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Chasing Silicon: The Race for GPUs

With the world constantly generating more data, unlocking the full potential of AI means a constant need for faster and more resilient hardware. In this episode – the second in our three-part series – we explore the challenges for founders trying to build AI companies. We dive into the delta between supply and demand, whether to own or rent, where moats can be found, and even where open source comes into play. Look out for the rest of our series, where we dive into terminology and technology that is the backbone of the AI, how much the cost of compute truly costs! Topics Covered: 00:00 – Supply and demand 03:03 – Competition for AI hardware 04:46 – Who gets access to the supply available 06:28 – How to select which hardware to use 08:59 – Cloud versus bringing infrastructure in house 13:26– What role does open source play? 16:48 – Cheaper and decentralized compute 20:16– Rebuilding the stack 21:31– Upcoming episodes on cost of compute Resources: Find Guido on LinkedIn: https://www.linkedin.com/in/appenz/ Find Guido on Twitter: https://twitter.com/appenz Find a16z on Twitter: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Subscribe on your favorite podcast app: https://a16z.simplecast.com/ Follow our host: https://twitter.com/stephsmithio 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.

Guido AppenzellerguestSteph Smithhost

Aug 25, 202322mWatch on YouTube ↗

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EPISODE INFO

Released: August 25, 2023
Duration: 22m
Channel: a16z
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

With the world constantly generating more data, unlocking the full potential of AI means a constant need for faster and more resilient hardware. In this episode – the second in our three-part series – we explore the challenges for founders trying to build AI companies. We dive into the delta between supply and demand, whether to own or rent, where moats can be found, and even where open source comes into play. Look out for the rest of our series, where we dive into terminology and technology that is the backbone of the AI, how much the cost of compute truly costs! Topics Covered: 00:00 – Supply and demand 03:03 – Competition for AI hardware 04:46 – Who gets access to the supply available 06:28 – How to select which hardware to use 08:59 – Cloud versus bringing infrastructure in house 13:26– What role does open source play? 16:48 – Cheaper and decentralized compute 20:16– Rebuilding the stack 21:31– Upcoming episodes on cost of compute Resources: Find Guido on LinkedIn: https://www.linkedin.com/in/appenz/ Find Guido on Twitter: https://twitter.com/appenz Find a16z on Twitter: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Subscribe on your favorite podcast app: https://a16z.simplecast.com/ Follow our host: https://twitter.com/stephsmithio 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.

SPEAKERS

Guido Appenzeller
guest
a16z special advisor and infrastructure/hardware expert; former CTO for Intel’s Data Center Group.
Steph Smith
host
a16z podcast host who narrates segments, introduces guests, and leads the interview.

EPISODE SUMMARY

In this episode of a16z, featuring Guido Appenzeller and Steph Smith, Chasing Silicon: The Race for GPUs explores why AI Compute Is Scarce and How Founders Navigate It Guido Appenzeller explains that exploding AI demand outstrips chip supply because of foundry capacity limits and bottlenecks in manufacturing chips and building cards, and new fabs take years and billions to build.

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