Illia Polosukhin: Why AI Agents Are Still Useless (And What Fixes Them) | NEAR Founder on IronClaw

**Illia Polosukhin** (0:00)
So one thing that people don't realize, when they use Antropic, OpenAI, or even worse, you use something else for inference, OpenClaw actually sends all your secrets to those services as well. Yeah. So somewhere in Antropic and OpenAI logs, they have everybody's access keys, API keys, and bearer tokens to access your Gmail and your Notions.

**Ryan** (0:25)
It's actually insane that we're doing that.

**Illia Polosukhin** (0:29)
Yeah. IronClaw fixes that, like the keys never touch a limb.

**David** (0:36)
Bankless Nation, we are joined by Illia Polosukhin, the co-founder of NearIllia. Welcome to Bankless.

**Illia Polosukhin** (0:42)
Thanks for having me.

**David** (0:43)
So Illia, you are one of the eight co-authors of the Transformer paper, the famous paper Attention is All You Need. The thing that kind of just broke open the doors of AI research to turn into some of the products that we know today, Chachi Buti, Claude, et cetera. And then in 2017, you left Google, where you were an AI researcher writing this paper, to go co-found Near. Question for you, do you regret leaving AI to go into crypto?

**Illia Polosukhin** (1:10)
Well, the story was that I left Google to start NearAI, which was a AI company. We were teaching machines to code, which is a fancy way to say live coding. And in 2017, everybody thought we were somewhere between delusional and doing science fiction at work. When I would go and tell people, no, no, machines will write all the code. Like, don't worry about it. People wouldn't believe me. And we were too early, right? That was a real, real challenge. And so what we were trying to do at the time was trying to get a lot more training data. And so we had students around the world, Eastern Europe, China, South East Asia, who were doing small tasks, small coding tasks for us to generate training data for us. And we had a challenge paying them, right? You know, students in China don't have bank accounts. They have WeChat Pay. Eastern Europe, every country has its own some kind of restrictions. And so crypto is a pretty natural, actually, like solution we needed for our own problem. It's like, hey, how do we actually pay people globally without like setting up a ton of entities, without, you know, needing to do all the kind of hard payment provider work? And crypto seemed like a solution, like, hey, you know, you don't need a bank. You don't need an entity in every country. You can just send people money over the internet. But this was already 2018 There was nothing that would like scale work, you know, in a simple and cheap way, right, to do this for, you know, we were paying 15 cents per task to people. And so that's kind of how we got into near blockchain. And so I would say at a time it made a little sense because it was clearly that to us, that blockchain was kind of a part of the story for kind of AI evolution. And at the same time, the hardware, the scale of AI itself wasn't there for what we were trying to do.

**Ryan** (3:04)
When you wrote Attention Is All You Need, how soon did you think LLMs would actually happen? Because within five years, we had sort of the famous chat GBT moment. I think that was maybe chat GBT 3 in 2022, kind of first release. And that's when the world started taking notice that this thing was huge, this thing was impactful, this thing could scale. So that was five years later. Did you think it would happen on that timeline or what was your sense for where AI would go after you published the paper in 2017?

**Illia Polosukhin** (3:38)
Yeah, so, I mean, the reason why we started Near AI in 2017 is because we thought it's going to happen like right now, at the time, right? So we actually were way more optimistic, thinking that we're almost there, right? We are on, like, kind of the curve we're seeing right now, we thought we were on that curve in 2017, 2018 And we were wrong. So, I mean, the main part was the compute wasn't there. Like, there was not enough, like, the individual and kind of cluster compute parts just weren't there. I think as soon as that kind of crossed the chasm, that's when this model started to scale.

**David** (4:14)
When you said that the blockchain component of AI was obvious all the way back in 2017, 2018, when you founded Near AI, people are now just starting to wrap their heads around the intersection of AI blockchain, like, today for the first time. Well, what did you see all the way back in 2017 about like why blockchains and AI go together? What made sense to you back then?