NEAR’s New Token Utility and AI Economy | Illia Polosukhin

What if staking a token could keep an AI agent thinking? NEAR cofounder Illia Polosukhin joins David to explain NEAR’s new staking-powered inference model, where yield funds access to confidential and verifiable AI. They unpack how NEAR AI Cloud, IronClaw, Intents, and the NEAR token fit into one integrated economy for autonomous agents, why AI outputs may need cryptographic proof when money is at stake, and how GPU compute could become a liquid global market. --- 📣SPOTIFY PREMIUM RSS FEED | USE CODE: SPOTIFY24 https://bankless.cc/spotify-premium --- BANKLESS SPONSOR TOOLS: 🔓NEAR | TRADE CONFIDENTIALLY, GET 20% BACK https://bankless.cc/near-pod 🔑BITKEY | GET 10% OFF USE CODE: BANKLESS | #bitkeypartner https://bankless.cc/bitkey ✈️COINBASE ONE CARD | EARN 5% BACK IN BITCOIN https://bankless.cc/coinbase-one-card 📊BITGET | TOKENIZED STOCKS 2.0 https://bankless.cc/bitget-stocks 🎯THE DEFI REPORT | ONCHAIN INSIGHTS https://thedefireport.io/bankless --- TIMESTAMPS 0:00 Private, Verifiable AI 4:57 Why AI Outputs Need Proof 9:13 From Staking Yield to Inference 16:16 NEAR’s Integrated AI Stack 20:01 NEAR as AI Money 24:27 Autonomous Businesses 28:51 Partners and Real-World Uses 33:26 What NEAR Ships Next 38:01 Where AI Value Accrues 40:56 The AI Operating System 48:07 Turning Compute Into a Market 51:48 Closing Thoughts --- RESOURCES Illia Polosukhin https://x.com/j0hnwang Near revenue https://revenue.near.org/ --- Not financial or tax advice. See our investment disclosures here: https://www.bankless.com/disclosures

• 0:02Speaker 00

  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. 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 agent. Here to help me learn a little bit more about how this all works is Ilya, co-founder of Near and co-author of the famous transformer white paper. Ilya, welcome back to Bankless.

• 0:34Speaker 01

  Thanks for having me. Yeah, very excited to talk about it.

• 0:37Speaker 00

  It seems like 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 things. You have like the actual near blockchain. You have the confidential and intense, and then the near AI cloud is like one of these pockets. How does the near AI cloud work? What actually is it? How does it work? Can you like paint a picture for me?

• 1:07Speaker 01

  For sure, yeah. So I think of near less as a collection and more as a vertically integrated stack. So each piece actually builds on top of each other. Intense is obviously using all the blockchain tech. There's a kind of our core cryptography primitives at the core. And so near AI actually builds on top of all of that, right? At the core, it's a confidential and verifiable computing platform. Think of cloud and it uses all of the blockchain primitives for encryption, decryption, provisioning, et cetera. But what you get as a user developer is an AI inference that is end-to-end confidential. What does this mean? There's nobody else who can actually access what queries you're putting into this, what

• 1:51Speaker 01

  prompts, what responses you get. And it runs kind of across, you know, different GPUs that support that that mode. We're using trusted execution environments. So there is some trust assumptions around like hardware manufacturers, but this is kind of a pragmatic assumptions right now, given where the kind of technology is.

• 2:12Speaker 00

  And part of the AI inference or the AI cloud side of things is you can do inference on it. And that inference has certain properties because of the nature of what it is. Maybe you could, what are the unique properties of the AI inference side of the AI cloud?

• 2:30Speaker 01

  So the, I mean, as I said, primary property is confidentiality, right? So again, nobody can see what you actually are running prompts. Nobody can, you know, filter in result, right? There is no kind of 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, OpenAI and Anthropic, but I've heard, 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, geometry, physics questions that like maybe touched on some like nuclear things or biology or cybersecurity, right?

• 3:14Speaker 01

  Right now, everybody is like, who wants to use some cybersecurity? So anyway, so this is all private.

• 3:19Speaker 00

  Wait, I have questions about that, about how uncensored it will kill, it will actually allow you to go?

• 3:26Speaker 01

  It's as uncensored as a model. So we are serving open weight models, right? So deep seeks and GLMs and kind of, you know, Gem, et cetera. So whatever is in that model, you get that, right?

• 3:38Speaker 00

  Okay.

• 3:38Speaker 01

  No more, no less.

• 3:40Speaker 00

  I see.

• 3:40Speaker 01

  And so if there is, you know, you know, untethered, uncensored models, right? Then you'll get that if this is the model that has been trained to do specific things, you get that.

• 3:50Speaker 00

  So you, near AI has kind of stripped out all of the like system prompts that OpenAI and Anthropic might filter before your prompt actually lands at the model. And so there's a filtering that Anthropic and OpenAI does to approve or disapprove of a prompt. But then the model itself might internally have been 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?

• 4:29Speaker 01

  Correct, yeah. We're just, we're serving these models. There is, I mean, in our roadmap, we have an ability for people to upload their custom models. Let's say you have, you know, untethered the model more and you want to upload that, like we will, we will support that. But yeah, effectively you get what model offers, no more, no less.

• 4:48Speaker 00

  We should call them unhinged models.

• 4:52Speaker 00

  Because like, it does kind of frustrate me. I mean, I asked a question to inside of Venice, which uses and integrates with the near AI cloud. Cause 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 commitments of individuals have in society, but that's kind of like a philosophical question that's not here nor there.

• 5:33Speaker 01

  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 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 kind of closed source OpenAI and Tropic, 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, open weight provider, like GLM provider, and you're asking it a question.

• 6:16Speaker 01

  They may be rewriting a prompt. They may be censoring you. They may be actually responding back with something that model didn't respond. So to give you a very specific example, I saw it on Twitter. So, 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 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

• 6:57Speaker 01

  on internet, they can literally like steal your files, rewrite 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 that you ran on this specific model, right? The hash on 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 provenance, 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 all the signatures

• 7:41Speaker 01

  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 bar, right? We're 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 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 OpenAI and modify the system prompt. Like people will, like the employees there may not even notice it because it's closed source, right? It's just a thing somewhere, you know, it's a string somewhere in the, in the code base. And like, I don't think it's guarded like as a, you know, like this is effectively a

• 8:25Speaker 01

  thing that in, in acts models to act on behalf of billion, of billion people who are using chat GPT. And so that, that string needs to be like effectively like locked in as, as, you know, as a like the Coca-Cola secret sauce, right? That thing. I'm pretty sure it's not, right? So because like, you know, subtly 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

• 9:09Speaker 01

  more for our own like decision-making, right? It's critical that like, if we're offloading a lot of this 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, as well as Brave is now offering, that is an option 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 with crypto. Now you still need to pay, right? And it's like a subscription fee or you need to pay per million tokens.

• 9:51Speaker 01

  And so it kind of limits, you know, like at least from my perspective, you know, you kind of have this as your recurrent bill now. And, and so what we effectively launched is this idea that if you're holding near, right? It should, 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.

• 10:22Speaker 00

  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 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 to the staking of the Near token in my Near AI account to the actual cost that somebody is bearing somewhere because of the electricity in the inference? How does that actually get paid?

• 10:51Speaker 01

  Yeah, very great question. And my ideal world is, you know, you will be able to pay everything in Near across the stack. But it's not, we're not there yet. So the way it works is you are effectively trading off your yield, right? The yield you can get generating on Near is now being paid for the AI inference and and capacity under the hood.

• 11:12Speaker 00

  Okay, so you would otherwise be getting yield on your Near that you're not getting that and that is going to, 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.

• 11:36Speaker 01

  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. There's obviously a lot of like questions around SLA and quality, et cetera, to really deliver that. But the goal is yes, to have third parties joining because it's confidential, right? They actually cannot see what data has been used. They join the network, they verify that they have the right hardware and now they can provide it. We've been bootstrapping it with our own hardware first. And so, yeah, the idea is effectively you stake, the yield is being generated by, I mean, staking, effectively you stake to Near AI validator right now, practically speaking.

• 12:19Speaker 01

  But, you know, there are going to be other ways this yield is generated. And then that is being distributed to the compute providers.

• 12:26Speaker 00

  The compute providers, okay, okay. So like 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?

• 12:47Speaker 01

  Yes, that's, that's effectively the plan.

• 12:49Speaker 00

  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, this is a way for us to

• 12:59Speaker 01

  To get more near.

• 13:00Speaker 00

  Internalize our own.

• 13:01Speaker 01

  Long Near, yes, exactly.

• 13:02Speaker 00

  Right, yes. But then also because Near is a decentralized ecosystem, it also, you can incent other third parties to also come into the marketplace. And 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.

• 13:33Speaker 01

  Correct, yeah. Yeah, it's effectively like proportional to how much you stake. And, you know, if you want like a subscription level, you need to stake like in Clips.

• 13:41Speaker 00

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• 15:09Speaker 00

  Or just visit Coinbase.com/banklist. Terms apply. That's Coinbase.com/banklist for more information. I've been trading crypto assets for almost a decade and I've used so many wallets, exchanges, aggregators, frontends, and I'm kind of always looking for the same thing. One interface with deep liquidity across a 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 cross-chain using post-quantum signing and has over five years of uptime.

• 15:52Speaker 00

  Near.com is the best way to be on-chain and be in control. Get 20% of your trading fees back through the Banklist link. It's in the show notes, not investment advice. Okay. And then this all kind of ties into the whole Near, Near Intents. Connect this part of the Near economy, if you will, to the Near confidential Intents economy. How do these three things connect? For me, the participant consuming inference, maybe I'm running an Ironclaw agent, maybe you can connect that as well. I'm like, my Near emissions are going to the cluster. How does confidential Intents kind of like fill the webbing between these systems?

• 16:27Speaker 01

  Yeah, so this is how, yeah, like it's a vertically integrated stack, right? So all the pieces kind of support each other. So, yeah, so starting with Ironclaw. So Ironclaw is our agentic harness. And effectively we have, as you stake, you get effectively a subscription similar to, you know, if you get like Cloud Code or Codex subscription, you get full agent, it is able to do anything, right? Write code, you know, ship software as well as, you know, do your daily, you know, email scans, reply in Slack, et cetera. The goal there is really to build something secure and optimized for privacy and optimized for organizations as well. And so, so that comes in, I mean, obviously organizations also can pay in dollars.

• 17:09Speaker 01

  But, you know, individuals and organizations as well can stake Near to get that capacity. So that uses the inference underneath. 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 compute capacity, right? So like we kind of showcased an MVP of this at NearCon, which is a compute marketplace. But that's the idea is like you can actually trade 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 Intents really connects, making this compute as an asset.

• 17:54Speaker 01

  Right? And then every transaction fee, every kind of that Intents captures, that goes to the protocol, right? And so that's where kind of this token, both you can stake to get the utility and there is kind of buyback mechanisms from the revenue that protocol generates on these transactions. And again, this all then goes back into our kind of core chain signatures and blockchain technology to really facilitate security for this.

• 18:20Speaker 00

  Oh yeah, this is a dashboard, this is revenue near.org, there's a dashboard of kind of like the Near economics. And it shows the, the confidence TVL, so Near Intents Confidential TVL, which is just like assets that are in the confidence Intents system. 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 by, by the protocol. Is that all the Near Intents products or like what else is contributing to that?

• 18:54Speaker 01

  Yeah, so this is Near Intents and the blockchain itself as well.

• 18:58Speaker 00

  Okay, and the blockchain itself.

• 18:59Speaker 01

  Yeah. And then with Near AI, the goal is like as that, so like we kind of similar how is Intents, right? We got to product market set through 2025 and then we turned on the fees in February 26. We expect similar thing happening with 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 does generate revenue, it just like creates it in this like yield way than, you know, direct payment way. But yeah, we will be adding some of this information here as well.

• 19:36Speaker 00

  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 blockchains, like mainly like Ethereum, which I kind of view as like a, like a pure manifestation of like a crypto economic system. Near is different in that it is like purely a blockchain.

• 20:18Speaker 00

  It's got like, it's got some of the core products, it's got block space, it's got the burn, like all these crypto economic 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 then Near Token is like being integrated as a first-class citizen into some of these products. So it changes the story of 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?

• 20:50Speaker 01

  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 AI capability. It needs to be a store, like true store value, it needs to sovereign to its own blockchain and block space and, you know, give the ability to program. Like 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,

• 21:33Speaker 01

  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.

• 21:46Speaker 00

  Based on the prompt or the, not the prompt, but the response of a model.

• 21:51Speaker 01

  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.

• 22:08Speaker 00

  Right, you can't have unverifiable inference because if money is, if there's a smart, smart contract 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 a complete verification of the whole supply chain because money is at stake on the other side.

• 22:26Speaker 01

  Exactly. Yeah, so you can have, for example, fully autonomous business, right? That runs on Iron Claw, uses inference, has near on a balance sheet to always have inference, right? Like that's, you know, one thing like this, but like you don't want business that like runs out of AI credits and now is not able to run, right? So 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 integrate with fiat through Intens, right? So it's able to interact and act on behalf of a lot of 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,

• 23:06Speaker 01

  there's like the same intent infrastructure that ensures that, you know, two assets are swapped. The work that agents are doing as well is ensured through that. So all of these 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 backend for trust, identity, settlement, you know, kind of coordination. And so like it is a backend, but it is, it is the core of security. It's a root of trust. It's where the kind of value settles and that needs the security of the token. It needs the, the token to be the kind of the, like the utility for the AI itself.

• 23:50Speaker 01

  And it needs to be this kind of global market for everything that the AIs will want to do. And this is what Intens are. And so all the three pieces really work together.

• 23:59Speaker 00

  The word autonomous is really coming to mind for me right now. And I think the world of the, that we are currently in with AI is very human led and human directed and human orchestrated. Like my own, my agents are in my, like my, like Claude or whatever. 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 broad strokes 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.

• 24:43Speaker 00

  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 you. Our brains are constantly thinking and like, there's a future world

• 25:29Speaker 00

  maybe like that we are trying to build, that we don't know exists yet, but we are trying to build 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's, and that's where near staking for an agent is producing always accessible inference. And there's like a, there's a special property of autonomy that near is producing for inference in 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 blockchain. Am I on, am I on to something here? This, this feels right.

• 26:07Speaker 01

  No, you're exactly right. This is, yeah, if, if you want true, like I call them autonomous businesses just to kind of distinguish it. Like agents, everything is agent now, you know?

• 26:16Speaker 00

  Right. And so like, yeah, the agent kind of lost its word, I mean, yeah.

• 26:20Speaker 01

  Yeah, I mean, this happens with every word, right? So, but yeah, so like if you talk, if you think of autonomous business, right? You, you want properties that it's indeed can run 24/7. 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, your treasury, right? As an agent to actually run this intelligence. Otherwise you're effectively going to lose, you know, like you, 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?

• 27:05Speaker 01

  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. I'm 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 is 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.

• 27:47Speaker 01

  Or it can be a business that actually like, you know, actually a supply chain, you know, finding the right producers of something and then making sure it gets shipped, insured and, and delivered, right? So all of that is effectively the commerce layer and that's what near and tense are really facilitating. And again, crypto is like a first, you know, FX and like kind of use case. And now that we have, you know, RWS, fiat, you know, all of these other pieces, now we can actually bring that closer and closer to real commerce. And so this is how, yeah, all of these pieces really work together.

• 28:21Speaker 00

  We're, 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. So we're still in that like boot loading phase, even though the, the pieces are coming together, we're still in the boot loading phase. And like at least on the, on the, the boot loading of, of 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 AI inference to, you know, boost the Venice product. Also Brave as well is integrating it and Brave has some three digit millions of, of users, I think.

• 29:04Speaker 01

  Over a hundred million, yeah.

• 29:05Speaker 00

  Over a hundred million users. And so like inference, like a prompt, 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 white labeled, white, white labeled by like Venice and Brave. And maybe you could talk about any other partners coming down, down the line. But just like, as like, all, all of these products, you know, Brave and Venice are very like human oriented, 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 have a bunch of spokes out there on the internet in Brave and Venice

• 29:47Speaker 00

  to like deliver some of these, you know, the same AI properties that you would get from Anthropic or OpenAI, but with these like human first, like properties that they have about them. Who else is integrating the near AI cloud? It with this hub and spoke metaphor, like who else are the, the spokes?

• 30:03Speaker 01

  Yeah, I mean, we're, we're working with few different partners, I mean, diff, different stages. So the ones that we've announced, for example, government of Bermuda, right? As a government, they wanna, like they're doing a lot of the financial kind of related use cases and want to provide AI help, aid, explanation to the, to the people. And so highly sensitive information, right? Somebody's like financial pensions, et cetera, and requires AI inference. And so that's a really great use case. Government, financial information, you know, kind of extreme privacy needs, extreme verifiability needs as well, making sure everybody gets the right thing. We have a bound we're working with who is remittance project

• 30:47Speaker 01

  and 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're paying dollars here, like that, like that requires payments, it requires, you know, finding right vendors, like all these use cases. It's 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, delivering that on the commercial layer as well.

• 31:32Speaker 01

  Because Avand also uses our kind of payment infrastructure underneath for, for stable coins. So that's kind of where like all of these pieces really fit together, right? A lot of the use cases where privacy matters is also financial use cases, right? Or at least like they're, you know, kind of critical HR, legal.

• 31:52Speaker 01

  And so that's why Iron Claw is kind of our approach to that, bringing that to market kind of more directly, where you actually get a full agent that is able to go and do a lot of these things. But yeah, I mean, generally as near right, we are kind of built for developers first and then turn it into a product ourselves as well. 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, through aggregators down 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 into near.com, making that experience better and better.

• 32:35Speaker 01

  But at the same time, offer all of this to other partners as well, who kind of share the same values, shares the same, same approach. So same on near AI side.

• 32:44Speaker 00

  What's next in near? So you guys, you guys.

• 32:48Speaker 01

  It's not enough like post quantum, dynamic resharding, you know, stake for inference.

• 32:53Speaker 00

  You guys are, you guys are post quantum, you guys are the first L1 to make it past post quantum. And 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 near?

• 33:04Speaker 01

  Yeah, I mean, a lot of it is like continue growing this product, right? I mean, I said it at near con, 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? 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 liquid as well. 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, right?

• 33:49Speaker 01

  Because really creating transparency, liquidity, 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 were like, 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.

• 34:29Speaker 01

  And then like, you need the verifiability and confidentiality on top, because you actually need to make sure that the hardware is there, that, you know, the people who providing it don't, don't actually get access to users data. So kind of really continue building out that stack. And then same as Iron Claw, really bringing it to market now. We just launched to kind of 1.0 to, to kind of enable like more secure, agentic experiences in organizations especially. Because 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 effectively capabilities of an agent. While everyone kind of is on the same system, is on the same instance for the organization.

• 35:12Speaker 01

  So things like that, kind of on near AI side, obviously Intense, you know, more assets, so like real world assets being added, a yield was added, like more, more different experiences, going to get out of like prediction markets, et cetera, to really offer it both to all of the partners as well as on near.com, to be like a single experience, you can come in and have access to everything you need across the crypto.

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• 37:11Speaker 00

  In the trad AI space, like the Anthropic's Open AIs, which I'm calling trad AI, and like the AI, the AI like stock market, like the stock market, there's like just 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 think maybe the two questions or two like story arcs of like the AI industry and the AI market right now, 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 like oil make it onto the CME?

• 37:51Speaker 00

  Like, oil is such a weird thing, there's 17,000 different types of oil, there's sweet fruit, there's like 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 fucking 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 emerge? I think that's one big question. Another big question out there, I think, is downstream of the whole Kimi K3 and the recent step up that Chinese open weight models have really introduced into the, the equation,

• 38:33Speaker 00

  where like OpenAI 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 95% of the cost. And so now it's like, well, it's like re-asking the question, where does value lie in the stack? 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 provides the model, because you guys can open up any model whatsoever.

• 39:12Speaker 00

  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?

• 39:38Speaker 01

  Yeah, I mean, there's a bunch of these, I think, like one, one that I'm kind of always pondering and like, I have a very strong view, but I don't, I also realize that like it may not align with some of the human psychology, is 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 SaaS, you know, SaaS 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 a fly, you don't need all these apps. You also don't need to go to webpages anymore, because your agent will do it for you, right?

• 40:22Speaker 01

  And it'll 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 is 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. I'm always too optimistic.

• 40:46Speaker 00

  Same.

• 40:46Speaker 01

  I think 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 to clicking on that icon and going into that experience, right? Versus, you know, you're 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 X, I have the feelings about this specific app. 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?

• 41:28Speaker 01

  Like Salesforce, you know, if you, you can build your own CRM on, you know, with AI. And yes, it'll 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 see, you know, compression of multiples, but we haven't seen like a true, you know, everybody cancel Salesforce for 2027 yet.

• 42:08Speaker 00

  Is kind of what you're asking is AI has this like interesting property of like ephemerality, 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 ephemeral. And but to your point, like people like looking at Instagram on their screen, there's a lot of very concrete things built by the SaaS 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 ephemerality of AI and the very concreteness of the products that came before it?

• 42:53Speaker 01

  Well, I would even say like, I mean, let's say Iron Claw, you know, 1.2 will will create the non ephemeral interfaces for you, right? It will have the Salesforce looking interface, it'll just generate 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 from your 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

• 43:38Speaker 01

  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 but like generally, that like SaaS software or Instagram for that matter, right, is like, you're gonna be seeing things like this, right? Like, it doesn't matter how you how you like to get information, you're gonna be seeing it like this, and we're gonna give you the feed, right? Versus my AI is 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 gonna do it like in the morning and, you know, you're not gonna spend your time scrolling, as we say, ever again during the day, right? So like, your AI becoming the driver of of how you perceive information, how you do

• 44:20Speaker 01

  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.

• 44:35Speaker 00

  Isn't that the kind of the question of like, is it going to be built, like your version of Instagram where you're showing 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 SaaS Poclopes thing of just like, you know, Figma, if Figma is going to be commoditized by AI, what if Figma just makes their product better than AI and your AI simply just fetches Figma to do it? And so I feel like that's kind of where the SaaS Poclopes things has ended up. It's like, it's actually not going to commoditize SaaS companies. SaaS companies are like, we're still going to hire engineers to do the job

• 45:19Speaker 00

  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 SaaS companies that do it. That's kind of like where I've netted out.

• 45:43Speaker 01

  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.

• 45:55Speaker 00

  Are you on the side that AI always wins? Like, it doesn't matter, like, cute human paired with AI agent, but like ultimately, the AI will always learn and the human will inevitably be redundant?

• 46:06Speaker 01

  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're 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. 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,

• 46:49Speaker 01

  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., maybe different. And so the 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 computers, yeah, indeed, a very different market from anything else we've we have. It has properties of oil, meaning like, there's, you know, million different qualities that matter. And then it also has property of electricity, because you cannot store it, right? Like,

• 47:34Speaker 01

  oil, you can store, you can have it in a tanker, you can, you know, have it in in some pipelines, etc. Compute you need to consume now, right?

• 47:42Speaker 00

  Right.

• 47:43Speaker 01

  It's, you know, maybe you can like de-energize 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 like massive contract, like multi-year contracts usually that are being done to because like it's just so hard to otherwise, 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 how we're approaching this. But I think, yeah, we have an opportunity to actually offer something where, you know, 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

• 48:27Speaker 01

  get the compute because it's digital, and you can get the actual inference or actual, you know, SSH into a GPU to to get your compute done. And it's all done, you know, verifiably, kind of even non custodial in many cases, from the market-based perspective.

• 48:45Speaker 00

  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 TLDR of just like, whatever information is available about near's involvement in building an AI compute market. How do you summarize it?

• 48:59Speaker 01

  I mean, just the TLDR is like, we're using intense, right? That intense is a great abstraction for for those general markets where you don't actually know, like, you have 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 under specified request. You cannot deal with that on the order book. You cannot deal with it on a traditional kind of trading 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, infinity band,

• 49:42Speaker 01

  at least whatever, you know, 4 gigahertz processors available on them. And because like, I know the workload I'm gonna run. And then now you can have solvers who are actually finding that compute right around the world and actually providing to you and you're negotiating prices, settling, you know, 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.

• 50:15Speaker 00

  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 these will be the topics of our our future discussion and probably where my attention will lead next. Ilya, thanks for coming back on the show and explaining to me all about the whole like near AI vertical. 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 at near. So I appreciate you guys keeping the ambition and the the large scope TAM inside the crypto industry.

• 50:52Speaker 01

  Yeah, I mean, all the economy is moving to AI and blockchain. So that's a TAM. Let's go.

• 50:58Speaker 00

  Bankless H, y'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 Bankless journey. Thanks a lot.