Unlocking Your AI Potential: Earn, Own & Grow with ConsumerFi

Captions as YouTube publishes them, usually machine-generated, so product and people names can be misheard.

1. 0:00

   Hey everyone, thank you for being here today. Uh John, David, really excited to talk about ConsumerFi with you. Uh let's just jump right in. Uh John, do you want to share a little bit about who you are and then David, you and then we'll go from there. Yeah, I mean great great to be back.

2. 0:15

   Always love these high high energy uh conversations. Yeah, I'm John. I'm one of the core contributors at uh Consumer. Uh my my background for those of you that that that don't know me spent the early part of my career building uh like advertising technology that then powers like consumer applications and games. Uh then I went to the uh the light side some people call the dark side actually making consumer apps and gaming content.

3. 0:42

   Um and then some of you might be familiar with uh Playmber. Uh we launched that around five years ago. Really building at the intersection of uh gaming and and blockchain initially trying to figure out what this all means for just really engaging consumer uh applications. And you know fast forward to the kind of journey now uh it's been it's been a lot of fun. We now have around 800,000 like monthly active consumers that every day in return for

4. 1:11

   Bitcoin are doing quests, surveys, tasks, playing games. And that's really where actually consumer came from because behind the scenes there we built this engine to to power these uh games initially and now we're pointing that at fun engaging AI powered consumer experiences and applications. But we're going to dive into that a little a little bit more. But um yeah, it's been a it's been a really fun journey and um you know, like everyone says in crypto, we're still early and uh we're really excited for this next chapter.

5. 1:43

   Hey guys, I'm David. I've been uh building in the near ecosystem for yeah, right around five years full-time. Uh before that, I was in construction engineering, but been heavily involved with a lot of uh DeFi initiatives on near a lot of different AI things.

6. 1:57

   Brought a few different um consumer AI applications to market. I am the C uh chief product officer at Consumerfi. Joined the team in March and have been going strong ever since. So yeah, it's been it's been incredible um taking the kind of core consumer base of the Play Amber um ecosystem and helping to to bring them into more of the AI side of things and and leading the the product development to make that happen.

7. 2:22

   All right. Well, cool. That that's really cool. Thanks for setting the stage of like who you guys are. And I like that you also put in there the journey of like where the project was and where it is now. So let's just go with it. What is consumerfi? Just explain it to the people.

8. 2:35

   So at at the most the simple level um it really came from uh our our journey right of trying to figure out um there's a lot of people that are kind of crypto natives. But there was all this talk about onboarding the the masses and we just saw this jarring fit of acronyms, chains, wallets, infrastructure, um agent frameworks and we were like who's actually using this stuff, right?

9. 3:03

   So that's really where this all started. Initially, we're just trying to find um PMF, there's another acronym, product market fit with this technology, consumer content, gaming content, and then incentive alignment. Can we can we create something that fairly rewards people for their time and contribution versus in web two typically we're all getting monetized somewhere, right?

10. 3:26

    Someone is monetizing us, right? Whether it's, you know, ads or we're sharing our data for a credit card provider, you name it. Um so initially that's where that's really what caught a light in the the playmber business and then um so back in well early back middle of last year we had lots of AI companies reaching out to us and we thought okay that's interesting we we'll kind of take the calls and the thing

11. 3:49

    that they wanted from us was our our data and I was like well that's interesting what what do you want our data for you're an I company I'm looking at your website you've got all these agents and these big visions of what what your AI technology can do. Um but of course for these agents to be intelligent they need to be fed in real time rich consumer data sets and profiles and segments. And of course where where do you get that from developers that have got rich firstparty data from real humans that every day are

12. 4:21

    telling you things about themselves albeit in a very indirect way. They're just having fun in your games and experiences. So that's really where this all started because we then okay well there's we have the games at the application layer but our product actually is this social graph consumer graph underneath that's turning all these individual signals into more personalized experience in our games you know at most simplistic level um you

13. 4:45

    know ads you watch ads you don't watch but of course it's more sophisticated um than that. So really that's where it was born and then we started to thinking about okay we're already building at the intersection of um let's let's call it consumer crypto right consumers and uh blockchain and then AI is something that was embedded into our processes in our analytics in our user acquisition I put a tweet about this several times I love that I just get off a plane and our UA

14. 5:11

    tooling's just done its thing and we've got more users it's all optimized so big fan of AI we just started to think about one um how AI can make our consumer graph smarter, more more intelligent to create more personalized experiences. And then we also started to think then on the application level on the front end, which you know David will go into detail on is well what what are those more you know engaging consumer products and services that people will use. So in

15. 5:37

    summary it's it's two parts. It's it's experiences and and and content and applications. We say applications, web 3 says agents that we believe people will will use. Um and then the engine room behind that that make these things interesting, relevant and and personalized. So and the the final part is then making sure that this is all equitable that whatever part you are contributing to as as Jared, right? It's

16. 6:02

    not a one-sizefits-all. You you know you you may just happily be doing an experience and providing data in well that that's valuable. You might actually want to build your own application on our protocol. That's valuable. you might want to um use your own data that you're providing and build your own application, right, to grow whatever it is that your subject matter expertise is. So, the summary is is trying to tie

17. 6:27

    all of those three together. Um, and that's really the ethos of Consumer. Oh man, there's a lot to unpack there, but here's a couple thing like I think the idea that we are getting monetized and and it's sort of been like web 2's dirty secret that in that terms and conditions when you click it deep down in there they're like you give us everything and it's just been like you get to use the web for free. I like that

18. 6:48

    you guys are spinning that around because I think once people start to realize they have value it's just going to like grow because once you actually realize you can get value from it, it's like a no-brainer to do it. So, I think that's a really I really appreciate you guys is what you're going for, your north star in that. Let's dive into some product real quick. I'm a user. I show

19. 7:06

    up. Let's just the consumer product. How do you see it? What do you what's it for a user and stuff like that? Yeah. So, as a user or consumer in the AI economy, you want to um earn as much as you can for for your contributions.

20. 7:23

    Uh and to do that it's pretty um unique to to you as a person. Um the various inputs that you have, your reputation, your expertise, your knowledge, what you what your preferences are, all of those things feed into how you as an individual can earn the most and own your portion of the AI economy. And so to do that, you need to start providing those various inputs so that you can build that profile. And so we call it kind of contributing your your digital DNA to the consumer graph. And when you

21. 7:59

    join um consumerfi.ai, that's kind of precisely what you start doing. You start connecting your onchain wallets, your social media accounts, and then you start answering training questions and providing kind of more of the first party data. Um, we have a few other browser extensions, things rolling out where where you can just start really building out your consumer graph and showcasing the the value and the scale

22. 8:20

    of your intelligent capital. And when you do that, the different opportunities are uh they start to present themselves. Um, if you are a content creator with a a large audience, then you can start to um monetize that way and and start to interface with with users at at a much larger scale um by by building agents.

23. 8:41

    And if you are someone that that might just be kind of an average everyday user, um the the ways that you earn in the AI economy will be slightly different. You might just be comfortable with with providing some of those those data inputs and and monetizing that, getting getting rewarded for that. um starting to benefit from very hyperpersonalized experiences when you're interfacing with your favorite content creators agent um and and also

24. 9:06

    different tasks based on but on your specific specialties. If you have um let's say you are someone that has a lot of uh experience in the medical fields, you might start to be able to answer different questions and and provide inputs to data sets like X-rays and things like that. But it it all um the direction and the trajectory is all based on what you're good at, what your background is, what your digital

25. 9:29

    reputation is. And when those components are defined, then those opportunities start to become um start to make themselves available to you. Okay, cool. So, and is there two sides of this uh product from consumers and like so you said there's data sets so consumers can answer data sets like I answered some questions. I thought it was really cool. I was like, "Oh, this is fun." And as your repetition's higher, I imagine that data set value.

26. 9:52

    Is there another side of like if a a company needs a data set, they can like also reach out to consumerfi and like then they can like actually set up like a data set to get answered like is there like a sort of a two-way that way?

27. 10:03

    Yeah. So that that's sort of one of the key components of the consumer graph. Uh and so it's kind of available in an SDK uh which is rolled out through the playmber ecosystem. And so you as as a company um will be able to start accessing that data and you as as a consumer with your individual consumer graph have full autonomy over how that data is used. So you you build that profile, you you bring all of that data

28. 10:29

    onto the consumer graph and you get to decide, hey, is this is this a company that I I want to share my data with? Um and and that package becomes monetizable to different companies. Um, but you you as the user, you as the consumer have the say on how and what that data is used for.

29. 10:45

    Okay. So, is this going to be something like um browser like your browser? You said you're going to have a plugin. Maybe you'll have like a you know a Chrome extension or something and it can actually like all your data you can get tracked on consumerfi. Is that the approach on this? I don't want to like go too deep into it. um for yeah for for the browser extension how how it's ruled out uh at least initially is simply you

30. 11:05

    drop in things that are interesting to you so it's it's it's completely you know optin you're you're finding something online that that you care about that you're interested in maybe you had a good LLM interaction you just want to copy paste that in that data is then stored on your consumer graph and and it's score it's scored and you're you're earning your compute credits based on kind of the quality of the data input but no it's not it's not tracking

31. 11:29

    uh anything. It's just you you are electing to provide things that are interesting or or relevant to you as a person. Okay, cool. I want that that that's interesting. Okay, so is it almost I I like trying to like uh visualize it. So, is it almost like I'm creating my consumer account, my my account, my consumer graph. Is that almost like a is my graph almost like a channel that I'm going to be building or gardening over time and making better and more valuable

32. 11:53

    over time thus um kind of increasing my user experience over time with consumerfi. Yeah. So I I think I think the way that we we we think for good about it and it's easy to understand at a at a consumer level is you know a bit a bit like in web two you have Experian and Equifax related to like the financial sector and you have your uh you have your credit score right and that's based

33. 12:15

    on your sort of financial history whether you pay your uh your bills on time etc. And based on the health of that score it gets you access to um you know certain rates or or discounts. When we when we think about that in web three, we try to look at it more holistically. Um, so we think about your you know your your social capital, your kind of network effects and influence, your reputational cap capital, you know, that's your kind of credibility and trustworthiness and then obviously you

34. 12:45

    know your financial capital i.e. you know your your your net worth. All of those individually of course have value, but to get that holistic value for you as uh as Jared is that's what we're trying to create is a a kind of a single immutable score that is owned by you and then off the basis of building and continually on a daily basis, you know, growing your score, validating your score. That's the that's the thing that has value. And to your previous

35. 13:18

    question, that's where the sort of B2B opportunities come in where, you know, brands, businesses, etc., all of these sort of web two financial products and services come on chain, you know, that's the thing that they can then start querying and and actually decide how to more accurately value you because they can they can see everything that you do.

36. 13:36

    or you know to the earlier conversation it's then about well how how can I productize that as a sort of intelligent score and feed that into um you know services or um you know agents or or workflows uh you know or tools that can continue to grow you know my my capital we can dive into some of those things that we're we're building. So that's initially what we're asking people to do

37. 14:00

    is basically come in and start um on the one hand verifying um you know things that you own you know integrate we just announced a partnership with eth ethos so integrate your ethos score for example start to build that single holistic view of you and then we will provide like the platform and ecosystem for you to whatever you choose to do to start you know monetizing your capital

38. 14:25

    your knowledge and your your expertise and um sort of an example of how that could look. Um there's the score and then there's the all of the data that that feeds into that score. And the data that feeds into that score is what um helps deliver those really personalized uh consumer experiences. And so onchain, if there is a yield optimization agent or or a trading agent, if I as a as a builder um kind of take my knowledge within DeFi and and build a a a workflow

39. 14:56

    that optimizes yields, that's great. But then with my consumer graph and all of those my my onchain histories, my my social media posts, what I'm who I'm following, what I'm interested in, that's able to guide exactly how that agent transacts. It's going to know my risk profile. It's going to know my goals, my time horizons, what sort of things I I want to invest in, where I'm comfortable, and so you're you're

40. 15:21

    layering in that that additional layer of personalization. So an agent builder, anyone that that wants to deploy an agent immediately gets rich deep context for every individual user. And so, yeah, for onchain use cases, it it's really important for how you trade, how you size, what you what you invest in, but it also applies to how you create content and um lots of other things. How you how you interface with with an agent and and a creator that you're interested in talking with via their their AI avatar, things like that.

41. 15:51

    Okay, cool. So, I'm going to kind of repeat back where I'm feel like I'm I'm understanding this just to make sure I understand that so people are understand. So you've got this platform consumer fee and that's where you can sign up or you you you set up an account and it creates your your graph right and then that is where you can like get reputation and all the things you talked about. So that's sort of level one. Then in this next section just what I'm

42. 16:15

    pulling there's AI agents involved. So let's maybe like separate those two because I don't want them to like I I do they mix or like where do the AI agents come into play? And then that'll then bring us to like I totally make sense why you guys are in the near ecosystem building with this because of this kind of stuff. But let's yeah the the agent layer of consumerfi is built on top of the consumer graph.

43. 16:36

    So yeah so that that's how it works. the the user, everyone that's contributing to their consumer graph, then when they utilize agents through the consumerfi ecosystem, um they're getting that consumer graph context applied to any agent workflow that's built through the consumerfi ecosystem. So am I like so I I could make an agent I could I could build an a so I've got my consumer graph. I've got my like high reputation

44. 17:01

    score. I'm an expert in say marketing or content. I could create an agent where I sort of give it several verticals of like all my knowledge and then that can go out there and be used. Is that or did I precisely? So you you have the opportunity if if you want if you are a subject matter expert in in marketing to create your own AI agent, whether you want to create that for yourself and

45. 17:26

    have it tap into your consumer graph to do things on your behalf or if you want to make that available to to other people to use. And you can then monetize your expertise that way in in a much more scalable manner where people are able to interface with your agent, leverage the knowledge that you've inputed to that, but the end experience is hyperpersonalized because it's always queried through my consumer graph or through the individual end users

46. 17:50

    consumer graph if that makes I don't know if that No, this is this is why we're doing this. This is great. Like this is like really important because I think like I totally like we're in AI all the time. Like this is what we do. So, I'm totally seeing your approach and it's awesome.

47. 18:03

    It's like, okay, the data then the agents and then that can like work on your behalf and you're getting more importantly some sort of like rewards or value for all this stuff that you're putting into it. Now, that how that looks might be refined or grow or something over time, but that's that's really cool. So, then so we've got that.

48. 18:21

    So, why use blockchain? Why AI? I think this is more of like a highle question of the sense of like why is the timing right now? Like where does the AI fit? Why crypto? by near, right? Like there's a couple high level things there.

49. 18:35

    Yeah, I I I can I can cover a couple. I mean, there's there's a lot of things that are happening at the the the same time. So, some of it you've got um you know, well publicized I mean I mentioned earlier like Experian Equifax, you've got well publicized you know data breaches fairly sizable just ongoing multi-industry. Um when we think about you know this concept that we we've spoken about before we can dive into of intelligent property right and we think

50. 19:01

    about AI we've got some real question marks over you know we talk about data and and owning data but well what about ownership of my voice my images I how I laugh and how I smile who who owns that well I I should right um you've got multiple lawsuits against you know web 2ai companies at the moment from you know all angles from the entertainment industry from collective groups of individual so I think you've actually got some you know real um you know IP

51. 19:33

    human problems data issues here that are all starting to come together um and initially I think that's that's partly where we're excited is we think that people are just looking at AI actually as um the enemy right rather than obviously something that near our um in incredibly passionate and championing is sort of userowned AI, right? Um and that's what we mean by intelligent property. So at a sort of a human cultural level there's lots of these things um all coming to a head that we

52. 20:06

    think you know blockchain technology is is you know absolutely perfect for obviously we understand it in web 3 but if we think about taking this beyond just kind of crypto Twitter and web 3 to something that consumers can understand that's where we think the technology can actually help them understand the simple concept of actually um making sure that that through the use of blockchain technology that you can verify that this is this is Jared talking to me right um you know the one

53. 20:37

    of the tools David mentioned like AI Jared I know that's actually Jared talking to me not a deep fake and it's actually Jared sharing his marketing expertise with me and the sort of link to his podcast you've just shared it's it's that concept of trust so I think that's why um you know we're very excited with bringing this to market because all of this stuff is happening and people are you know rightly concerned about it but they're just not

54. 21:03

    sure sort of where to look and I think at a consumer level they they don't want to complicate themselves with the technology they just want to use the thing and something that they can trust whether it is you know something that they directly control or own or something they've contributed to but it's through a creator or you know subject matter expert that they also trust but they just know that it's verified that it's them. So I think

55. 21:27

    blockchain just brings that um that holistic element of you know verified um ownership and and trust um that just simply can't be questioned. And uh just I I guess to quickly build on that I think what's what's interesting to me is how crypto can solve a lot of the problems that exist within AI and AI can solve a lot of the problems that exist within crypto. So in AI, you have the ownership problem where

56. 21:55

    all of the value is being concentrated and distributed to these big trillion dollar companies um where really it's the individual users, the people that are are providing their their data. They're that are interfacing. They they're the ones that are are, you know, producing the raw materials for this value that's all going to OpenAI. Well, blockchain enables that to be owned by users. And there's a means to monetize that and and receive kind of a just

57. 22:26

    permissionless income and and um revenue from the value that you contribute to the AI economy. And then on the other side, on the crypto side, especially with consumer crypto, AI can abstract away the complexity of crypto. Um, and it's if you have uh the existing user experience, everything that we're all quite aware of in regards to the friction points that exist with with AI and various agents, you have people that have knowledge that have, you know, edge

58. 22:59

    um that can input their knowledge into DeFi agents or trading agents. Well, now me as a consumer, all I need to do is deposit some money, use the near technology that allows my um transactions to to be multi-chain, and um all I need to do is deposit some money and pick my favorite creators agent, my my favorite um DeFi K's agent, and let it do all of the work on my behalf. And that's AI abstracting away all of the complexity along with the near technology stack as well. So,

59. 23:32

    dude, go I I'm I'm I'm right with both of you here. This is awesome. Like this is because I think this is needed because like you said something that there's sort of this like humans, we're all going to get to be we're all getting farmed and whether we want to farm ourselves or let five billionaires farm us, it's kind of like there's no other option right now and you guys are creating an option which is like what

60. 23:53

    I've been like shouting hoping exists sooner than later. So really stoked what you guys are doing. Um that's awesome. So I think we can maybe take this into the next level which is like okay I see some things like are there some terms with the consumerfi that would help consumers like machine room or any of these things like within the product are there certain sort of like mini product verticals that we can talk about within

61. 24:15

    it that would help round out the story or are we pretty good? Um, yeah. I I and John, I don't know if you want to take the lead on this, but I would say kind of the the two main elements to understand with consumerfi is the consumer graph. And that is where I'm providing my digital DNA into a kind of portable thing that I own and control. And all of those inputs, my my social media presence, my onchain

62. 24:46

    history are going to fuel a profile that I can either monetize or I can use to enrich my consumer experiences and using that context to enrich different agent interactions and and consumer application interactions. So you have the consumerfi the thing that you are providing your information to your your data so that you can own and control that in a single place and then you have the agentic layer which is utilizing the

63. 25:16

    consumer graph to deliver those personalized agent experiences. And the agent lay the agentic layer is for people that are going to have more intelligent capital. People that have those strong reputations, those um the you know the followings, people um that have unique knowledge and skill set that they can apply to a vertical agent that people want to to utilize and and whether that is just you as your own you you your own use case or you at at scale

64. 25:47

    to to monetize your agents for an audience. Those are the two components that I think are are important to note. And um the the agentic layer comes with a lot of different kind of baseline tools and uh aims to make it very easy for anyone that's a subject matter expert to easily deploy uh their own vertical agent workflows. Yeah, sorry for the confusion, J.

65. 26:08

    Initially, I was trying to figure out what the what the question was and listening to David. I think the the bit to add on which I think also you're asking is the the different vertical use cases as well. Is that also what you were asking a little bit as well? one I really like how David just separated that like really explain those two sides because I think for anyone using this that's really the important part like okay and now the third the

66. 26:31

    vertical use cases I think we should go into now like what are some vertical use cases I I really like that so I think I mean we've mentioned this before sometimes I think web likes talking about agents the way we believe this is going to work in terms of consumers is um I put a tweet about the other day if you just sit around a dinner table and you talk to your friends and family about an agent and ask them what what that looked looks like you just hear funny things. You're like you talking about James Bond like

67. 26:55

    they so when we internally when we design these products initially like we've kind of proved this out on games at an application layer we just think about content. then this gets a lot easier because when you think about agents, you you go down this web three rabbit hole and and and you know, Dave has talked about some of the tools and services we're building for web 3. It's really important because there's, you know, a lot of very intelligent, smart people, liquidity there, but when we

68. 27:20

    think about consumers, that's the that's where everything's gone wrong before because we're trying to give them something that they don't understand or even recognize. When we think about agents, well, a couple of examples of that. um one that we can kind of share with you to overlay on this, but you know, it's what we're calling like your your AI, your digital twin, right? So, what does that look like as a consumer experience? Well, I'm literally seeing and hearing an AI version of myself or a

69. 27:44

    celebrity or a sports star or a life coach, whatever that is, right? They they're not going to think that's an agent. That's just, oh, that's cool. That's like an AI avatar of of someone. And then it's just like, well, what's the kind of BTOC experience? I'm I'm then engaging with my favorite person, sports star, celebrity, life coach. I'm interacting with them. I'm talking to them. I'm I'm tapping into their AI brain for their their knowledge. And

70. 28:10

    then you can use that on the on the B2B side as the as the owner of the avatar to do whatever you want to to further grow your fan base, monetize your audience. So when we think about it like that, it's actually pretty straightforward that anyone would would would understand the the advantage for you on the on the creator side is is relatively simple is that and we're having these conversations at the moment and that's where we like to build products from. We think this is interesting. We go and talk to um the people that I've just mentioned and ask

71. 28:43

    is this in interesting? How would you like to use it? And then we put that into a product sprint. So the things we heard back were like, "Okay, what you're telling us in terms of I get the tech, how would I use this?" Well, basically what you're doing is you're is you're unlocking and productizing uh time, right? Um if we take it a step level, you're making time limitless because you can now effectively engage your audience

72. 29:05

    while you're asleep. You can engage your audience while you're on a a plane. You can engage your audience while you're completely off cell coverage up a mountain. They can be engaging with your AI mind talking to you. I've just seen this this TV program. I saw you in it.

73. 29:20

    How did you do that thing? How did you do that stunt? I've just listened to your podcast and you mentioned um Ilia said this thing about the near AI stack. Can you tell me more about that? and and you can engage in conversation and then not only can you engage them but then they might hit a level where okay they've maxed out on credits or there is a thing that they really like your content or hey have you checked out my latest podcast or I've actually um

74. 29:45

    record an audio book on this click here to purchase it you can then structure an entire audience experience fan experience around that that simply wouldn't be possible without the the technology so I've covered I've covered a lot there but there there are lots of interesting vertical use cases when like David said you abstract all the jargon away and you just sit simply talk to people that you know have got these

75. 30:10

    large audiences and you give them a few examples and they start throwing things at us. So that's really how we've tried to build these products you know alongside and handinhand with subject matter experts and creators. So say someone's a registered dietitian and they see people in an office. Now I'm a person who needs to see this RD expert.

76. 30:31

    Where do I go? Is there is there an app? Is there another end to this that I'm not that we haven't talked about yet? Yeah. So that would be the agent app store. Um which is sort of the the interface for the agentic layer. And yeah, that's where where you are going to go and say, "Hey, I want to get my my nutritional tips, my my dietitionian trip tips from Jared, who's this renowned dietician." Yeah. And um what what's cool is, you know, I I can pay to have, you know, an

77. 31:00

    hour's worth of of interactions with with that agent, but instead of it just being a surface level, you know, basis of of tips and and um general guidance, it's exactly fine-tuned to to me as David, as the 250 pound guy that eats too much McDonald's. Um and and that's because my consumer graph, you know, has a lot of those key heristics. And so that that can be applied to any sort of um use case with an in agent where

78. 31:29

    you're you're delivering a fan experience or any sort of just professional that has has information. I'm going to be able to receive that information in a manner that is completely packaged to myself based on all of the inputs that I've provided to my consumer graph.

79. 31:45

    Whoa. Okay. So it could get to a point where my digital twin talks to their digital twin first. they kind of sort out ideas and then maybe if we needed to we can meet for a much more abbreviated amount of time or whatever sort of thing or we don't even ever have to meet and it just kind of my RD digital twin could see 10,000 people in the same hour.

80. 32:04

    Yeah. Precisely. And so the um we we utilize what's called agentic graph rig. So you're the how how an agent on consumerfi would be interacting with a user would actually that's actually the flow because it's the agent is traversing the graph and and understanding you know completely all of the relevant data points necessary to answer the the respective query. So, so yes, that that is actually happening when you're interfacing with with any agent and um the use cases are are you

81. 32:36

    know how how we're looking at them of course is content creation and then onchain workflows uh and and both of those based on the the consumer graph inputs that we have now which are you know social media uh web two accounts and then also primarily onchain accounts you're able to deliver personalized risk tolerance kind of positional information on the onchain side and then on the the content side, whether it's for content

82. 33:00

    that you're creating or or just receiving from a user. All of those those inputs are are very relevant in how you can deliver personalized um context and advice for for that user. Guys, my favorite part of any conversation is when that moment happens where I'm like, I get it and like it's so cool. So now I totally see the whole like suite that you guys are putting together for this product.

83. 33:22

    Yeah. And um I guess this how how we've been building product is is really testing in the market. So we have a few kind of key examples that that we can share. Um just last week we started rolling out the AI avatars. also been posting a bit on on X with my AI avatar and we we have different creators that will be doing the same kind of talking with how we can build pipelines for near

84. 33:46

    content and just start really uh what what is it channeling the fire hose or or concentrating the fire hose and focus the fire hose and and just blasting out near content with um different people's AI avatars. Uh but then more so on the there's text to video pipeline. We've seen great success with our um workflow there. I scaled a fresh account to like I think it's close to six million views

85. 34:11

    within the first two weeks across Instagram and Tik Tok reels. And there's a a pretty cool analysis on the different um user engagement, user comments and and using all of that that feedback on videos to then uh optimize how the next set of of AI generated video content is created. So, we've been very happy with how that's been received and the and the level of of interest and and just um viral outcomes that that's

86. 34:36

    produced. And then uh on the onchain side, uh our first big lift has been just kind of the the intense buildout. So we have um that coming out just kind of more of a manual swapping interface at first, but on the on the back end what we're doing is laying the groundwork so that intents can power a wide v wide range of of various agentic workflows from kind of just the standard

87. 34:57

    dollar cost averaging, price-based buying to more uh advanced event driven things, sentiment driven things. And so, um, yeah, just building out tons of really robust data pipelines, processing pipelines, and at the same time, all of these little components that that go into the various workflows are are are quite modular and and designed so that the agentic layer is available for any subject matter expert to take any of these little Legos and and build their

88. 35:24

    own workflows and and apply their expertise to a pre-made set of of components that can easily be made into agentic experiences. my mind's spinning with ideas that I want to start using it for, which is always cool. Yeah, we gota we gota chat. That's the that's the that's the thing is we that's why this conversation is great once you distill it down and people get it particularly this AI avatar like we're

89. 35:45

    very lucky to have um you know a diverse range of investors and everyone has got a slightly different use case which is that's the whole point is if the technology works you should be able to immediately apply it to like you your brain was going to you know like the clinician point of view another one of our um investors has got like a a media property so their idea was okay well this is genius I can spin

90. 36:08

    a bunch of virtual avatar avatars for like the vertical my media properties in on Instagram and Tik Tok and bootstrap a whole load of top offunnel awareness pointed at this product and then as David's explained it they're all effectively training each other feeding off each other learning what everyone's engaging with and you've just got this automated machine so then while you're sleeping you've just basically just

91. 36:31

    bootstrapped an audience acquisition strategy that you probably used to spend thousands of dollars on agencies on creatives, on analytics, etc. Um, so that's the thing that once once people see AI David and we're going to start releasing AI John soon, people just get it. They're like, "Okay, great. Can I do this with it?"

92. 36:48

    So, um, I think the challenge is at this stage is not, you know, like we're doing here. I mean, there's so much it could do. It's just the more the more simple digestible use cases and the more people actually just see and hear things like this and we strip away, you know, like that's why the near technology is great.

93. 37:04

    Once you strip away all the complexity and you can see the thing, the outcome of the thing, this just gets real easy and then it's just a question of, well, how fast, you know, how fast and efficiently can I make this thing work?

94. 37:16

    Absolutely. Yeah. And and I'm starting to see also your history with like gaming and stuff is going to be so crucial for this because you're going to make you're you're you're focusing at it from a consumer point of view and you've always been that way. We've talked a couple times in the past and you've always been very aware of like the consumer which is really refreshing and um so that'll be cool.

95. 37:34

    Yeah, I I think that's I think that's still where I mean even now I feel like even I don't know what cycle we're in now but we're still talking about my god mass adoption and this stuff and it's just just stop just you you've e you've either got numbers that prove it or you don't. If you're still talking about it then you may be a bit late. So yeah, for us it's more about again that's why abstraction you don't need to see it. They just want to

96. 38:00

    use this product. I want to use this thing and I want to pay for it. So that's where gamification is is is is crucial. Taking someone from free to premium to a subscription model. That's again at the core of all the products we're releasing. You know try this thing for free. That's how these credits then fit in. You know you've given us some contribution. you might want to use some of these credits on um you know your first you know 10 video recordings of AI

97. 38:24

    Jared that's great and then you hit that pinch point where oh no I really like this now so the the concept of paying 10 bucks a month for something you've already got proven value from is great rather than straight out the box oh no you can't even use this thing it's $10 it's like well well I'm just going to use this free product first and try it so just real simple stuff like that is again baked into I mean it's it's we try and hide it in the platform a little

98. 38:48

    bit, but um you know that's there and even the platform itself just gified things to make people come back each day and check what's my score you know what's my my score compared to someone else. It's all, you know, it's all part of, you know, the the the game of trying to keep people interested and um not not um feel like, you know, basically gamifying LLMs and gamifying AI training

99. 39:12

    without actually, you know, it should be fun. So that you know, if if we can't do it given our background, then um we we probably should just retire. But yeah, you're absolutely right. We're trying to deploy everything that we've learned in an understanding of just, you know, consumer mindset and psychology into just this next suite of products. Can you tell me a bit more about like why you chose Near? And I noticed that you

100. 39:34

     guys are doing Near Intense. Can you talk a little bit about that? So I think one of the reasons we we we picked Near is is total alignment on the vision. So Near's vision of like userowned agentpowered open web is really exactly one of the core principles that we've built on. You've got nearpowered agents, you know, hyper personalized uh multi- aent stacks.

101. 39:55

     You've got shade agents. You've got near AI uh running private inference and uh automating advanced on and offchain workflows. We've got onchain adoption. So, when we think about our gaming portfolio, we've got the in-game um BTC rewards that can be run on near BTC and also our our plan CFI token economy all leveraging your intents. And then you've got the composable identity layer. So

102. 40:19

     incentive aligned user owned digital identity with proven traction. So I mean really we um you know have got the full near stack um heavily embedded behind the scenes. But that's the great thing at a consumer level. They just get the thing that they are looking for out of it that hyperpersonalized engaging entertainment experience or service.

103. 40:43

     Yeah absolutely. So there's the the core technology itself, the um stakeholders that design the technology uh for an AI product. There's a lot of uh no-brainers there, right? Um Ilia and Co knew what they were doing when they developed the Near Protocol.

104. 41:01

     Uh and so kind of at its day zero state, it was designed, you know, to be a blockchain for AI. Uh and so that that alone there's a lot of um reason and you know we don't need to to dive into too many of the specifics there but more of the um true product components that are are built on top of near that have you know recently started to gain traction uh provide immense value for any sort of consumer crypto

105. 41:27

     application. Um and so when you're when you're looking at a user um accessing the best things that crypto has to offer you can't just look at one single blockchain. You need to look at every chain and how do you do that in a way that is consumer friendly. Well, uh you need to have unification and you need to have a single account that can transact transfer funds across near Salana, EVM.

106. 41:52

     It can do all of the things. It can use utilize the best web3 applications that exist no matter what blockchain there are and what what blockchain they're on. And so that's exactly what what Near enables. Uh, intense is a way to do that and and transfer funds across chains.

107. 42:07

     Uh, shade agents are a way to um, basically have a contract that you're depositing funds into and allowing an LLM or an agent to to execute transactions in in a trustless um, non-custodial manner. And so that that's exactly what we needed. We needed a way for a user, someone that is uh in consumer crypto, that someone that's not deeply advanced in in web 3 to simply be able to get an account, deposit some funds from Coinbase and pick an agent

108. 42:38

     for their their favorite DeFi creator or their their favorite YouTube creator and and simply do the things that they they see them do, you know, all of these different trading schemes and and things that that they could only dream about doing. Now, all of that's possible by just depositing some USDC into your wallet and letting Near Intense, letting Chain Abstraction execute those those

109. 43:01

     trades on Hyperlid, the yield optimization strategies across whatever um DeFi protocol has the best lending or borrowing yields at that time. And and that's exactly what Near enables in regards to just one account transferring, executing on any chain, on any application. And um yeah, there there's lots of other um supplementary upside, but I think immediately that is why why we're so excited about how we're building on Near right now. Man, it's great chatting with you, man.

110. 43:32

     You just really you just succinctly described like all of Near's strengths, I think, in that like little run right there. Absolutely. The account model, like all of it makes so much sense. Okay, so now that we got a really good understanding of what ConsumerFi is, let's talk about how people listening can get involved. what are some next steps? What are you guys doing? Things like that. And then I think you even have a a TGE coming up that we can like briefly talk about.

111. 43:55

     Yeah. So, at at the moment, uh we have um we had to be a little bit sensitive to kind of scale up um in terms of letting people in just to stress test a few things. So, we have kind of invitation only at the moment. We'll we'll share some codes and we'll kind of pin it to the the tweet and the content going out so people can uh have access.

112. 44:13

     Um but that's where we're at at the moment. So um we're we have users that have uh been coming in beta testing some of the sort of product stress testing some of the technology and some of the workflows for us and we're starting to announce we're trying to do one a week at the moment um announce some partnerships. So each of these partnerships in some way is enriching that consumer graph like they're bringing an interesting uh data set in that makes um that makes the data sets

113. 44:39

     more interesting but for you as an individual there's that additional thing that you can verify. So the most recent one was Ethos. You know, it's got a lot of great traction in web three. A lot of people spent a lot of time on there building up their web three reputation score. So it was, you know, and and Serpentin is an incredible um founder and vibe coder. So there was just a lot of alignment. So we're rolling um those

114. 45:01

     out as well. and um also starting to get out, you know, a lot more content and start telling the story of, you know, the bigger vision, but also, you know, as you alluded to earlier, sort of phase one and then what's coming in um phase two, so people understand what I'm doing in phase one, why I'm doing it, and and how I might be able to use that um you know, that that personal level of intelligence and knowledge and capital

115. 45:24

     um very soon. Um so that's that's really where we're at um at right now. Um, of course you can follow us on uh on on Twitter. Um, very soon you might be able to follow us on other channels with our own suite of like virtual uh consumer agents as well that you can engage with.

116. 45:43

     Some of those under wraps at the moment. So if you find one of them, maybe we'll give you some bonus points. Um, and then yeah, we are obviously on a path of um, you know, it's definitely not the end goal. The next step in our journey that we've been planning for for a while is um you know bringing the uh CFI token to market um in in due course. Can you repeat back in under one minute what consumer is? Me?

117. 46:09

     Yeah. You Yeah. Okay. So here's how I think consumer is. Right. So consumer to me is Okay. It's a platform, right? It's kind of a multi-stage platform. The the first stage is a place where I can actually create my graph, right? like it's my it's my place where I can upload my my reputation, my knowledge, my subject matter expert and things like that. So that's sort of the step one and I can connect wallets and get like it's

118. 46:34

     kind of my reputation score in web3 score. So that's the first step. Then there's the next there's a layer on top of that where I can actually leverage that graph and create an AI agent or maybe multiple ones. I don't know yet, but an AI agent where I can focus my knowledge and have that agent do actions on my behalf using my actual knowledge that I own. Now, that those two things right there are already just awesome. There's a third layer that you guys are there's a there's an aspect there of making profit from my knowledge

119. 47:04

     and data which is already happening in web two and someone's getting that profit and they all have yachts and billionaires but instead I can actually get some value for who I am my data my knowledge and stuff like that especially in an AI world it's going to be huge and then lastly the part on the consumer fi app is that from a other user side there's going to be an AI agent marketplace where people can go find

120. 47:27

     agents I imagine I can share my agent, someone can find it there. Things like that where my audience can easily find and interact with my agent to various degrees. That's kind of what consumer is to me. Like, how how'd I do? That's great. Can we can we get that clipped, please?

121. 47:44

     Yeah, I'll I'll totally clip it for you guys. Yeah. Yeah, it's great. That's the thing. It's like that's just the point we're trying to get to. So, that that's great. Cool. Rather than us saying it's like here's what Jared thinks it is. Done.

122. 47:55

     Done. And well, thank you guys so much for being here today and thank you for taking the time and really looking forward to everything that consumerfi brings into the whole ecosystem. Thank you guys. Yeah, thanks for having us. I really enjoyed that.