Is Agent Finance The Next Trillion-Dollar Wave? | Sam Green

**Robbie** (0:00)
We are back from the Capital Summit here at the Ritz-Carlton in Miami, Florida.
This is our AI show, AI Supercycle. I'm here with Sam Green. AI Supercycle is powered by Nier. They're doing tremendous things with Iron Claw, Nier Intense, all the likes. And we're gonna be at a Nier event on Thursday. But now we're here with Sam Green, the founder of Cambrian Network, and we're gonna be talking about Agentic Finance, Agentic Commerce. You recently did an entire Agent Finance report. And so we're gonna be talking a lot about this.
Sam, how you doing, man? How you finding Miami?

**Sam Green** (0:37)
I'm doing great. This is my first time in Miami. I've been listening to the show the past few days, and it's been excellent.

**Robbie** (0:42)
Appreciate it. We are on the homestretch here. We did a full slate yesterday at the Tokenize Capital Summit, and we're rounding out on the homestretch here at the Capital Summit.
Agents has been one of two conversations, primary fields of interest here at the conference. It's been agents, and it's been tokenization. And so I want to get a sense of this Agent Phi report that you put out there for Q1 2026 You tested a bunch of agents. What did you find when you were testing these agents, and what did you put into this report?

**Sam Green** (1:19)
Yeah, so the Agent Phi landscape is super interesting right now.
It's about a year old, with serious efforts. And the Q1 report was our latest report. We did our first one last year. And we're going to have another one coming out in a month or so, so updating it. And I think what's interesting, if we like maybe a little bit of context.

**Robbie** (1:40)
Please.

**Sam Green** (1:40)
Ethereum went live 2015
Aave Uniswap, they went live about eight years ago. And now about a year ago, we started to see these new layer built on top of the existing DeFi primitives. And yes, on my team, we tried about 100 of the projects. So it's a very popular space. And we filtered out basically the 40 of the most, of the projects that are most mature, that are shipping, that have users. And I can go into, I can break down what the different types are.

**Robbie** (2:15)
That would be fantastic. Yeah, let's do it.

**Sam Green** (2:18)
So what we're seeing that had, the initial segment that had the most PMF was yield agents. So these agents would basically look on chain, look for yield opportunities, monitoring, let's say, all the vaults, and then basically deploy their users' capital across all these vaults. And then when API, APIs changed or became, just changed, they would rebalance. And so many of these are rebalancing multiple times a day, trying to get their users the best yield, also trying to protect their users from risk.
For example, what happened with the KelpDAL. Several of the companies that we work with, they proactively withdrew their users' funds before they were locked within Aave.

**Robbie** (3:01)
Oh, that's incredible. And they did this with AI agents?

**Sam Green** (3:04)
Yes. So maybe let's break this down, and then we'll come back to the rest of that market segment.
So what we saw was that last year, most of the agents that were getting PMF, were getting, starting to manage their users' capital, they were rule-based. They were what we would call bots. And then over time, over the year, what's happened is they're using more and more AI in their stack. So I'll highlight Sale. Sale is a yield agent. It's a company that provides yield agents. And now I'm going to explain how they're using both like the rule-based logic and AI in their decision making. So what they're doing is they have a risk engine that they use. And for the deterministic rule-based logic, they're looking at how much yield is being distributed across all the vaults.
What are the APIs available? And then they're also looking at volatility of the assets. They're looking at if there's capital flight from the vaults. And they're looking at Twitter. They're looking at crypto Twitter sentiment. And this is all within their autonomous risk engine that is on top of their rebalancing engine. And so you can see that it's basically a blend of rule-based logic and AI to be able to automatically rebalance portfolios and protect their users' portfolios. So I expect to see this combination of things a lot in the coming years, both in TradFi and in crypto, where you have deterministic optimization methods that you use for well known things, and then you're going to be using AI to, let's say, look at the news, see if there's something that could destabilize the market coming up, and that can help basically protect users.