The Unexpected Investment Case for AI Safety

Ariana Salvatore (0:00)

Welcome to Thoughts on the Market. I'm Ariana Salvatore, head of US Public Policy Research at Morgan Stanley.

Michael Zezas (0:05)

And I'm Michael Zezas, Deputy Global Head of Research at Morgan Stanley.

Ariana Salvatore (0:09)

Today, we'll be talking about AI safety and regulation. It's Wednesday, September 23rd at 10 a.m. in New York.

We put out a note last week on AI frontier capability gain and the associated safety risks. Those have been in focus in recent weeks, and as a result, we've gotten a number of questions about the path forward for government regulation. So today, Mike and I are going to get into some of the newest developments, where we think things are headed, and how the midterms could shape that path.

Michael Zezas (0:37)

Yeah, and this is pretty important because the concern is that if AI safety scrutiny increases, it's going to slow everything down. You might have less capex, fewer model releases, and there's all sorts of downstream effects for the pace of US growth and investment strategy in equities and throughout the AI investment theme. But Ariana, you and the team landed in a bit of a different place and are arguing that a bigger focus on AI safety could end up being a tailwind to compute spend rather than a break on it. Can you break that down for us?

Ariana Salvatore (1:18)

Sure. So the way we see this playing out is there are five potential states of the world. Some include industry self-policing, some include the prospects for heavier government intervention.

Across all of them, as you mentioned, we actually think this is a pretty big tailwind to compute spend and capex more broadly. That's because as the labs integrate greater safety monitoring infrastructure, we think that spend is only going to accelerate, especially as LLM capabilities increase at a non-linear rate.

Similarly, on the regulation front, we think there are a few things that prevent something like a large comprehensive AI regulation bill from coming to fruition. We think there's really three key obstacles to something like that happening. The first is the politics. So the president himself has said he's against some large-scale regulation. The second is the procedure. So mechanically speaking, there would need to be a legislative vehicle for this thing to ride on. That's hard to see emerging in the very near term.

The third is precedent. So historical precedent here tells you that usually regulation is catalyzed by some sort of high salience event. That's why our framework for government reaction here hinges on two components. Incident salience, as I just mentioned, and instrument availability. Instrument availability basically reflects the extent to which the government already has a tool that it can pull in this direction. So that's how we think about it going forward. That doesn't mean all policy action is off the table, but that supports our expectation for higher capex, higher compute spend over the coming years.

Michael Zezas (2:47)

Right. So the idea is that the spending continues, and the things that would otherwise limit that spending, you don't see as real plausible policy options at the moment. And can you break this down a little bit more, because there's a lot of different proposals floating around Washington, DC from policymakers right now.

What are you paying attention to?

Ariana Salvatore (3:08)

We don't expect an overarching AI regulatory authority in the near term.

Now, importantly, we also don't expect sweeping open weight model regulation.

The reason for that is threefold. First of all, we think the US is keen on maintaining this managed stability relationship with China. We've written about the expectations around the U.S.-China summit. That's kind of a delicate balance that we think is likely to persist. So overly restricting open weights models might throw a little bit of a wrench into that equilibrium that we see. So that's the first reason. The second reason is diffusion. We think the US administration wants to see the proliferation of open weights models. We know that companies are using some sort of hybrid of open and closed weight. So to the extent that banning these models with slow adoption, we don't think that's in the interest of the administration. And the third reason is purely mechanical. It's really hard to enforce these sorts of restrictions. Once a model weight is published online, it can be really hard to clamp down exactly who and where it's going to. Obviously, companies can download them, customize them, etc. So the enforcement picture here is also really challenging.

That being said, we do think that the executive can continue to lean in and sort of make some incremental adjustments or changes on the regulatory front. But we think it's likely less severe than some of the proposals you're seeing in Congress right now, things like the Kill Switch Act, for example, which basically mandate that companies can maintain an ability to shut down models at a moment's notice, right, if a certain threshold is crossed. So that's something that we see as less likely to come to fruition. But again, setting safety standards, guardrails, all that from the administration we think is possible in the near term.