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

As the world generates more data, unlocking the full potential of AI means a constant need for faster and more resilient hardware.

But how much does this all really cost? In this final segment of our AI hardware series, we tackle that question head on.

Be sure to check part 1 and 2, where we explore the emerging architectures and the momentous competition for AI hardware.

Topics Covered:

00:00 – The cost of compute

02:20 – Is this sustainable?

03:23 – The cost to train a model

05:39 – Computation requirements

09:05 – The relationship between compute, capital, and technology

11:15 – GPT4 commenting on the technology with help from ElevenLabs

Resources:

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Please note that 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. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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