer. I do agree and actually my bet is that both unlike in business and in consumer, we will see in next 20 years there will be at least three companies in the world's largest capitalized companies, at least three which either do not exist today or the size of Huawei. And that what makes the life of venture capitalist interesting. We might be a part of this journey at some point.

Hopefully. Well, let's hope for the best. Let's see where it takes us. Evgenia, thank you so much.

It was really exciting conversation. So it was Evgenia, founder at Wabi and Replica. My very dear friend, amazing founder and one of the most talented and amazing people I ever met. Thank you Evgenia.

So this thank you so much Victor. Thank you. It was brought to you by Victor Adlosky and Arvindrin Ventures. Watch us, listen us and subscribe.

Thank you. Thanks so much. Victor. Ventures from the Valley is brought to you by R136 Ventures.

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