AI’s New Rules of Engagement

Our Head of U.S. Public Policy Research Ariana Salvatore explains how U.S.-China tensions, export controls and domestic regulation are reshaping where AI is built, who controls it and what investors should watch.

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Ariana Salvatore: Welcome to Thoughts on the Market. I'm Ariana Salvatore, Head of U.S. Public Policy Research at Morgan Stanley. 

Today, a look at how government is increasingly determining the future of AI in the U.S. – from where it's built to which technologies US companies and consumers can use. 

It's Friday, August 7th at 10am in New York. 

AI is rapidly reshaping the economy and society, so this is a pivotal moment for government to consider the rules governing that development. The first area to watch is technology restrictions, particularly in the context of U.S.-China competition. 

Now, for much of the past decade, the government's approach has been to restrict a relatively narrow group of technologies with clear national security implications while maintaining broader commercial ties. But as export controls spread across more sectors of the economy and AI moves from software into physical infrastructure, the definition of what qualifies as national security has become broader. 

The Department of Commerce could, for example, expand the entity list. That would require US cloud providers, software companies, and model marketplaces to remove or stop supporting models tied to designated Chinese developers. 

Congress could then make those restrictions more durable through things like the annual defense bill or other policy vehicles. We're keeping an eye on several legislative proposals, like the AI Overwatch Act, which would tighten controls and give congressional oversight around exports of the most advanced AI chips; and the MATCH Act, which would extend restrictions further upstream to semiconductor manufacturing equipment and seek closer alignment with allied producers. 

These measures wouldn't directly ban Americans from using a Chinese model, but they could constrain China's ability to train future frontier systems. 

But it's not just the US that could impose a set of restrictions. China has a parallel set of tools focused more on integration and market access. Regulators could block four models or APIs. They could require locally controlled deployment. They could impose Chinese data and content standards or use cybersecurity and entity list authorities to promote domestic substitutes. 

The likely result is an increasingly distinct pair of AI ecosystems. That's our two worlds thesis in practice. Over time, we think that means a bifurcated global AI market into separate technology ecosystems. 

That looks like the U.S. relying on export controls, allied supply chains, and largely closed frontier model platforms, while China emphasizes domestic hardware, open-weight models, subsidized compute, and localization. Over time, that bifurcation could produce different chips, models, standards, data rules, and distribution channels, while third countries navigate between the competing stacks. 

The second area to watch is domestic regulation. Today, the landscape is pretty fragmented. States are moving first on certain specific issues, including automated decision-making and child safety. Now, at the same time, Congress is confronting competing objectives from industry, consumer groups, and national security officials. 

So far, we think the evidence suggests that the administration's preference is for a light-touch approach, a largely voluntary national framework rather than a broad new licensing regime. But it's also moving toward more direct oversight of the most advanced models. That includes the possibility to play a more active role prior to model release to ensure that certain protections like cybersecurity and intellectual property are met. 

Publicly outlined priorities from industry seem to broadly overlap with that approach: a consistent federal framework, clearer liability standards, access to data, compute, and power, and copyright rules that don't materially limit model training. 

But of course, the industry isn't monolithic. There are some important nuances between frontier developers and other players. 

So, what does all this mean for investors? The government's reaction function will be critical to the way AI is developed and diffused throughout our society in two key ways. 

First, we see regulation altering not only the pace, but also the geography of AI infrastructure. 

At the same time, we think these constraints could strengthen the investment case for bottleneck solutions like on-site power generation, fuel cells, storage, and more. 

Second, greater technology bifurcation supports investment in parallel supply chains. 

The key takeaway here is that the government is no longer simply regulating the industry from the sidelines. It's helping to determine how fast AI develops through domestic rules, where it develops through infrastructure, permitting, and sovereign AI policy, and which technologies are accessible through export controls and market access restrictions. 

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