播客讨论AI算力供给受EUV与先进制程产能约束，未披露公司新增订单或产能动作

【播客逐字稿片段 · Dwarkesh Podcast】
单集:Why smarter AI models could drive up compute prices 10x
片段:第 4/5 片(全集切片后按关键词筛出本片) · 本片命中:ASML、wafer、euv、capacity
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But more generally, I think the supply of compute is much less elastic and much less capable of absorbing large demand shocks and much less capable of being accommodated by using different substitutes than the extraction of different metals is.

to illustrate why I think this 3x in comput capacity year-over-year is hard to budge or potentially even sustain is that I don't see how any of the three elements that constitute that 3x can be much accelerated.

So 1.4x of that is coming from Mo's law.

Far from increasing it, I think it'll be a miracle if we can just keep it going for a few more years.

1.2x is coming from building new fabs.

This process is ultimately going to be bottlenecked up to 2030 and potentially even beyond by just building new ASML EUV machines.

Uh Dylan when he was on the podcast a few months ago talked about this in great detail.

And 1.8x comes from the fact that AI is absorbing a lot of wafer allocation that was previously going to smartphones and PCs.

This is probably going to hit a wall by the end of next year when at the leading edge N3 nodes at TFMC AI will have gone from 60% to 86%.

At some point you have just absorbed all leading edge way for capacity for AI and you can't keep increasing this number.

So I don't know how we get even to continue to do 3x comput scaling year over year for the next few years much less go beyond that.

At the end of the month I go through the time honor tradition of closing my books.

I start by opening Mercury, which is my banking platform, to make sure that all my transactions are properly categorized.

Autocategorization rules handle the predictable stuff pretty well.

But I'm constantly working with new contractors, you know, tutors and researchers and videographers.

And I'm also trying new tools.

Manually categorizing all of these transactions would add a couple of hours of overhead every single month.

So instead of going through them one by one, I have Command, which is Mercury's built-in AI, take a stab at all of them at once.

command proposes a category for each transaction and provides its rationale.

I just review, I fix anything that's off and [music] I approve.

And once all this work is done in Mercury, it syncs everything with QuickBooks and commands judgment calls are genuinely good.

It does the obvious things like looking at the vendor, but it also investigates who on my team made the purchase and looks at notes and memos to build up as much context as possible.

This is just one of the ways you can use Command to automate the back end of your business.

To learn more, go to mercury.com/command.

Mercury is a fintech company, not an FDIC insured bank.

Banking services provided through Choice Financial Group in column NA members FDIC.

AI generated responses and suggested actions may vary and are not guaranteed.

Now, I want to clarify that at some point in the future, compute will get cheap again.

At some point, we'll just have robots that can convert shores of silica sand and mines of copper into new computer chips.