Can Policy Solve AI’s Chipflation?

AI’s appetite for memory has turned chips into an inflationary factor. Our U.S. Public Policy Strategist Ariana Salvatore looks at what policymakers could do to reduce that pressure.

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Ariana Salvatore: Welcome to Thoughts on the Market. I'm Ariana Salvatore, Morgan Stanley's U.S. Public Policy Strategist.

Today, I'll be talking about chipflation and what policy tools can or can't be used to address the memory bottleneck.

It's Wednesday, June 17th, at 10am in New York.

Last week, you heard my colleague Shawn Kim talk about chipflation and the surging cost of memory. Today, I'll get into what policymakers can and can't do about it.

As listeners will know, memory chips are becoming an increasingly strategic resource because AI infrastructure depends on them. And when a resource becomes strategic, governments tend to get involved. The challenge is that policy can help at the margin but probably can't solve the problem quickly.

There are three reasons for that. First, many U.S. policy tools all take time. Direct subsidies, tax credits, procurement guarantees, and faster permitting are all things that can support new fabrication plants, packaging facilities, and testing capacity. But memory supply is not going to appear overnight. This new capacity has to be built, equipped, qualified, and ramped – and that process can take years.

Second, China may be able to add some supply in conventional memory markets, but not enough to close the broader gap created by AI demand. That's especially true for high bandwidth memory, the more strategic type of memory for frontier AI systems. Supply there still remains highly concentrated, technically complex, and difficult to scale.

Third, our base case is that U.S. policy remains more restrictive, not less. We don't expect a broad loosening of export controls given the strategic imperative of this technology. Instead, we think policymakers are likely to continue to prioritize supply chain resilience, trusted capacity, and geopolitical de-risking over the near-term price relief.

Now, from a policy perspective, we think it's important to split memory into two categories. The first is AI strategic memory, high bandwidth and advanced DRAM. That's the memory that enables the most advanced AI systems. And for that reason, we think policy here is likely to focus on protecting strategic capability, limiting geopolitical vulnerability, and expanding trusted supply across the U.S. and its allied countries.

The second category is commodity or legacy memory. That's the memory that you can think of as being used in autos, industrial systems, consumer electronics, and other non-frontier applications. Now here, we think policymakers could consider more flexible options, like differentiated licensing or targeted support for critical sectors. But even then, the limits are practical: permitting, workforce, tools, qualification cycles, and production lead times.

China is the other major variable. Chinese producers are expanding in conventional DRAM and NAND. In some consumer-grade applications, that supply could act as a relief valve for buyers that have been crowded out by AI-related demand.

But still, there are limits. Chinese producers face yield and technology gaps, even if policy is supportive. And China alone will not solve the high-bandwidth memory bottleneck. The regulatory backdrop reinforces that point.

Some Chinese memory producers remain subject to U.S. restrictions or even heightened scrutiny. Access to the most advanced lithography tools also remains a hard ceiling. Without that access, scaling leading-edge memory becomes much more difficult.

So, the bottom line is this: policy can mitigate chipflation, but it's unlikely to end it in the near term. For AI strategic memory, policymakers are more likely to defend access, deepen allied coordination, and encourage trusted capacity than to loosen restrictions. For commodity memory, there may be room for some targeted flexibility.

But of course, geopolitics and timing still matter.

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