There's very few computational problems that complex that mankind has actually undertaken.

How do you think about the relationship between compute, capital, and then the technology that we have today.

Yeah, that's a million dollar question or maybe a trillion dollar question.

The expectation at the moment is that the cost of training these models may actually sort of top out or even go down a little bit as

the chips get faster but we don't discover new training material as quickly.

With software becoming more important than ever, hardware is following suit.

And with the world constantly generating more data, unlocking the full potential of AI

means a constant need for faster and more resilient

hardware. But how much does all of this really cost? In this final segment of our

AI Hardware series we tackle that question head on.

But if you're just catching up, be sure to check out Part 1 and Part 2,

where we explored the emerging architectures and the momentous competition for AI hardware.

And today we're joined again by A16Z Special Advisor, Gido Appanzel, someone who is uniquely suited for this deep dive as a storied infrastructure expert.

With experience like Intel's Data Center group dealing a lot with hardware and the low-level components,

so it's giving me sort of,'s giving me a good insight how large data centers work,

what the basic components are, they make all of this AI boom possible today.

Here is Gido touching on the reality of these models and how much they cost today.

Training one of these large language models today is not a hundred thousand dollar thing,

it's probably millions of dollars thing.

Practically speaking, what we're seeing in industry is that it's actually more of a tens of millions of dollars thing.

As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund.

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