Virtual NEAR Day Live

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1. 0:00

   more money out of the trade or if you're putting liquidity there's always a way to withdraw protocols and you know as you go down the line. So that's kind of the fundamental setup and yeah I mean Alex been working kind of for the past year uh to develop a bunch of stuff on this side so I'll pass it to him.

2. 0:22

   Yeah. Yeah. So please. So one thing uh before before I talk about what we've been building I I guess it's uh it's worth defining a little more what forification is because on the recent news like probably everybody have heard that entropy have has formalized uh the last theorem uh and reading hacker news comments or like Twitter comments it's pretty clear that people are not quite uh they don't quite grasp what for notification means right so So like for

3. 0:57

   example entropic says hey our proof is 13 million lines of code uh right and uh you know like have someone looked at those 13 million lines of code does anyone need to look at them etc right so so I think let's use that example very quickly to understand what it is right so so formal theorem says that for no numbers uh you know a a a a cubed plus b cubed is never equal to cubed something like this right and so in that in that example right we have the statement of the theorem that says hey it is the case or rather hey

4. 1:29

   this is what we want to prove right and that's one line of code right and then we have 13 million lines of code of the proof right and then you if you feed them into lean lean is the tool everybody uses these days or or like it's it's one of the biggest tools people use uh the lyn says hey the theorem is correct right so I think what's important to understand here is that you do need to trust that one line of code right so so if someone misspelled this line of code you know

5. 1:53

   like they use like for example You know like a very simple example they can forget to specify that a you know like the two numbers need to be greater than zero or thing like that right that would be an incorrect like you know a proof of a wrong thing is a useless thing right but if you do trust that one line and if you do trust that lean is correct uh then you do not need to trust the 13

6. 2:14

   million lines of code right those are uh machine checked uh and if you know if you trust the first line and the link says hey the proof has checked out you do not need to trust the 13 million lines of code they can be as sloppy as it gets you know they they can go all over the place but if it says it's true it is true right and so in the context of code right you can the now the flow

7. 2:37

   is you have a smart contract and the smart contract would be very large right like you can imagine it has like a lot of logic a lot of special cases a lot like maybe I don't know optimizations etc right and reading the code of a smart contract is relatively involved process and so we have auditors and you know we pay them a lot of money to read it right and Then you would have some statement uh that you really care about like as a liquidity provider for example imagine I'm providing liquidity to some

8. 3:01

   sort of unis swap v4 which is you know like unlike unis swap v1 is actually a relatively complex system the only thing I care about is if I deposit money right now at any point in the future I would be able to withdraw right and then then there's like you know there's some rule in terms of how much money I should be able to withdraw right and so then what you would do is you would have this statement that says hey uh if you deposit money as a user Then no matter

9. 3:25

   what happens to the contract you know like any like other users call any public interface is available at any point in the future you will be able to withdraw you know you know assuming you did not withdraw before that. Right. Right.

10. 3:37

    Right. And so and so you you put that statement in. Right. And so that statement is much shorter than the than the smart contract itself. Right. You read that that statement and then you need a proof that it is the case. Right?

11. 3:50

    And so until recently, until like I would say half a year ago, that proof was what was making it all impossible in practice because the proof like you know like if if it takes you like a week to write a smart contract, it would take you several years to prove that it is correct, right? [snorts] And so that was Go ahead. No, I was just going to ask is this this um this lean product that you're describing here, this kind of

12. 4:14

    consolidation of all of this kind of complex, you know, code is is is is what you're describing this this concept of lean. So, so lean is a tool that will verify the proof, right? So, so it's also a language in which you will express it, right? Yeah. And so, [snorts] until recently, writing those proofs was prohibitively expensive like like an example of FMA theorem, right? you would need to write 13 million lines of code.

13. 4:37

    As a matter of fact, there was a an effort that started in 2004 like multi-year effort with a huge team under Kevin Bazard and Imperial College of London, right? And you know like and then Antropic did it in 11 days, right? So so that is an example of how of like what changed, right? So today proving became solved and the reason it became solved is that because at the end of the proof it's either one or zero. It's an outstanding real environment and so every single model every single lab has

14. 5:04

    lean as an environment and so they became extremely good at proving lean right and so today proving is practically solved right like if you have a theorem uh like you know for my last theorem was proven if you were following this debacle of openi and the neier stokes theorem that was proven in lin as well it wasn't just like a paper it was an actual formal proof that they have it proven right and so now the only question is uh can we write down this statement in a way that we can trust it

15. 5:35

    right because the statement that you know like when you say hey I'm not going to lose money in in English it says easy to express in lin not so much right so you need to express it you need to read it you need to trust it right so now the frontier move there uh and the second question is how do you prove chiefly right so like there's a benchmark called putnam bench uh in which we recently announced uh we we solved it and we're on the first place right now uh and it

16. 5:59

    is uh sorry it's 250 times cheaper than the second best solution submission and the second best submission was like $20,000 right which is quite pricey and ours was 111 right so that is the biggest thing we're working towards is like you know how do we prove like like proving became feasible uh but you know it doesn't need to be just feasible it needs to be cheap right and so how do you do it cheaply that's what we've been working on right so like the the effectively we have three directions right now one is how do

17. 6:26

    you prove things very cheaply second one is how do formalize smart contracts in a way that you can trust the formalization uh you know and the third one uh is something I I mentioned at the beginning uh is that you also need to trust the LIN kernel and LIN was designed for people who are friends with each other to trust each other right like you're a mathematician you have a mathematician colleague you're sending proofs to each other you would never have incentive to

18. 6:51

    to trick each other right but now that you you know uh if I'm if I'm giving if I'm publishing a smart contract and I'm giving you a 13 million lines of proof that it is correct. I actually have a very strong incentive uh to lie to you.

19. 7:05

    Right? So at this point uh breaking link kernel becomes actually very desirable. Right? And so we're working on uh uh we're working on making sure that link kernel is correct, right? We're reducing it to a uh much simpler uh code base to much simpler for system. So actually Ilia I'd love just to like your general reaction to like what the Navier Stokes like you know this whole situation how it's

20. 7:34

    evolved obviously there's the privacy and the confidentiality of open AI potentially reading you know uh the chats of the researchers that were working on this problem but it sounds as if the frontier is no longer trying to solve the problem. it has been solved according to lean, but now people are questioning whether lean is effective and airtight in expressing these proofs.

21. 7:58

    Is that kind of the right way to think about like where this problem stands right now? I realize this is a little bit of a side quest, but it feels relevant to formal verification. So, I'm just curious like what is your general reaction both on the privacy element of this, but then also where Navier Stokes stands right now?

22. 8:14

    Yeah, I mean I from my perspective it's it's the best highlight of everything we've been saying for past three years on the privacy side, right? I think it's a pretty critical that the AI belongs to you, not to the company that provides you the service, right? And this example is just kind of you know yet another example in in a string of kind of recent uh problems. I think the poller was the one who kind of

23. 8:48

    introduced this idea that like hey the companies are effectively taking your data and you know turning that into their own products and there's been a lot of rumors that even if you're like asking to not train on your data they would create synthetic data out of it and then train on that. Uh and I mean obviously it's rumors so like there's no clear evidence but this example is effectively kind of confirming that like hey guys you know they effectively like

24. 9:20

    took something that had like an insight around how to do it and and and kind of expanded that right by spending a ton of compute to uh to finish a proof. I think where on the broader side the challenge as Alex mentioned has been in formalization itself. So like whenever you have a problem right now it's like how do you know that it's a correctly stated problem in the first place to solve to prove and I mean Alex maybe has a better better kind of

25. 9:50

    understanding of where that stands uh latest yeah but there's no question that the proof that openai presented is correct I mean that that is that's what I wanted to convey at the beginning is that if lean says something is correct it is correct right with the uh discounting for the fact It could have a bug, but it's uh it's extremely unlikely that either thermal Ethereum or Navier Stokes uh actually used any bugs in Lynn,

26. 10:19

    right? And and also Lynn has two kernels. So, sort of like you know how Ethereum has two clients, Lin has two kernels and Ethereum is only correct if it passes both and they independently developed. Uh it is it's still not like if the model was malicious, it could potentially find two different bugs in both, right?

27. 10:37

    create like you know like like little u uh you know little hidden gems in its proof that that utilize both to prove it eventually right but it's uh yeah it's it's highly unlikely that this is the route it took right and luckily we can still inspect the reasoning right so hopefully both entropic and open looked at those and like you would see if it says hey I cannot prove it let me just find a zero day uh right you you you

28. 11:01

    would notice it yeah yeah so Alex one of the things that you were saying um you know kind of previously explaining the value of what you guys are doing with lean-in formal verification is just this drastic reduction in cost to basically formally verify and um get to the conclusion of verifiable truth you could say. And I actually had a really interesting podcast here a couple days ago with a with a fellow big-time investor who

29. 11:27

    spoke about SpaceX and how their cost to bring one kilogram of matter to space has gone from $20,000 previously, you know, prior to SpaceX to now down to about $100. So, you know, a massive massive massive reduction in cost. And then what is the result of this is this massive opening of a new TAM of if we can get a lot more matter into space, we can do a lot more up there. We can build

30. 11:55

    data centers up there. We can we can do anti-uclear, right? So there's this whole kind of like concept of and in any kind of market reduce the cost to you know achieve an outcome and then scale. And so when it comes to this by reducing the cost of formal verification by reducing the cost of this kind of proofs Ilia I'm curious from your perspective more from like the founder side and then Alex on the tech side what becomes possible right like what are you what what are you taking advantage of from

31. 12:21

    the near side due to this drastic reduction in cost um and and how do you kind of expand the market for what you guys are building? Yeah, I think so from a software engineering perspective, from kind of building products, the slow part is not anymore the building, it's validation, right? Like my agent can write, you know, millions of code overnight, but I will have no idea, right, if it's doing stuff correctly. And so the bottleneck effectively moved from writing code to

32. 12:55

    validation, verification, and relying on human reviews just doesn't work, right? you're effectively still using an agent to review it. And so that kind of gets into like, you know, AI reviewing AI, which we know uh doesn't always work as well. And so for formal verification as a framework allows us to uh shift from this human validation and you know AI validation to actually have a formal validation of what the code does, right?

33. 13:23

    And this is especially you know important when you're dealing with a lot of money, right? when you this is blockchain code, this is smart contracts, um you know intents, etc. or anything else that's mission critical, you want to have a validation of correctness of that code down to the properties you care about, right? So you the money you you know trading in and saying you expecting this outcome, right? It should come out come out on the other side. And again before this was both slow and expensive, right?

34. 13:57

    before like we would write a smart contract. we would you know give it to auditors maybe you know there's like few firms that were doing kind of manual formification and you know three months later they would give you an audit report right maybe you fix some stuff but like the development speed of of kind of this engineering was extremely slow and so the idea here is that you have something that's fast intelligent

35. 14:21

    and can plug in directly into your kind of continuous integration process where you can effectively verify that you know whatever change you did still uh proves down the properties that your users care about. And then from there, this should be expanded beyond just, you know, smart contracts, money, blockchain, but to things that, you know, we're really uh relying on that people don't think about, which is like

36. 14:47

    networking stack and, you know, other layers of kind of computing where we actually have a lot of zero day vulnerabilities that keep getting found, right? And uh I mean there was like I don't know if you remember like a year ago there's a vulnerability where somebody could text you a image in on iPhone and it like effectively root your root your iPhone and like steal your crypto right and [snorts] people were

37. 15:10

    targeted in crypto with that right so this type of things you know should be formally verified to ensure that this kind of things doesn't happen so but that requires you know like iPhone iOS code base right there's millions of lines of code so you need something that can actually like verify cheaply across large code bases and have broader and broader properties. But I think the the big kind of question mark

38. 15:36

    and this is still active research is how do we easier and easier now formalize and define what are the criteria that that needed to be proved, right? So beyond you know simple like swaps and kind of multiGs etc which are pretty simple to do uh like for something as complex as like you know no way to exfiltrate your private keys. Uh there would be a really nice property right that if your private key never leaves

39. 16:01

    your machine if you can just prove that for your operating system now you know at least you know it's not possible. So um so this is kind of things that you know will become possible now with this technology becoming cheaper and cheaper.

40. 16:15

    given that um you know you guys are sort of taking uh a pi like a leadership role in formal verification one of the few blockchains that has gone and done this so far. You guys are implementing this into Near and we anticipate that this is something that is going to fit into pretty much not only every facet of blockchains and crypto but also broadly software stacks you know outside of

41. 16:40

    crypto. Is there a chance that you know the work that you guys do internally at near you'll kind of create like a formal verification product and it won't really look like a blockchain product but it'll be something that maybe like I don't know Near Labs is going to add to its product stack and then you know you can you guys can kind of offer that to companies that are looking at increasing formal verification across their own product stack.

42. 17:07

    Yeah, potentially. I mean we have right now the neari the the agent that Alex built that effectively scorn and put bench it is under neari um it is open source people can run it and then yeah as we continue advancing this u it makes sense to productionize and kind of offer neari suite and then Alex just kind of uh closing out here on the kind of world that we're heading towards right with just kind of the we spoke spoke to E-mod, the exfounder of stability AI here on our AI

43. 17:41

    show with Ne and he basically described that we've we've now reached what he calls competent intelligence with AI, right? So, we're kind of at the point where it's it's getting to that point where it's pretty damn competent. Just talk to us about this future world of accelerating intelligence and why you feel that building these formal formal verification systems are so crucial to this future world that we're entering.

44. 18:07

    Yeah, that's that's a great question and I I think a very good way to think about it is like like I'm pretty sure everyone today who's who's using like cloud code or codeex, right, have experienced in many many times this feeling where you you build like you might still want to review code that it's writing, right? is just becoming it's producing so much code so much um uh yeah it creates so much that it's it becomes completely infeasible for us to review it in any meaningful way right so I think a very large number of people at

45. 18:38

    this point switch to the mode where either they like build with one review with another or just build with one and like yeah it's probably correct let me let me use it as is and uh and it's only going to be getting worse right yeah is getting better and better uh faster and faster right so we're going to be creating more and more uh uh uh artifacts with it, right? And I think formal verification here is extremely helpful because it can it it allows you to create a very large code base, a very large product, right? And then a

46. 19:10

    relatively small spec that you as a human actually is able to read, prove it, right? And then and then you can say, hey, hey, AI created for me this massive thing that I wanted, right? Right? It is way beyond my capability at this point to review it. Right? But I want I cared about this this and this property and here I have formal proofs that the thing it built does satisfy the properties, right? So I can use it, right? So that will allow us uh to to trust AI in building much more complex

47. 19:37

    systems that we can reasonably trust it today uh and still be certain uh that they are correct and uh effectively still be able to an extent inspect what is what is actually being created without without trusting it blindly. Awesome gentlemen. Great work and we appreciate your time. I want to take this last minute here to just introduce the Let's get to the full screen now, please. Um, we're going to do the rollup persp launch, guys. Um, we have now launching the roll-up persp.

48. 20:14

    So, right now, you can go to the rollup.ne.com. Yes, all you people in the chat who are bullish on Nearcat. Yes, all you people in the chat who are saying near is going to rip. You guys can trade, you guys can swap, you guys can do intense all on the rollup.

49. 20:33

    This is the initial launch of what we're doing. And so basically, it's super simple. You just come in here, you create you create an account. All rollup traders that sign up through our website keep 20% of their platform fees in near tokens. We are also going to be doing a giveaway that we will announce towards the end of the stream and after the stream. It's it's a near.com front end.

50. 20:57

    You come in, you click create account, it redirects you to near.com and then you're good to go. And so this is the initial iteration and we can come back to to us and close out here. And just to kind of give you guys our vision, the rollup has this media and distribution channel. We have this massive audience.

51. 21:15

    Near.com is, you know, and obviously they're a partner of the show and we appreciate Alex and Ilia. This is the one of the best onchain trading experiences I've ever used. We've traded I've been trading on it for months now. Per, swaps, transfers, cells, everything. It is so goddamn easy. Pass key. So easy. Any chain. You don't need to think about chains. You don't need to think about wallets. It's just all there. Think about gas as well.

52. 21:41

    Nothing. Yeah. It's just like I just want to get long or shorter. And so guys, we're going to be trading live on the show a ton more. We're going to be incentivizing the usage of this in the uh initial period. So I'm going to drop the link in the chat to all the channels. Guys, sign up. We're doing a thousand USDC giveaway to give this uh a big kickoff. And the roll-ups, pers, and intents are now live. Um Ilia, Alex, any closing thoughts on this?

53. 22:09

    Well, I mean, excited for you guys to launch this. Um I think everybody should roll up their sleeves and uh go use it. Uh but yeah, I think the the broader vision of like this unified liquidity that is private that is, you know, unites kind of AI and blockchain is life, right? And I think kind of the privacy is now the core thesis both for AI and for blockchain. I think privacy is freedom. Privacy is peace of mind as well. like when I you know do anything

54. 22:41

    on chain now through near.com I don't need to think about you know where this data will come out and similarly when I use you know nearai I don't need to think about you know like I actually got banned by open AAI for for some stuff I was doing on like doing like actually doing trying to test cyber uh security stuff of a system but like you know it saw them effectively trying to attack

55. 23:05

    right so they banned me so like this is where you need you know uh privacy because you can actually use stuff and not like double think of what's going to happen. So yeah, excited excited to partner with you guys. Excited to see this uh rolling out. Uh and yeah.

56. 23:21

    Yeah. Awesome. Thank you, Alex, for joining us as well. Thank you, Ilia. I think we're going to move on to our next panel here. I really appreciate your time. And just one last word for me is that you know this is kind of like the very beginning. It's like our V.01. We're already talking with different teams about launching rollup vaults, about expanding veneer AI, uh, with the Venice API capabilities into the site to kind of have more of like a, you know, robo advisor. Um, and

57. 23:49

    there's just more and more that we're trying to build into this into our own website as well and kind of bring this all together. You know, we think we think that the future of media and markets are going to go hand in hand. If Robbie and Andy have good ideas, they are long a position, short a position, they're, you know, there's a rumor that's buzzing, we we want people to take action on that. Um, and we want people to take action on this website on the rollup.nneer.com.

58. 24:14

    So guys, thanks for being a part of the show today and we got a long road ahead. So, uh, appreciate you guys being the first guests. Thanks so much, gents. Couldn't do without you guys. We'll see you again soon. All right, panel number one.

59. 24:31

    You're absolutely right, Andy. I mean, you know, the way that I kind of see this product is, you know, these uh these guests that we bring on, we couldn't do it without them. And you're absolutely right. Sometimes you and I are, you know, getting long. We're talking about our takes. We're talking about our thesis, but our thesis is, you know, it's this like broad synthes synthesis of all the guests that come on the show. Yep.

60. 24:53

    And so the guests have takes, too. And we want people that are watching and listening to our guests takes and our takes to have a product and access to act on those takes right alongside where they're ingesting those takes and they're listening to uh to our guest.

61. 25:10

    So, I'm excited. I think uh you know building out what a native place looks like for people to act on this information y um is going to give people a lot more uh a lot more opportunity to uh basically take action and uh you know I there's this like information slippage concept where like sure you might be taking in all this information and it happened just last weekend you sent me a chart you were like wow this thing looks great I was like yep I was away from my computer. I want to get long on Venice.

62. 25:45

    I wanted to take the action, but it wasn't easy enough for me to take. And that's what we're trying to do with this product is we're trying to make it easier for people to act on uh some of these ideas. So, yeah. Hello, chat. Hello, Godfather. Hello, G. Hello, Nearcat. Hello, N1. Hello, Miss Gun.

63. 26:02

    Hello, James. Carlo Verde, Gong, Nearcat, Char, We got a lot of people in the chat today. JDC, OCO, MX, Ariel, great to see everybody. Great to see you. Felix, great to see you. Rollup, Army, Shay, Nicholas, great to see you. Uh, Deneron, Ireina, everybody, good morning. Lil, Miss Gun, Joe Ho, Kevin Hawthorne, great to see all you guys in the chat. Let's keep this show rolling. I think we have

64. 26:27

    another amazing panel. Um, coming up next, got my run a show here in front of me. We got uh AI agents controlling money. We got Jeremy from the Near Foundation. Um, and Marco from MetaMask. Newly renamed. Newly renamed. So, uh, I think we've also Yeah. So, we we've got Marco and we've got Jeremy. Guys, welcome to the show.

65. 26:53

    You're live. Hi, guys. Happy to be here. Great to see you. Great to see you. AI agents are controlling money. I was just telling Ilia and um Alex that I spoke to E-mod from stability AI big AI guy who kind of got ousted from SF because I don't know you know whatever and it was you know it's kind of when AI was like crypto in 2017 back then and so what happened is you know we got on the show and he's like AI is reaching competent

66. 27:21

    intelligence and like this has happened the last couple of months and it's got to the point where you know it's just good enough to be like rival humans and you know we're at the era of surpassing human intelligence now and it's crazy to even say that and so when we're thinking about agents moving money you obviously want competent people moving your money or competent agents. So just maybe just like break us down here and I'd love just a quick

67. 27:48

    intro from each of you guys as to where you guys think we truly are at. Set the foundation for AI agents managing money. How realistic? How practical? just kind of give us the blunt truth and then we can build from there and would love a quick intro. Jeremy, you can start first.

68. 28:03

    For sure. Yeah, thanks you guys for having us today. Excited to be here. Name's Jeremy, go by it flow on Twitter and then at Near Foundation doing kind of market intelligence. So all things from research side, narrative building, storytelling, and then just like overall exploration. Marco, I'll let you introduce yourself then we can hop into the questions.

69. 28:23

    Marco, I've been entrepreneur in education then in web 3 now focused on AI. Um at MetaMask we have just built and released a couple of months ago the Metam mask agent wallet that enables agents to spend and trade autonomously. Beautiful. So maybe just give us a lay of the land gentlemen. Where are we in the era of a gentic finance? Where are we today?

70. 28:45

    Yeah, I can start us off and hand it off. So I think still super early days. Uh, we haven't really seen a full like autonomous agent loop that's able to spend safely with the proper permissions and like proper signing scheme, stuff like that yet. I think we're close. Uh, same thing just like general AI landscape. People are just starting to realize how powerful these tools can be.

71. 29:04

    And if you're already an above average user, you can do really spectacular things. And if you're relatively below average, you'll get up to that average. So these tools as we create them will kind of unlock a lot of just like economic activity hopefully and just make and enrich people's lives.

72. 29:23

    Very cool. Marco I would clearly divide uh payments from trading. Uh tradings is definitely more major. For payments you need demand and supply right demanded service of services for for agents and supply of services offered by by agents. uh and uh we are not there yet in terms of readiness mainly of the demand said that I'm super bullish about what's happening with X42 and MPPP uh I'm super

73. 29:48

    interesting um in the microp payments upgrade that X42 did a few month few months ago but if you look at the like overload number as a transaction in the last but compared to December unfortunately we're not there yet and actually some key is are going down and not up totally different stories for for trading for trading you don't need like it's like agents directly trading with the products that are already out there.

74. 30:12

    You don't need like a demand and supply. It's working. If you look at the like the number of uh hyperlquid SDK downloads over the last six months is just shocking. So the the direction is clearly more and more programmatic and uh we already have I think the tool to limit and that's what what we will discuss today uh agents in what they're trading and so it the the big challenge is all about like the intelligence of

75. 30:38

    agents in deciding what to trade and the willingness of the human to delegate some authority to those agents. Yeah. And it feels like we're starting to make breakthroughs on both of those fronts. Uh it feels like willingness is becoming a little bit more open. People are seemingly and I think they go hand in hand because the models are getting more intelligent. People realize that they are more intelligent and therefore are more willing to give them access and control over some source of funds. And

76. 31:09

    you know, most recently there's been two. Obviously, Astra came out from Chat GBT and OpenAI, but then also Grockbot. I've been seeing so much in in terms of the amount of trust and just pure, you know, admiration for these models.

77. 31:24

    People are trusting them more, realizing how intelligent they are. And from what I've seen, it seems like people are actually starting to make money. They set this thing. uh there's already the infrastructure built for these things to take trades, but now they're actually performing well.

78. 31:40

    H I mean, maybe you guys have seen some internal testing, maybe you guys have tried these things out. When you basically unleash an agent, you give it some money and then you set it free in the wild and you tell it to go take some trades. How much progress has been made?

79. 31:54

    Where were we maybe six months or a year ago in terms of profitability? And how much progress has been made up until now? Yeah, I think six months a year ago, we didn't really have like a the formalized word agents per se. Like you had people running bots which would basically just be like snipers going on Pump Fun now like Pon and all these places. Those are just kind of programs that were running and they were making money, but you kind

80. 32:20

    of just like set it once and it just does its thing. So that that was a very lowlevel agent. But now like with Grockbot and like other autonomous kind of trading agents and terminals, you're seeing kind of the proliferation of just set some guard rails and then it just kind of takes the intelligence and goes and just trades. And that's more like the edges you can get is like kind of with your harness and what like directives you give it, what guard rails

81. 32:43

    you give it. So current state now, I think we're definitely early stages, but people are starting to do cool things. People are starting to make a lot of money. So it's just do time and do experimentation to really get it a little bit more mainstream and that will probably come with kind of a formalization of some sort of interface that kind of shows the power of this and like being able to customize it.

82. 33:05

    Yeah. And I know Jeremy you guys are working towards that from the near foundation side right where it's signing permissions. Could you maybe just give us what the POV is from near on what agent permissions should look like, what signing permissions, what guardrail should like if you were just to, you know, give a really, you know, bird's eye view here of like what should be the baseline.

83. 33:27

    Yeah. So there's no like key product that's public available yet, but like in an ideal world, if we could ship it immediately, it would be something where you can kind of move the authority, not just like your assets. And a lot of that goes down to kind of Near's core design principles back in the day. Uh like like their key signing signatures and stuff like that. And like most recently we're just passing a new like governance proposal for top level accounts which

84. 33:52

    will right now it's more for like human readable and like humans. But soon it will be kind of like a gentic. So you'll be able to kind of delegate and scope authority out to different agents based off your top level account. You can kind of like en envision different threads of different agents being able to do different things and different functions whether that's for trading or even if you're like another foundation or a business if you want to have like a operations agent that can control all

85. 34:17

    operation spend within like that scoped permission guardrail and then it like doesn't really connect to your top level like authority wise so it can't take your top level money. So it's all about kind of like scoping the permissions to make sure you don't lose your private keys and that all the execution is safe and secure. And then obviously like near is an intentbased system. So that kind of gives you access to all the chains that we have supported which is a little

86. 34:40

    bit broader than kind of all these other like crosschain solutions. Yeah. And then Marco on the MetaMask side is is it a similar kind of view here as far as like I know you guys have some really cool X42 and API stuff happening where you guys can kind of let agents operate within some sort of you know guideline here. What's your perspective on this permissions guidelines guard rails kind of uh perspective here?

87. 35:05

    The kind of problem is not entirely new because delegated authority to other humans has always been around in web 3. So the basic technologies at the end of the day in terms of signing and protection are similar. You don't really want you want to divide the vaserp and the key that controls the asset with very scoped keys that can be used by a human or by an agent to do specific things. That's why MetaMask agent wallet

88. 35:27

    has two modalities. One is called guard mode and the other is called beast mode. They are managed by a trusted execution environment. So the agent can't access the assets directly and in in guard mode the agent can only uh operate on like whitel listed protocols and and protected tokens. On this mode uh there is no limitation on the universe of assets that the agents can trade on and the user can also set limits on like

89. 35:57

    specific like limits per day and the request in that case is is escalated to to the human. The needs are very diverse based on users to user I have to say. So there is it's very important to have an extreme granularity. So one of the like most common requests that we're receiving now is to have like custom guards where the the developer wants to trade only on hyperlid specific assets.

90. 36:20

    Yeah. All this to to achieve what getting back to your question on like the agent that you switch it on and it prints money. That's I think like the the wrong framing. The holy grail of like agent creative money where uh while we sleep.

91. 36:34

    I'm sorry. No. Yeah. I'm sorry. Keep it rolling. Keep it rolling. And um for for for economical reason of course people have been trying to create algorithms producing money for it. it just it's not the uh while uh if you speak to met MetaMask users the most common uh way to use AI and agents in trading is to get a second opinion and and to form conviction before doing the trade. So the the new flow is that I

92. 37:04

    hear about some alpha from something I read from a friend from like socials from TG and I use my agent to develop conviction of if is it really worth or not. Um and and finally I use the agent and the delegated authority to execute to do some automation and to work less.

93. 37:22

    For example, I decide that I want to play a grid strategy on an asset. I decide the boundaries of the two thresholds and then okay the agent can be autonomous in executing within that strategy. But people are still in control. People still want to think that they are special and that they have a better strategy than somebody else. And uh at the end of the day we feel that we are smarter than our agent in in in taking responsibilities in what we trade and that's fine.

94. 37:49

    Yeah. You know you know what's a like a element of aentic finance that has only recently popped up but I I don't think anyone really saw coming was that you know now like Muse just got released by Meta, right? And people are basically telling their Muse agents to go like submit all of these claims for unclaimed property and they're just finding like thousands of dollars that are just out there. And then, you know, also maybe

95. 38:16

    more notably, you know, this Navier Stokes problem we were just talking about during the last panel as well. Well, that's a millennium math problem. You know, they're going to end up receiving a million dollars from that, right? And so obviously they spent a lot more than a million dollars on compute and everything to ultimately you know receive that. But it feels as if there's kind of like these this peripheral edges

96. 38:40

    the e edges of the agentic economy where you can you know don't tell your agent to go trade directly. go tell it to like the most basic example is probably build a business, right? But then it's also like, you know, go try to find unclaimed property. Like go kind of to the edges where there is latent value and try to basically just fill those gaps. And I'm just curious if you guys have come

97. 39:05

    across any other use cases of this that are are kind of like, you know, they're making people money. They're part of the Agentic economy, but they aren't really what people expect when they think of the Agentic financial buildout because it's not directly in finance. It's kind of on the periphery. Is there any any use cases that you guys have found for agents to go out and kind of make money in these more unique ways?

98. 39:29

    Yeah, I I would say it's a really interesting point because it's like everyone has access to this. everyone has access to some level of compute and however much money you have can kind of correlate to the direct like output that you can get. So like when you're scoping for certain things like we can make a cool trading agent that can go trade shitcoins and like make maybe a couple m or you could take a lot of compute go

99. 39:51

    give it to some really cracked researchers and go have them find a new drug or something like that. So I think with kind of this the Savior Stokes problem that kind of is given another green light to the labs that hey we can put a lot of compute behind very specific industries that can create breakthrough technologies that have multi-billion dollar rewards. Yes, it might take a couple hundred million

100. 40:14

     dollars of compute, but it could just be like a shift of focus to those kind of like outside edges that weren't really possible until we had models this intelligent, right? It's kind of like a reconfiguration of the capex. Like, you know, there is speculation that a lot of these labs are going to become like biotech companies. And there was a tweet went pretty viral. It was after this

101. 40:37

     Navier Stokes thing. It's like, oh, you know, I heard there was a rumor Anthropic was pretty close to solving all forms of cancer, right? And they were kind of just they were like making a joke that now Open AI is going to go, you know, take all of their research and go solve cancer. Like that would be an incredible outcome for these AI models.

102. 40:57

     They will need to start inventing new diseases after that they solve them. Yeah. To to show them that they're bad. Yeah. I I think that um it's really a matter of arbitrage. Of course like agents are better than us in collecting signal and come up with opportunities that we couldn't have spotted but if we all have these tools this will be true for everybody right sure and same is for how much like intelligence I need to

103. 41:19

     discover that mathematical problem we are all discussing but there is like a lot of opportunity here for who wants to build this building now wants to be fast but the opportunity comes from the arbitrage of this not being yet in the hands of everybody or if you're creating a consumer app in making like this agents that aggregate signal uh faster more efficient now but I long term this like intelligence everywhere will be

104. 41:44

     like so distributed that all this like alpha will be like owned by everybody in a few years. Yeah. So Mark, I want to take that back to the top. What you said was this supply and demand problem or just situation if you will, where the the commerce side, let's say, of a gentic finance puzzle is far less developed than the trading side, right? Because I mean we've had quantitative trading down here in in Wall Street for like the last I mean what 30 years, right? Since the since the internet.

105. 42:15

     Um and so that's happened and now obviously you have like agents trading on chain. Like this is obvious, right? Cool. Great. But yeah, this commerce question based on what you're saying here, you know, just about kind of this equality of um you know, competence or intelligence leading to a kind of level playing field for for everybody who wants to kind of uh participate maybe in markets or in these kind of uh giveaways or or bounties. When you apply that

106. 42:43

     framework to the commerce side like is are you seeing any like spark you know that is going to light the supply and demand equation you know at a steeper slope of adoption or usage like we have level intelligence or sorry we have good intelligence every human has compute they can use it okay so then what so then we're good to go why are we not you know why is this commerce piece still kind of slow then the The main blocker is not the intelligence, it's the connectors

107. 43:15

     between the intelligence and us. Let's make a real example. I want on a Saturday morning to create an agent which is able just to book my restaurant for Saturday night. Incredibly easy, right? And I start with my army with my open claw and I start putting keys to make phone calls. I don't know if you've tried that but even if you're technical it takes like six hours and it doesn't work. It doesn't work smoothly, right?

108. 43:36

     So but it's the problem is not the model. the model I can solve like as we just discussed mathematical. So and this is changing every month and it's improving a lot. So how much it's easy for a consumer per for a consumer that has no technical background to create an agent that is able to book a hotel to to buy some sneakers. Let's start from the basics what people do. This is still not easy enough. So that's the main blocker to me. The second second um it's still

109. 44:08

     the cost. So if you do the math on like the impact on a consumer how much it costs to to do all this with tokens all day long it's they would like spend thousands of dollars of tokens that is not like so the velocity of token per second the cur velocity of token per second and the the cur of cost per token that is like very steep will improve a lot. I'm pretty bullish that in a few months uh both problems will be uh largely mitigated.

110. 44:36

     Yeah. Jeremy, anything to add there? Yeah, I think more maybe on the the trading side. I think we're seeing a rise in social trading with with FOMO and stuff like that. So, I think it's cool and it's a really cool paradigm, but I think we'll also see an increase in like spoofing as well because now you can put out kind of fake data. like we already see people aird dropping small even sometimes large amounts of tokens to some of the top traders and then that kind of like pumps up the chart and goes

111. 45:04

     back down. So we also need to come up with like certain types of guard rails to protect consumers and decision makers and agents as well because when you think about even like when claw code came out there used to be like you have to like you have to accept each change you want to make right and then they put oh now you can just auto accept all changes and basically remove every guardrail possible and now you just have some machine going crazy on your

112. 45:29

     machine. That's not the safest. So we need thoughtful design again. Uh, I know it's super easy to make software these days, but it's almost more important to take a lot of time and consider what you're designing, how people are going to use it, and like what are the edge cases of how people can then go and destroy it because now they can just prompt an agent to go try to destroy your software. And if you didn't give that thought, you're just kind of screwed. Yeah. Yeah.

113. 45:54

     Yeah. I mean there the implications of this feel like they're going to be farreaching both on the you know security side of things as well as you know the the social trading composability side of things. Um Marco just curious you know how you guys are approaching this from the wallet perspective given that you know this is kind of a new paradigm. You guys have released new products before. How do you balance innovation and security?

114. 46:19

     Yeah. And also just given the the the consensus news as well this week, it's uh I don't know if it affects anything the user doing. One thing is scoping the what the agent can do and another problem is defending the agent from doing things that are within the scope but shouldn't happen because like the counterpart convince the agent to do that. So these are two different problems. The the wallet

115. 46:47

     tackles the the former. How can you limit the scope of an agent? Security for metam mask is like number one priority always. It comes even before innovation. Uh it's at the end of the day with self- custody security of the asset is the most important thing. And the the the agent doesn't have access to the assets themselves that are in a TE.

116. 47:09

     All the access is always scoped with like two different levels as we were discussing. you can limit the access and I think that 7702 that has been released uh six months ago has really helped in general the entire industry because you can just pick any new way keep the key of AOA out of a like not in the agent so please don't give your agent your private key and then you can temporarily like evolve the EOA in a smart contract with all the custom dynamic that you want also using a smart contract and and that's it if you don't

117. 47:42

     like te because as we know like this is a general term that we use often but the security of the is really depends on which e architecture we we we are talking about. So this is the wallet part but then there is another part that other players are tackling which is how can I create an output guard that if the agent sends a request that is allowed from the wallet but is against what's happening

118. 48:06

     into the conversation. Let's say that in the system prompt that is written don't refund clients from Europe for some compliance reason the wallet doesn't read the conversation right it just receive the request so you need output gar that are deterministically on the hotspot in less than 100 millisecond able to read the conversation and stop the agent and that's like another important problem uh uh to to answer your question on on the consistent news

119. 48:34

     of this week so yeah uh the protocol those group of consensus that run all our like uh Ethereum efforts are not part of consensus anymore and has been spun out in a new company that is called consensus while the current consensus that so metam mask basically is renamed to to MetaMask. So it's a straightforward way to align to users with what like the brand that people uh have known and loved.

120. 49:04

     Yeah. Yeah. Awesome. Well, guys, this is uh as fascinating as as it is eerie. I mean, how about that guy from Anthropic the other day, crashing out on the timeline two months before his vesting cliff start or ended or whatever. So, not even trying to like, you know, vesting cliff ended, got rich, get out of here. Just before it even started or, you know, ended, he's just like, I'm out

121. 49:27

     and this is going to kill people. It's just like, whoa. Okay. I mean, the hell's going on? What the hell's going on in Silicon Valley, guys? too much acid. [snorts] It is scary, but it's good. I mean, every innovation comes with ups and downs, right? And people are afraid of change. We just have to make sure it's thoughtfully designed and people are thinking about it properly. We're all humans. We'll make errors, but hopefully we make more good decisions than bad ones.

122. 49:54

     I love it. Well, guys, looking forward to the future of the agentic finance world that we're entering that you guys are building out. I know we're a little past time. Thank you for your time, Jeremy and Marco. awesome session and I look forward to seeing what you guys can cook.

123. 50:08

     Have a good one. Bye, everybody. You too. Thanks, guys. All righty, Rob. The agents are coming. They're coming for your They're coming for your coins. Yeah. Well, we'll see. Um, you know, again, AI is going to kill people. Buy Zcash. Just feels pretty, you know, by near. It just feels pretty like, you know, there's a there's an anter for these things. No, just like the the AI industry aura I

124. 50:35

     think has been drastically um on a downhill spiral since Trump and the US government was like getting in to bed with open AI while simultaneously talking trash publicly about anthropic. It was like that was the kind of splitting point. And it was a couple months ago where it was like, "Oh, so there's these two companies going headtohead doing different things. The government clearly doesn't like one of them. Interesting. I wonder why." Well,

125. 51:11

     ideally because they're not giving the information to the government that OpenAI is. And OpenAI is like, "Yeah, great. The government's on our side. Let's pump it." And you know, we're going to IPO after these guys. We need all we all the liquidity that we can get. Sure, we'll give Trump 5%. Why not?

126. 51:27

     Let's turn this whole entire company into communism. Might as well, right? And then since then, I mean, it's just there's been a numerous things that have happened that have like completely calmed down the animal spirits, I think, in the industry. And so, oh, I think animal spirits are alive and well. I also think that anthropic, the reason that the government doesn't like anthropic is because how do you have an AI company? Like a frontier AI company

127. 51:57

     that is also a doomer like how does that work? How do you have a doomer AI frontier company? Like uh it's pretty nuts because these guys obviously are, you know, on the frontier. They're contributing their time, energy, and resources to building out like this sand god, but then they're fully aware of what the negative repercussions of this are.

128. 52:25

     And so, you know, it's a dangerous time for everyone. Um and so I I mean what I think we're gonna see is like yeah the government is taking um like this crony capitalism you know perspective and they are taking equity positions in these companies. Now what kind of bailout could get into effect if these companies you know are in a position where and and this is the concern is that yeah these these companies are making money handover fist. They've got so much revenue coming in to the tunes

129. 52:59

     of, you know, potentially hundred billion dollars. But that could be overshadowed by the level of expenses that these companies also have. I mean, the pure, you know, compute expenditures that these companies have on their books is gargantuan. Potentially, you know, very, very close, if not more than, you know, the revenue that they have coming in.

130. 53:24

     And right now we don't have what the cash flow story is from, you know, Anthropic and OpenAI. And so we don't know what the cash flow perspective is, whether these companies are profitable or not until they actually do IPO and we get, you know, a public form of those books and records.

131. 53:45

     And so I do think that we're going to get a government bailout of AI, uh, if it does come to that. You know, hopefully these guys are somewhat responsible, but I don't understand how they can justify building this thing out, spending their time every single day to build this sand god when they're very bearish and doomer about what the implications of their work are.

132. 54:12

     And I think I and I think the government realizes that and they realize that they might have to step in both financially and like ethically if it comes to that. I mean the bull thesis for us right is AI is exploding in value and in capex and so the government has to keep you know uh keep that afloat right and so what does that mean realistically it means that we have a life boat for fiat debasement assets right

133. 54:47

     because you keep increasing capex you keep spending ing on AI in the name of innovation, in the name of the disinflationary nature of the potential future that AI could bring. And what does that do? Well, it increases the money supply. It drives short-term inflation. The war drives short-term inflation, too.

134. 55:09

     And so yeah, you kind of get to this world where well, Bitcoin, gold, you know, you can even put ETH in there, Zcash, maybe, right? you start to this world where this trade powered by fueled by AI capex is leading to I mean it's going to be a print like co it's not going to be like co like they were doing what a hundred billion dollars of treasury buybacks a day or sorry a month you know Besson just did a

135. 55:49

     $6 billion one this month right but you can start to see where this is going because the politicians cannot admit that they over spent on AI. They cannot admit that they were wrong about the capabilities of this technology. And I think the technology is fantastic.

136. 56:06

     I mean, we use it every day. It's getting better by the minute truly. But it's just hard to it like we had such a crazy AI boom in the stock market and yeah, it's just kind of like times have changed. Times have changed. So guys, we've got our next panel coming up. Um, and it's going to be a good one. It's going to be a good one and I'm looking forward to it.

137. 56:40

     Um, it is going to be the privacy panel, Matt Henderson, Aaron, Igor, and Ebeck. And so, might as well fire it up and get this show started here because we've got the privacy panel coming up. Confidentiality becoming nonnegotiable.

138. 57:03

     Um, I mean, that's true. So, let's get this started and let's get these guys up. Confidentiality is becoming nonnegotiable. Amy Henderson of the fuseear.com. Aaron Houdini of Houdini Swap, Eigor Siddillesca of Infinex and Ebec of Unstoppable Wallet. Gentlemen, welcome to the privacy panel. Welcome to the confidentiality panel. Quick round of introductions before we get started and let's get it started. Matt, we'll start with you.

139. 57:33

     Hey. Yeah, my name it's great to be here. My name is Matt Henderson and I'm the uh product manager of uh Near.com. Awesome. Good to see you again, Matt. Aaron, hey guys. Jim, thanks for uh thanks for having us on. And Robbie, good to see you again, man. Our our days go way back, so uh maybe we'll crack into the lore later, but uh yeah, it's Aaron from Houdini. I'm the chief revenue officer over there. Um but yeah, pleasure to uh to be on today.

140. 58:01

     I love it. It's good to see you, Aaron. Eigor. Uh, hey guys. Uh, Igor from uh from Infinex. We run a a super app. Uh, I run Treasury there and commercial. And uh, hey everyone, good to be here. I'm IBECK and I'm a founder of Unstoppable Wallet. Uh, been in crypto since 2014.

141. 58:26

     Built a lot of stuff, but yeah, mostly that's the product we go by. Beautiful. Well, it is virtual near day. So, I'd be it would be a miss if I didn't start with you, Matt. Near.com just brought in privacy first, confidentiality first as a default on Near.com. Uh we are active power users.

142. 58:47

     We just also rolled out our rollup front end for Near.com. So, exciting day today. Maybe just walk us through kind of what that product feature stack uh roadmap looked like because you guys launched and it was kind of a toggle button, but now it's the default. talk us talk us through your perspective on why that is the priority and kind of what steps you had to go through to get there um just to kick us off here.

143. 59:12

     [clears throat] Sure. Well, when we first built uh Near.com, it was just built on um on near intents uh on the near blockchain and we wanted to build um a financial app for um for the people coming to blockchain. We wanted to uh to deliver the the benefits of blockchain to to everyday people. And um the first kind of uh way we we did that was by abstracting um most of the primary blockchains and and assets. So people go [clears throat] into their near.com account, they see

144. 59:49

     all of their assets in one place. And in the long run um we believe that um that the people who are per who are non-native uh non-crypto native people uh coming to near.com they will have an expectation of privacy um because that's that's um kind of their expectation to some extent in the in in where where they are. So we want to meet them there.

145. 1:00:13

     We want to meet them where they are. And so the organization began developing the the near confidential uh technology and uh as soon as that was available we began uh we immediately added it to near.com. Not all of the features that we have uh could run on near confidential intents at that point. Um and so we u over time started uh getting um all of the feature parities for the sort of the trans the transparent side

146. 1:00:45

     and the confidential side. And once we had pretty much everything running uh in confidential, we decided to uh basically disable the non-confidential side so that when people come to the to the product, they don't have to they can't really make a mistake. They can't uh they can't end up in Yeah. So that's how we got here.

147. 1:01:09

     Very cool. Awesome. My my question now is like what you know why why now like what is the biggest component or contributing factor to confidentiality and to privacy frankly catching the bid both psychologically from a narrative perspective it feels like privacy has the most momentum than it ever has and also you know obviously in in the in the financial markets as well but we're

148. 1:01:34

     seeing it in Zcash we're seeing it in confidential infrastructure Matt like you just described and I take it you know everyone else here that's building in the privacy segment has also seen an uptick in activity. And so that's that's kind of my question is generally what is the trend that you guys are seeing now?

149. 1:01:50

     How real is that uptick and why do you think it's happening right now? Open question to the rest of you guys if you want to start Aaron. Yeah sure I'd love to go ahead. Um yeah man I I think it's a I think it's just the nency of the industry to be honest Robbie like I I think as uh crypto becomes more sophisticated and uh really what I mean by that but like as individual retail and institutional adoption becomes larger and like the

150. 1:02:18

     metric for that being like you know more self-custodied assets as a percentage of overall net worth um privacy starts to become a real concern and a and a real value ad um for Wales. So like you know one anecdote towards that is our average transaction size at Houdini swap is somewhere to the tune of 3750 to to $4,000 right which is pretty significant compared to like a like a unis swap or a sushi swap or a pancake

151. 1:02:47

     swap which is just obviously just pure you know lowest cost fastest speed uh public routing right um and so I think it really just speaks to retail getting more and more um not only sific sophisticated, but as assets under, you know, personal management grow, I think privacy becomes a real concern, there's a lot of issues to just having, you know, public wallets with hundreds of

152. 1:03:13

     thousands, if not millions of dollars, uh, you know, within them. You become susceptible to social engineering, to to all sorts of fishing, to, you know, wallet poisoning. And I think privacy, uh, is really just merging into the category of of OPSSE. Um, I think it's really easy for crypto Twitter to to paint narratives on privacy around moneyaundering and terrorist financing and you know all the uh, you know, like the the kind of like the social clipping and you know those are the narratives

153. 1:03:42

     that that hit the hardest uh, from a views and and you know awareness perspective. But what privacy really is is is kind of in that category that that we spoke around around OBSC. you know, it's like VPN, Brave browser, you know, Houdini for transactions and, you know, cold storage. That's kind of what we call the the the crypto starter kit. Um, but it only it only resonates with people that, you know, are are sitting on serious net worth, and that's just uh that's just a product of time.

154. 1:04:11

     I I think it's like uh the issue with privacy is uh um people just never cared. It's not like it's been it's not like it's a new thing, you know, it's been around for forever. like they no one really cared. But the the the reason that exists uh now is so prevalent is because it's like the last bastion that we haven't we haven't conquered.

155. 1:04:32

     We have had uh everything from uh onchain exchanges through to uh NFT marketplaces, but we still don't have like a good privacy solution for Ethereum. And so now you're starting to like pick this up as people are starting to look for areas where they can make they can make money from their investments. And it's it's this kind of like a a you know crypto is a giant casino. It's a uh price gets narrative

156. 1:05:03

     price narrative. And so the more that something goes up people are like oh we should we should really take this seriously. And then the more people that get involved, they're like, "Oh, actually privacy is a thing and the real world just takes it for granted because it's there by default." And actually maybe we should focus on this. And then you start to you start to see like uh organizations like Near Intents create

157. 1:05:26

     privacy for any asset and you're like, "Oh, okay. Wow, this thing actually works." And then so you've got this kind of uh narrative building effect, but it's not like it's new. It's the it's the absence that the market just has nothing else left to build. And that's why that's why like, you know, for Zcash, they say it's it's the last it's the last 100x that we'll see in crypto that isn't like this random memecoin.

158. 1:05:50

     Uh, and that that's probably true because you you you don't have the opportunity to have like a new unis swap competitor that that will come and do 100x. Like, what are they going to do that unis swap hasn't done? like what will they do that doesn't exist on Salana already? Probably nothing. And so that's why you're uh you're attracting the you're attracting the people that are attracted to the price. And that that is the way that cryptos worked over time. Um you know it's really exciting.

159. 1:06:21

     You want to come in, you want to make money, you want to start learning about a new thing and then suddenly that is the narrative and the narrative has been formed. Yeah. Anyone else have something to add there about why now? Yeah, [clears throat] from my perspective um the kind how I um how how I view it is the the first level was the distinction between permissionless and permissioned and I think a lot of us or most of us um are

160. 1:06:50

     experiencing more and more friction in the in the financial system. Um we make bank transfers, they get stopped, we get called, we get asked for source of uh funds documentation. um we have to go through endless KYC's and then KYC's have to get repeated and then uh there's breaches every day of KYC companies and so I think most people um we in the crypto community kind of take it take this for for granted but we

161. 1:07:18

     begin to want to have a part of our life which is permissionless and over time it feels like that slice of life that's permissionless and and sort of self- sovereign begins to grow relative to the other side and this trend will probably continue into the future. So, as more and more people outside of crypto begin to experience the same thing and they come into the space, they're going to have this expectation for privacy. and

162. 1:07:43

     um I think is we're just fortunate that the technology is coming together now where we can deliver that that privacy um on any spectrum that they need whether from state level uh anonymity to something like what we offer at near.com which uh is compliant uh allows you to uh get an attestate an an attestation of your transactions and so um yeah we that's why um I think this is this is

163. 1:08:10

     happening today because that that slice of our life which we want to be per uh permissionless is is growing for everyone. Yeah, you're you're absolutely right. It's growing in market share and I think it's you know it it is resonating with more and more people as you know the surveillance state kind of continues to creep in. People are just thinking okay well you know if you know the the the you know people are always watching with their phones and the government's got

164. 1:08:35

     these things and they're out there you know they want to protect themselves. to want to protect their money and kind of shield it from prying eyes. Um, but at the same time, we've got this convergence on our hands and tradi and defi are converging right before our eyes. Like we've all seen the rise of these stock tokens. You know, you can point to Robin Hood, you can point to, you know, another chain, but the tokenized equities push is increasing.

165. 1:09:00

     Tra more and more trades coming onchain and getting tokenized for better, for worse. And so these are kind of bringing about what we're calling the compliant era of crypto. And so we've got these really weird competing forces because on one hand it feels like we're returning to our philosophical roots. We've got the cipher punk ethos that is returning. People are embracing privacy again.

166. 1:09:22

     Feels like that hasn't really been seen since, you know, 2013 to like 2015, 2016. But at the same time, we're getting all these compliant assets on chain that don't really mesh well or harmonize with, you know, the cippher punk ethos. These are compliant things.

167. 1:09:39

     They require like shareholder rights and and, you know, disclosures and compliance and all these things. They don't necessarily align or, you know, agree with one another. And so when you guys are building infrastructure, I mean, we've got, you know, all across the docket, right? Infinex, Houdini, Near, etc., Unstoppable. like how do we how do we reconcile these two forces when we're building infrastructure that

168. 1:10:02

     in theory is supposed to support both sides of this movement? Again, open question. Feel free to just jump in. Whatever you guys What do you give whatever you guys have? Yeah, I I mean I can go first here. Like you know, so like like I've been at Houdini since the start and you know, like I've helped build this tech from the ground up. So I think it's um you know from our perspective it really depends on on this the stakeholder right

169. 1:10:28

     like as a as a user of the platform you expect all the you know the things that we've already talked about permissionless you know non-custodial um you want safety you want reliability you want privacy you want price and speed aka execution right but as an owner and an operator which is what we are of a business you need to make sure that you're you know you're protecting uh yourselves, right? And like to, by

170. 1:10:53

     the way, to get reliability and trust and security and be around in crypto for four years, you you need to have some, you know, level of compliance that ensures that you can have staying power in crypto, right? Or else you just see a project, you know, come out and market themselves as the cheapest and the fastest. They put compliance on the back burner and then, you know, we don't see them in four months, right? So, that's how we've been able to to stick around

171. 1:11:16

     for four years. I think a lot of the um the lessons and the principles that we used when shipping privacy tech was what were the options available four years ago, right? It's like yes, you can go custody your assets at a centralized exchange and it's you know it's an off-chain order book, excuse me. So you get so you get privacy but you lose you lose things, right? You lose custody, you know, like you know you can you you

172. 1:11:40

     take on FTX risk, let's just call it that, right? Then you had the other side of privacy which was like the onchain cowboy pure libertarian decentralized mixers like the tornadoes, the coin joins, the wasabis and you know like retail loves it. Volumes go through the roof. You know tornado gets the Vitalic shill and and everybody's wag me wag me wag me. But then you know like Roman

173. 1:12:04

     Storm and the devs are are in front of the SEC fighting for their lives, right? So we try to kind of take the best of both worlds. So from a user perspective from Robbie using Houdini, you get all the aspects of you know what like self-custodying permissionless abstracted privacy. You're not jumping through a million hoops. You get the price and speed that you're comfortable with. But from an owner operating perspective, we know that we're screening, you know, all the wallets for

174. 1:12:36

     for AML and ATF and OFAC sanction list and just doing everything we can on the compliance side to ensure that, you know, Houdini doesn't doesn't file Chapter 11 in the next week because, you know, we're we're fighting in front of the SEC for our lives. So, it's just kind of like it depends on your perspective and and these these two forces are always going to be push pull, right? like the user wants more cowboy,

175. 1:12:59

     the operators want to feel, you know, that they can go to bed at night knowing that, you know, no illicit activity or criminal activity can go through their platform. So those two forces are always going to be, you know, opposing, but like look at, you know, look at any technology that's really cracked through and made it, you know, to the big leagues. Like if we were talking about getting in a stranger's vehicle, you

176. 1:13:23

     know, that wasn't KYC 10 years ago, you'd probably be like, "What's up with that?" You know, and that's just that's just Uber, right? You'd probably be like, "Oh, sports betting plus prediction markets plus cryptocurrencies. Like, that's a moneyaundering powerhouse." Like, guess what? That's that's just Poly Market partnering with CNN these days, right?

177. 1:13:43

     Like these these people that push the agenda, they almost and and that do it right. and then like you know are professional about it and act with like class and operational excellence are usually the biggest winners of the cycle, right? hyperlquid taking complex margin options trading you know high net worth derivatives accredited investor style stuff and just shipping it to retail into you know 10x levered up 10x you know short down on crypto assets and RWAS like they've simplified it um you

178. 1:14:15

     know like they're they're the biggest winners for the reason because they they kind of push the the tech and they push the compliance but in a in a way that makes it uh you know profitable and and just like a product that everyone can love and trust.

179. 1:14:31

     Yep. Anybody else have something to add there? Um, okay. Yeah, let let me try. So, I think there are no standards yet for compliance. I mean, if you look at uh I I mean, everyone does KYC now, right? or IML and it's in many cases it's just ridiculous to the to the extent it can go you know you have people complaining that they're not able like to get their funds out from the exchange despite like

180. 1:15:03

     giving everything they could so and the problem is that that operators like like if it's exchange or some other organization they are and the compliance itself they are kind of separate you know like in traditional finance uh you'll often like have a situation where a compliance department is actually independent from the from the bank itself. So and they get to decide like uh what will really happen to the to the to the case that specific case.

181. 1:15:34

     So uh going back to crypto, we are right now at the stage where everyone tries to maintain the status quo of making money if you are making money some one way or another by providing by trying to connect traditional finance with crypto, right? So you you you still want to keep that money flowing but at the other hand you need to stay compliant and depending on like the services you provide you

182. 1:15:59

     there are just a you you pretty much have to do a lot of things. I mean if you are like serving European users, you need to like get Micah license. H and I I'm guessing like the the list is really large. I mean depending on on your area of uh of of your business. So uh so yeah, the problem is there are no standards yet. So and everyone is trying to uh just like stay compliant by uh

183. 1:16:31

     yeah by by just trying to uh uh integrate whatever like uh IML or KYC solutions that are out there. So that is one thing. Secondly, the crypto itself right now is not um is fragmented and in my opinion we have like three types of people. We have OG's people who came to crypto for the self self-s sovereignty right and there are like were a lot of us I'm guessing prior 2017 and then we have digents you know who are like trying to make money from from the meme tokens and the new generation of

184. 1:17:11

     meme tokens are tokenist stocks which are kind of getting popular right now and then we have people who are uh kind of trying to actually trying to make uh traditional finance and crypto work and I think near is a good example of that.

185. 1:17:26

     So uh so the point I'm trying to make uh the standards are going to emerge and right now we're like in this wild west where everyone is try is first trying something and later finds out whether uh is that business model is actually sustainable or not uh or whether like uh yeah they need to wind down or just introduce some draconian uh KYCML measures you Yeah.

186. 1:17:57

     Can I can I just play devil's advocate here? Like like the one of the problems that we have in crypto is where we're exceptionally good at creating products that people don't want. And this is like it scares me a little bit because this is a this is a one of those things uh that we we think that we really need where everyone needs to be compliant and everyone needs to do KYC and ML and then we'll build a solution for it. and we're like great we have this fantastic product and there are no users because

187. 1:18:28

     actually the demand is through the floor and so I I think you you need the demand side to come in in a really aggressive way uh before you have like a a genuine breakout product and a solution and it might be you know the the guys at NIA I'm I'm sure that Ilia will come out with a uh a ZK passport or an encrypted passport that you can you can do a a chain signature or a proof to say yes, this is my account or my transaction or this is this is my KYC and here is the

188. 1:19:00

     you know here's the hash and you'll have someone like Infinex or or or another wallet um just integrate those and you can you can in a private and secure way just attach one to the other um but I'm telling you if we if we create that now and we're like guys we have the compliance [clears throat] solution no one will care no one will care and it won't be used and it will just fade away into the distance. You really need the

189. 1:19:26

     demand side to be like pushing against the door in order for something to break through. Well, I just I just don't think we have it. Like, correct me if I'm wrong, but I just don't think that we're like so desperate for everyone to to be compliant and KYC that we just we need a product in this space. Hm.

190. 1:19:49

     And so you think that a as we continue even despite like uh kind of the necessary compliance metrics that we have coming from like Tradf getting onchain, they're they they won't really proliferate. They won't really stick because the demand isn't there.

191. 1:20:05

     Yeah. They'll just they'll just do what they've done before and they'll just they'll have their own avenues. They'll they'll use an exchange or they'll use a proxy or they'll use a market maker or an OTC. they they just if we think that you the only thing stopping the the large players from coming on chain is the fact that there's no there's no compliance or KYC um that's just not it's just not a barrier like if you are a if you are a big uh big

192. 1:20:32

     financial hub big multinational if you're if you want to get on chain in any way shape or form there are plenty of ways that you can do it it's just uh it's just not it's not really a barrier I just don't see that like that that uh that's the thing that's stopping them, the fact that there's no there's no KYC.

193. 1:20:48

     Like there are plenty of avenues to buy tokens. There are plenty of avenues to trade. You've got Koshi if you want to do any any sort of sized market making for for compliant KYC purp. Uh you you can absolutely get on Hyperlquid. There there are so many market makers that will that will execute your orders and your orders will be perfect. Like it just doesn't exist. I I think that I think that we we need to be a little bit

194. 1:21:13

     careful in trying to convince ourselves that like this is a you know this is one of the this is one of the hurdles that we need to get over if it's not if it's not a real hurdle because otherwise then we'll just create product for product's sake and then we'll uh we'll we'll create it and wait for the wait for the institutions to come and then they'll they'll never end up coming. We're like oh what happened? We're so compliant these days. Yeah.

195. 1:21:36

     Well J I think we're a bit over time here. Um, lots to kind of mull on. Appreciate each and every one of you kind of just, you know, sharing insights and thoughts. Um, yeah, a lot of a lot of weird forces kind of coming together at once as you as you've just described there. And, um, agreed on the kind of product for products sake in the past, but it does feel like we have some

196. 1:21:58

     really strong exports of the industry now to be proud of and, you know, kind of stand on and build around. So I think with that in mind, I think that's kind of like where a lot of investors are looking to allocate capital and I think entrepreneurs are looking to build products around kind of, you know, tokenization, stable coins, per prediction markets, vaults, you know, just general exports of things that we know are going to work that we know are

197. 1:22:21

     working. Um, and so that's how I'm thinking about it. But guys, thank you for your time. Um, thanks for being a part of the virtual near day and hope you guys have a great rest of your day. appreciate you spending of it spending some of it with us. Appreciate it. Thanks, guys. Thanks, guys.

198. 1:22:37

     Guys, take care. Cheers. All righty, Rob. Near day continues. We're on. We're on. We got a uh private an AI meets privacy panel. Couple days after Venice ripped face near Venice duo, the dynamic duo for the AI crypto convergence.

199. 1:23:04

     And yeah, I mean about halfway through here, guys. Um, such good dense conversations. Near always brings guests that are just really solid and technically sound and fundamentally sound, and it's one of the best things about their entire ecosystem.

200. 1:23:22

     just the talent in the ecosystem is very strong. Yeah. And it's also just, you know, the evidenced by the conversations today, the protocol is very well-rounded. Um well-rounded in the sense that, you know, obviously there's the AI component and and I think it's actually indicative of the market as well. Like there's the the AI component. Um there's the privacy and confidentiality component and then obviously there's the blockchain digital

201. 1:23:50

     assets per you know asset class component and so near is almost like this little microcosm of the industry itself where you know it kind of represents what I think are like some of the primary exports of the industry like people you know when whether it's Silicon Valley or you know they're looking at like kind of the the bleeding edge of fintech They're looking at privacy. They're looking at, you know, kind of like an evolution of finance to make things more efficient and composable, make markets more efficient and composable. And then they're looking

202. 1:24:23

     at AI. And I think Near has somehow been able to kind of wrap their their arms around all of these y narratives and these components. And so I think they're incredibly well positioned. I mean, if I were just to think out loud here, I mean, the the the only push back, you know, that I could imagine here, the alternative would be like, you know, if you try to do too much, you're going to spread yourself too thin and then, you know, you end up,

203. 1:24:50

     you know, not doing any one particular thing strong or well enough. Yeah. But I mean, Andrew, what do you think on on this? You know, because to me, it seems as if Near has done a good job kind of, you know, building a well-rounded protocol.

204. 1:25:03

     How do you think that they've been able to avoid kind of like that pitfall? I think in the beginning they had, you know, a lot of these L1 chains wanted to be everything for everybody, right? And like you just saw like the classic narrative hopping, right? It was like, you know, there was trading and DeFi summer and then there was RWAS and then there was memes and it was just like this constant kind of hopping. But I think I think Near avoided that pretty well by by like focusing on multiple

205. 1:25:29

     product verticals, right? And then now seeing them all come together is like really bullish. like you've got the intent side, you've got the confidentiality side, you've got the agent side. Eventually, the agents are going to start to use intents, right?

206. 1:25:40

     You've got the uh private infence side with Venice. Obviously, Venice is exploding. So, that's now be that's now going to be something that, you know, benefits near. And so, you just kind of see like all these, you know, you know, kind of discriminant products, but all there's like a longer term and a lot of it is dependent on AI kind of coming together. So, that's how I would do it. I think we have our guests ready. I think so too. So, let's go ahead and bring them live. Um, and I

207. 1:26:06

     think that's absolutely right. Just to kind of kick this off here, which is that, you know, obviously in the in the in the moment, it seems as if, uh, you know, everything is kind of disperate and these things aren't particularly connected. Let's go ahead and bring our guests live. In in the long run, when we have the benefit of hindsight, we can kind of understand how these things connect. And I think that is one thing as we're kind of looking at AI and

208. 1:26:29

     fintech and uh and privacy uh you know kind of skating to where the puck is going. These things ultimately all do connect in the long run as we think through kind of a longer time horizon. Uh but for the next panel we're going to be talking specifically about model routing and privacy. So two of those components maybe a little bit less on the fintech component but obviously AI and privacy are significant components of this buildout. topic here is model

209. 1:26:56

     routing doesn't work without privacy. Um and I I mean I think everyone is aware of you know the Navier Stokes problem open AI and you know anthropic researchers both kind of embarking on you know the journey to solve this problem. Open AI potentially maybe spying on those chats and then using them in their own research and uh you know therefore creating uh one of the biggest moments for private models [snorts] that we've seen in in recent

210. 1:27:27

     history. Uh and so uh we're going to welcome Sergey, Luke, and Sabrina uh to the stage. It's a pleasure to have all of you. Uh why don't we do a quick round of intros just to start. Uh, I think a couple of you guys have been here before. Uh, Sabrina, it's your first time on the show. Welcome. Uh, welcome to the rest of you as well. Sabrina, why don't we start with you? Quick intro and we'll go down the line here. Absolutely. Thank you so much, Robbie.

211. 1:27:50

     It's a pleasure to be here. Uh, hey everyone. I'm Sabrina, head of API at Venice and developer experience, too. So, basically making sure everybody get the best experience and and has a great time using our API. I'm based in Brazil and yeah, I've been in Venice for about one year, a little bit over an year now.

212. 1:28:11

     Fantastic. Uh Luke, why don't you uh do a quick intro next? I think you've been on before and then we'll go to Sergey. Yeah, I'm Luke Muls. I'm the VP of business operations at Brave. Uh you know, we have browser, search engine, AI, API, all the good stuff.

213. 1:28:27

     Very cool. Very cool. Sergey, how's it going? Hello. Um I'm actually first time here too, so nice to meet everyone. Um my name is Sergey. I'm the head of product at Near AI. So I focused on everything AI uh within the Near ecosystem and uh the private inference is one of the focuses that we're going to be talking about. Uh I've been with Near for uh a little bit over a year and I'm based in San Francisco.

214. 1:28:52

     Very cool. Very cool. And uh I think that there are deep interworkings between you know all the all everyone on the stage here Near working with Venice Near working with uh uh Brave and then maybe even Venice and Brave. So I I'd love just to kind of get a highlevel understanding of how we've kind of arrived here. Um you know model routing as it stands is sort of still a very early question. I think we've seen this in a couple different ways. You know Venice does this in a private way. Near's also

215. 1:29:23

     got its near AI component and then Brave obviously has you know privacy elements to it as well more so in the browser perspective. Why don't we start on the Venice side. Sabrina, maybe you can give us an understanding of, you know, in short, how the Venice product and and specifically the API works today and how you've been able to implement privacy into something that again, you know,

216. 1:29:46

     especially evidenced with, you know, this this millennium math problem in the last week, you know, seems like this is extraordinarily important. Maybe you can tell us how Venice solves for this. Absolutely. So, um, Venice has a strong privacy philosophy in mind. So, this obviously translate to the API. We started out as only a web application as as a chat but now you know we grown into having this place where we offer every

217. 1:30:10

     single model out there and we try to offer it as with the best privacy guarantees that we can. So um in the API you're going to find every private model in a zero day retention way. So basically um Kim K3 uh GLM models every single one and you're also going to find anonymized models when you're talking to closed source models. So we try to make them all available at the same place as for example you can also talk to fable uh you can also talk to GBT Astra but those

218. 1:30:43

     uh are going to be anonymized through us so basically the end provider never actually sees that you are the one talking um to those models but of course there's um many things that comes to place when you're talking about model routing when there's so many things that come into place um when you're talking to a model that has that privacy guarantee and you cannot break that contract. So always something that we

219. 1:31:08

     need to to keep in mind. Um but just the products tries to bring intelligence and bring AI um as best as we can uh to everyone in the private way. Yeah. Uh Sergey, I want to hop hop over to you and kind of compare and contrast a little bit. Sabrina just gave us a great intro to how Venice's model routing works and how they do it in a confidential way. How similar or different is near AI's cloud when you're thinking about model routing and how you're doing this privately?

220. 1:31:37

     If we talk about uh private inference specifically and uh serving models, it approaches are actually very similar. Uh one thing I wanted to highlight is like the overall vision of uh near AI. We believe in user owned AI. Uh this is where because the world is moving to AI more and more. uh we believe that the owner of the AI, owner of the data should be the user, not the corporation that serves the model, the not the corporation that builds models, not

221. 1:32:08

     anyone but user and privacy is a key component of that. So we uh built the inference engine so that the user has full control of their data. uh similar to some extent to Venice as well. We have uh private models that we host ourselves and serve um to the end users.

222. 1:32:29

     Uh those models deployed behind the TE. No one has access to that. Uh not even us developers at near can see um the input output of the models. Uh we serve them directly to the users. We serve them through partners. Uh Brave for example, we talk about that in a second.

223. 1:32:48

     Uh and then we have the we call them incognita models or anonymize models basically similar concept to Venice. Um and it's great to see that we across the board at at least within this panel understand the important of privacy and how that leads to the user owned AI.

224. 1:33:08

     Absolutely. And Luke, I mean, I I'd love just an understanding, you know, how you guys approach this both at the browser perspective, but also you've got Brave Leo and, you know, this sits next to, you know, some pretty uh sensitive data sometimes in terms of browsing history, in terms of history, login, passwords, all these things. How how do you guys manage that and make sure that you keep LEO segmented from an existing browser profile?

225. 1:33:35

     Yeah. No, it's I mean, it's like one of the one of the powerful things about having the uh browser, having the AI built into the browser is having access to that context, but also kind of making sure that it's isolated uh and and that we're guard railing that as much as we can is is obviously like a super important piece. I mean, we're in a interesting category too because we have like 126 million uh monthly active users

226. 1:33:58

     and so we've been working with Near since like November of last year. I think we rolled out um private inference in in our nightly channel which is very developer focused and and uh where a lot of people are being very picky about which models they're using but like as you get to the broader segments uh you know you see a lot more auto you know selection being key so right now we've actually got that in our release channel

227. 1:34:22

     um with GLM 5.1 using Near's um uh private AI like inference and so um we're we're marketing that out and seeing how users take to it and move to it and and all that. But um you know with everything we're doing it's user first and and trying to kind of even where it's not fully private um trying to sanitize it as much as possible uh whether it's proxying things or or you know making sure that that you know brave our whole ethos is that we don't want to know who you are you know but we want to just kind of get out of the way.

228. 1:34:54

     Got it. Okay. And so the way that the the the routing works here and and you kind of point it to like the auto mode which you know I'm typically in as well like you know you kind of put it through your LLM of choice but then maybe it will pick the level of sophistication that it needs to answer your question correctly or effectively. And so as the model is kind of passing it to what it thinks is the correct level of sophistication, you're trying to maintain privacy when

229. 1:35:27

     it's kind of going and sending that data or that prompt along. And as I understand it here, correct me if I'm wrong, that's what you you know near AI is for is that when it goes to route this prompt to another model with a different level of sophistication, it can do so privately via Near's AI.

230. 1:35:48

     Yeah. Like ideally every model is doing what Near's doing, right? Like you have you have I mean like in an ideal world like it's all private. Um and I think that's kind of where it needs to go. But right now, just think about it as like the the Leo will pick the right tool for the right job, right? Like and and so ideally all those tools are private for every job. Um we're we're not quite there yet, but we've made an important step in getting this out there to our

231. 1:36:13

     release channel um for for all of our monthly active users to to start using uh and and yeah, kind of like see how how we even message that, right? like and uh and and and actually marketing that to people too because I think a big problem with the privacy story in AI is that there's there are only a few players that are actually doing it. Um and that people aren't even really aware

232. 1:36:36

     or people might make assumptions that things are private that aren't necessarily private. Yeah. And I think that's what I think people are now waking up to, right? Even if you know it was just open AI and it was a matter of hey you know there's this you know pretty significant thing that everyone not everyone but you know some some very smart people were working on okay now we are kind of realizing or waking up to the fact that you know open AI might have been spying or reading or

233. 1:37:05

     training itself based on these things. I mean question to all you guys just feel free like can you can you provide any clarity here because I think there is still some misconceptions like even just in the last few days you know we're starting to wake up to the fact that maybe OpenAI is listening for lack of a better term like are they or is it still kind of just on like a hit or miss basis yeah Sergey what what what's your what's your read on this well I don't know the answer to this

234. 1:37:36

     question uh I have some speculations and I think there are a lot of speculations uh in the world as well and this is where I think our approach is important. If you protect your data not on the policy level but on the encryption level this is what we do like we deploy models within the TE no one has access to it you don't need to trust anyone you can actually validate that no one has access

235. 1:38:03

     to your data through the attestation that's the right approach uh I believe uh and then in terms of what open AI and that's uh the recent story about the math uh problem um yeah there's so many opinions it's hard to tell which one is true. there is like the discussion that did they use uh the data did they create synthetic data from the all the conversations that um are happening u we don't know uh but I prefer to live in

236. 1:38:37

     a world where you don't need to ask those questions where everything is private and secure um and routing is a big pro problem uh about that too like the routing also has to happen in a private environment We're working towards that solution like when you decide which model to use that model that makes the decision should also ideally write uh run in a private environment so that the data again is not exposed to anyone and the cost of

237. 1:39:07

     error also is high like oh if you route it to wrong model then your data is exposed you don't want to have that happen all these problems they pile up and um yeah require a thorough solution. Sure. Yeah. Uh Sabrina over to you. I mean you know Venice was at least in the market a huge benefactor of you know what happened with this potential you know breach in privacy. What what what was your reaction internally at Venice?

238. 1:39:37

     Um no surprise really. uh it feels like at least you know it it has been a little clear that these big companies they they do benefit from from the conversations and I I feel like they have been open to it at to some extent as well. So um it's it's honestly not the first story we've heard about something like this happening. I think we we've all also seen some other cases where uh you know big companies reach out to to other folks on on

239. 1:40:06

     conversations that were had within the platform. So there is of course some level of monitoring happening and I feel like um the big thing here is really that the user sometimes isn't aware um of the contract they're making when talking to these models through these platforms. Right? So it is assumed that that the user has the information and and it is aware of what can happen. Um, but it's it's just not the case. And this is why we prefer just defaulting to

240. 1:40:36

     private um, and letting the user make that decision when they want to use a closed source mo model more consciously um, than to have that be the default of every conversation. Yeah. Absolutely. And so, you know, Lou, come coming full circle here. I think you know we've got different levels of granularity or different levels of confidentiality when we're considering how these models are sharing prompts both you know how they're routing within

241. 1:41:05

     their own sort of closed environment but then in the case of some more of these open environments how these things are getting passed back and forth where are you current and I think Brave Leo is kind of a good example here where are some of the the the stop gaps like the gaps internally at Brave Leo where you know I I think Near and Venice both have taken an approach where they've kind of made privacy a non-negotiable like hey

242. 1:41:29

     if you want to use us for AI you're going to do so in a private way Brave Leo still kind of has a choice open why is that the right case I take it you know significant user base you want to have optionality but how what is the migration plan as you go to a you know kind of uh privacy choice to a privacy by default regime theme inside of Brave.

243. 1:41:54

     Yeah, I mean I think it's balancing demand for new models with what's available and like you know when we can uh kind of self-host um and and sanitize as much as possible we do. Um and so I think it's a matter of time where in market maturity like as things get people become more aware of the need for uh these private defaults which everything should be defaulting that way anyway. But I mean just just thinking back on the last, you know, set of questions and responses, it's all just deja vu. Like the same thing happened

244. 1:42:25

     with adtech and with uh with monetizing the web where, you know, before we know it, we looked around, everybody's racing um you know, running with scissors to basically scale monetization and users don't realize how much of their information they're giving away. Well, now you not only like AI is essentially kickstanding the entire economy and um you've got these companies that are at a break pace like just running very fast

245. 1:42:50

     and um and so you know that's why it's so important that people on the frontier are innovating at this level with privacy because if you're not doing this it's just going to be a disaster when you think about what AI can the potential there and and and all of the um you know the vulnerabilities that are out there with the web and and also just from a user perspective and a B2B perspective And that you you preempted my next question actually that I think the last

246. 1:43:13

     question as we kind of come to a close here. Uh question for you Sergey you know on on the B2B side on the enterprise side of things you guys have you know flexible and gen general tech that can apply to both the retail perspective that we're seeing you know adopted on Venice and across the board at Near AI but then also in an enterprise perspective how you know there are sensitive business secrets and

247. 1:43:38

     information that is proprietary that enterprises want to protect via a private model. How are you seeing adoption progress on the enterprise side of things and how how do you see it keep pace with the retail side of things? What are you seeing at Near?

248. 1:43:54

     We constantly interview people uh and ask them what's important for them in the AI. And if you ask that question out promptly it's like quality of the models cost and then there's like additional factors ease of use and rarely the privacy comes up right out of the box.

249. 1:44:14

     uh especially like say last year beginning of this year that was not on people's radar and now we see uh more and more stories where data was leaked there's like 100 millions uh conversations was leaked from one of the websites uh then there's this um kind of controversy around open AI people starting starting paying more and more attention to privacy um I agree with Luke there's a lot uh in educ ation and uh user behavior change that still needs to happen.

250. 1:44:48

     uh and uh I hope that happens soon because the AI adoption is going so fast and um the more contexts AI has the more powerful it is and I want to share this context with the AI but I'm like concerned who's going to own this data who's going to own this AI at the end and the privacy I think is the only approach that uh make sure that uh those AI agents they represent me uh not the company that built them if if uh it's done in a proper and private

251. 1:45:28

     manner. So to answer your question people start recognizing it more and more and it becomes a bigger topic of conversations and privacy goes up and up and up on this core values. Fantastic guys. Thank you so much. Um, I think this was a very relevant conversation uh based on everything that we're seeing in the AI space and how there is this suspicion and this growing suspicion of an invasion of privacy. Um,

252. 1:45:55

     couldn't do it without you guys building and uh fighting the good fight out there for private AI and model routing. Um, and so we're going to keep things moving along. Thank you so much. We'll see you all again soon. Thank you. Thank you. Thank you. Cheers.

253. 1:46:13

     All right, so let's keep going. Uh, we're gonna bring back up Andy, my uh my co-host. Uh hopefully he had a little snack and uh did what he had to do. There he is. Don't have your audio, but everything is well. And uh yeah, we're going to keep things moving along. I think we've got two panels left. Uh we've got stake near run private AI should be a good one. I know that is a uh new tokconomics uh development in the near ecosystem.

254. 1:46:43

     And so if you guys are unaware uh you can actually stake near now and what you get is uh you basically get like credits. You basically get uh inference at a cheaper rate. The yield on as I understand it the yield on the stake actually pays for the compute which is a entirely new primitive. uh something that's pretty much only possible in uh the DeFi world and so stake near uh run AI is uh very very very cool and uh I

255. 1:47:13

     think you know we're kind of seeing how these AI economic models develop and then uh we're going to wrap with crosschain beyond the chain you're on and that one is going to be with uh near layer zero and thor swap and so we are going to get a crosschain intense uh portion of the conversation in there as well, guys. I mean, extremely relevant as we were talking about how Near has been able to basically skate to where the puck is going. I think if you take a longterm time horizon, then you

256. 1:47:44

     understand that these uh these components all fit together quite nicely in the long run and Near has been able to carve out a future part of the market that they are skating towards. So, we anticipate that this is going to continue to develop and then uh yeah, we're going to close out two panels, crosschain and staking near uh a lot of conversation about privacy and AI up till now. I think we're going to round

257. 1:48:08

     out with the economic conversation as well. Yep. Yep. Yep. Yep. Uh market's down a little bit today, guys. Nothing crazy. Um not too worried, honestly. Um, yeah. I just I don't know. Not surprised. I think we continue to dump into FOMC and then I'm going to buy the dip into FOMC.

258. 1:48:30

     I think maybe the inflation data, the CPI comes in tomorrow. Maybe it's cold, maybe it's hot. No one really knows. But depending on that, I kind of think it's going to be really close to estimated, maybe a smidge higher. Gonna push the market a little lower. I think as long as we hold like 70s, I think we're good. And no matter what happens with the FOMC, I think uh even if it if they hike, I think it's going to be a little bit slower off the line.

259. 1:48:55

     But if we uh if we hold rates steady, we're going to pump. And if we hold and it and if we do hike, I think maybe it's another week of chop. But I think we're going to end up pumping anyway. So yeah, I'm buying I'm buying the FOMC dip. I'm buying the inflation dip.

260. 1:49:10

     I don't even know if I guess Yeah, I think this might have been it. I personally think this might have been it. I don't think we're going to get any kind of reaction until CPI 8:30 a.m. tomorrow. Well, that's what I mean. But I think this might have been it on the downside.

261. 1:49:25

     You think if I think this is like this was like, oh crap, if PPI is this high, then we're probably screwed on CPI, nuke the market. Oh, CPI is not that bad. We're gonna go higher. Like I I think we I think we pumped this weekend. I I I think we I think we're ripping going into FOMC and we'll see what happens.

262. 1:49:48

     I think what you're saying is a bet on a pretty chill CPI and I have no insight into that. I have no idea whether or not like how do you know what CPI is going to come in at? I Yeah, can't say I do. To me, it could come Who knows? It could come in either way and that's going to dictate what we do into FOMC. Oh, I agree. Yeah.

263. 1:50:14

     And then but you know coming out of that I think uh both paths result in the same outcome. Sure. That's kind of my read but it's it's cho I think if we hold man I could even see us breaking 70 and hitting a six handle if they hike but like holding 65k and like we set that as like a higher low and then we're off to the races again in like a matter of weeks. You think we could get a really if they hi if they hike I think we could

264. 1:50:46

     push into the 60s. If they hold I mean look if if inflation data comes in it's not good then that might push things like pretty close to 70. Yep. If they hike that could push things close to 65 or so. Yeah. And then I think I but I I don't think we go any lower than like the range that we were in before.

265. 1:51:11

     All righty, guys. We've got Cameron Dennis set to join us. We had a panel with Cameron in Cameron's a man. I believe it was Miami. We had him on the show and yeah, it was really great. And I think there's just so much to talk about here with that article that you saw, Rob. So, let's let's bring Cameron up um and let's get this let's get this show started here. So, I think yeah, like you said, Rob, there was so much happening in this private inference world that I mean I mean you talked about that that

266. 1:51:42

     that piece that came out that pumped Venice. I think that's probably a good place to start. Yeah. So, let's go ahead bring up Cameron. Um we had Venice on the last panel, so we talked a little bit about it, but I mean this was the highlight of the last like I mean this is an this is a math problem that went unsolved for a millennium. This is a millennium math problem. There's a million dollar prize for anyone that solved it.

267. 1:52:04

     And uh and you know, both anthropic researchers and OpenAI researchers were working on this at the same time. The speculation is that OpenAI sort of stole the researcher the researchers research from Claude. Cameron, we've got we've got you on the stage now, man. I mean, we've been talking about this a lot, but it feels super relevant. We'll get into everything that's going on with staking

268. 1:52:25

     near and all that good stuff, but welcome to the show first off. and what are uh you back and it's good to have you back and uh we're curious how you're feeling about this uh this potential invasion of privacy by OpenAI. Uh it's probably not new. Uh OpenAI has leaked stuff before. Um I think the biggest question that needs to be asked is are they creating synthetic data sets on data that they're not training on but they're creating synthetic data from and then training on that. Um I don't know.

269. 1:52:56

     I don't I just have no idea. Um, but that is something that I don't want to have to guess. I would like But why is that important, Cameron? Sorry. Yeah. Talk talk us through like what those synthetic data sets are. What do they mean?

270. 1:53:08

     Oh, yeah. So, like they in their privacy policy in terms of service, they say like they don't train on your data. Um, but they could take said data and sort of create a synthetic version of that data and then train on that. Um, and so the I mean I don't know if this is 100% relevant for uh you know this whole issue with this problem but because like the pre the pre-training was done quite

271. 1:53:31

     a while ago for this model and like there's a bunch of back and forth of whether like the date of uh the pre-training was before or after uh when they started working on it. But the whole point is like they might not be training on your exact data but they might be sort of cloning your data and yeah but I don't have the answer to this. This is like and I and I just have to trust what they say. I want to be able to verify it like

272. 1:53:54

     cryptographically. Um and so this is where all this like TE sort of verifiable privacy comes into play. Yeah, man. And I mean, look, we saw that tweet go out with the highlighted text in yellow about those about this specific kind of incident and then instantly somebody picked up that Venice and Near are the best way to express the bullish thesis on private inference and both tokens are up like 30% or something from the from that post. And so I guess you know not to put on an investor hat

273. 1:54:28

     we'll stay on the private inference side and the builder side and the tech side but you know do you think that the world is starting to wake up to the value of private inference and that there are private inference providers out there that are established um you know these tokens are catching a bit obviously crypto's had a bit of a turnaround but like where do you think we are in the awareness curve of private inference?

274. 1:54:50

     Uh very early uh still very early. I'm still educating like CISOs uh like chief security officers on what verifiable privacy and AI looks like. And so we are still very very early in this. I mean confidential compute has been around for quite a while.

275. 1:55:06

     Um and even at the last confidential comput summit it's started to feel like there's a you know different energy in the air from previous ones. Um but it's still a bit early candidly. And unfortunately humans, you know, we have to uh have there has to be a car crash before we put the stop sign where it needs to be.

276. 1:55:24

     Um, and so this is kind of what's probably going to have to happen in AI for people to care about this. But the other key thing is just like open source is getting so much better. And so like DeepSeek V uh 4.1 came out today, Flash, and it's doing as well as Astra. And this is at at literally like like 95 or so percent cheaper. And so it's not just about confidential compute. It's about open source being

277. 1:55:51

     able to perform as well at a fraction of the cost and have it be private. And so the goal is make privacy make sure there's no trade-off or there's even a cheaper option or you know as performant uh than frontier models. Um, Cameron, we're going to get back into staking for inference and confidential compute and all these things, but the community members in the chat here are crazy. First of all, they're they're asking you if you enjoy Mircats, and they say you would make a

278. 1:56:23

     fantastic Mircat mob leader. Do you do you are you aware of Mircats and how do you feel about them? Uh, so I don't know much about Mircats to be totally honest. Uh, and I'm sorry to the community, but um, you know, near cats were actually like the first sort of like u like internal meme of near.

279. 1:56:41

     Uh, and this is, yeah, we were all near cats uh, way back when. Um, so I guess this is a play on that and I don't really want to be a cult leader to be totally honest, but I really appreciate the, you know, all right Cameron, let's get into it.

280. 1:56:56

     Um, so staking Neo for private inference, right? And thinking about the scale of the TAM here, obviously Near AI is powering, you know, the the Venice API and can power pretty much any front end that wants to tap into verifiable inference. We just launched our rollup front end for near.com. So, congrats to us. Um, and we're we're like, all right, what is the Rollup LLM look like in the

281. 1:57:20

     future? Right? like is there a is there a $20 a month rollup website LLM offering that comes with like some better perks that is powered by Near AI people can stake near to get access to you know this or they can purchase it on a subscription plan so we're like iterating a lot of these ideas right and we're like we're at v001 of this like what we're calling like the markets arm and so from my perspective

282. 1:57:45

     if I'm going out there and I'm like hey you know what guys use our LLM use our rollup AI because it's powered by near. You know, we use the Venice API. It's uncensorable. It's private by design and the inference is powered by Near. You can stake near. All of my ducks are in a row here. This is a really good offer.

283. 1:58:04

     Um, you know, what do I have to trade off here? What like what is still missing from the private inference that you guys are doing compared to the Frontier Lab inference? Like, let's just let's just get it out on on the floor here. I would say it's like about the types of models. I mean there are really good like let's say voice models. I don't know how you guys use AI every day but like text to voice and like voice to

284. 1:58:27

     text is something that I think is growing exponentially. Um I have you know people in the office are like talking to their area AI sometimes even more than typing. And so you know having open source versions of these models come out that are competitive with uh the closed sourced ones. I think this is like a big step forward like thing that needs to be done there. Um, but honestly like we're at this precipice that there's not going to be a trade-off. Um, I would say the

285. 1:58:52

     other piece is like uh easy like model routing because in open source land it's very confusing to constantly have to stay up to date with like the best model for the best task. And so how can you have like a classifier that uh essentially routes a prompt to the best model according to the task? And then it's up to you guys to sort of make sure that those models are readily available for your users. You have to abstract all

286. 1:59:17

     that away because no one wants to hear, you know, GLM 5.3 flash versus GLM 5.4 and Y is better. Like people don't give a And so um you need to abstract that away. And I think that's probably like one of the missing pieces. But the quality though just to Yeah. So like the so the quality of the model is can be the same, right? It's just like as far as the the level of intelligence that I'm able to get on near AI with private inference, it's the same level of intelligence. Correct.

287. 1:59:51

     Yeah, it is. I would just say it's like vibe. Like you need to provide the vibe for your users. Like I don't know if you've interacted with a lot of these like you know Chinese open source models um but it doesn't sound the way you're sort of used to navigating chat GPT um and sometimes like the answers are much longer or like if you ask it to generate you a UI you know maybe it's a little bit more clunkier the edges

288. 2:00:16

     aren't rounded like that's all work that absolutely can be fixed and is actually not that hard of a problem to solve um but that's the work that needs to be done but in terms of like intelligence and reason capability, it's as competitive.

289. 2:00:31

     Gotcha. Okay. So, as far as staking near goes, could you please explain some of the mechanics there and maybe just start with the thought process around why this makes sense right now? Yeah, for sure. So, in short, uh we want people to be able to continue holding their near. So, you don't need to sell your near in order to get free inference. And the way that this works is you stake it to a validator that Near

290. 2:00:56

     AI manages and that money that is generated from validation is essentially converted and just given to Near. Well, not converted to cash. It's just paid to the Near AI team in exchange for inference. We're essentially just accepting Near as payment, which makes sense because we're near AI. And so, um, the whole point there is now you don't need to necessarily spend any additional

291. 2:01:20

     money to get your AI paid for. You can just hold your asset, have that money that's generated through staking, and have that pay for near or pay for inference. It's it's pretty clear. So, no card, no account, you just stake. It's that easy.

292. 2:01:36

     Yeah. I mean, you have you have to have a near account, but yes, no other accounts. Yeah. And can you talk through the economic implications for near because I I think a lot of people, you know, they this will resonate with them as a product and it'll onboard a lot of people. But what does this mean for the for near economics? What do we expect to take place here?

293. 2:02:01

     So there's a couple parts to this. One, it's a really cool new utility for the token that previously didn't exist. And so there's an entire new, you know, economy that opens up around, you know, having near being able to pay for intelligence. And so this sort of falls into the long-term goal of like making near token AI money. And so there's a very important piece there. Um, now for

294. 2:02:27

     the mechanics of how like that the the value of those that inference gets calculated is based upon the inflation rate and the amount of tokens staked on the protocol. And so the the the caveat here is that and there's a little bit of a competing interest because there's a huge interest to have the inflation rate of the protocol go down. We don't want to have to overpay for protocol security

295. 2:02:52

     and so there's a interest in making sure and there was a huge inflation cut. So near's almost like not quite deflationary but the goal is to make the protocol and the token deflationary. Um, now that's competing with the stake for inference piece because for stake for inference, you want the inflation rate to be higher because you're going to want for more tokens to then go pay for your inference. There's two things that can solve this. One, the price of goes

296. 2:03:18

     up. The price of goes up, you get more credits to pay for inference. Everyone's happy. The other thing is the cost of AI tokens goes down. We get better hardware. We get more efficient models. you know that part is also happening and so this to me is incredibly exciting. We have all these new like TPUs and types of like AS6 that are you know making inference a lot more effective. Um I expect the price of

297. 2:03:45

     tokens to continuously go down as well as more efficient models. And so um put these two things together I think this is going to be sort of the foundation for sustainable open source AI economy. Um and this is just a step like in that direction. And so can you just kind of talk to us about the TAM here like what would you consider how much near staked are you looking to acquire here and you know AI is just continuously growing. So

298. 2:04:13

     this is you know this is practically infinite as far as inference demand goes and also you know as Venice grows the demand for private inference directly correlates to near AI's demand. So just kind of walk us through like what would make this a success for near AAI? How much nearstake? How much inference demand? Give us a bit of a sense of some of the metrics that are like you know your your northstar.

299. 2:04:42

     The amount of nearstakes to this validator is important because this is going to determine the amount of revenue in near tokens that nearai receives. And so definitely like the amount of near staked. Um but I also want to flag a lot of this right now given the sort of lower price of near as well as uh the low inflation rate. Um you this product is kind of designed for whales right now

300. 2:05:07

     and I want to be kind of clear about that because you do need to stake a lot of near to get access to a reasonable amount of inference to power your AI that's let's say always on like your agent that is scraping the web and doing things for you. um that sort of like always on workload would probably be around 60,000 near so about a hundred 20ish,000 you know $25,000 and so but the cool thing here and this is what people like need to wrap their head around they're

301. 2:05:39

     not paying $125,000 they're holding that asset they're just foregoing the reward from staking and instead receiving inference and the cool thing here and again this is Not tax advice, but and talk to your tax professionals about this, but if you're a giant whale and you have a millionaire, you can receive inference credits instead of receive your tokens which are taxed.

302. 2:06:11

     And so right that is something that and again it varies per jurisdiction and I know you have viewers all around the world and so I don't want to like flag anything but like that is an interesting use case is like instead of getting tokens back get some sort of like utility that is going to actually be spent to run your everyday life like AI tokens right and specifically non-taxable utility depending where you are. depending on where you are. For For I said I said

303. 2:06:42

     that Cameron didn't say that. Lawyers stay where you are. Um Cameron, I want to I want to dive a little bit deeper into the inflation that you talked about because I think we understand how, you know, right now the Near token is fully diluted but the protocol still has some inflation mech you know me mechanics.

304. 2:07:05

     There was a comment in the chat actually just now says you're not ready for deflationary near. I think people are kind of looking at that. They're not really sure whether it's going to happen, but they're they kind of see it looming. And then at the same time, we've got like these these near campaigns like the 333 campaigns and and some others going on. Can you just talk us through like what is the current inflation dynamic on the near protocol and how does it work? what are

305. 2:07:36

     the inputs and is a deflationary mere really something that is in the realm of possibilities? Yeah. So, uh just to kind of start from square one like the inflation i.e. new tokens coming to the economy um are normally are spent to pay validators to maintain the security of the network.

306. 2:07:57

     And so that is very important. But there's also an argument to be made that you know there's a cap of how much money you should be paying for security. And if you're overpaying for security on something that is pretty damn secure, never been hacked, never gone down, all this other stuff, you don't need to overpay for it. And so the goal is to make sure to pay for security at the right rate. And so that is like one of the main drivers of like the governance posts to drive down the inflation rate.

307. 2:08:31

     So that means the validators won't get paid as much. And so um that is a huge argument and debate that's going on that candidly I just want a sustainable new economy. I don't want to have a giant horse in this race. Um but that is like one piece and so um there was a proposal done last year to cut the inflation in half similar to like you know bitcoin havingss and so uh that did that passed

308. 2:09:01

     um I then there's another layer of all this which is like near intense near ai some of these like near foundation affiliated projects um will take the profits that they that they make and use them to either buy back near do something with it, stake it. I don't I don't have the the the clear, you know, this is what's happening. Um but that is to take more tokens off of the market and so you know decrease supply increase uh yeah decrease supply sort of

309. 2:09:30

     increases the price and so uh that part is uh pretty much dependent on candidly my ability my team's ability to make near AI money and that is done by you know selling inference tokens uh to highly regulated industries people who need private AI and as Andy flagged there's almost an unlimited demand for AI tokens. Um, intelligence is in the AI term being tokenized. And so, um, there's an unlimited amount of intelligence needed for solving the

310. 2:10:03

     world's unsolved problems. So, that's the gist. Yeah. And I I I think you paint a more clear picture than I've heard others articulate. And it's a difficult paint picture to paint because we're just so, you know, fixated or consumed or just bought into the fact that, you know, we have US dollars and that's what we spend to buy goods. Like frankly, it's the world's reserve currency. You know, it it's psychologically

311. 2:10:30

     like we're psychologically monopolized to thinking that the US dollar is the only form of money. Sometimes, you know, if you think way back a hundred thousand years ago, you know, people were using gold and whatnot. Besides the point, future looking is more interesting when you start to think about how we could denominate some goods in the form of other forms of currency particularly

312. 2:10:54

     like you're saying making near the currency through which we denominate intelligence where you know if you're buying credits or AI compute tokens input tokens output tokens near AI you're going to be spending near and that is just the base form of money that we use to denominate intelligence. And I've heard other people try to make this case, you know, around Ethereum and DeFi

313. 2:11:18

     and that sort of thing. And it it often times does fall flat because people just can't really wrap their head around using a different form of money to denominate the goods and services that they're buying. And so as you're talking to chief security officers, you know, obviously the first step is to get them to buy into confidential AI, private AI, near AI. Once they understand the protocol,

314. 2:11:43

     how easy or difficult is it for you to overcome the next psychological hurdle? The idea that they're not going to be spending dollars on near AI, they're going to be spending near on near AI. Do you abstract that part away from them?

315. 2:11:56

     Do you say, "Hey, just give us dollars. We'll do the swapping in the background." Or how do you explain, you know, that component, the money component to these chief security officers? I don't talk about the near token at all. um they need to be I need to meet them where they are. Um they also don't like like some youngl looking guy telling them something that they don't know. uh you know there's a lot of ego with a lot of these people and you know I need

316. 2:12:21

     to really match their energy and so um I say hey pay in dollars pay in you know I'll invoice you at the end of the month like just traditional receipt based pricing whatever they're used to and um yeah we'll take their money and use that to buyer it's it's like super simple and then that's also where like intense is quite interesting um you throw this like giant you know liquidity network being able to swap anything for anything. Um it's that part can be like make everything seamless.

317. 2:12:53

     And so there's like a lot of really cool things that we can talk about um when it comes to you know uh AI commerce and how do you price intelligence and like also not just intelligence but the data. So like one of the most important things right now is real-time data for these labs to trade on. So like OpenAI is paying a ton of money uh to get like real- time uh like business data or you

318. 2:13:17

     know Google made a giant uh like investment into weirdly I think Spirit Airlines after they went bankrupt for all of their business data. Why? It's because they take it to train their next model because all these labs are literally trying to become the last company on earth. They're trying to replicate everything. And so what do they need to replicate everything? Real time data. And that's where everyone's

319. 2:13:41

     prompts and how their agents interact with tools, when things fail, all of that matters a lot. And I'd consider the data piece a very important pillar of AI money. Um, and so there's a whole side quest that I'm happy to unpack at some other point uh talking about this.

320. 2:13:58

     Yeah. I mean, Cameron, I guess the other obstacle that I'm curious about outside of this kind of monetary, you know, I mean, which seems like it's solved, you know, is this, you know, when I go on Venice, I find myself before different types of queries. I'm like, all right, I'm in an incognito window. I've got the privac I've got the model set to the GLM private model.

321. 2:14:23

     And then I'm like, "All right, first, can you please pull some context from any of my previous conversations just to kind of test it, you know?" And he's like, "Nope, I can't find any of your previous contacts because you cleared the storage, cleared the cache, and cleared all the browser history and went into your Mac like files and deleted the file that was on your local browser and in your computer." So, I'm like, "Okay, cool." And then I'm like, "Okay, is this private?" Like, "Yes, it's fully

322. 2:14:48

     private. Here's why." I'm like, "Am I anonymous?" "Yes, here's why. Is there any data that's gonna be shared? It's like, okay, no. All right, I'm trying to make a a bomb. No, I'm just kidding. And right. And so it's like, how do you get that mental leap solved? Like, how do you solve that problem? Because if I'm doing this as somebody who knows all the intricacies of what you guys are doing with Tees,

323. 2:15:13

     with Venice, I know Eric, we've talked to him in person. Like, I'm like, I'm very in the know and I'm doing this. How do you solve that for someone who's not so in the know? Yeah, we need outcome. We It's outcome driven. Like we're not going to convince people on mechanics. Uh we got to convince them on outcomes.

324. 2:15:31

     Sure. And so the you know takeaway here and this is something that actually Mark Zuckerberg and others have been talking about on the Facebook side or meta side. Um talk about CVMs, these things called confidential virtual machines. And this is essentially a black box that you're able to run things in. It could be AI models. It could be, you know, like like databases. And what everyone eventually

325. 2:15:54

     will have is their own sort of black box that lives in the cloud that you have access to. This is like the long-term vision. Uh that you have access to and you get access through something like a near account or a crypto wallet, something that you verifiably own yourself. I think of like the near account as like the off like the authorization in order to access your blackbox in the cloud. And that blackbox

326. 2:16:20

     has all your context that has all your previous files. It has all the you know your photos and understands you. Um now Meta is likely going to say, "Hey, this is your blackbox. We're going to throw our little like advertising engine or something in here and we're going to like learn about it and like kind of see what you store and all this that the other. It's yours. It's private, but it's going to like push

327. 2:16:41

     certain data to, you know, our servers to run you ads. And so this is where um we differ from let's say like some of the big labs like we're like we're not going to run any of that stuff in there. Um you can have your own blackbox off into it with your near account that you verifiably own yourself and then you call the inference APIs to get access to your private intelligence. So that context layer, we started building some things called like a private portable memory layer. Um, so you can actually

328. 2:17:10

     like move, let's say, all your chat history from OpenAI to this, you know, your personal server so it actually understands what you're doing. And so you kind of carry your context with you from thing to thing. Um, and you can take your context that is in a place that is like, you know, I don't know, they're training their their models based on your data just because you unknowingly agreed to that or

329. 2:17:34

     whatever it is or, you know, this synthetic data clone and you can take all that context, put it into this black box that has that is stored on your local machine that has all the properties of the privacy embedded into it. And then you can leverage that context again in another private environment. Um, and then after your session, you can still have that context be built up in that black box, clear all

330. 2:17:58

     your history, clear all your cash, clear all your session data, and then come back and unless somebody is like logged into your Venice or or your near AI account or can get access to that somehow. Maybe you maybe you like password protect it. There's like a okay, would you like to use context in this conversation? Yes. Okay, tell me your PIN.

331. 2:18:18

     Right? And then so you can build a blackbox of your own data but have individual private sessions. That sounds really really really interesting. Like that sounds like a breakthrough. It is coolest part key. So like near supports like SECP I think it's like SEC 2 or I forgot the exact like you know encryption algorithm but like you could just pass key into anything and so no more having to write down these 12word seed phrase you lose it this that and the other like it's all

332. 2:18:45

     backed by pass key. um completely abstracts like the all the crypto complexities away and it's really about user ownership. It's really about, you know, the things that we've all like the things that got us into the space to begin with. And so, um, yeah, this is all possible.

333. 2:19:01

     Um, it's not super seamless to build. Um, I will tell you it's like, well, the idea in theory sounds great. Like bring your contacts from all your like all your skilled agents that you've built out, port them into your own black box, right? and then be able to have individual private sessions with context because that's the biggest downfall to Venice or to Near AI, right? It's just like every if you really want this to be

334. 2:19:25

     private, every new chat is like starting with a blank slate, which is tough and it's that's it's that won't win in the market like candidly like you're going to need that context. And so how do you do that context in a flexible way? Something that I've always wanted and this is like a pipe dream of mine.

335. 2:19:40

     Hopefully someone listening can go build it is I want an agent that has access to my email that's verifiably private and then it scrapes everything that I've like look at and do and like anything that I sign up for navigates the terms of service of that website and then goes makes a request to get that data from my site like export that zip because you could do this at like you know Instagram or Spotify or whatever export that zip to my email and then that is the thing

336. 2:20:04

     that manages my context like that is my context but the hard part is like largecale context management like things like obsidian and things like this is all really cool stuff like critically important. Um but when you throw like a sh a ton of data at you know this like small context window the AI essentially stops working well and so overwhelmed it overwhelmed it's overwhelmed. Yeah.

337. 2:20:26

     And so how do you manage a ton of context um in a seamless way? That is the engineering problem to solve and I'm confident someone will solve it. Uh, we are not doing this quite yet. It's all comp. It's all compression, right? You got to compress a large amount of context into something smaller and analyze it.

338. 2:20:49

     Yeah. Synthesize it or whatever. Yeah. Yeah. Well, Cameron, I would love to have you on as you make more progress on that specific vertical because I'm all in on that. I will be the first power user of that. I will test that thing until non-stop. I love I love the private uncensored LLM. It is my favorite favorite thing. Like I just love how near's inference uh powers Venice and just how it's able to just I can talk

339. 2:21:18

     about anything. Like they were all like, you know, they're always like, "Oh, AI is going to be your doctor and your therapist and your companion." And I'm like, "The hell it is." Yeah. Sam Olman's going to be my my therapist. I'm not having that. No. You know what I mean? Yeah.

340. 2:21:31

     But push direction you wouldn't normally have gone like it's uh 100% things change y well cool man thanks for your time Cameron awesome work and stoked to see this um keep growing and I'll have to look into that uh is that private virtual machine that you speak of or is it PVM or CVM is that is that like can you just buy that somewhere and use it yeah you can actually uh follow network has CVMs for sale like for everyday people. Uh you can go spin up your own.

341. 2:22:04

     Uh Nearai does not sell CVMs as a service. Uh we actually just run like agents that we build in CVMs. There's also a lot of other amazing use cases for CVMs. Mark Zuckerberg is making this more popular right now. I think there was a clip going around yesterday him talking about this magical thing called a CVM. Um but this has been around for quite a while. And uh the the one thing I want to kind of walk take like walk

342. 2:22:28

     away with is like what we're really trying to do is create a sustainable open source AI economy. Because like right now AI is not that sustainable. It's like powered by debt. Like debt is underlying like all these giant data center buildouts. There's all these like all these giant labs have all this like potential offbook debt that's like not even popping up in like SEC filings and stuff. And so how do we make AI sustainable? Um, it

343. 2:22:54

     starts with things like stake for inference. It starts with things like controlling like how inflation gets allocated. It's very low-level and technical. But at the same time, this is the hard problem to solve. Like how do we make sure AI is accessible to everyone around the world, no matter where you come from, you know, your economic status, this, that, and the other. Like intelligence is the thing. I love it.

344. 2:23:15

     Thank you, Karen. Thank you for your time. Awesome. Thanks. Awesome session. Thank you so much. See you again soon. Thanks. See you buddy. Wow, that was awesome. You know, Andy, just uh while while we bring in our our final group of panelists, I um the guys at FA Network are incredibly smart. I sat next to I sat next to uh an engineer on my way, I think it was to Korea last year on the plane.

345. 2:23:40

     Yeah. Sat next to someone who worked at Fala and you know, lo and behold, we both work in crypto. you know, one thing leads to another and we start talking about, you know, the pros and cons of TEES versus ZK versus FHE. Yep. And basically like why someone would use one and not the other. I mean, this guy incredibly bright and worked at Fala. They also were extremely ahead of the curve when it came to revenue. They don't I don't know if they have a token. I don't think they

346. 2:24:14

     do, but they were basically doing like privacy as a service for like all of the ZK and FHE and TEES like had like 30 $35 million of revenue annually back then last year. So doing incredibly well in terms of both like, you know, the technical depth and knowledge, but then also had product market fit and like you heard, you know, had these private virtual machines or confidential virtual machines

347. 2:24:47

     like as a service that I think a lot of these guys were using. So they're a uh they're they're a a participant in the privacy segment market. Love it. All right, guys. We got our intense and crosschain panel. We've got Allan from Thortop and Will from Near Intense. I think we're missing a friend from layer zero, but that is okay. We're going to bring them up. Last panel of the day. Let's talk intense. This is the

348. 2:25:13

     product. This is the stack. This is what people know near for. This is the I mean, I don't know if this is, you know, confirmed, but I think it's the winning interup model. I think it's pretty clear. You know, we had a lot of like different attempts at things over the years and it just seems like, for lack of a better term, market makers or solvers, just fast, cheap, any chain,

349. 2:25:36

     any asset. Here we are. Will Allan, great to have you guys. Great to be here. Yeah, gentlemen. Um, what a what a last six to eight weeks in the market. Um, what a last couple months in the institutional adoption of this industry.

350. 2:25:52

     And I think this is a great place to start as far as like where we're headed, right? So I think we've moved effectively from a chain centric and I I'm sure this is not new, a chain centric view of the world. You've got all these blockchains, you got to connect them all to now it's much more of an asset centric view of the world.

351. 2:26:10

     We've got tokenized equities, we've got memes, we've got um assets across different venues, you've got different chains with different assets, you've got launchpads for different assets. It's all about what what do people want to buy, sell, hold, trade, and how do we get it to them in the cheapest, fastest, easiest way possible. And when you throw in the institutional adoption of the space, we're now seeing another layer.

352. 2:26:32

     You've got tokenized equities, securities, money market funds, commodities, all coming in with different compliance, KYC, you know, and different types of requirements. And so the interop space, crosschain space has expanded much more into the asset centric view with now kind of entering the compliant era of the world. I always wanted to kind of like lay that foundation and if you guys want to fill in any holes here, Will, Allan, what

353. 2:26:56

     what am I missing about the state of crosschain today? Um helped help to kind of fill in the foundation here. Yeah, I mean I guess from from my perspective, uh crosschain's come a pretty long way. Um there was a time when you were paying like insane slippage to jump from one place to another using like a really dodgy exchange in a really you didn't know if your funds were going to be able to like

354. 2:27:20

     be withdrawn. Uh you would just sort of send USD do some trade and then maybe you can withdraw it, maybe you can't. Um we've come a long way from there. Obviously like there's been a lot of hacks, there's been a lot of like crazy things. There's been a lot of protocols like going up and down. Um, but I think we're at a point where a user can ape into a memecoin on BNB and then very quickly in one or two clicks swap that

355. 2:27:46

     into a stock on Salana and then if they want to earn yield on that stock they can do that and uh they can do it from their telegram they can do it from their phone they can do it from their browser. Um so I think we've come a pretty long way. Um, obviously I believe that the best way to execute that is through intents and I think we're proving that with like our volume and a lot of the the flow that we're getting through a lot of the sources that it comes from.

356. 2:28:14

     But there are certainly different different models to do it. But um but yeah, I I think we've come a long way and I think the next leg is probably getting even better price execution, getting even better compliance, um attracting bigger players with larger liquidity that want to trade things other than memecoins and NFTs, but like RWAs and like important more important assets, if you will. Um but yeah, we're

357. 2:28:38

     definitely on in the right in the right path. Inside you there are two wolves two wolves will there's the RWA and the and and the meme wolf if I may will and then Allan I'm super curious to hear your perspective especially around the immutability of of Thor swap which often gets you guys in some trouble but I'm super curious your take there is will on the near and near.com front end when you

358. 2:29:02

     were doing swaps or sending um you get this um slippage notification where it says like 0.1% is like the max accepted. But then sometimes when the when you actually do the swap, it says like the minimum you'll receive and sometimes it's a scary number. Sometimes it's like I'm swapping, you know, Zcash to near and it's like maximum amount you're going to pay six Zcash. I'm like, whoa, whoa, whoa, whoa. This is like a 20 grand swap. You're telling me I might lose six $8,000 on this? Like, so I

359. 2:29:36

     don't know if that's a front end thing, but I don't know if it's a legal thing that that you have to do, but I just wanted to present that to you as a user. Just some some feedback. It's kind of a scary It's kind of a scary site sometimes.

360. 2:29:47

     Yeah. So, I think that's really good feedback. And I think with near.com specifically, um, this product only just shipped in February. Um, but it's built on top of Intent. So, before that, the front end of Intense, if anyone's used it, it looks ancient compared to what we have now. Uh and intense like near intense as a whole has all these features. We have oneclick earn where you can go into any yield opportunity

361. 2:30:11

     that that we have integrated. We have all the swaps. We have rwas a lot of other things. We have per like they're trying to put everything together. So yeah, I would probably fault that down to like a like a like a platform issue like a front end bug. Um but having but having said that uh on the slippage side um most users don't really understand what slippage is and how it comes about and I think what you need to

362. 2:30:36

     think about is like you know some users think okay I want to swap Zcash for near these are two very volatile assets and if I create this intent right now in 10 minutes or whenever Zcash the Zcash chain has passed that on to near um the quota is going to be the same but like at the moment With this volatility, we're seeing Zcash go like insane numbers up and down and near as well to

363. 2:31:00

     be honest in a matter of seconds. And so often the quote that you you get is stale and the the slippage is a lot different than you might expect. Um this is what sometimes causes swaps to get stuck. Um sometimes causes uh worse price execution. Um there was a good example recently on FOMO where there was a a trader that lost like 40% just by swapping out of an asset and they were sort of blaming FOMO and it was like

364. 2:31:26

     well actually it's just you're trading a meme coin that's super liquid and super volatile so like it's going to have this. Um, so that's just like a side note on slippage because a lot of people they want to like I don't know one click 100k clips in and out of an asset and like there's going to be some slippage regardless like even if you went to Binance and you swap 100k with the Zcash just a market order no limit orders like

365. 2:31:49

     you're going to see some slippage. Um, but we have some cool features coming out for that soon as well. So hopefully that won't be a problem for long. Very cool. Very cool. Very cool. And and Alan, how how do you approach this? you know, obviously when there's, you know, times of volatility, it kind of stress tests infrastructure. How do you manage this at Thorro?

366. 2:32:08

     Yeah. Um, I think this goes into some other questions we could dive into, but uh on a higher level, um, we've we've been building, you know, basically a decentralized um, exchange front end since 2021. Um, the name comes from 4chain. uh and even before that you know it was really looking at um atomic swaps like you know previously as a kind of a peer-to-peer solution for you know doing you know uh decentralized permissionless trading crosschain especially right um

367. 2:32:33

     and I think amm last cycle you know defy summit was the solution um and I think now intense has proven to be a more you know uh even more capital efficient you know model on top of that um but certainly uh because we aggregate so many different providers and now you know I think uh in times of volatility sometimes AMM models actually um do provide a benefit in that you know um uh if the pool just rebalances then you

368. 2:32:57

     know you might get a better execution um or you know there there may be other models where it's kind of like you might want to t-wop like like slightly split up a trade into smaller chunks so that you don't get you know hit with one um one big slip you know in in one trade um but uh I think the benefit of us you know you know you know u you know using near intense um we're one of the first to integrate like you know uh I think

369. 2:33:20

     July last year um and you know Zcash and everything really took off and um we've seen the volumes like obviously you know tremendous volumes on on for swap and um uh you know through near intents and that's purely because of the you know both the the the pricing um and execution and uh and yeah just the amount of like chains that can be accessed that was previously siloed. So, um, it's funny you mentioned about slippage

370. 2:33:46

     cuz I remember in the DeFi summer was kind of like, oh, if you want to buy a meme coin, you know, you should set slippage to like, I don't know, like 10%. 20 10 10 to 20% or something or else it'll never fill. Um, but but I think folks are very used to um now with, you know, Salana and just like sniping, you know, into meme tokens. Um, it's gotten very very um aggressive, you know. So,

371. 2:34:08

     um I mean to be honest, it's not really something that we we tend to um uh that's not really like our platform because it's not really a trading platform as much as it's a um yeah, it's more of a kind of OG cy cipher funk like you know exchange you know for for self custody you know and that's one of the things I love about what you guys have built and I think uh thither is going to come in as well so we can send him over. Um, and

372. 2:34:34

     Allan, I mean, you guys get yourself a fair share of flack for not giving in to authorities, to uh bounties, to all these things because you guys just persist a immutable opensource transparent um system and then obviously like there's privacy embedded in when you cross chains and things, but you know, I just am curious to understand your perspective on why the value of immutability is so important. important in crosschain systems, in decentralized systems, you know, what is Allen's take

373. 2:35:07

     on Thor Swap's ability to remain immutable no matter what happens, right? I think just just to clarify first, um so you know, we were founded in 2021 um alongside Thor Chain um but we're actually independent team. So, you know, it's um at the time was like basically the main and only front end.

374. 2:35:27

     Um and to be honest, you know, all of this I think I called a previous panel and it was really good. You know, it covered a lot of the topics about, you know, regulation and compliance and you know, everything. Um so for us, it wasn't really a you know, a huge issue until I would say 2023. Um when the topic of like, okay, is moving as crosschain considered um a mixer so to speak if a elicit like a bad actor

375. 2:35:51

     happens to use it, right? Um because it's technically all onchain. is actually transparent and traceable. But um for platforms um like protocol holder like protocols or just you know front ends directly serving users um you know uh like is there a solution where we're not like you know con like forever going to be essentially seen as um the only you know use case is serving bad actors

376. 2:36:15

     right so I think obviously centralization is a spectrum and then when you're talking about managing like funds across you know moving funds that's like um a very difficult you know way to like u um mechanism to say like you cannot interfere right so um I think in in terms of that like we've like since you know the past few years um on a front end level and we we I think the some of

377. 2:36:39

     the previous topic um covered this is you know we try to do our best to uh enforce like um AML so we know you know we can try be on top of that and work with the best you know um teams in the space to make sure that we're not deliberately serving bad actors um because to be honest like we intend to be around for another you know five years and we want to be serving users who who are going to be here for another five years and not because they just

378. 2:37:01

     like you know exploited some protocol it you know so um I think our intent like you know not to use a pun here but is our intent is to serve good users um who needs a you know financial sovereignty and in self-custody so u we try to do our best and then we work with protocols that are doing you know a good job of this as well such as narence so um I know chain has their own like every protocol has their own stance on it to

379. 2:37:24

     be honest and um you know and if people choose to use protocols directly you know whether um it's you know through onchain messaging or what have you um then you know the protocols will have to respond and you know um build mechanisms you know for that but for front ends I think our first of all is because we cannot freeze funds you know we do not take funds into custody so you know the first instance is basically saying like if you can you know um observe that you

380. 2:37:49

     are a bad actor and work with you know teams in space to make sure that we all do a collectively do a good job to prevent you know, these things from happening, then uh the least we can do is not to serve them, right? Um regardless of how much revenue is at stake, you know, it's just not the business that we want to be in. So, got it. Appreciate that. Um thither, do we got you thither?

381. 2:38:11

     Close. It's like uh slither slither with a th at the start. Um and yeah, sorry, a little technical difficulty joining, but uh Hey, no worries. Thanks for having me. Um yeah, man. I mean, we would love to get you involved. So, we're talking just kind of generally about the growth of intents and just how the space has progressed um with the lens of entering this like compliant era and I mean it's kind of boring but I saw you guys got

382. 2:38:36

     your SOC like one and two licenses I think like yesterday or two days ago you guys dropped that. So, congrats on that. Um, I'm just curious like from the layer zero perspective, maybe just like what's the biggest um, like focus point, right, for expanding the interop and kind of crosschain stack you guys have?

383. 2:38:55

     Obviously, we're in this institutional era. We're also in this like Robin Hood chain meme era and this like tokenized equity crosschain era where we're seeing a lot of the fragmentation issues that we had in like the early days of crosschain stable coins. just kind of, you know, paint the picture for us of what layer zero is tackling right now as far as uh priority lists.

384. 2:39:15

     Yeah, I think, you know, in terms of priority, there there are kind of these two things that we're doing as a company right now, right? There's the interop business that we, you know, have been a player in for five, six years now. Um, and then there is the upcoming L1 that we're launching at some point, you know, hopefully before the end of the year. Um I think the two do play on each other and they do certainly overlap a lot in terms of the you know very institutional use cases and and partners that we're

385. 2:39:40

     going after for both of them. Um the sock one and sock two stuff. Yeah, I appreciate that. I think that that you know definitely is confidence inspiring for people in that institutionally facing uh bucket. But at the end of the day, I think, you know, a lot of these teams are they're they're led or featured, you know, with people that have been in the crypto space for a while who kind of

386. 2:40:03

     understand the way that the, you know, ecosystem of chains and the the ways to move assets and use cases for assets and all of these different DeFi related concepts have have grown and evolved. Um, I think the ball just rolls much much slower um in that world when you compare it to DeFi, right? And so there are things like, you know, proof of concepts, pilot programs, um, extensive testing, you know, in private before teams are willing to be able to actually

387. 2:40:29

     launch public mainet products. Um, and I think, yeah, we're seeing that, you know, certainly with interop, but to an even greater extent with zero with the chain. Um on the interop side, yeah, we work with obviously a ton of RWAs with, you know, virtually all of the tokenized equity issuers with a lot of these different um stable coins that are rappers for things like like private credit for example. Um and yeah, I think

388. 2:40:52

     at the end of the day, uh giving these people the confidence that they need to succeed is only half of our job. The other half kind of comes from the regulatory environment on things like whether or not it's you know legal for money market funds to exist on blockchains or for uh major institutional market makers to trade perpetual futures and things uh of that nature. So it's a you know it's a

389. 2:41:15

     rolling or a moving target and we're trying to stay one step ahead of it on both of these uh different business lines that we have. Yeah. And Will, from your perspective, right, you know, Robin Hood chain pops up out of nowhere kind of to what to what I'm sorry, Slizzard's saying. I I'm I'm going to mess it up. Um and um you know, you guys have to make a a liquidity decision, right, of supporting

390. 2:41:40

     this new chain with with your with your solver network, right? And you you know, you want to be there quickly. Um and also this app FOMO is obviously like really growing. How do you guys make decisions about porting solvers and market makers to support new assets and new chains? Um because yeah, I mean we're seeing tokenized equities take take the world by storm and a lot of it is like this like initial bootstrapping kind of meme phase, but it's obvious where this is heading, right? And so how

391. 2:42:12

     do you guys enable that future? And then you know what was the Robin Hood chain story for you guys? Yeah, sure. So um actually first of all like it's very difficult to assess whether a chain is going to be successful or not. Uh if we look at [snorts] like last I don't know last year everyone was sort of ra or two years ago everyone was raving about uh bar chain and monad and and not to say

392. 2:42:34

     that those teams weren't sort of successful but I think it's fair to say they didn't have the the the great crypto impact we thought they would. They didn't go to like a top 10 as many people at least at the time were saying. Um we obviously we chose to support support both of them and we're happy we did. Um great partners but obviously like ahead of time there was there's nothing to to say what what is going to make Robin Hood be like what Robin Hood is or what is going to make it be not as successful not have the same hype. So

393. 2:43:02

     it's super difficult to tell ahead of time. Um it's not just a case of like getting liquidity over and getting solvers to to compete in those markets. That's like one part of the orchestration of it. But we also have to have our, you know, the bridging team actually integrate the chain. We have to look at um how does the chain work specifically? What would um what are the risk factors within the chain? And this

394. 2:43:25

     takes some time. If it's an EVM chain, it's relatively simpler because a lot of concepts and things are sort of copied over uh when people make a new EVM chain. But if it's like a like a Zcash, uh that one's obviously like a little more complicated because there's a whole new technology. It's also got these privacy features that are really difficult to think through. Um, so yeah,

395. 2:43:48

     it takes a lot. I think it's a thing of like it's a balance of like how difficult is it to to support the chain? How easy is it to get solvers and liquidity on there? Um, and then how big of a priority should we make it based on the volume we think it's going to have.

396. 2:44:04

     So, in hindsight, Robin Hood, we should have had it from day one. uh and we didn't and we don't have it yet, but we um but you know, it's it's very tough to know that Robin Hood is going to go and pull that. Um now we have another a couple more exciting chains coming up and the question is like are they going to have the same effect as as Robin Hood or is it going to depend on the price of Bitcoin at the time or like

397. 2:44:26

     what what's going to cause it? Um so yeah, that's kind of how we try and make the decisions, but it's definitely like a moving a moving target and um I don't I think it's more of like an art than a science. like it's very difficult to predict what is going to pop next.

398. 2:44:43

     Yeah. Um guys, as we kind of come to a a close here, I know it's uh tight on the hour. Just kind of thinking about the tokenized equity part of the qua part of the equation. Are are we anticipating a massive sum of crosschain activity to be non-crypto assets in a similar capacity that you know HIP3 did extremely well on Hyperlid during like the commodities and AI run? Are we are we expecting something similar in the kind of asset transfer and movement of assets on chain space or do you

399. 2:45:16

     anticipate that it'll be consistently dominated by crypto assets for the foreseeable future? I'll open. I feel like Yeah. No, no, you Oh, thanks. Um, yeah, I think we've already seen the needle move considerably, right? I mean, even if obviously every, you know, everyone's seeing what's happening on Hyperlid, um, all of these other issuers and exchanges and apps that are launching their own flavors of tokenized equities in

400. 2:45:44

     particular, but even if you look at a lot of the like memecoin activity, especially like Robin Hood chains memecoin activity, um, you know, virtually all of the big tokens there are paired on the back end with Robin Hood tokenized equities, right? And I think like it is cool for you know I've been in in crypto for probably a decade now and it's cool to see the convergence of these things that you know we all know

401. 2:46:09

     and love on kind of the more den side of crypto uh coupled with the things that we've all been talking about for years now you know in terms of wanting to see on chain like tokenized equities so I think that'll probably continue um but yeah it's certainly been a fun last few months on chain in that sense Yeah, Alan, right? I was going to say um I think we started this conversation by talking about you know with the state of like crosschain but um it's really about I think you know not necessary crosschain multi chain but really about like you

402. 2:46:40

     know onchain now right so whether it's to equities or commodities um the good thing is they are onchain assets and I suppose the other good thing is all things considered they all blue chips you know because most of them have a like you know they've been around for much longer than um you know altcoins or memecoins in particular so uh I think that's pretty bullish to me in the sense that like you know as long as when the market shifts liquidity is not going

403. 2:47:05

     offchain we you know it means the the whole ecosystem is thriving right and our job is basically to try to um you know connect like build the rails and then offer them to to users so they can still um have access to them you know at any time right um I think uh you know the models of whatever is is the latest hottest thing will change but um the fact that we just have more you know

404. 2:47:29

     market cap in general moving on chain is is a really bullish thing you know um so yeah I I think um especially I think feel like we're moving into a into like a recovery phase now from the bear so you know this this can only be good yeah yeah I agree on the number of high quality assets I think that's probably kind of more of what I was getting at is just you anticipate to to see a lot more demand and volume traded and swapped and

405. 2:47:53

     sent around of these higher quality assets as investors kind of want to invest into these and less into more speculative things and it's also quite interesting as you say about the timing Allan where we we've gone through this bear where we've been you know we call it the revenue meadow where it's like the assets that are being bid are the ones that are making money that are giving money back to their token holders they're doing that they're building an

406. 2:48:17

     onchain business and so simultaneously you've got a lot of squeezing of the fat or the trimming of the fat where it's like a you've got better quality assets stocks equities on chain now to invest in so that's taking flows away from, you know, governance tokens, you could say.

407. 2:48:31

     And then B, you've got um high quality crypto assets now, right? And so you've kind of got this like dual force that's just squeezing the the market for legitimacy, right? And I think that's super super bullish for the market and for onchain finance as a whole.

408. 2:48:48

     Yeah, for sure. Because I think the one other thing is the tough part is the liquidity, right? you know what you're talking about like if you want to build these rails is liquidity is very very hard unless you just completely abstract everything away and it's all just on a centralized you know platform it's like you know trust me bro I owe you you know I've got the I've got the liquidity

409. 2:49:04

     which we've seen doesn't work you know and um and I think you know that we just saw like the laptop token basically like people not really falling for it anymore right so basically you know like the tokens that stick around have are going to have a better chance of you know having liquidity available and as long as you have deep liquidity um there's going to be, you know, opportunities for you to to not only keep building in in in in in the space, but also um yeah,

410. 2:49:28

     like actually have a reason to be there, you know. Um so, yeah, like the meta will change, but um I think we we're all a bit more, you know, I don't think the transfer has anything to do with this. It's just like a natural evolution of the space. I think, you know, we've just gotten a lot better at this, you know, and um then because we've learned from the mistakes. I would like to say that.

411. 2:49:47

     Um, I put out a post yesterday that was like unpopular opinion memes are good because I've been sending my my friends from like the fifth grade of my FOMO link and like sending them like $500 of memes. I'm like, "Guys, I know you're going to blow money this weekend on the NFL opening week on gambling. Just like try this instead." And people are all my comments being like, "Dude, you're an idiot. Like this is not any better." And I'm like, "Okay, fine. At least they can like think about growing a portfolio and like they can go to bed at night and the money's going to be there the next day.

412. 2:50:19

     They're not going to zero out their account on some like plus 500 odds bet. And then Joe Biden's son, Hunter Biden, responds to my tweet and goes, "I agree. They onboarded me." So that's the state of affairs. Yeah. So Jens, thanks for your time.

413. 2:50:38

     Yeah. I hope people learn about uh uh FTVs and market caps like from this whole thing. Yeah. Yeah. If anything, that's that's a good thing to learn. Yeah. Yeah. Jens, thank you so much. Have a great rest of your day. Enjoy the bull market. Stay long. Stay strong. There's no reason to be bearish. Don't let Rob's macro takes and the rollup get to your head. Stay long. Stay strong. We're all good here.

414. 2:51:00

     Yeah. Great. Always great to talk to you guys, man. Yeah. Yep. Thanks for having us. Thanks, Chad. You guys take care. Cheers. Alrighty, Rob. Virtual near day continues. That is the last panel indeed. Um, what a show. I mean, amazing guests, amazing content. It's been a while since we've done a marathon like that. I'm I'm feeling it. I'm feeling it.

415. 2:51:25

     Yeah. Yeah. You uh you backpacked that last one. I was rather quiet. Um so, appreciate that. Hey, man. Looks good, man. Ah, I uh you're bearish. You're so bearish right now. I uh I I have no edge when it comes to predicting the inflation data that that's going to determine whether or not we're uh macro policy.

416. 2:51:48

     That's going to determine whether we're rich or poor. And I have no edge predicting that. So, I'm just going to let let it do its thing and react accordingly. But guys, um it's a good show. It's a great show. I mean, so many factors.

417. 2:52:01

     Yeah, we're doing a thousand USCC giveaway. Um, I think we'll probably do it at the end of the show tomorrow uh to a a handful of folks. We're going to split it up maybe into five F. So, five $200 giveaways. All you got to [snorts] do is we have this rollup. I'm going to pull up on the screen. We have this near times the rollup website. It's the rollup.nneer.com.

418. 2:52:25

     I'm going to paste it in the chat one more time across all the platforms. And all you got to do is go to the rollup.n.com near.com and click create an account. Click create an account. It'll take you to near.com, right? And then you just start trading.

419. 2:52:46

     You get 20% off of platform fees in your tokens. Create an account. Pass key. No KYC, no custodial, nothing. It's fully self-custodial, private, pass key only. Create an account, deposit some crypto. Um, and yeah, this is not available to US people or other restricted jurisdictions.

420. 2:53:08

     Leverage trading carries a sub substantial risk of loss. It's not financial advice. But guys, sign up, be a part of this, and deposit some crypto, swap something, stake something, use the vaults, and we'll we're going to do a giveaway to all the first users. We're going to send it to your near account.

421. 2:53:29

     So you'll have you'll have it there. And then we're going to be trading on this using this and more to come. So guys, that's the closeout. We'll see you for the giveaway tomorrow at the end of the stream. W's in the chat. Debbie's in the chat. Deb's in the chat. W's in the chat.

422. 2:53:48

     Thor swap. Yes. Thanks for coming. And guys, stay long, stay strong. CPI tomorrow at 8:30. Pull the screen down and we're on. We're on. I'm bullish. Rob's bearish. And I'm I'm neutral into tomorrow. He's neutral. He's neutral, guys. Guys, guys, he's neutral. He's neutral.

423. 2:54:19

     Misrepresenting my He's neutral, guys. He's feeling he's in a very neutral part of his life. He's in his neutral era. Certainly am. All right, guys. Much love, Rob. Much love, audience, everybody who's being a part of here. If you're watching on YouTube, smash the like. Smash the subscribe. Means a lot to us. Goes a long way. Helps the channel grow.

424. 2:54:37

     Easiest way to support us. Smash the like. Smash the like. Smash the like. Appreciate you guys. We'll be live tomorrow, same place, same time from the financial capital of the world, the tokenization tower. Rob down in the rollup HQ. Shout out to Near. Shout out to the virtual near day.

425. 2:54:51

     all the guests, all the fans, all the NearCat holders who made this possible. You guys are amazing. Thanks for being part of the show today.