Quotesby GlyphDavid Cahn quote, Jun 20, 2024
Partner, Sequoia Capital · Jun 20, 2024 2 years ago · Essay, AI's $600B Question
AI’s $200B question is now AI’s $600B question.
Words found on the source page on Nov 6, 2026.
Across Glyph
More from David Cahn
- Dwarkesh Patel’s recent podcast interviews with Richard Sutton, Andrej Karpathy, and Ilya Sutskever are a demarcating line; the new consensus is that the AGI window will be in the 2030s, at earliest.
- In 2026, we’re going to see the emergence of a $0 to $1B club.
- My prediction, especially coming off of the successful Gemini 3 launch and hype around TPUs, is that the TSMC constraint could become material in 2026.
- On the one hand, it will be a year of delays, first in data center buildouts, many of which will fall behind schedule, and second, in the AGI timeline. At the same time, AI adoption will continue its relentless rise.
- But we need to make sure not to believe in the delusion that has now spread from Silicon Valley to the rest of the country, and indeed the world. That delusion says that we’re all going to get rich quick, because AGI is coming tomorrow, and we all need to stockpile the only valuable resource, which is GPUs.
- GPU computing is increasingly turning into a commodity, metered per hour. Unlike the CPU cloud, which became an oligopoly, new entrants building dedicated AI clouds continue to flood the market. Without a monopoly or oligopoly, high fixed cost + low marginal cost businesses almost always see prices competed down to marginal cost (e.g., airlines).
On the same topics
- If the top U.S. labs have to give up on what is supposed to be really important, they will look very “catchable.” The policy creates a big incentive for some other parts of the world to invest a lot more in compute.
- A world with sufficient compute is one where intelligence is a commodity, and in commodity markets margin comes from a superior cost structure, which I would expect the leading model providers to have, in part because they have a meaningful lead in scaling and can apply superior AI to their infrastructure.
- I have my doubts that the labs can afford to spend a constant portion of this compute on internal R&D as the total volume goes up, especially with plans to IPO, as they face increased scrutiny on basic economics.
- It is important to not confuse massive steps in inference-time scaling, a dynamic which should be fairly predictable, with being the outputs of RSI, which is highly uncertain.
- If you have five hours, you’re probably going to do a lot better. The AI models are pretty similar.
- But in my view, the net effect of the last decade of “Doomer” talk (Coxon appears to be a card-carrying member of that party) has been to make the Frontier AI companies wealthier and more powerful.
About this quote
- Said by
- David Cahn, Partner, Sequoia Capital
- Where
- AI's $600B Question
- Topics
- Investment, Compute
- Company
Sequoia Capital
- Source
- sequoiacap.com/article/ais-600b-question/
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