NEAR’s New Token Utility and AI Economy | Illia Polosukhin
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What is NEAR’s staking‑powered inference model and why does it matter?
Bankless Nation, there was a big announcement and evolution in the near part of crypto. The NIR 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 NIR and co-author of the famous Transformer white paper. Ilya, welcome back to Bankless.
Thanks for having me. Yeah, very excited to talk about it.
It seem it seems like a a pr one of the larger upgrades to the NIR token that I've seen in a while, in order to really understand it, I think we kinda need to just start from the basement with the near AI, like part of NIR. Near itself seems to be like a collection of of things. Yeah, you have like the actual near blockchain, you have the c confidential and intense, and then the near AI cloud is like one of these pockets. How how does the near AI cloud work? What actually is it? How does it work? Can you like paint a picture for me?
For sure, yeah. So I think of NIR less as a collection and more as a vertically integrated stack. So each piece actually built 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 NIR AI actually built on top of all of that. At the core, it's a confidential and verifiable computing platform. You can think of cloud, and it's Uses all of the blockchain primitives for encryption, decryption, provisioning, et cetera. But what you get as a user or 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 prompts, what responses you get.
And it runs kind of across 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.
And part of uh the AI inference or the AI cloud side of things is uh you can do inference on it. And that inference has certain properties because of the nature of what it is. Maybe you can what what are the unique properties of the AI inference side of the AI cloud?
So the I mean, as I said, primary property is confidentiality, right? So again, nobody nobody ca can see what you actually are running prompts. Nob nobody can, you know, filter in result, right? There's no kind of censorship, additional censorship or blocking or whatever that's happening on top of this, right? I don't know if, you know, if you've tried asking some sensitive questions to, you know, OpenAI on Tropic, but I've heard I I because we have near AI, I mostly use that for any s sensitive topics. But I've heard of multiple people who got banned for even pretty like reasonable like you know geom geometry physics questions that like maybe touched on some like nuclear things or biology or or or cybersecurity, right?
Right now everybody's like who wants to use some cybersecurity. So anyway, so this is all private.
Wait, I I have I have questions about that. About how uncensored it will kill it will actually allow you to go. It's because as
uncensored as a model. So we are serving open weight models, right? So deep seeks and GLMs and and kind of you know Gemma, et cetera. So whatever is in that model, you get that, right? Okay. No more, no less. I see. And so if there is, you know, uh you know un untethered, uncensored models, right, then you'll get that. If if this the model it has been trained to do specific things, you get that.
You uh near AI has kind of stripped out all of the like system prompts that open AI and anthropic might filter before your prompt actually lands at the model. And so there's a filtering that anthropic and open AI 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
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Chapters
8 chapters
1
What is NEAR’s staking‑powered inference model and why does it matter?
0:02–6:50
2
Why do AI outputs need cryptographic proof when money is at stake?
6:50–13:12
3
How does staking yield get converted into AI inference credits?
13:12–18:58
4
What components make up NEAR’s integrated AI stack (AI Cloud, IronClaw, Intents)?
18:58–25:41
5
How does the NEAR token function as ‘AI money’ within the ecosystem?
25:41–32:22
6
What are autonomous businesses and how do they use NEAR AI services?
32:22–38:23
7
Which partners are integrating NEAR AI Cloud and what real‑world use cases exist?
38:23–44:52
8
Where does AI value accrue and how will NEAR turn compute into a liquid market?
44:52–51:10
Speakers
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