AI avatars and creator alignment, with Avi Fein

Avi Fein, founder of Meebo, discusses how AI can be used to extend people's capabilities rather than replace them. He explains the differences between Meebo and ChatGPT, and how YouTube's success is due to its product definition and monetization engine. He also talks about the importance of trusting individuals rather than brands when it comes to moderating the internet, and the road to monetization. A great and wide-reaching conversation.

Transcript

John McDonnell: Okay, so we have with us today, Avi Fein. Avi is the founder of Meebo, which is a platform for building personalized chatbots. Prior to that, he was a member at South Park Commons, and previously worked at Neeva and YouTube and Google. Avi, welcome to Pioneer Park.

Avi Fein: Thank you. Great to be here.

Bryan Davis: Yeah. Welcome. Good to see you. So we've been having some conversations prior to this, which I think at some point we all realized oh, we should probably turn on the microphones just so we can begin to capture some of this. And I think we were just on the con topic of talking about how to master chat and really like [00:01:00] some of the challenges of chat.

So first, can you just tell us a little bit about Meebo?

Avi Fein: Sure. So Meebo is a platform where we build chat bots out of creators of various topics. We look for people who are usually like experts in a certain thing and, really have proactively shared their knowledge. And then on the other side, there's people who trust them and want to connect with them to get almost like one-on-one advice for recommendations for, questions that they may have.

Where so much we're going to. I would say Instagram and TikTok and YouTube nowadays as being the place that we want to get knowledge out of and we want to get information from. But those are still static and a distant in many ways where, they're not relatable to you. They, can answer, really connect with things that you're interested in, and we want to break down those barriers and really use chat as an interface to make it interactable such that you can have conversation and go into the depths of both you and how it connects to that person and their knowledge and their content as well.

Bryan Davis: Cool. So I guess something that's really top of mind, I think for a lot of people right now is ChatGPT differentiate. Tell us [00:02:00] how Meebo is different than just run of the mill vanilla ChatGPT

Avi Fein: yeah. It's interesting cuz we started working on this before ChatGPT even came out, but I Very hipster of you.

Yeah. But I would say the foundational ideas and principles actually cut across. Even the post ChatGPT world and that one, what we wanted to do was break apart knowledge to not have it be a monolith anymore. And if you looked at what a lot of people experienced with the web and the internet today through products like Google and now ChatGPT it's relatively generic.

You get the same answer independent of who you are. Like if you do a Google search, if you do a ChatGPT like Q and A, we're all gonna get the same thing back. And our belief is that it's a much more. Delightful. And not only that, but like trustful experience when you can blow that up and go into the distribution of different perspectives and different niches of knowledge where someone's gonna have a slightly different take than, person A versus person B on a whole slew of things.

And so for us it's how do you take, [00:03:00] some of the technology that ChatGPT is good, but apply it to the diversity of human perspectives and knowledge in many ways. I think the second part that we build on, That's beyond ChatGPT is playing with the idea of how do you use the technology to extend people versus replace them.

And a lot of what people talk about in AI now is like these virtual assistants, which are just. SNTs of like humans where it's oh yeah, we've trained on a million of you and now this can do what all million of you can do. Like you should just use this one like AI bot. And that's true for art.

It's true for now chatGPT and knowledge of like why would you talk to anyone else about ChatGPT it knows the entire internet? And I think what goes unsaid in those things is that when you do that, you lose the integrity of in the nuance of all those individual people and all the individual relationships and the trust even that you may have in that.

And it becomes not to go back to the same [00:04:00] idea, but there's like monolith of just, the average across everything. And what we wanna lean into is, The individual. And it is like the personal, and it is the idea that we are all unique in our way and like how can it, how can AI extend us to give us superpowers versus just act as like a replacement of us all?

John McDonnell: So when you talk about that uniqueness, certainly from a in the first comment you were saying, oh unlike ChatGPT, we wanna be really personalized. How are you able to achieve that?

Avi Fein: I think it starts with people, and I like, we said before of what Meebo is like our building block, our atomic unit, was an individual creator, was as someone on YouTube. And really it's actually cutting crosses. It's like the person has, they're represented on YouTube, TikTok, Instagram, and even their website like that is like your identity. And so we started with the identity as being the atomic unit and then build up from that and the, Philosophy behind that was that you can capture their unique [00:05:00] perspective and their, you're their unique point of view, and then make that accessible and shareable with the world.

And by doing that, you can maintain this like boundary so that it's no longer like the aggregation of them, plus 10 others who are like them. And then you actually lose texture and you lose the nuances of like their experience of the world. And you also then from the other side as a user, Know who you're talking with and you can have a trusted relationship versus being like, you have to take this leap of faith with ChatGPT, though what they're saying to you is the authoritative truth of the internet, and you're like, we're in a post truth world.

What is the truth of the internet?

Bryan Davis: Yeah. It brings to attention some of the interesting issues. A lot of the complaints about ChatGPT and related products have been that they hallucinate that the things that they spout so confidently aren't facts. Which I think has been a warning sign for a lot of people.

But it is also true that the perspective of an individual creator is also not necessarily a fact. So I'm curious to hear your perspective on two angles. One is the ability to take a creator's perspective and actually [00:06:00] represent that faithfully. Like how do you ground your technology in the actual perspective of creators and how do you feel about creators being obligated to be truthful?

For instance, fake news. Like what's the, what are the risks or maybe down the line for how Meebo could be a voice of people who you don't necessarily want to expand their voice.

Avi Fein: Yeah.

I'll go in reverse order because I think the first question is almost like the harder question than the second one, at least for us. On the second one, and this connects with the idea of like, how do you not think of the world as being a generic monolith of information that we all trust in that we're not trying to give you an opinion around what is fact and what is not fact in the world.

By virtue of talking with an individual, you are establishing that you trust them, or at least like you like, like they're their source of knowledge and they're the source of information, not us. And having worked in this space before, at least at like Neeva and seeing some of these [00:07:00] dynamics and that one of the drawbacks of the, of those types of products is that trust is inferred into the brand such that I trust the first result on Google because Google said it's the first result.

And the actual sourcing of the individual things that like go into it, start to fade away and not matter anymore. And then Google becomes responsible for moderating the internet. And then Twitter becomes responsible for moderating Twitter. And then Instagram becomes responsible for moderating those things because trust flows up into the brand versus like staying down into the individual.

And they all say oh, that's what we, we don't want to have this responsibility, but they design the products and they build the products to, because they like, become the aggregation point and become the, they come the center point to do that. And for us I think it is about not meddling too much into those worlds and letting the individual points of view, the individual facts still sit where they like lay. Like we're not gonna strip it out like of someone's chatbot just because we may disagree with it. Because you on the other side are an [00:08:00] adult and like we trust that you will be able to form your own point of view on whether you can have that trust with that person or not. And that's the complexity of life and I think the reality of it.

On the first part of how do you actually do a good job of this? That's like the long arc of technology and I don't want to claim that like after three or four months we've solved some massive problem and be like, ah, guys, like this is done.

Like we we've done it here. What I can say that we lean into and we think give us tailwinds to be able to tackle this number one is that we come from a background in search. And what that means is that we spend a lot more time and energy and effort into. Retrieval as being like an important problem and understanding what are the facts or what are the opinions, or what are the things that this person has said and how do we like, make sure we're relying on that.

And what that does is it gives you a boundary in terms of the AI and what it like when you are generating a response or when you are trying to [00:09:00] Yeah, like generate like leverage that you can know apriori how far you may be deviating from what that person has said before. So imagine like when it comes in, you have now GPT3 where you give it a prompt, and when you're assembling that prompt, you could say this person has Neevaer said anything about this topic before. Or like they've said something, 20% about this topic, 80% about this topic, 70% about this topic.

You can at least now have like thresholds to say they have not talked enough about this to really give a response and just be like, I'm sorry I can't answer your question. For example, and that's like a very simplistic I think approach to it. But in concept, I think once you have a source domain that is like boundary incense on it you can apply some of these techniques to.

Not let the model hallucinate or at least know when you're hallucinating more.

John McDonnell: I do wanna double click on something here around these recommendations because you worked previously at Google, YouTube, and Neeva. And one thing that I've always found so these platforms do end up becoming editorial and I've actually always found that YouTube seems to have the [00:10:00] most wholesome recommendations relative to other platforms.

I find that like when I get recommended things on YouTube, they're often educational or interesting and relevant to my interests, but not in a perverse way. Whereas, say TikTok is clearly just trying to addict me to their platform which is fun, but doesn't necessarily feel as wholesome.

Why is that?

Bryan Davis: Or perhaps how could that differentiated experience be created?

Avi Fein: Yeah, no I love this question and I love YouTube And I feel the same way. I think that's like definitely a very true observation with insight into it. If I had to speculate on the potential reasons for it, I would guess it relates to both the product definition of long form video itself.

And then also monetization and how those relate to why this, like maybe manifestation of it. One is that I think more nutritious edu educational content is hard to make bite size. And it is better in like a long form format. And I would also say, I would also guess intuitively that the people who want that type of [00:11:00] content don't want it to be like overly reductive and to be like this like hot take TikTok type of thing, where it's no, they're interested in going deep and actually like learning about this thing which is not well suited to those short form bite size types of.

Like platforms. And so I think there's just a natural product definition that causes more of those things to flow into a YouTube or it's a better fit for both the audience who like want to engage with it. And for the content itself in many ways. I think the second reason for monetization is that outside of YouTube, it's actually very hard to make a living generating content on TikTok or Instagram. They're just not the same type of monetization engine for creators as like YouTube is. That's largely related to both. YouTube's an amazing monetization engine of like they can Make billions and billions of dollars of advertising. But they also share all of that with these creators.

And if you take the power of Google's advertising machine and then you [00:12:00] give 50% of that, not exactly, but roughly 50% of that out to creators. You're sharing a lot of wealth and they have, they show more wealth with creators than any other platform by far. What that also means is, Some of these things which are less popular and less like mainstream, like educational informational things are, can survive and make a living on YouTube where they really can On TikTok or Instagram, if you are like an info entertainer, nutritious content creator you really aren't gonna make a living on TikTok or Instagram.

You'll probably do it for a few months and then realize how hard it is and how much it's just like running up Mount Everest effectively and probably burn out and not do it, and fade away. We're on YouTube. You can find your niche and you can find that audience because of the platform, be, and then be able to make money that comes back to you on it to reinvest and actually have a content business that like comes out of it.

Good.

Bryan Davis: What a, what allows for YouTube to be that platform in a way that Instagram is not. Both of them have enormously high user counts. I would imagine that the kind of number of crevices and interests that one can [00:13:00] fall into are just as deep on all of these platforms. What do you think makes YouTube distinct?

Avi Fein: In terms of its monetization, you mean?

Bryan Davis: Or if it's, I guess its ability. Yeah. One thing, it sounds like one of the biggest differentiators is the ability to make a living. Yeah. What incentivizes YouTube to keep that open? Because it sounds like if they wanted to squeeze artists, they could, but they're choosing not to.

Is that motivated in the core leadership of YouTube, or is that something else?

Avi Fein: Oh, ab absolutely. There's YouTube caught onto this far earlier. To me than other products did in that content creators are your supply of unique, differentiated content and also then the relationships that people are coming back for like more and more onto it.

And if you want to have. A healthy marketplace, which these products are ultimately marketplaces, where like content creators are coming in as your supply and you users are there to consume it. You need to make sure that your suppliers are able to[00:14:00] reinvest into their businesses to make it better and are able to increase the quality of their content and their output over time.

You don't want to actually squeeze your suppliers so much that you're effectively selling like trinkets that are, low value, just like commodities like. Crap effectively versus able to build up into the value chain and offer better and better things. And I think there is a deep insight that YouTube had there of we want to make sure that our supply gets better over time.

We wanna offer a higher and higher quality product, which means we can't squeeze margin out of our content creators and keep it all for ourselves. Because what you'll do is you'll undermine yourself in the long term. And I think that was in many ways an early insight of the partnership program and it's something that has allowed it to thrive and and achieved a lot of these goals.

Bryan Davis: A good note for us, John. Yeah, very much yeah.

John McDonnell: How do you think this is gonna end up playing out for Meebo?

Avi Fein: The idea of like revenue or monetization. Yeah. Or the like, How do the dynamics of like education or like advice or [00:15:00] information then connect into Meebo, like chatbots?

John McDonnell: I think, you listed out essentially two reasons why YouTube might have more kind of nutritious recommendations. And I think the first one was a medium is the message type of thing. Yeah. Oh, just YouTube medium is just suited to high investment content.

Yeah. And then the second one was around essentially the incentives of the creators. Yeah. So as you. As Meebo expands out, what, what's gonna be the character of Meebo and how are those two elements gonna play out?

Avi Fein: Yeah, I think it's no surprise, that when we talk, when I talked about before around, where we're starting is in the more of like expert and advice area.

And the reason is that when you say medium is the message, right? Yeah. And that we got really excited by. Chat bots and chat agent conversations in this context because it allows you to go into the nuances and into the one-on-one and the personal. And that is like where the medium shines of chat. And I would actually like then, juxtapose this against ChatGPT, where it does really bad.[00:16:00]

This has a chatbot. It's actually not an if you like, were to compare a conversation you would have with someone where you were, give it the same input or the same prompt as you give to ChatGPT. That's not how a person would respond to you. No it, it it's not, it's a chatbot only in the sense that you can have follow-ups that like iteratively build on it to have this idea of like memory, which Google does like is doing in the background but doesn't, explicitly do in the product interface.

But I think what is really exciting about chat and about dialogue, which we were talking before, is in the back and forth, and it is in the nuance, in the details to say " Hey Bryan tell me specifically like what you've learned here, what you've done so far, and how can I apply my specific knowledge into that?"

And to me the medium of chat is very much. Like about you both working together to strive to those goals or, and it could even be just getting to know each other better as well. I think on monetization, we're a typical startup of build an engagement. And make build a successful, engaging thing first, and then monetize [00:17:00] it.

I'd say in the spirit of the product and of the company and the spirit of me, if we can monetize, oh yeah, we're sharing that with creators, we're sharing that with people on the other end because the exact same reasons that I said, and just in the fundamental values that these are the people who are the authors, are the creators and the ones who are the thriving suppliers of the product.

Like to me, like I see them as like the blood of these products. And we haven't talked about Neeva yet, but even one of the reasons I went to Neeva was feeling that products like Google don't value the web and they don't value the, like the free publishing that everyone gives them as a supply of the web, and they are extractive at the end of the day and they act as like a gatekeeper in a toll really for discovery and for connecting with those people.

And it just was contrary to what, to me, felt like sustainable, equitable ecosystems that have like fairness and have the idea of, rewarding people for good content built into them.

Bryan Davis: We're now going through [00:18:00] this massive wave of interest in generative ai. It's a huge sort of hype cycle. We're talking about products that can be built off of this and products that can be based on this, basically grounded chat that represents soMeebody.

Where do you think this current wave is overhyped? Where is it over promising and where do you think there's potential to where it's oh, there's an unexplored direction in this way?

Avi Fein: You mean specifically within chat? Within like

Bryan Davis: specifically within, let's say, generative text.

Avi Fein: I would say it's most at risk of being over-hyped in the deep domain use cases, and we were talking about those before.

I think there is a risk that it's overhyped in like B2B where everyone wants to flock into, because typically B2B businesses are like money making things that investors love and other people love and stuff like that. The risks there are, the expectations of B2B are really into nuance and getting down [00:19:00] into a deep domain depth of someone's individual business.

And a lot of things that are hard to capture in the digital world. Even of oh, Lucy talked to Mike about z like, and how does this AI bot not know about that? And you're like there's not really a good record of that anywhere. And if there is, it's like very partial and hard to even connect with what you're expecting out of it.

And I would say in the workplace, people I think will have different expectations and those ex like of these AI agents and those expectations will be hard to live up to given the information available. And I think the difficulty in accessing even all of that information in different warehouses and places that it lives in unstructured ways, in the messy world that like a business is.

And I think that's actually less true in consumer. I think that you have far lower consumer expectations actually, of these things and like what they can do. And you have far more information available that will help people with the 80 or 90% of the use cases that [00:20:00] they will actually have in practice.

So it's more well set up. I think, than people probably give it credit for where do I, where I would say where things are under hyped. I guess consumer I touched on that one a little bit.

John McDonnell: Although ChatGPT is consumer.

Avi Fein: What? Yeah. Yeah. ChatGPT is everything, yeah.

Yeah. I wouldn't really, yeah, I guess it's is consumer, but it's like you've seen it most in like the B2B use cases, take off. I feel like it's, that's like my impression of it, of like people see it, of applied at their job of or applied as a student, which is almost like a b2B use case if you look at it in many ways. Yeah. That's interesting. I don't think you're going to chat GBT to ask it what to wear out to a party tonight or something like that, or like what to do this weekend or those classic like consumery types of things.

John McDonnell: I have used it to plan a party.

Avi Fein: Yeah. Oh yeah, you did. Like you did have, yeah. I'd be curious on how it worked for that.

John McDonnell: I think ChatGPT is very good at generating generic checklists.

Avi Fein: Yeah. And so you got a checklist of what to do for your party.

John McDonnell: And that sounds trivial, but it's really not in some way. I think, there's the Atul [00:21:00] Gawande checklist manifesto, et cetera.

When you're doing something moderately complicated. It can be easy to miss something important. And if you just have the source that can just generate your checklist, then maybe you would've just generated the same checklist on your own, but you can double check it against theirs and you can just say oh, did I miss anything?

Bryan Davis: Yeah, a generative work. Like I can imagine having it generate a workout for me and like that being something like, oh, okay, that's reasonable. I had a good goal for the day.

Avi Fein: So I think that but those are such good places to juxtapose against because I. Yeah. The magic to me that's like super exciting to work towards is you're planning a party and you're like, I want this party to be special.

I want something different. I want like an idea that is not the generic. I don't know that ChatGPT will be able to get that to you and a friend will. Yeah. Like I, I think like ChatGPT, if you say Hey, ChatGPT, gimme like, special ideas for my party. I still think it'll like, spit out something to you, but I think you'll still kinda be like, eh, I don't know, none of those really hit the mark with me.

And same thing for like an exercise routine. Like it'll give you an exercise routine. My guess is you're on the [00:22:00] other side being this doesn't really hit the mark with me. And I, and there's a, there's a. There's a, there's something to bridge that gap there. And I think AI can bridge that gap.

And I think like we have the capability to do it. And I think that's there's like an under hyped goodness. And I can't articulate or even know exactly what there is there, but I'm like, I know the tech is there and I know the information is there. And that bridge, I think is through ...

Bryan Davis: retrieval.

Avi Fein: It's through retrieval, but it's through like it's through people and it's through nuance and it's through the idea of like, how do I get to know you, Bryan and you John, and like, how do I use like the unique things about you and the unique things about the people that you like to talk with to give you something more special for you?

John McDonnell: It is interesting. So I feel like say MidJourney has done a good job making their platform produce art that people are just more into. Do you feel like that's just, oh, they just exercise editorial control and that maybe there'll be like a dozen different mid journeys with different aesthetics or something or are there somewhat generic ways to just make chat more interesting?

Avi Fein: I think there are definitely more generic ways to make chat more interesting. Even now with like hyper parameters [00:23:00] of GPT3 of you can change a temperature and like you don't get back very

John McDonnell: like random and interesting are not synonyms

Bryan Davis: more or less spicy.

Avi Fein: These are like on the spectrum, but like they definitely will have like impact on like the end user experience for people. So you can do these things. I. One thing I wrestle with a lot is this middle ground place where, I talk a lot about individuals and creators and identity, and then you have ChatGPT, which is like the monolith on the other end, and there's an absolute middle ground where you're like what if you take a basket of people. What if you take 10 artists, 10 creators, 10 this, and then you build like an AI agent out of that kind of like they're nearest neighbor to each other. Or maybe they have some kind of value in like this, like mass together. And that's gives you like an interesting output. Yeah, I think it's a really cool exercise to do and think through what are the implications of that and what would be like the end user experience?

And I hope that's something we can play around with, but I I don't know. But that's certainly, I [00:24:00] think some things that you could have in like a mid journey type world where it's oh, there's some level of curation that like you do on the back end to say Hey, you know what really makes great art for this area is to do these 10 things.

And like we're gonna put those things, those 10 things together and that'll actually move us further down the spectrum from the generic into like where we are on the other end, which is like everyone has their own special kind of, point of view.

Bryan Davis: One of the dynamics I think that's interesting is just how bland ChatGPT is, and that's done intentionally.

It's by, it's very unopinionated, intentionally politically neutral or driven to be politically neutral. And, any sort of risque content, it shies away from any sort of attempts to basically address preemptively the AI safety concerns.

But the result, I think is A consumer experience that lacks luster in a lot of domains that lacks color, that may not be appropriate for a lot of purposes.

Avi Fein: It's boring.

Bryan Davis: It's boring. So what do you think about the, how do we confront the potential AI risk community when trying to build things that are potentially more exciting, more spicy, more interesting?

Avi Fein: You said you take risk you, [00:25:00] you cannot innovate with fear. Like you cannot constantly be worried about the downside risks of something in order to. Unexplored the upside potential of it.

Bryan Davis: So let me throw you a curve ball there. Let's say one of your creators is a bomb manufacturer.

Yeah. And soMeebody wants to come in, they're like, oh. Like I really want to know, like I, what are the household ingredients I can use to make a bomb? Yeah. This guy's content. Maybe it's I guess you're somewhat limited by the content you're consuming. You're trusting that those sources will be will be doing a lot of the sort of censorship and maintenance of content control.

Yeah. But hypothetically, that person could have a private blog where they aggregate these things. Yeah. Just, that just like driving the nail home here. Yeah. What about that case?

Avi Fein: I'll actually push back on it in that I think that case is less exemplary because you don't do anything that's gonna break the law or cause like extreme outcomes like that, like where you can have people end up in harm or death or things like that. And those are actually very easy policing types of things where it's yeah, we like, we're not gonna do things that like break the law or [00:26:00] enable to people to easily break the law.

There's a lot of other like nuance stuff in between there of misogyny and hate and just like meanness that I think is harder to reconcile with. But I think that case at least is like more straightforward.

Bryan Davis: So what do you think the solution is for something like misogyny or hate speech and things like that where there are people certainly on YouTube where they've devoted their platforms to being assholes, right?

And I totally understand the sensitivity or the conservativeness of OpenAI and companies like this that are attempting not to build something that kind of perpetuates that. Yeah. What's the, I guess what would be your guide to the appropriate boundary?

Avi Fein: I think most of the first order risk is in like the discovery experience or this type of thing where you get into like recommendations of people. And I think for where we are right now, that's less something we have to worry about. Where it's oh, what happens if we like promote like a, a hateful chat bot that like then breeds more hate in the world.

That's just not a problem that we're gonna face for the next [00:27:00] like year probably. At least. Can there be those that exist and are created or are powered on it? Yeah. Yeah. And I think that that is a potential downside of opening up to more diversity of perspectives to more, free speech to more of what is humanity and the ultimate criticism of things like ChatGPT, or of Google, or of these like behemoths, is that in the fear of these issues, they water down humanity and they like water down to like very basic, like basic levels and make us all become more like, like, not us all to become more similar, but basically like our experience is to become more similar on those products.

And so you end up with they come become further and further from what the world actually is to become like the own bubble where it's oh, this is like the culture of TikTok or this is the culture of [00:28:00] Twitter. And they don't represent maybe the culture of the world as much. And so I think it's just interesting to take a risk and let those things flourish and open up and see where they go and then, deal with the issues that come out of it as they arise.

John McDonnell: One thing that I love about the angle that you're coming at Meebo from is your background. Yeah. Cuz you were at Google for a long time and YouTube and then Neeva was founded by people that were sick of Google. And and now you're going in yet another direction. Maybe it's one interesting thing could be the level set on the state of Google.

So I, I think we've had this conversation at one point, but what is Google's moat?

Avi Fein: They have multiple moats that are incredibly powerful. The first one is distribution in that one of the hardest problems for any company, any startup, especially in the consumer space, is just distribution. How do you, like,

easily reach your end customer at scale for like very efficiently and things like that. And the most effective [00:29:00] way oftentimes to do that is to be pre-installed slash the default of something that already has distribution. And Google very early on was the default for Safari. And then, they very smartly took advantage of The wave of mobile and now own 50% market share for Android, where they are the default through, very like many OEM agreements as a the search provider and they very smartly have Chrome.

Where they are the default for it and able to, use their own browser as like their distribution. And overcoming that alone is a very haul hard hill to climb and is very independent of the quality of your product. Like you could build something that is 20%, 30%, 50% better than Google.

You won't overcome the distribution gap there as a startup. You really need to be 10 x or have some smart unique angle around like why you can penetrate the market in a way that, someone hasn't thought of before. But beyond that, from a product standpoint and technical oh, if you actually wanna go rebuild Google and you think you can solve distribution there's two other moats [00:30:00] that they have.

One is, one is slowly getting torn down, which is I'll actually on that one. But the second one is the crawl of the web. They have the entire web content, but not only that, but they have it annotated with structured information about everything there. So they can say oh, this webpage is talking about these people, this location, these topics, imagine if you had that for anything where you could just run a query and say Hey, find me all of the webpages that are about this place, or about this person, or about this idea. That's incredibly, you can't do that today. Yeah, they're. Thousands of millions of businesses spend millions of dollars crawling the web and trying to extract information out of it in order to create a sliver of what they have. Yeah. As just like a baseline default for retrieval and for, other fancier future building.

And then the third one, which is eroding now has been their feedback loop in terms of like ranking and that they have query click pairs. So you know that when people do this search that they will click this result in this website and that is the most [00:31:00] relevant, Website to them which can act as a feedback loop into ranking for you and also is very virtuous and just you can effectively leverage that for things like memorization where it's just oh, I've seen this query a million times.

So like the next time I see it, not only do I not even need to like do retrieval anymore because like I know exactly what someone's gonna click on, but I can now even do fancier things because I understand a lot more in depth than that. I've known that these words are associated with these like pages and these topics and things, so I can build an AI system on that as well.

I think on that one, that's where we're seeing the biggest kind of disruption with embeddings for relevance and ranking, for example. And also with LLMs, which have in many ways like intent, understanding and have content and knowledge baked into them that you don't need to necessarily go rank, the world's web to be able to get the answer out of it.

John McDonnell: I was actually curious about your take on embeddings. So one kind of thing that I've had to explain to people about embeddings is that they are, they represent the semantic content of a document. [00:32:00] And something that's magical about Google that's not included in those embeddings is that intent isn't the same as semantic similarity.

But it sounds like I guess do you think, are you an optimist about embeddings? Will they end up actually like enabling. That kind of intense discovery.

Avi Fein: I'm absolutely an optimist about embedding, and I also think that like embeddings are a reflection of what they've been trained on, like everything else in ai, right?

And so if you train a bunch of embeddings on. Google's data, if you had Google's data, they'd be really good for those use cases. Would be really good at intent understanding, like when I say these three words, I know that these three words are probably about, this thing or these, like these sets of things.

And that's big biggest issue and that I think people naively see them as This sledgehammer where it's oh, open eyes and beddings. That's amazing. Like I can just use those and you're like no, it really matters what you like, what they've been trained on for your use case. And if they haven't been trained on what you're trying to use them for, they're not gonna be very good for what you're trying to use them for.

Yeah. And so I'm definitely bullish of them because I think what is magical [00:33:00] about embeddings and I think what is magical in general about where we are in AI and deep learning. Is you can move into the smearing. And I go a lot and we talk a lot about like nuance and details and like that, like the world is complex.

Information is complex, everything is like much more nuanced than we give appreciation for. Words are reductive symbols of that complexity and the minute you can put them into a much more complex like embedding space or latent space, you're able to capture much more complex associations and ideas that are more reflective of reality.

And so I think that that and continued investment in that is like a breakthrough thing, but they're not gonna be like the I turn on embeddings and suddenly I have a search engine, type of thing.

Bryan Davis: How can small businesses take advantage of embeddings, which seem to be a technology that really is requires first that you have a data set that describes that intent?

It describes that purpose. Purpose-built use case?

Avi Fein: Yeah. I think anyone who's like starting to mess, that's like starting to play with them. You continue to use like a hybrid system, even be like the [00:34:00] biggest tech companies still use hybrid systems where they're doing keyword based retrieval in addition to embedding based retrieval.

Like they're not a mutually exclusive thing. And it's not like one is a drop, like embeddings are a drop in replacement for some of these issues, I would say. And so if you're gonna add 'em in, I would you want to add them in and then be able to at least start retrieving them alongside. And then you can even do evals and say okay is this actually.

Doing a good job and augmenting what we're already able to get with keywords. Are we getting what is like in search world, like recall, like we're getting things that we Neevaer knew we were retrieving on before that are related because they're semantically similar to at least assess the quality of your embedding.

The second thing I would then say, based on what that assessment looks like for things that are out of the box. And a lot of the things if you're getting into it, is just go do research and choose the right embedding. What's the right model to generate your embeddings from? That's most similar to your use case.

You can then try to, potentially take a swack at either fine tuning or even like building your own based on your own like data. But I would say that's like an extra level and really is more dependent upon you getting [00:35:00] extreme value out of that capability. And it's like worth the time and investment to do it because that's not even an easy thing in itself to, to go down that path.

John McDonnell: Actually maybe one more question about Google before we get into Neeva, but It seems like a Google should be the big winners here. They have this amazing web crawl. They invented the transformer. And cuz as you were saying oh a model trained on Google's data would have the most amazing embeddings for intent ever. It seems like Google probably should or probably actually does have that.

Are they going to be able to exploit those advantages or if not, why?

Avi Fein: The problem google and Bing have is when you are the monolith of information. The expectations for users are unbounded and technology's not at that point yet. I don't care how good your data is, I don't care how good your AI is, like we've all seen the fail cases of Bing.

We've all seen the fail cases of these, of chat, g p t still and like these other things especially when you're talking [00:36:00] more about facts and knowledge. And when you take that, that that product concept of being like, this is just a universal chat bot that like, has all of the world's knowledge into it that you can trust.

And then you put that into a corporation which has a trillion dollars on the line that is not set up for success. Because the amount of risk that you have in revenue that is introduced by this vector, which is Uncontrollable and isn't ready for prime time makes it very hard for you to actually take advantage of the wave.

It is said another way, it is the innovators dilemma you like they are at the crossroads of the innovators dilemma, where the risk for them of innovating is so high that it's hard for them to even get started there and the closest they can do is do very quiet behind the scenes things. But at the same time, they're getting just lambasted in the PR sphere for not doing the big things.

And you're like the tech isn't there. Like the people who can take risk and the [00:37:00] people who can take the this and produce a good output are the startups, are the people who have nothing on the line. I don't have a trillion dollars to lose. I, like people can go and have a terrible experience with any chatbot and it's really painful for me. But it's not gonna put entire company at stake. And I think Google faces that type of dilemma in practice as one.

The second thing I would say, outside of that, they have an absolute leg up from all of technology data, all those other things, side chat experiences are not, chat experiences are not the same.

It is not a drop in replacement. People's query behavior. Super ambiguous. It is very generic itself. Google has actually taught you to use few and fewer words over time as you're less expressive in search than you once were a decade ago. And the reason is because they've gotten so good at intent understanding when it's like, Hey, when you put these two words in 70% of the time, you probably mean this.

And then 30% of the time, like you'll do a refinement on your search or you'll like, you'll do follow [00:38:00] ups or you'll get to what you want anyway. Yep. Chat from what we said is almost the exact opposite, where people are much more expressive of their needs and much more expressive of their desires and the shape of that data and the shape of the product and everything comes out of it, is actually to be, think quite different than even what Google has.

And that is Blue Ocean. No one I think has good quality real chat data for having a conversation like around a topic like you would with a friend. Yeah.

Bryan Davis: Do you think that people want to be expressive to entities that don't reward them for that?

Avi Fein: You have to be rewarded.

Bryan Davis: My, my example comes from my earliest interactions with any sort of chatbots we're often very curious and open-ended.

Yeah. And I desire to find the boundaries of their ability to simulate human behavior, simulate chat behavior. And I almost inevitably converge on use cases, which are really mundane. Yeah. Like the way that I use Google Assistant right now is like almost [00:39:00] exclusively to set timers and play Spotify.

Yeah, when I first encountered Google Assistant, I approached it with more curiosity and open-endedness. I think I asked it about its emotions, asked it whether like for advice about things. I don't do any of that anymore. Yeah. And I wonder whether we're going through like a bubble of thinking that these technologies are in some way different.

But really they're not. Yeah. And like we might all end up disappointed by that.

Avi Fein: I think that's a risk. My experience thus far, and this is the optimist startup founder who's headlong into it, is that we have crossed a Rubicon. And it is not that like these problems are solved, but it is that we have the tools in place to be able to create completely new experiences.

That break out from what you felt and I felt, and we all felt with Siri, with Google Assistant and stuff like that. We have to deliver on that. Like I, I think that like you have to deliver on that and we talked about this and I like what I think about and what is the most stressful thing [00:40:00] is I, execution is the most important thing.

Execution is the most important thing. And how do you simultaneously execute in building out a delightful, magical product? While, you're still flying the plane at the same time and you need people to use it and you need to go to market and you need investors to believe in it, you need users to believe in it.

You need to build faith in it along the way. I think that is like a real challenge, but I think the pieces are there to, to overcome it. You can disagree on specifics by evidence.

Bryan Davis: Yeah. I don't know if I have a grounded disagreement other than the fact there's like some criticism I think of this potential comes from previous in this conversation.

Yeah. Just looking at how things get bland. Yeah. And also I think looking at my own engagement with these things has also followed a wave. It was like initially an expansive, exploratory process and increasingly has become very practical. Yeah. I also wanna counterweight that opinion with the observation that companies like replica were replicant, I can't remember.

Replica. Replica, yeah. Which is produced [00:41:00] basically AI romantic partners. Yeah. Went through this massive wave of interest, but I think they maybe have just discontinued a product or something like that. There was some controversial news recently. Yeah.

Avi Fein: The, we were talking saying this before, like the most popular use case that I've seen from like data for chatbots is around like adults and intimate interactions and nsfw like the data points that say that is if you look at, searches and queries where, like what are people actually looking proactively for a chatbot? Generally it's that, which is not all that surprising.

Like the top search term on YouTube is like porn. The second one is prawn. The same thing for Google, like the, like adult content leads innovation oftentimes. And so replica that was a huge use case for them. And like they turned off like the explicit, I think part, of the capability. And I think the same thing was true for, there was another startup that was like doing something similar where they also had the same disabling capability.

I like, there's there's a giant theory all around how this stuff will play out. And I like, [00:42:00] I don't think the Google assistant generic bland, even if you inject personality, but like your assistant now has personality to it. I don't think that's like the starting place for the, for interactions with AI and for interactions with AI agents.

It's very hard for me to make that leap beyond the people in tech who are like very interested in playing with these things and like the leading edge, but for the average day person where you say oh, this is actually like a thing that will be used by, people you and I know that are in different fields than tech so to speak.

And I think the likely entry point for those experiences, probably more like productivity, utility, more like we were actually getting value out of it. That or the very extreme end where you can get into more of the like, fun slash like emotional empathetic type of things. And that's like talk therapy.

Those and some other fun experiences I think are like ends of the spectrum where it'll start and then it'll be who's fastest to converge to complete the rest of it.

John McDonnell: I did wanna follow up on what you were saying about [00:43:00] Google's moat and then what Neeva was trying to do. Certainly one challenge that you listed for Google is that, oh, they're just the incumbent and have innovators dilemma, and it seems like Neevaa probably shouldn't have that since they're early enough stage. What's up with Neevaa? Are they gonna, are they gonna win? What's their strategy? How's it going?

Avi Fein: I'm no longer there, so I can only say what I as yeah, of course. Know as like an outside of server. No, they absolutely have a ton of opportunity in front of them and I'm really excited for that team.

They just launched I'll give them a hype for their own product. They just launched an app called gist. Which has AI basically baked in throughout it. It takes a query and then on the fly assembles an Instagram like story that summarizes the, that topic or whatever you're looking for as an example.

And I think one of the real advantages that they have is that they have a corpus of the web. They have that crawl. Yeah. They're incredibly strong team, so they have annotations and references and stuff on it, to know what that crawl is about. They have the power of ai. I think a lot of the challenge for [00:44:00] them will be around the product and positioning and like, how do you find the entry point and how do you find the wedge and what is the experience that wins and what is the audience and product that actually gets you there.

And I think that's it. Like from the outset. That's the journey that we were on. And that was the journey I would say, like Neevaa was on. Yeah. Was like, how do you find that? And it was like an incredibly hard thing. Like we all came in, like everyone would just be like, this is a really hard problem. This is a very hard problem.

But yeah it was baked in observing these like larger trends that The modality of search in Google was becoming outdated, that the business model was undermining the user experience and that technology was providing tailwinds to try and, provide something new and different. Getting, building up to there is not faster easy.

Building out your own crawl thing to go get the internet is not an easy feat. But at this point in time, they have all of those tools at their disposal and I think, it's just blue skies in front of them to take advantage of it. And how's Meebo gonna find a wedge?

We're [00:45:00] in the same bucket but less. Yeah. With scrap and hustle and with connecting with our users and building through empathy I would say. No, like in practice, like I said, there's two things I think about every day. Number one is how do we iterate and build a high quality product experience?

And I think the path to that is mostly, Getting data as much as possible and through Revs to understand how can we create, the most delightful chats and conversations possible. Yep. And I think number two is how do we bridge the gap while we're building that to get people on and trying it and using it who are partners to us and believe in the vision along the way.

Yes. That allow us to build up to there. And those are the two things that I think about most. And then everything else is just an input of like, how do you make that possible.

Bryan Davis: Avi, we like to finish our interviews with a recommendation. Yeah. So it could be a movie, a book, a poem, just something that you would like to share that's been like on of top of mind for you recently.

Avi Fein: This is [00:46:00] not a great recommendation. I'm sorry, but it's, it is both connected to what we've talked about and what you said before. I find a ton of inspiration from Westworld, from Westworld, season one and season two just only watch those and assume that, the rest is unnecessary .

I would say if you haven't seen it and you're interested in this area, I think they did a wonderful job exploring the topics that we've talked about. And the reason that is, is that they very much explored what does AI look like in a diverse world without a monolith. And they do that simultaneous, and they, it gets worse, like as they go on because they have this like Rehoboam, like they have this like central ai, which is like the overall seeing like, singularity.

But at the end of the day, what their take is that you have consciousness through individual, unique, diverse representations of ai. Like every it's like a chatbot, but every person is, there's no singularity. There is no oh John and Bryan and Avi we're all using the same [00:47:00] AI model, like behind the scenes.

And I think their exploration of that and how the second and order and third order side effects of that in many ways is wonderful. Is wonderful. Yeah. And I think it very much like to me, connects with what I find inspiring about, technology A and where we're at more than anything.

John McDonnell: Cool.

Bryan Davis: Thanks for being part of pioneer Park.

Avi Fein: Yeah, thank you for having me. This was a wonderful experience. Hopefully I didn't talk your ear off too much and it was somewhat interesting .

Bryan Davis: Just the right amount.

John McDonnell: It was perfect. Thank you.