Can AI answer the $3 trillion question? | TechCrunch
techcrunch.com · 583 words · saved by 1 readers
The AI ROI debate has returned and the numbers are even bigger, as are, perhaps, the consequences.
Three years ago, Sequoia partner David Cahn was one of the first people to do the math and put a number on the implications of Silicon Valley’s titanic spend on AI infrastructure. In 2023, he was reacting to Nvidia’s reported annual GPU revenue of $50 billion. Starting with that figure, and adding in the implied costs of operating the data centers and the margins for their operators, he deduced that $200 billion in revenue would be required to pay back the up-front investment. He took it as a challenge, asking entrepreneurs to come up with AI products and services to make use of, and…
saved by
related reading
- IIIa. Racing to the Trillion-Dollar Cluster - SITUATIONAL AWARENESSsituational-awareness.ai
- AI’s $1.5T Question - David Cahn's Substackdcahn.substack.com
- Big Tech Needs $2 Trillion In AI Revenue By 2030 or They Wasted Their Capexwheresyoured.at
- AI’s $600B Question | Sequoia Capitalsequoiacap.com
- AI Is Slowing Downwheresyoured.at
- Why compute might get 10x more expensive in coming yearsdwarkesh.com
- Navigating the High Cost of AI Compute | Andreessen Horowitza16z.com
- Lifespan of AI Chips: The $300 Billion Question - CITP Blogblog.citp.princeton.edu
- Is AI Profitable Yet?isaiprofitable.com
- Deep Dive: Economics of the AI Build-Out | Contrary Researchresearch.contrary.com
- AI’s $200B Question | Sequoia Capitalsequoiacap.com
- AI Is Really Weirdwheresyoured.at