Shifts in Credit Markets for the AI Buildout

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

Stephen Byrd: And I'm Stephen Byrd, Head of Global Thematic Research at Morgan Stanley.

Ariana Salvatore: Today, we'll be talking about AI sovereignty, what it means, what countries around the world are doing to advance their own goals, and what a more fragmented AI ecosystem could mean for investors.

It's Thursday, August 20th at 2pm in New York.

Stephen Byrd: And it's 9pm in Helsinki.

Ariana Salvatore: As AI becomes more powerful and therefore more important to the global economy, countries are asking a basic question: How much of it do we need to control ourselves? That's at the heart of AI sovereignty, making sure governments around the world can access the computing power, data, energy, and technology they need even as geopolitical tensions may rise.

Stephen Byrd: And that seems to fit into a broader trend we've been talking about for some time, a more multipolar world where governments are increasingly willing to intervene in markets around strategically important technologies.

Ariana Salvatore: Exactly. We describe this as a potential ‘two worlds dynamic.’ The U.S. and China have been gradually de-risking from one another, particularly in advanced technology.

We've already seen policy tools, including export controls, tariffs, and incentives for domestic manufacturing. And as AI becomes more strategically important, our expectation is for policy intervention to increase rather than decrease. But what's interesting is that the U.S. and China aren't necessarily pursuing sovereignty in the same way.

Stephen Byrd: So, let's unpack that. Can you start with the U.S.? What does the American approach look like?

Ariana Salvatore: Yes. We think the U.S. is trying to do two things at once, basically. On one hand, it wants to preserve national security guardrails around some of the most sensitive AI capabilities. But on the other hand, it has an incentive to make sure the American AI tech stack is broadly available to allies and partners.

So, there's an inherent tension there between those two objectives. Obviously, if you restrict access too much, you can encourage other countries to develop alternatives,. But if you allow unrestricted access, policymakers may begin to worry about losing control over strategically important technology.

So, the way that we chart this is through a middle path. We think the direction of travel looks less like complete technological separation and more like selective access – tighter controls around sensitive capabilities alongside an effort to maintain the global reach of the U.S. AI ecosystem.

Stephen Byrd: Whereas China's approach is more focused on building out an indigenous ecosystem. Specifically, we see policymakers in China pursuing greater self-sufficiency across the AI stack, from chips and computing infrastructure to cloud and models.

Our China strategists argue that bifurcation could actually increase China's incentive to build a larger China-compatible AI ecosystem abroad, particularly across the Global South and other markets that aren't firmly aligned with the U.S. ecosystem.

China's model emphasizes lower-cost models, open weight ecosystems, subsidized compute, cloud partnerships and infrastructure exports. So, the competition could increasingly be about not only which country has the most advanced model, but which ecosystem can achieve the widest adoption.

Ariana Salvatore: That's right, and that brings us back to this idea of two worlds.

So, Stephen, is the implication here that we're going to be heading toward two completely separate AI systems?

Stephen Byrd: Not necessarily, I'd say. You know, the supply chains are still deeply interconnected, so our research does not suggest a sudden decoupling. But we could see greater duplication and less globally fungible infrastructure.

Countries may increasingly want compute located domestically or regionally. Sensitive data may need to stay within particular jurisdictions, and companies may need different cloud cybersecurity or distribution arrangements in different markets. And that means the same global level of AI demand could require more physical infrastructure than it would in a completely integrated world.

Ariana Salvatore: So, fragmentation, like other themes within multipolarity, are more economically inefficient. But potentially pretty important for the investment cycle. We think sovereign AI can make the system more redundant and more capital-intensive as a result. Our research teams think there are potential beneficiaries from that across semiconductors, data centers, networking, power, cloud, cybersecurity, and infrastructure software.

Let's look at data centers specifically. If governments and enterprises increasingly require local hosting and greater control over sensitive data, you will inevitably need more geographically distributed infrastructure. Colocation operators, we think, can benefit because they provide the power, cooling, space, security, and interconnection that can allow customers to keep workloads in specific jurisdictions.

So, the fragmentation we're talking about may introduce inefficiency at a system level while simultaneously creating incremental infrastructure demand. 

Stephen Byrd: And there's another constraint here that we probably shouldn't overlook, which is energy. Compute ultimately needs power. So, access to reliable, affordable electricity becomes part of a country's competitive position in AI, which ties into our politics of energy theme that we outlined in January of this year.

But as we've also noted, that creates a political constraint. Our thematic work has highlighted rising concern around the impact of data center growth on power prices and on local infrastructure. This has really shown up in a big way in the U.S. And that can mean more pressure to protect existing rate payers, more emphasis on low-cost power. And greater interest in behind-the-meter or off-grid power solutions that allow data centers to secure electricity without putting the same pressure on the grid.

Ariana Salvatore: Which suggests that there's a cost, in fact, to AI sovereignty as well.

Stephen Byrd: Absolutely. And if countries want more domestic compute, duplicated infrastructure, localized supply chains, and greater redundancy, the system may become more resilient, but potentially more expensive – and we're certainly seeing signs of it being more expensive.

Compute and power are already constrained in many markets. Add to that regulatory requirements, localization, and potential restrictions on technology transfer, and reducing dependence can carry an inflationary cost. So, for investors, I think the question isn't simply whether sovereign AI increases spending. It's also where that spending has to occur, what gets duplicated, and which parts of the stack become strategically indispensable.

Ariana Salvatore: So, Steven, to frame this for investors, the way we see this theme unfolding suggests that sovereign AI reinforces rather than undermines the broader AI CapEx cycle. We think competition between the U.S. and China is intensifying. Countries outside those two ecosystems increasingly will want greater national resilience and flexibility. And that combination can support additional spending on compute, data centers, networking, and power for years to come.

Lastly, an increasingly important question is who controls and supplies that infrastructure, energy, standards, and supply chains that will allow those models to operate at scale?

Stephen Byrd: And that may ultimately be the most important thing to watch. Sovereign AI is another example of geopolitics moving directly into the technology investment cycle and potentially changing not only where AI gets built, but how much infrastructure the world needs to build it.

Ariana Salvatore: Steven, we'll leave it there. Thanks so much for joining me.

Stephen Byrd: Great to be here, Ariana.

Ariana Salvatore: And thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

Welcome to Thoughts on the Market. I’m Julia Rizzo, Latin America Agribusiness Analyst at Morgan Stanley.

Today: how El Niño could move from the Pacific into commodity markets, grocery prices, and investor portfolios.

It’s Wednesday, August 19th, at 10am in Sao Paulo.

You may not follow rainfall patterns in Brazil or cocoa-growing conditions in West Africa. But you immediately notice when chocolate, groceries, or electricity cost more. And you can connect the dots to El Niño -- a warming cycle in the Pacific Ocean that disrupts weather globally. It changes where rain falls and shapes the outlook for crops, power markets, transportation, and inflation.

There is now a 95 percent chance of a very strong El Niño in the fourth quarter of 2026. It could end up being among the most powerful events in more than 75 years of recorded history. Timing and location matter greatly. Crop damage often depends on whether heat or heavy rain arrives during a narrow planting, flowering, or harvest window.

The most direct effects are likely to appear first in commodities. Sugar is on the list of commodities most exposed to favorable price dynamics from weather conditions. Cocoa also looks tight. Grains are more complicated. Soybeans need evidence of a net South American production loss. Problems in northern Brazil may be offset by stronger crops in Argentina or Brazil south. Corn is even more dependent on timing. The key near-term catalyst remains U.S. weather and crops.

What happens next matters well beyond agricultural markets. Food is the main channel through which El Niño reaches the broader economy, and the effect usually appears after a one-year lag. That makes inflation primarily a 2027 story.

In Latin America, the largest incremental inflation risks are concentrated in Peru, Brazil, and Colombia, with most of the pressure arriving in 2027. That matters for central banks. Weather shocks can fade. So, policymakers often look through an initial rise in food prices. The greater concern is that higher food costs may begin to influence inflation expectations, wages, rents, or other prices across the economy. Colombia stands out as the clearest case where those second-round effects could complicate monetary policy.

India and Indonesia also face meaningful economic exposure. Agriculture accounts for a large share of output and employment in these countries. India is especially sensitive. Agriculture represents about 18 percent of the GDP, 43 to 45 [percent] of jobs, while food makes up roughly 36 percent of the consumer price basket. Record food reserves may provide some protection, though a poor growing season could still weigh on rural incomes and keep food inflation elevated.

The economic consequences will vary widely. Higher agricultural prices can support farmer income and benefit some parts of the food and agricultural supply chain. They can also raise costs for households, food producers, and businesses that depend on grains and sugar. Utilities may benefit in markets where hotter or drier conditions lift electricity prices, while heavy rainfall could disrupt transport routes and airports in those exposed regions.

Historical asset-price signals are limited, so this is less of a broad macro trade than a detailed assessment of local exposure. Rainfall, crop timing, inventories, and the ability to pass higher costs on to consumers will determine where the pressure lands.

El Niño may begin in the Pacific, but its market footprint can travel from cocoa farms in West Africa to a grocery aisle, a power grid, or a central bank meeting.

Thanks for listening. If you enjoy the show, please leave us a review and share Thoughts on the Market with a friend or colleague today.