Hey, I’m Matt. I’m a former Bloomberg News reporter, and you’re reading AI Street, where I report on how Wall Street uses AI.
Milos Maricic has a unique view into how the world’s largest asset owners are using AI.
Maricic and his team at SPEC Research work with sovereign wealth and pension funds as they figure out where the technology actually helps with investing.
Over the past six months, his team has also tried to answer a straightforward question: What are the world’s largest sovereign wealth and pension funds actually doing with AI?
They reviewed more than 1,000 public sources, incorporated information supplied by institutions and contacted the funds directly to assess 27 sovereign wealth funds and 26 pension funds. And today they released their SWF AI Maturity Index report.
The big takeaway:
The world’s largest sovereign wealth and pension funds are rapidly adopting AI, but none can show that it has made them money.
Or as CPP Investments CEO John Graham summed up in the report: “Will [AI] help us make faster decisions? Yes. Will it help us make better decisions? TBD.”
What Maricic and his team found was a wide gap between adoption and proof.
Singapore’s GIC has built a Virtual Investment Committee that includes an AI devil’s advocate trained on 44 years of internal deal data. The agent argues against an investment before the real committee sees it. CPP Investments has issued more than 2,100 Microsoft Copilot licenses across a firm of roughly 2,100 people, with 73% actively using them.
The strongest claim comes from Norway’s sovereign wealth fund, which says AI saves it about $100 million a year in trading costs. The fund has not published a methodology attributing those savings to the technology. More broadly, most funds do not track which parts of an investment decision came from AI and which came from people, making it difficult to tell whether AI improved a decision or simply made the process faster.
I spoke with Maricic about how sovereign wealth and pension funds are using AI. We talked about what sovereign wealth and pension funds are actually doing with the technology, why managers’ claims of AI-generated returns often fall apart under scrutiny, and why the growing volume of AI-generated research is creating another problem: the cost of verifying it.
I turned this interview, with the help of AI, into a new episode of my Alpha Intelligence Podcast. It has been dormant, but I plan to share more interviews going forward in this format, alongside the written transcripts.
Matt: How long have you been putting this report together?
Milos: About six months now. It is the first thing of its kind, so it has been ramping up really fast. I am just looking at how the response from the institutions has changed. When we launched the first one six months ago, it was like, “Do you want to comment on this?” And they were like, “No. Who are you? Just get out of here.”
Now it is more like, “Ah, yeah, sure. Let’s have you talk to our AI team. Let’s calibrate that.” So it has been getting some pickup.
ICYMI
Matt: So you are reaching out to these funds directly, saying, “Hey, we are trying to get a better sense of how you are thinking about this technology?”
Milos: Yeah. There are three things. First of all, the baseline job is to go through everything that is publicly available. The index has a database of more than 1,000 public sources.
Some of these institutions have internal information about themselves or their clients, and they allow us to use it specifically. Sometimes they do not, and that is fine. The third layer is that we reach out to them and say, “Hey, we are going public with this in a couple of weeks. Please help us understand what is going on.” More often than not, they come back, so we feel like we have a relatively robust picture of where the use is. Is it perfect? No. But this is a super-fast-moving field.
Matt: Right. And there are not many other resources looking at it like that.
Milos: That is the thing. Sovereigns are a niche to begin with, and even those who tackle that niche do not necessarily talk about AI use within it. So it is a niche within a niche.
Matt: What made you start it?
Milos: My last company was an AI-driven capital-allocation platform that got acquired by a sovereign.
So a lot of sovereigns and pensions were clients in the latter stages of that business. Business with sovereigns is a who-you-know business. It is very relationship-driven. We felt it was interesting for the team to continue in the same direction. We also felt we were doubling down on the advantage of being a little bit of insiders, knowing the right people, and so on.
Matt: I think of your role as somewhere between the vendors and all these solutions. If you are working at a sovereign wealth fund or you are a money manager, all this AI stuff can be too much.
So you are helping them sort out what is actually working, right?
Milos: Exactly. If it is overwhelming for you and me just trying to follow what is going on, it is even more overwhelming for them because they have all that capability. But what do you do with the capability?
Even if you have bought the story that AI is going to be big and you want to be part of it, what do you do? Where do you deploy? Do you deploy in the biggest, most inflated-valuation companies out there? Do you look for the nuggets? Do you go through managers?
If we focus on the manager side, a lot of what allocators do is manager selection. There is an explosion of managers coming to them and saying, “We use AI. Look at our backtests. Look at our track record. It is all amazing.” Allocators are prudent about it, and they have every reason to be, because a lot of this is just bogus.
Some of it is interesting, but you have to distinguish between the two. One of the things we produce is a framework called the SPEC test that we coach allocators to use with managers. It breaks down their AI claims in a structured way to understand what holds water and what does not.
One priority for the second half of this year is working with the Institute for Sovereign Investors to take the SPEC framework and turn it into a more industry-wide due-diligence framework. It is sorely needed. Right now, what is happening within allocators around AI claims is completely ad hoc.
Matt: What are fund managers saying they are doing with AI?
Milos: The most typical thing is: “We backtested this, it worked amazingly well, and our Sharpe ratio ended up being three.” Then you look into the backtest and realize the model had a little glimpse of the future. It actually saw where the market was going.
Another thing is riding heavily on the track record of the managers themselves. But there is loads of research showing that, if you use AI to do fully automated investing, the aspects that make up a manager’s track record are what AI can replicate most easily. The more idiosyncratic parts are more difficult to pick up.
When we make managers go through the framework, we ask them to decompose their claims. We probe them. We say: “Tell me about a time when the model was wrong. What happened? How did you adapt it? How did you fix it going forward?” Or: “Tell me about a time when it was overruled by a human.” That gives you a window into their internal process, so you can understand where the boundary is between human capacity, human oversight, and AI. The framework has about 14 questions that we drill into.
Matt: A sovereign wealth fund will call you up and say, “XYZ reached out. I want you to take a look. Do you know about them? Maybe you already do.”
Milos: Yeah, exactly.
Matt: What are some red flags you have seen?
