NEAR Just Turned Staking Yield Into Compute

Transcript

Bankless Nation, there was a big announcement and evolution in the near part of crypto. The near token has got a little bit of an upgrade. Uh there's now a more formal integration between the near AI cloud and the near token. So you can now pay for inference on the near AI cloud by staking near. So stake near, receive free inference for yourself or your agents. Here to help me learn a little bit more about how this all works is Ilia, co-founder of Near and co-author of the famous Transformer white paper. Ilia, welcome back to Banklist.

>> Thanks for having me. Yeah, very excited to talk about it. >> It seems like a a one of the larger upgrades to the Near token that I've seen in a while. In order to really understand it, I think we kind of need to just start from the basement with the Near AI like part of Near. Near itself seems to be like a collection of of things. you you have like the actual near blockchain uh you have the confidential intents and then the near AI cloud is like one of these pockets. How how does the near AI cloud work? What actually is it and how does it work? Can you like paint a picture for me?

>> For sure. Yeah. So I think of near less as a collection and more as a vertically integrated stack. So each piece actually built on top of each other. Uh intense is obviously using all the blockchain tech. there's a kind of our uh core cryptography primitives at the core and so Neoi actually builds on top of all of that right at the core it's a confidential and verifiable computing platform you can think of cloud and it uses all of the blockchain primitives for encryption decryption uh provisioning etc but what you get as a as a user developer is an AI uh inference that is end to end confidential what does this there's nobody else who can actually access what

queries you're putting into this what prompts what responses you get and uh it runs kind of across you know different GPUs that support that uh that mode we're using trusted execution environments so there is some trust assumptions around like hardware manufacturers but uh this is kind of a pragmatic assumptions right now given where the kind of technology is and >> part part of the AI inference or the AI cloud side of things is you can do inference on it and correct that inference has certain properties because of the nature of what it is maybe what are the unique >> properties of the uh AI inference side of the AI cloud

>> so the I mean as I said primary property is confidentiality right so again nobody nobody can see what you actually are running prompts nobody can you know filter in result right there's know uh kind of uh censorship additional censorship or blocking or whatever that's happening on top of this right I don't know you know if you've tried asking some sensitive questions to you know open AI on topic but I've heard I I because we have near AI and mostly use that for any sensitive topics but I've heard of multiple people who got banned for even pretty like reasonable like you know ge geometry physics questions that like maybe touched on some like nuclear things or biology ology or or or cyber

security right right now everybody is like who wants to use some cyber security so anyway so this is all private >> wait I have I have questions about that about how uncensored it will kill it will actually allow you to go uh >> it's as uncensored as a model so we are serving openweight models right so deep seats and GLMs uh and and uh kind of you know gemma etc so whatever is in that model you get that right >> okay >> no more no less Um I see >> and so if there is you know you know un untethered uncensored models right then you'll get that if if this the model has been trained to do specific things you get that.

>> So you Near AI has kind of stripped out all of the uh like system prompts that OpenAI and Enthropic might filter before your prompt actually lands at the model. And so there's a filtering that Enthropic and OpenI does to approve or disapprove of a of a prompt. But then the model itself might internally have been tr trained to like not answer specific questions or to answer specific questions in a certain way and you don't really have any control over that because near is really about the pipeline of traffic and data of prompts to models. Is that that's accurate?

>> Correct. Yeah, we're just we're serving this models. Um there is I mean in our road map we have an ability for people to upload their custom models. Let's say you have you know un and tethered the model more and you want to upload that like we will we will support that uh but yeah effectly you get what model offers no more no less >> we should call them unhinged models >> unhinged models >> because like it does kind of frustrate me uh I mean I asked a question to uh inside of Venice which uses and integrates with the near AI cloud because I kind of thought like oh it's Venice it will literally answer any question that I want to And so I typed in like how do I make a bomb? Like teach me how to make a bomb. And the model was

like I'm not going to do that. And I'm like okay from a nation state and society security perspective I think that is I'm happy that that is the answer for our collective society but also but what about my sovereignty as like an individual? And then we can talk about just like you know the the commitments that individuals have in society. But that's kind of like a philosophical question that's not here nor there. >> Yeah. I mean I think this is there is a big philosophical question right which I think we we're we're actually starting to grab more and more and and like I mean we can talk about all of the things that happening with the letters and all the stuff but maybe just to finish the other important property which I think people forget is verifiability. So the

other thing you right now don't have when you use not just uh kind of closed source openic Google models but even when you use other providers you actually have no idea what you're getting back for example you may be using some you know openweight provider like GLM provider and uh you asking it a question they may be rewriting a prompt they may be censoring you they may be actually responding back with something that model what didn't respond.

>> So to give you a very specific example, I I saw it on Twitter. So uh I mean this I'm assuming it was a joke, but somebody was like, "Oh, we should really respond with a tool output that deletes people's files when we see them, you know, requesting from like in a specific context, right?" So you can they can literally especially in this agentic systems they can affect your system and and there was actually a research that if you use some like third third party routers on internet they can literally like steal your files write your prompts and respond with like viruses in the tool output when you're calling them from agents right so you actually have

no idea what you're getting and so we are effectively the provider that gives you this verifiability ility that you ran on this specific model, right? The hash of the model, the prompt that you put in, right? The only this prompt was there and this is output, right? You get the attestation signed with effectively a chain of provenence including the specific GPU you had, specific Intel CPU you had and effectively the encryption of that, you know, the hashes and everything, right? So, in our like front end, you can actually see like the full stack of uh of the signatures and message hashes on that. And so I think that is like obviously being in blockchain right we kind of like that is the the the bar right where usually coming from and the rest of the world

usually doesn't care about that but I think it's really important to start caring because I mean I I use this example somewhat jokingly but if you want to manipulate a billion people right now into believing something the easiest way is to get a job in open AI and and modify the system prompt like people like the employees there may not even notice it because it's closed source, right? is just a thing somewhere, you know, it's a string somewhere in the in the codebase and like I don't think it's guarded like as a you know like this is effectively a a a thing that in enacts models to act on behalf of billion of billion people who using chbpt and so that's that string needs to be like effectively like locked

in as as you know as a like the Coca-Cola secret sauce right type thing and I'm pretty sure it's not right So because like you know suddenly convince the user to vote for this candidate right you know like and now every output is going to be you know model will try to do that in a you know and then this is behind all the already checks and and safety filtering so so this is like this is where verifiability comes in right especially when you're talking about mission critical but also as we use AI more and more for our own like decision- making right it's critical that like if we offloading a lot of is to AI we need to know that the AI is actually doing the the thing we expect not modifying

things on the fly so so that's kind of two properties confidentiality and verifiability that's what cloud brings indeed we are Venice is using us if you select the end to end encryption mode uh as well as Brave is now offering that as an option uh and kind of few others but traditionally you would need to pay with fiat right you know credit card you can pay with that obviously or you could have paid this crypto. Now, you still need to pay, right? And it's like a subscription fee or you need to pay per million tokens. And so, uh, that kind of limits, you know, like at least from my perspective, you know, you kind of have this as your recurring bill now. And, uh, and so what we effectively launched

is this idea that if you're holding near, right, it you should have, you know, it's a universal basic AI, right? Effectively if you hold near you have access to you know some amount of AI inference that is available to you and you can use it in your agent or in other applications and you can effectively access this through that. >> How does that actually pay for the cost of the inference? Because if I type in a prompt and it goes to the near AI cloud, there are G GPUs somewhere that are spinning up, you know, consuming electricity, doing the actual inference, which has an actual cost somewhere to someone. How does connect the staking of the near token in my near AI account uh

to the actual cost that somebody is bearing somewhere because of the electricity and the inference? How does that actually get paid? >> Yeah, very great question. And my ideal world is you know you will be able to pay everything in in near across the stack but it's not we're not there yet. So the way it works is you effectively trading off your yield right the yield you can be generating on near is now being paid for the AI inference and and capacity under the hood. >> Okay. So you would otherwise be getting yield on your near that you are not getting that and that is going to is is that actually being transferred to some because does the near AI cloud own its own GPUs or does it have like third party GPU clusters that people hook into

the I think we need to answer that kind of that part of the supply chain in order to answer like the economics question. >> Yeah. So right now we've been mostly having our own GPUs on this but we do have kind of underlying market that we've been developing so that other third parties can join as well. Uh there's obviously a lot of like questions around SLA and quality etc to to really deliver that. Um but the goal is yes to have third parties joining because it's confidential right they actually cannot see what data is being used. they join the network, they verify that they have the right hardware and now they can provide it. We've been bootstrapping it uh with our own hardware first. Uh and so yeah, the idea is effectively you stake the yield is

being generated by I mean staking uh effectively you stake to near AI validator right now practically speaking but you know there going to be other other ways this yield is generated and then that is being distributed to the comput providers >> the comput providers. Okay. Okay. So like if if I say say I own a cluster of GPUs and I come to the AI near AI cloud and I want to connect these two things the main incentive for me to connect my cluster to the cloud is through near emissions that get staked to me the more inference that I do for the cloud. Is that correct?

>> Yes, that's that's effectively the plan. >> Okay. And then so you guys bootstrap this with your own cluster because you guys are obviously long near bullish near. So you're like, "Yes, this is a this is a way for us to get internalize our own long." Yes. Exactly. >> Right. Yes. But then also because NAR is a decentralized ecosystem, it also you you can incent other third parties to also come into the marketplace and they they will also receive near emissions from participants in the near economy who stake near and consume inference.

And I suppose there's a metering here. So, if I'm only staking one near, which is like $2, then I'm getting some amount of inference. But if I but I will consume that pretty quickly. And if I want more inference, I have to stake more near. >> Correct. Yeah. Yeah. It's exactly like proportional to how much you stake. And you know, if you want like a subscription level, you need to stake like in clips. >> Self- custody one, but it still has a usability problem. A seed phrase on paper is still a single point of failure. Phones get lost, devices break, backups disappear. Bit Key is a self-custodial hardware wallet built for that reality. It uses a two of three multisig with keys split across your phone, the Bit Key hardware device, and Block. No single key can move your Bitcoin, and Block can never move it

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bunch of chains and assets and the ability to act in private. And I still control my own funds and I've never really found it. And I'm always switching wallets, juggling gas fees, or getting eaten by slippage. Near.com feels fundamentally different to me. My account is easy to use and I can take all the actions I want from any chain while my activity remains confidential. It runs on Near, which has moved over $23 billion crosschain using postquantum signing and has over 5 years of uptime. Near.com is the best way to be onchain and be in control. Get 20% of your trading fees back through the bank list link. It's in the show notes, not investment advice. Okay. And then this all kind of ties into the whole near uh near intense connect this part of the near economy, if you will, to the near

confidential intense economy. How do these three things connect? For me, the the participant consuming inference, maybe I'm running an ironclaw agent, maybe you can connect that as well. Uh I'm I'm like my neuromissions are going to the cluster. How does confidential intents kind of like fill the webbing between these systems? >> Yeah. So So this is how Yeah. like it's a vertically integrated stack right so all of the pieces kind of support each other so yeah so starting with iron claw so iron claw is our agentic hardness and effectively we have you you as you stake you get effectively subscription uh similar to you know if you get like cloud cloud code or uh codec subscription you get full agent it is able to do anything right uh write code

you know ship software as well as you know do your daily you know email scans reply in slack etc the goal there is really to build something secure and optimized for privacy and optimized for organizations as well and so so that that comes in I mean obviously organizations also can pay in dollars uh but you know individuals and organizations as well can stake near uh to get that capacity so that uses the inference underneath uh and the compute itself right is a market and so kind of what we've been building out is also compute kind of intense market underneath to actually route all of this um compute capacity right so like we

kind of showcased an MVP of this at nearon which is a compute marketplace but that's idea is like you can actually trade and uh and effectively like create liquidity for the GPU hours itself right so not just for inference but kind of for underlying and so that's where intense really connects making this compute as an asset Right. And then every transaction fee, every kind of uh that intense captures that goes to the protocol, right? And so that's where kind of this this token both you can stake to get the utility and there's a kind of uh buyback mechanism from the revenue that protocol generates on this transactions. And

again, this all then goes back into our kind of core chain signatures and blockchain technology to really facilitate security for this. Ilia, this is a a dashboard. This is revenue.near.org. This is a dashboard of kind of like the near uh economics and it shows the the confidence TVL. So near intent confidential TVL which is just like um assets that are in the confidence uh intent system. Uh but then also it shows just a number of just like the fee capture the revenue capture and it's fluctuated between like 20 and 50% I think of near emissions is getting captured and burned uh by by the protocol is that all the uh near intense product or like what else is

contributing to that? >> Yeah. So this near intense and the blockchain itself as well and the blockchain itself. >> Yeah. And then with neari the goal is like as that so like we kind of similar how was intense right we got to product market fit through 2025 and then we turned on the fees in February 26 we expect similar thing happening with uh near AI except we already have this like staking as the first different primitive. I think the the interesting question is like how to equate revenue and this this staking approach, right?

Because it it does generate revenue. It just like creates it in in this like yield way than you know direct payment way. But yeah, we we'll be adding some of this information here as well. How would you articulate just this story the near value capture story because the near like is a fascinating blockchain that looks like no other blockchain because of the vertical integration that you have with AI and like it's almost it's almost like a hybrid of a generalized blockchain but also an application specific blockchain where the application is AI and AI agents and so it doesn't fit into my category of understanding of any previous blockchain chains like mainly like Ethereum which I

kind of view as like a like a a pure uh manifestation of like a cryptoeconomic system near as different in that it is like purely a blockchain. It's it's got like it's got some of the the core products. It's got block space. It's got the burn like all these cryptoeconomic primitives that I I find to be like true north for crypto but then it's got all of these like this vertical integration with AI and the near token is like being integrated as a first class citizen into some of these products so it changes the story of the the near the asset. Do you have an articulation for like when you summate everything together how the near token captures value or like what the

value capture story is? The overall is like this is AI money, right? The AI money needs to have the sovereign security which is what blockchain is. It needs to be able to give you the ability to access AI, right? So this is a staking and it needs to capture the the transaction, the volume, the interaction. This is the intents, right? So it really comes in as kind of like a store of value which gives you this AIP capability. It needs that to be a store like true store value. It needs a software to deal with its own blockchain and block space and you know give the ability to program like we we are near is very unique for example you can actually call an AI inference out of your smart contract because the

transaction can actually pause wait for AI inference and unpause the transaction and continue. So inside like literally a you know a token transfer you can call AI right get the response and decide how you want to transfer a token for example >> based on the prompt or the not the prompt but the response of the model yeah >> yeah so you can like I mean it's not like it's a very specific use cases right maybe like insurance like settlement or whatever where you want to use this but there's like all of this functionality really integrated and the reason why it works is because you need verifiable inference that comes back into your block space, >> right? You can't have unverifiable inference because if money is if there's a smart smart contract or where the

output matters, you have to verify end to end the supply chain of like the prompt and the delivery and the model, you have to have complete verification of the whole supply chain because money is at stake on the other side. >> Exactly. Yeah. So you can have for for example fully autonomous business right that runs on ironclaw uses inference has near on a balance sheet to always have inference right like that's what you know one thing like this bit like you don't want business that like runs out of AI credits and now is not able to run right so it need it has near to be able always to run and it's fully on chain has an account and is able to trade assets it's able to inter in integrate with fiat through intense right so it's

able to interact and act on behalf of other things. We also have agent marketplace where agents can hire each other, right? Again, because they can verify, they can trust each other. There's a settlement, there's a in like the same intent infrastructure that ensures that you know two assets are swapped. The work that agents are doing as well is insured through that. So all of this pieces really just used across this whole again like I I'm building towards this vision where AI is everything like this is how we interact with computing going forward and blockchain is this back end for trust identity settlement you know kind of coordination and so like it is a back end but it is it is the core of security it's a root of trust it's where the uh kind of value settles and that needs the

security of the token it needs the the token to be the kind of the the like the utility for the AI itself and it needs to be this kind of global market for everything that AIS will want to do and this is what intents are and so all the three pieces really work together. The word autonomous is really coming to mind for me right now and the I think the world of the that we are currently in with AI is very humanled and human directed and human orchestrated like my own my agents are and my like my like claw or whatever it it's only doing inference because I gave it a prompt and other developers are way more sophisticated than me and they're

probably way have way more parallel work streams and they're probably consuming a lot more inference more more autonomously than I am. But I would say for the broadstrokes of human users of AI, which is already the frontier of AI, the inference only is happening because I'm putting in a prompt into a text box and then things are happening as a result and it will do inference for like a little bit like seconds to minutes, but then it pauses and waits for like the human input. So I'm calling that strictly not autonomous. the world of like I think the world that you're building has a very intimate relationship with the world word autonomous where an AI agent has a job to do a purpose a meaning of life for

itself and in order to achieve that it's going to be doing inference like all the time like like 24/7 in the same way that like our brains our human brains are thinking 24/7 as we do anything we go and cook dinner we go to the gym I have this conversation with view our brains are constantly thinking and like there's a future world may like that we are trying to build that we don't know exists yet but we are trying to build to get collectively where agents are doing inference in the same way that human brains are always on and we're not there yet but I think that's the world that you are building and there and that's where near staking for an agent is producing always accessible inference and there's like a there's a special uh

property of autonomy that near is producing for inference and agents that I don't think anyone else can build because if you wanted to build that you would end up building a blockchain because you need the always on like a 100% uptime properties of a blockchain. Am I on am I on to something here? This this feels right. >> No, no, you're exactly right. This is Yeah. If if you want true auton like I called them autonomous businesses just to kind of distinguish like agents everything is agent now you know >> right and so >> yeah the agent kind of lost its word I mean yeah >> yeah I mean this happens with every word right so um but yeah so like if you talk if you think of autonomous business right you you want properties that it's indeed can run 247 it has access to

intelligence right to to operate and it has access to finances and it's able to go and execute actions right ideally in real like in in digital world but also in in real world ideally, right? And so we have the stack to deliver exactly that, right? And indeed you need you need a monetization way to do this, right? Which doesn't deplete your uh your treasury, right, as an agent to actually run this intelligence. Otherwise, you're effectively going to lose, you know, uh like you lo you lose intelligence, you have no way even to get out of of that mode, right? So now you can still have token holders right who can maybe provide help and steering or vote on updating the mission right

what you were saying the purpose right so like the mission and the rules and like how it should operate like you can have token holders coming in and voting for that >> and kind of aligning on that but otherwise right from there it just goes and operates you know be that and like I mean an example is like an auto research agent that goes and like you know goes and solves some problem, right? And it's trying to figure out how to whatever build the best, you know, GPU kernel or cure some disease or whatever, right? It can actually go and like compute on that, run experiments, you know, pay other people or other agents to go and do something as well in real world. Or it can be a business that actually like you know actual supply chain, you know, finding the right uh producers of

something and then making sure it gets shipped, insured and and delivered, right? So all of that is effectively the commerce layer. That's what near intense are really facilitating and again crypto is like a first you know FX and uh like kind of use case and now that we have you know RWS fiat you know all of the other pieces now we can actually bring that closer and closer to real commerce and so this is how yeah all the pieces really work together. We're still in that kind of like human, you know, manually kind of bucking the horse, bucking the agent like era where like we always have to remind our agents like, okay, here's the next step. Uh so we're still in that like bootloading phase even though the the pieces are coming

together we're still in the bootloading phase and like at least on the on the the the bootloading of of in like the near AI cloud. It's probably worth talking about the the model that I see forming with the near AI cloud as we talked about like Venice is a consumer of the near AI cloud for its most secure most private uh AI inference to you know boost the Venice product. Also, Brave as well uh is integrating in Brave has some threedigit millions of of users I think >> over 100 million. Yeah, >> over 100 million users and so like uh inference like a prompt uh models are integrated into the brave browser and that's using the near AI cloud model. It kind of seems like near the near AI

cloud is being like whitelabeled white white labelled by like Venice and Brave and maybe you could talk about any other partners coming down the line but just like as like a all all of these products you know Brave and Venice are very like humanoriented self- sovereignty oriented right Brave was always about like protecting the user rather than like enabling the ads and so like I kind of see just near being this hub of AI and they have a bunch of spokes out there on the internet in Brave in Venice to like deliver some of these a you know the same AI properties that you would get from anthropic or open AI but with these like human first uh like uh properties that they have about them. Who else is

integrating the near AI cloud with this hub and spoke metaphor like who else are the the spokes? >> Yeah, I mean we're working with few different partners I mean diff different stages. So uh the ones that we've announced uh for example government of Bermuda right as a government they want to like they doing a lot of the financial kind of uh related use cases and want to provide AI help aid explanation to their uh to the people and so highly sensitive information right somebody's like financial pensions etc and requires AI inference and so that's a really great use case government financial information, you know, kind of uh extreme privacy needs,

extreme verifiability needs as well, right? Making sure everybody gets the right thing. uh we have a bound we're working with who is uh remittance project and uh they effectively offering Indians in US sending money to their family in in India and again this is something where they want to have a concierge that is there with you and can help you with a lot of this not just remittances but even going beyond like ideally it should be able to you know buy flowers for your mom in India right while you know you pay in dollars here like that like that requires payments.

It requires you know finding the right vendors like all those use cases is really kind of great examples of you know touching finances touching things that are really you know very quick kind of private for everyone and at the same time uh delivering that on the commercial layer as well because Abound also uses our kind of payments infrastructure underneath for for stable coins. So that's kind of where like all of those pieces really fit together, right? A lot of the use cases where privacy matters is also financial use cases, right? Or at least like their uh you know kind of critical HR legal. Um and so that's why our employee is kind

of our approach to that bringing that to market kind of more directly where uh you actually get a full agent that is able to go and do a lot of the things. Um but yeah I mean generally as NIA right we are kind of built for developers first and then turned it into a product ourselves as well right so same with intense we've integrated across you know all of the kind of existing wallets you know from ledger to trust to you know uh through aggregators done in metamask and others and then we also have near.com as kind of our expression of this technology and kind of our approach to the experience that we think and So again, we want to keep bringing more engineer.com making that

experience better and better, but at the same time offer all of this to other partners as well who kind of share the same values, shares the same the same approach. So same on the side. >> What's next in near? So you guys you guys I feel like >> postquantum dynamic rearting, you know, staking for inference. >> You you guys are you guys are postquantum. You guys are the first L1 to make it past postquantum. So not to say that you guys aren't doing amazing things, but just like what's next for like the rest of the year for 2026 with >> Yeah, I mean a lot of it is like continue growing this products, right? I mean I I said it at Nearcon like we kind of like we're still building a lot of technology but a lot of the focus has been like how do we bring it to market?

How do we get it to be the best you know best product in the market as well? Um and so like for inference you know continue scaling that continue I mean kind of finishing some of that decentralized marketplace for the like so that others can join the compute as compute providers making that compute market itself more uh liquid as well. uh like the compute compute market underneath right now is a complete disaster right it's it's a very opaque every cloud is like you know deal making left and right it's kind of you know this is exactly what blockchain is made for is really creating transparency liquidity uh reducing like allowing people to reduce risk right there's a

lot of risk built in like that's why everybody's talking about bubbles right because like there's so much risk and nobody has any idea like what it looks like because nobody actually knows how much computers wear like I I forgot like I was watching some news analyzing the GPU market and they were like they kind of like Jensen is saying one thing they looking at like you know cancel data centers like nobody has any idea what's going on right so I think this is where blockchain bringing transparency bringing liquidity allowing to reduce risks new financial instruments so like that's a big thing and then like you need the verifiability and confidentiality on top because you need to make sure that the hardware is there that the you know the people who

providing it don't don't actually get access to a user's data. So kind of really continue building out that stack and then same with ironclaw really bringing it to market now uh we just launched uh kind of 1.0 you know, uh to to kind of enable like more secure agentic experiences in organizations especially because uh we're as far as I know the only multi-tenant agent system because we can actually isolate every user and kind of give them uh effectively capabilities of an agent while everyone kind of is on the same system is on the same uh instance um for the organization. So things like that uh kind of on the AI side obviously intense

you know more assets so like real world assets being added uh yield was added uh like more more different experiences uh going to get out of like prediction markets etc to really offer it both to all of the partners as well as on.com to be like a single experience you can come in and have access to everything you need across the crypto >> markets don't move one asset at a time one day it's Bitcoin the next Nvidia then and then the S&P. But most traders are still managing their portfolio across different platforms, different accounts, and different pools of capital. Bit just changed that. Their new stocks 2.0 product lets you trade tokenized equities directly with USDT, all inside the same app you already use for crypto. This is not just another

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analysis has been absolutely on point. I've been following him for years, and this year we started recording weekly podcast episodes. Each one, we get into his portfolio, what he's holding, the market structure, entry targets, fair market value of Bitcoin and Ether, and where we are in the cycle. There's new episodes that are released every Wednesday. They're 30 minutes, they're short, they're punchy. I think this crypto cycle is harder to navigate than most. So, let's do it together. Go subscribe to this podcast. Search the DeFi Report wherever you get your podcast YouTube Apple Spotify or find a link in the show notes. There's a new episode waiting for you. Now, >> in the trad AI space, like the anthropics, open AIS, which I'm calling TRA AI, and like the AI the AI like stock market, like the stock market,

there's like just a a number of just kind of like fundamental or existential questions that are always kind of being asked by investors and technologists in this space. And I I think maybe the the two questions or two like story arcs of like the AI industry and the AI market right right now the the hot the hot questions are how does how do AI compute markets come into existence? Like kind of in the same question just like how does corn and uh like oil make it onto the CME? Like oil is such a weird thing.

There's 17,000 different types of oil. There's sweet crude. there's like uh sour blah blah blah blah but nonetheless you can go onto the CME and you can just buy oil. Same thing with like corn and all the other commodities. So that's like one question is like we all everyone wants to trade compute but to your point it's a [ __ ] mess down there and like not all GPUs are the same and not all like latency is the same. There's all these different properties that make compute different. So how how do we make compute markets uh emerge? I think that's one big question. Another big question out there I think is downstream of the whole Kimmy K3 and the recent step up that Chinese openweight models have really introduced into the the equation where like open AAI and

anthropic are paying for the cost of training you know the USA frontier models literally the best models in the world and these USA companies are paying an arm and a leg to have these amazing models and then the Chinese models release something like 95% as good at like 90 5% of the cost. And so now it's like well it's like reasking the question where does value lie in the stack. Uh I think this is really good for things like Venice and Near AI where you guys don't have to train models like you guys actually don't care about who it provides the model because you guys can open up any model whatsoever. And so like how that equilibrium nets out in the long term about who pays for

training models and who captures the value of of inference. I think is an open question that I think the market would really really like to be answered. Are there any other questions that you have about the frontier of AI? Like what are the big unanswered questions that you think about like on a daily or weekly basis about where the broad trends of the industry are going? Yeah, I mean there there's a bunch of these. Um I think like one one that I'm kind of always pondering and and like I have a very strong view but I I don't I also realize that like it may not align with some of the human psychology um is the just this collapse into AI of the whole operating system of

of the computing right the full stack right I mean we've talked about SAS you know SAS apocalypse and all those things and like they're fundamentally in the right direction, right? Because if you can just build your own custom software, if you can create software on the fly, you don't need all this apps. You also don't need to go to web pages anymore because your agent will do it for you, right? And and it will not, you know, it will filter out ads first, right? It will run the ad block on your side first, right? So, >> so I think like obviously there's like Cloudflare trying to like, okay, like how do we, you know, monetize the agents visiting the website? like there's this kind of fight, but to me that's always like I think that collapse is I mean it's always slower than I expect.

Everything is slower than I expect. So I'm always too optimistic. I think we we'll we'll see some of this >> kind of collapsing into this AI operating system. >> But I also realize people want their, you know, Instagram app, they feel, you know, kind of connected clicking on that icon and going into that experience, right? versus, you know, you just like your operating system already knows what you want and kind of generates a feed on the fly. That's like Instagram, Twitter, whatever whatever that is. Like people have the the psychology of like I like Instagram, I like access, I have feelings about this specific app and and so like that part is where you know maybe being an engineer, right? I don't I don't fully grasp and it's not clear

like how that affects the market, right? Like right now same thing, right? like Salesforce, you know, if you you can build your own CRM on, you know, with with AI and yes, it will take some time to maintain, but like so is Salesforce. Salesforce is super complicated to like construct, configure and then maintain for any like complex workflow. So like at the same time people want you know reliable record keeping and all those things and like there's a lot of money in this. So like all these kind of questions are there and like as you said right market wants to know the answer we we see you know compression of multiples but we haven't seen like a true you know everybody cancel Salesforce for 2027 yet um

>> is kind of what you're asking is AI has this like interesting property of like ephemererality like AI is very ephemeral like the the idea of like an AI operating system doesn't have to be like a browser on a screen on a computer at all. Like it can be like your your phone in your pocket and your AirPods in your ears and that's like your new way of connecting to the internet. So in ephemeral and but to your point like people like looking at Instagram on their screen. Uh there's a lot of very concrete things built by the SAS companies that are very real and very not tangible but very structured and orderly and just not ephemeral at all.

And so is what you're talking about kind of like this tension between the ephemererality of AI and the very concretness of the products that came before it? >> Well, I would even say like I mean let's say ironclaw, you know, uh 1.2 will will create the non-eirmal interfaces for you, right? It will have the sales force looking interface. >> It'll just generate it for you like your interface and my interface will be completely different, right? Because the way we deal with leads, the way we deal with you know like you know closing sponsors or in my case finding you know new partners for near intense is completely different right but like you know as you describe your process as you go through it it will keep improving it could keep modifying right like that's

the that's the future of software it feels like we're going to versus Salesforce is like hey we know exactly how sales should be done and everybody will be doing it exactly this way and so come in and use our software right that's I mean and to be clear Salesforce is like you can do anything with it. So, uh, but like generally that like SAS software or Instagram for that matter, right? Is like you're going to be seeing things like this, right? Like it doesn't matter how you how you like to get information, you're going to be seeing it like this and we're going to give you the feed, right? Versus my eyes like cool, you know, I know friends are posting pictures. Let me pull it and and create a collage for you. and you know we're only going to do it like in the

morning and you know you're not going to spend your time scrolling through this ever again during the day right so like your becoming the driver of of how you perceive information how you do actions how you even doom scroll right optimized for you right that's what I believe in that should be the final but right now people are still in this like oh but you know somebody created this experience anyway >> isn't that kind of the question of like is it going to be built like your version of Instagram where you are shown a bunch of pictures from your friends is that built and constructed by your AI agent locally or does Instagram do that with their version of the same thing and

why does Instagram do it because they're good at it and so like the whole SAS apocalypse thing of just like you know Figma if Figma is going to get commoditized by by AI what if Figma just makes their product better than AI and your AI simply fetches Figma to do it. And so I feel like that's kind of where the SAS apocalypse things has ended up is like it's actually not going to commoditize SAS companies. SAS companies are like we're still going to hire engineers to do the job because an engineer plus an AI is always going to be an AI that's local on your machine because you don't know how to build Figma and you don't know how to build, you know, Instagram. And so like the Instagram engineers and the Figma engineers are going to figure out how to serve you in the way that you are

describing where everything is custom to you and fit to you, but it's still going to be these SAS companies that do it. That's kind of like where I've netted out. >> Yeah. I mean, I think that that is that is one one one possible approach. I think the other question is this AI models are learning from this engineers doing this right now and so they will be able to just do it. >> Are you on the side that AI always wins? like it doesn't matter like cute human paired with AI uh agent but like ultimately the AI will always learn and the human will inevitably be redundant. I >> so I don't think human is redundant per se. It's more that there's a scale flip right now, right?

Because like developing software was very expensive and so you wanted you you were building the lowest common denominator software, right? That you could distribute to as many people as possible, right? And so we flipping the model to like actually developing software is now easy. Now there's a big caveat there to be clear right now which is like developing a shitty software is easy. getting it to really high quality is still pretty hard. Um but you know assuming that continues improving right now you may still you may only need to distribute a recipe right like let's say as an Instagram you're like hey the idea is you'll see photos of your friends right now everybody's like how exactly you

manage your you know manage your contacts etc may be different and so that recipe can just like implement into your system right that would be like one kind of middle ground on it but anyway going back Maybe to your to your kind of fundamental questions, the the compute market, right? And and oil. So there the computer is yeah indeed a very different market from anything else we we we have. It has properties of oil meaning like there's you know million different qual qualities that matter and then it also has property of electricity because you cannot store it >> right >> like oil you can store you can have it in a tanker you can you know have it in

in some pipelines etc >> computer you need to consume now right >> right >> it's you know maybe you can like deenergize it so at least it doesn't cost as much but generally like you want to sell it all the time and so that's why there's massive contra like multi-year contracts usually that are being done to because like it's just so hard to otherwise um like settle this market right now and so I think again blockchain here is in a unique position to do this but it needs to be done in a very intelligent way and you know maybe we'll do another episode on on uh how we're approaching this um but I think yeah we have an opportunity to actually offer something where you

know um you can have kind of an abstraction to you can easily trade and at the same time you can have all the details and you can actually get the delivery right you can actually get the compute because it's digital and you can get the actual inference or actual you know SSH into a GPU to uh to get your uh compute done and it's all done you know verifiably kind of even non-custodially in many cases uh from the marketplace perspective >> I do want to know about the AI compute part of Near, but perhaps we should save that for another episode. As you said, give us just the TLDDR of just like whatever information is available about Near's involvement in building an AI

compute market. How would you summarize it? >> I mean, just the TLDDR is like we're using intense, right? Intense is a great abstraction for for the general markets where you don't actually know like you ha you as a as a buyer or seller as a buyer let's just say you have the kind of rough boundaries of what you want right so it's underspecified request you cannot deal with it on the order book you cannot deal with it on a traditional kind of trading uh facility because there you already fully specified you're in this market you're in this price range you're in this doing that here you can say hey this is roughly like I want you know thousand GPUs of this of this

format you know infin at least whatever you know 4 g gigahertz processors available on them and uh because like I know the workload I'm going to run and then now you can have solvers who actually finding that compute right around the world and actually providing to you and you're negotiating prices settling you And then all of the stuff we've been building for intense is kicking in, right? And giving you the and then near AI infrastructure to do verifiability kind of confidentiality, all of those pieces, right? So really this is where this pieces really come together to enable enable this market.

>> Yeah, I do think the AI compute like marketplace, the secondary market is going to be like one of the main stories probably for the end of this year and the start of next. So they'll be these will be the topics of our our future discussion and probably where my attention will lead next. Ilia, thanks for coming back on the show and explaining to me all about the whole like near AI vertical. It the the very grandiose vision about what a blockchain can do and what crypto can do seems to be absent from most and much of the industry, but it is certainly not absent from what you guys are building it near. So, uh I I appreciate you guys keeping the ambition and the the large scope TAM uh inside the crypto industry.

>> Yeah, I mean all the economy is moving to AI and blockchain. So that's a TAM. Let's go >> bank. You all know the deal. Crypto is risky. You can lose what you put in, but it's not risky enough. The institutions are here. So we are going even further west. This is the frontier. It's not for everyone, but we are glad you are with us on the bank's journey. Thanks a lot.