Chris Miller

And if you listen to what's publicly reported about open AI or anthropic

their margins on their inference business are not just positive, 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&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&D and AI, just given all of the extraordinary improvements that we've

seen over the past couple of years in terms of capability.

And so when you start breaking it down and ask, well, which specific investment do we think is excessive?

Which specific investment would we 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.

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, where you'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 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 AI chips and servers that we need.

an arms race of source what arms races of sorts excuse me yeah yeah i i think arms races is not a bad analogy when you listen to uh tech ceos they'll speak in the same language they're struggling to get access to all the computing power uh that they need that they envision needing more tomorrow than they've got uh today and if you listen to chinese uh technology leaders what you'll hear from them is challenges and getting access to

uh computing power and the primary reason they've struggled uh 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.

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.