
David Cahn
Articles
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Dec 9, 2024 |
sequoiacap.com | David Cahn
Last January, we compared ChatGPT to AI's "Big Bang" and predicted that 2024 would be AI's " primordial soup " year. The AI ecosystem was abounding with new ideas and potential energy. It was a ripe moment for new entrepreneurs. "There is much potential in the air, and yet it is still amorphous," we wrote at the time. "Vision is required to convert it into something real, tangible and, ultimately, impactful."Today, the AI ecosystem has hardened.
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Aug 12, 2024 |
sequoiacap.com | David Cahn
Here's the question now being asked all across the AI ecosystem: Is there a way for someone else to take on the demand risk from AI, while I capture the profits? Today, Big Tech companies have stepped up to alleviate some of this tension. They are acting as risk-absorbers within the system, taking on as much demand risk as they possibly can, and driving the supply chain toward greater and greater CapEx escalation.
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Aug 5, 2024 |
sequoiacap.com | David Cahn
The race to model parity has been the defining project of the last 12 months in AI. This phase was characterized by the search for new research techniques, better training data and larger cluster sizes. The next phase in the AI race is going to look different: It will be defined more by physical construction than by scientific discovery. Up until now, you could fit your training cluster into an existing data center via colocation or retrofit.
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Jul 16, 2024 |
sequoiacap.com | David Cahn
Imagine you knew for certain that AI was going to be as transformational as the internet, and that you control the only AI company in the world. How fast would you build CapEx? I believe the answer is: You would take your time. "AI CapEx" is a euphemism for building physical data centers with land, power, steel and industrial capacity. If you were the only company in AI, you'd wait to digest some AI revenues. You'd see how liquid cooling systems perform, and alter your data center designs as needed.
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Jul 8, 2024 |
sequoiacap.com | David Cahn
We have written here extensively about the revenue side of the AI infrastructure buildout. My last piece, AI's $600B question, focused on the implied revenue expectations for AI, and questioned the time horizon in which we'll be able to meet those lofty goals. This piece turns to the cost side of the equation. In particular, we will focus on the data center buildout, the rise of the "AI factory," and its implications for energy, construction and the industrial supply chain.
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