Chip Craze: Framing Industrial Policy for the AI Era
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Why is the AI‑driven capex cycle considered the biggest investment story of the century?
Welcome to PGIM's The Outthinking Investor, exploring the forces shaping global markets, past, present, and future. Join Dalip Singh as he talks to economists, policymakers, technologists, and veterans of global financial markets to explore what could happen next and why it matters. Now, over to Dalip.
It's not exaggeration to say the AI CapEx cycle is the biggest investment story of the past century. The five largest hyperscalers, they're on track to spend almost $800 billion on AI infrastructure this year. Most analysts say that number will exceed a trillion next year. And as a share of USGDP, AI-related CapEx already exceeds the peak of the late 90s internet boom. It dwarfs The CapEx spend on the Eisenhower Interstate Highway System, the Apollo space program, and the Shale Revolution. Only the railroad build-out rivals the AI binge. And even that may not be true for long, since today's AI CapEx depreciates in years, not decades. And at the center of the AI CapEx boom are chips. They represent anywhere from 25 to 50 percent of the cost of an AI training cluster.
The same chips that power frontier AI models also guide precision munitions, power satellite comms, and determine whether a military can see and act faster than its adversary. So this goes well beyond the economic exposure. Which is itself staggering, the national security stakes are potentially existential. And whoever controls the leading edge of chip production therefore doesn't just have an economic edge, they have an overwhelming strategic advantage. And it's why governments have been in a race to shore up their supply chains. Of course, the US passed the CHIPS Act in 2022. Europe followed suit with its own CHIPS Act. Japan legislated around Of subsidies. China has made a $150 billion push. Every major economy at roughly the same time has decided that leaving the chip supply chain to market forces is an unacceptable liability for national security.
And here's the problem: legislating industrial policy to shore up supply chain resilience and executing are two very different challenges. My guest today lived in the execution layer. Mike Schmidt was the first director of the U.S. Chip's Office. He was the person charged with turning more than $50 billion of U.S. taxpayer money into functioning fabs while operating inside a massive federal bureaucracy against the clock. He's now a distinguished fellow at Princeton's Center for Economic Policy. and his research focuses on developing a principled framework for industrial policy. Mike, welcome. Thank you for joining.
Thanks, Sleep. It's really great to be here. Thanks for having me on.
Mike, you mentioned this relentless effort to cut costs and you were in contact with chip executives, I'm sure, many times a day, every day for multiple years. Do you make a distinction between the demand for training compute, which does look insatiable and is driving the demand for the most advanced chips versus inference compute? which is much more about efficiency gains and where you've seen models like Deep Seek show that perhaps you can have good enough computing power to run applications. Do you think we might end up with two different stories playing out?
I think
that's
right. But my sense is that what you're also seeing is a greater appreciation for how important inference is gonna be. So if you look at the Intel story right now. And we can get into Intel later. Yeah, I want to. A big part of their tailwind has been what part of their core products business is manufacturing CPUs for data centers. And in recent quarters, it's become clear that because of inference, there's gonna be a huge amount of demand for CPUs. Whereas earlier, like when I was in government, The whole AI story was on the GPU side. Now, because of the importance of inference, there's like really strong demand on the CPU side as well. And that has given Intel a tailwind that was hard to foresee a year or a year and a half ago.
So again, it's a story of you're driving for the efficiency, but also with agentic AI, you're going to need the inference.
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Chapters
4 chapters
1
Why is the AI‑driven capex cycle considered the biggest investment story of the century?
0:03–11:46
2
How do training‑compute demand and inference‑compute demand differ for AI chips?
11:46–19:14
3
What did the 2022 CHIPS Act actually fund and how is its progress measured?
19:14–30:22
4
Why are building a fab and its supply chain so costly and time‑intensive?
30:22–37:35