Chris Miller (2:36)

I think if you look historically, certainly you see cycles up, cycles down, but there are moments when you see step changes in terms of demand for certain types of chips. We saw that with smartphones, for example, there were was no demand for smartphone chips. And then now everyone needs a new smartphone every couple of years. And what we're seeing right now is that type of step change, a huge increase in just the baseline amount of chips that we're going to need for data centers driven by AI. And so I think we shouldn't expect cycles to be over. Certainly there'll be ups and downs in the future, but it's now very clear, I think, that we're just going to need a lot more compute for AI purposes in the future, and as a result, we'll need a lot more of the AI chips that go inside of data centers.

Co-host (possibly Carol or another Bloomberg host) (3:18)

In your view, is the, the promise that many people think AI is supposed to deliver? Will it, will it actually be delivered? Like, what's the future that you envision after all this capex is spent?

Chris Miller (3:34)

Well, I think it's in some ways funny when we ask, will I deliver? Because if you look just three years ago, even after ChatGPT was released, there are so many things that were not possible that are possible today. Whether it's the number or the scale of hallucinations and answers that chatbots give you, or the scale of work that you can put together today that just wasn't possible in the past. We've already had so much new capability generated just in the couple of years since ChatGPT that in some ways I think it's an absurd question to ask, will AI deliver? It already has in a lot of ways, but I think I understand why. There's plenty of questions about what? About the, the investment that's happening right now. Will that investment pay off? And I think there are reasons to think carefully about how much each company is putting in over what time horizon. But I guess when I, when I zoom out, I ask myself, do I want a world in which there's more access to compute or less? And it seems to me that we should, rather than being concerned that there's too much compute being put in the ground, be excited that there's actually companies willing to invest in the infrastructure that's going to deliver all of these capabilities?

Co-host (possibly Carol or another Bloomberg host) (4:44)

Well, I think it's an important question to ask for a few reasons, and one of those is because if you look at just the CapEx that these companies are spending, you know, one company, $200 billion in a single year, that's a big commitment, and that's something that they have to convince investors that is money that is going to the right place, that's money that has to be earned back. Plus on companies coming to a hyperscaler and saying yes, we think that this money is not only being well spent, but then we can use this compute to actually create a product that will provide a return on investment. So yes, we've seen a lot of the hyperscalers benefit the, you know, the anthropics and the open eyes and the mistrals. Like that's amazing stuff. But at the end of the day there are a lot of companies that are not necessarily technology companies that maybe aren't yet necessarily seeing an increase in productivity as a result of these tools. So my question is, do those companies start to see that? Do we live in a world where this is a layer, just like the Internet was a layer of technology?

Chris Miller (5:49)

I think there's no doubt that we're going to have AI as a layer that's embedded into all technology that we use. And when I look at the economic question, I say first off, is it profitable to serve AI systems today? Not train the next generation, but serve today. And if you listen to what's publicly reported about OpenAI or Anthropic, their margins on their inference business are, are not just positive but, but quite good. And so that I think is pretty strong evidence that the delivery of already existing AI services is a pretty profitable business. The next question is should we be investing in R and D in the next generation, which is of course very, very expensive, but I think it's hard to argue we should dramatically slow down R and D. And I just given all of the extraordinary improvements that we've seen over the past couple of years and in terms of capability and so when you start breaking it down and ask which specific investment do we think is excessive? Which specific investment would rather not be doing? Would you really like to be the CEO of the only big tech company that's not investing in AI? That doesn't seem like a very comfortable place to be. And I'd rather have a situation in which the big technology companies are investing more rather than putting money in the bank or buying back their stock because they didn't have any profitable investment opportunities.

Host (possibly a Bloomberg host, e.g., John or Emily) (7:01)

Well, you know, I would say Apple's doing it differently and I guess time will tell whether or not their approach works out. Chris, I am curious too. Who do you talk to? What research do you follow? What are the leading voices? What are the leading companies that you watch to figure out how the semiconductor space is evolving? We still know tsmc is still the big manufacturer of all chips in the world, and there's geopolitical tensions to that. But give us an idea of where do you think investors and just the world at large need to be focusing their, their attention on when it comes to the semiconductor world. Is it Nvidia? Is it China? Is it something else?

Chris Miller (7:43)

I think the hard problem is that it's all of the above. And we've seen this play out in the GPU supply chain over the past couple of years. We've had shortages in different types of components, different materials, and the memory chips that go next to GPUs and AI servers. Each part of the supply chain has had to dramatically ramp up its production capacity to respond to this surge in demand. And so if you only look at one part of the supply chain, you miss the, the challenges that other parts are often facing. And so it's the chip designers, it's the chip makers, but it's also the materials suppliers, which are often not even thought of as being semiconductor firms, but produce many of the capabilities that are critical to actually manufacture the chips and servers that we need.

Host (possibly a Bloomberg host, e.g., John or Emily) (8:31)

Do you think the world, or do you think the US specifically does need to be restricting sales of its most sophisticated chips to China, Iran, others? I mean, we did see Nvidia, they still face some uncertainty in China. That's their largest market, or which it is the largest market for chips. The government granting some licenses to ship a small amount of some of its processors to customers there. But Nvidia is not sure if the Chinese government will give its approval. So there's still some back and forth here, but is it still an arms race of source? Arms race of sorts. Excuse me.

Chris Miller (9:05)

Yeah, I think arms race is not a bad analogy. When you listen to tech CEOs, they'll speak in the same language. They're struggling to get access to all the computing power that they need, that they envision needing more tomorrow than they've got today. And if you listen to Chinese technology leaders, what you'll hear from them is challenges in getting access to computing power. And the primary reason they've struggled to deploy AI products at scale is because they've struggled to get access to all of the computing capabilities. And it's on a regular basis. We see new Chinese models launched that can actually be deployed at scale because they don't have access to the advanced chips that they need. And so this does, I think, seem to me that this is still a very powerful card that the US has to play. And so I think we should be very careful or on any decisions to give China more access or at least make sure that we're getting something in exchange for that.

Co-host (possibly Carol or another Bloomberg host) (9:59)

On that, Chris, the U.S. support of TSMC and the U.S. support of intel, different types of support. And I guess you could call the TSMC1 encouragement to build here in the United States. What is the right industrial policy to reduce reliance on companies outside of the U.S.

Chris Miller (10:21)

well, it's a really hard problem because TSMC is such a capable and efficient manufacturer in their home base in Taiwan. But I think the US Government is right to say that we need a more diversified manufacturing base for the world's most important semiconductors. I think we've learned over the last couple of years there's there's no silver bullet. President Trump's tried tariffs. That comes with obvious downside. President Biden tried subsidies. That worked to a degree, but only to a degree. Here's the reality. The chip industry has involved hundreds of billions of dollars of Capex over the last several decades. And so it's just not going to move fast. And so if you want to slowly change the structure of where chips are produced, you've got to plan for years of years of implementation of measures designed to shift the economics of the chip industry.

Host (possibly a Bloomberg host, e.g., John or Emily) (11:11)

All right, going to leave it there. Chris Miller, thank you so much. Good to check in with you. Professor of international history at the Fletcher School at Tufts University, author of Chip Ward joining us on this Thursday.

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