https://www.sequoiacap.com/article/ais-600b-question/
What has changed since September 2023?
1. The supply shortage has subsided Late 2023 was the peak of the GPU supply shortage. Startups were calling VCs, calling anyone that would talk to them, asking for help getting access to GPUs. Today, that concern has been almost entirely eliminated. For most people I speak with, it’s relatively easy to get GPUs now with reasonable lead times.
2 GPU stockpiles are growing:Nvidia reported in Q4 that about half of its data center revenue came from the large cloud providers. Microsoft alone likely represented approximately 22% of Nvidia’s Q4 revenue. Hyperscale CapEx is reaching historic levels. These investments were a major theme of Big Tech Q1 ‘24 earnings, with CEOs effectively telling the market: “We’re going to invest in GPUs whether you like it or not.” Stockpiling hardware is not a new phenomenon, and the catalyst for a reset will be once the stockpiles are large enough that demand decreases.
3. OpenAI still has the lion’s share of AI revenue: The Information recently reported that OpenAI’s revenue is now $3.4B, up from $1.6B in late 2023. While we’ve seen a handful of startups scale revenues into the <$100M range, the gap between OpenAI and everyone else continues to loom large
The $125B hole is now a $500B hole: In the last analysis, I generously assumed that each of Google, Microsoft, Apple and Meta will be able to generate $10B annually from new AI-related revenue. I also assumed $5B in new AI revenue for each of Oracle, ByteDance, Alibaba, Tencent, X, and Tesla. Even if this remains true and we add a few more companies to the list, the $125B hole is now going to become a $500B hole.
It’s not over—the B100 is coming:Earlier this year, Nvidia announced their B100 chip, which will have 2.5x better performance for only 25% more cost.
One of the major rebuttals to my last piece was that “GPU CapEx is like building railroads” and eventually the trains will come, as will the destinations—the new agriculture exports, amusement parks, malls, etc. I actually agree with this, but I think it misses a few points:
Lack of pricing power: In the case of physical infrastructure build outs, there is some intrinsic value associated with the infrastructure you are building. If you own the tracks between San Francisco and Los Angeles, you likely have some kind of monopolistic pricing power, because there can only be so many tracks laid between place A and place B. In the case of GPU data centers, there is much less pricing power. 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).
Investment incineration: Even in the case of railroads—and in the case of many new technologies—speculative investment frenzies often lead to high rates of capital incineration. The Engines that Moves Markets is one of the best textbooks on technology investing, and the major takeaway—indeed, focused on railroads—is that a lot of people lose a lot of money during speculative technology waves. It’s hard to pick winners, but much easier to pick losers (canals, in the case of railroads).
Depreciation: We know from the history of technology that semiconductors tend to get better and better. Nvidia is going to keep producing better next-generation chips like the B100.
1 Winners vs. losers: I think we need to look carefully at winners and losers—there are always winners during periods of excess infrastructure building. AI is likely to be the next transformative technology wave, and as I mentioned in the last piece, declining prices for GPU computing is actually good for long-term innovation and good for startups. If my forecast comes to bear, it will cause harm primarily to investors.
What has changed since September 2023?
1. The supply shortage has subsided Late 2023 was the peak of the GPU supply shortage. Startups were calling VCs, calling anyone that would talk to them, asking for help getting access to GPUs. Today, that concern has been almost entirely eliminated. For most people I speak with, it’s relatively easy to get GPUs now with reasonable lead times.
2 GPU stockpiles are growing:Nvidia reported in Q4 that about half of its data center revenue came from the large cloud providers. Microsoft alone likely represented approximately 22% of Nvidia’s Q4 revenue. Hyperscale CapEx is reaching historic levels. These investments were a major theme of Big Tech Q1 ‘24 earnings, with CEOs effectively telling the market: “We’re going to invest in GPUs whether you like it or not.” Stockpiling hardware is not a new phenomenon, and the catalyst for a reset will be once the stockpiles are large enough that demand decreases.
3. OpenAI still has the lion’s share of AI revenue: The Information recently reported that OpenAI’s revenue is now $3.4B, up from $1.6B in late 2023. While we’ve seen a handful of startups scale revenues into the <$100M range, the gap between OpenAI and everyone else continues to loom large
The $125B hole is now a $500B hole: In the last analysis, I generously assumed that each of Google, Microsoft, Apple and Meta will be able to generate $10B annually from new AI-related revenue. I also assumed $5B in new AI revenue for each of Oracle, ByteDance, Alibaba, Tencent, X, and Tesla. Even if this remains true and we add a few more companies to the list, the $125B hole is now going to become a $500B hole.
It’s not over—the B100 is coming:Earlier this year, Nvidia announced their B100 chip, which will have 2.5x better performance for only 25% more cost.
One of the major rebuttals to my last piece was that “GPU CapEx is like building railroads” and eventually the trains will come, as will the destinations—the new agriculture exports, amusement parks, malls, etc. I actually agree with this, but I think it misses a few points:
Lack of pricing power: In the case of physical infrastructure build outs, there is some intrinsic value associated with the infrastructure you are building. If you own the tracks between San Francisco and Los Angeles, you likely have some kind of monopolistic pricing power, because there can only be so many tracks laid between place A and place B. In the case of GPU data centers, there is much less pricing power. 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).
Investment incineration: Even in the case of railroads—and in the case of many new technologies—speculative investment frenzies often lead to high rates of capital incineration. The Engines that Moves Markets is one of the best textbooks on technology investing, and the major takeaway—indeed, focused on railroads—is that a lot of people lose a lot of money during speculative technology waves. It’s hard to pick winners, but much easier to pick losers (canals, in the case of railroads).
Depreciation: We know from the history of technology that semiconductors tend to get better and better. Nvidia is going to keep producing better next-generation chips like the B100.
1 Winners vs. losers: I think we need to look carefully at winners and losers—there are always winners during periods of excess infrastructure building. AI is likely to be the next transformative technology wave, and as I mentioned in the last piece, declining prices for GPU computing is actually good for long-term innovation and good for startups. If my forecast comes to bear, it will cause harm primarily to investors.
Sequoia Capital
AI’s $600B Question
The AI bubble is reaching a tipping point. As we continue to follow the GPUs, navigating what comes next will be essential.
Founders and company builders will continue to build in AI—and they will be more likely to succeed, because they will benefit both from lower costs and from learnings accrued during this period of experimentation.
Sequoia Capital
AI’s $600B Question
The AI bubble is reaching a tipping point. As we continue to follow the GPUs, navigating what comes next will be essential.
Forwarded from BZCF | 비즈까페
인공지능 검색엔진 퍼플렉시티 창업자 '아라빈드 스리니바스'. 1조 이상 밸류로 투자를 최근에 또 받았다. 어떤 사람인가 궁금했는데, 마침 이번에 팟캐스트 올라와서 바로 번역했는데. 그중 가장 인상 깊었던 부분은 아래와 같다.
'엄마가 영어를 잘 못하신다. (인도 출신) 검색을 하면 가끔 정보가 잘 안 나온다. 이유를 보니, 검색이 잘못됐다. (문법이나 단어 등 오류) 그러면 엄마 탓일까? 제품 탓일까? 진짜 좋은 제품은 그것까지도 맥락으로 이해해서 소비자가 원하는 답을 제공해 주는 제품이어야만 한다. 나쁜 유저는 없다. 모든 유저가 만족하게 만드는 게 좋은 제품이다. 나쁘지 않은 제품, 좋은 제품, 진짜 좋은 제품은 여기서 나뉜다.'
이 철학을 듣고, 머리가 띵- 했다. 정말 높은 수준의 제품은 이러한 창업자의 마음가짐과 디테일에서 나오지 않나 싶었다.
'엄마가 영어를 잘 못하신다. (인도 출신) 검색을 하면 가끔 정보가 잘 안 나온다. 이유를 보니, 검색이 잘못됐다. (문법이나 단어 등 오류) 그러면 엄마 탓일까? 제품 탓일까? 진짜 좋은 제품은 그것까지도 맥락으로 이해해서 소비자가 원하는 답을 제공해 주는 제품이어야만 한다. 나쁜 유저는 없다. 모든 유저가 만족하게 만드는 게 좋은 제품이다. 나쁘지 않은 제품, 좋은 제품, 진짜 좋은 제품은 여기서 나뉜다.'
이 철학을 듣고, 머리가 띵- 했다. 정말 높은 수준의 제품은 이러한 창업자의 마음가짐과 디테일에서 나오지 않나 싶었다.
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업을 찾는 과정에서 struggle하고 그 과정에서 배우고 나다움을 찾고 우직하게 큰 목표를 향해가는것. 젠슨의 인생 교훈이네요.
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Forwarded from BZCF | 비즈까페
오늘의 영상. 젠슨 황의 칼텍 졸업연설. 세계 일등 기업 만드는 데 몇 번 망할 뻔 했다고. 그냥 꾸준히 했다고... 그러다가 망할 것 같으면 피벗 피벗 또 피벗.. 그러다가 세계 최고 기업 됐다고. 영상에서 이렇게 말한다. '서두를 것 없다, 시간은 충분하다'고. 목표가 명확하면 시간은 ‘충분‘ 하다고. 멋진 말.
영상 : https://www.youtube.com/watch?v=O5vFGHRbiFE
영상 : https://www.youtube.com/watch?v=O5vFGHRbiFE
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