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Rohan Paul (@rohanpaul_ai) “Jensen Huang thinks Dario Amodei's prediction of $1T in AI revenue by 2030 is to” — TopicDigg

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Rohan Paul
@rohanpaul_ai
Compiling in real-time, the race towards AGI. The Largest Show on X for AI. 🗞️ Get my daily AI analysis newsletter to your email 👉
加入 June 2014
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Jensen Huang thinks Dario Amodei's prediction of $1T in AI revenue by 2030 is too conservative. "I believe Dario and Anthropic are going to do way better than that. Way better than that. And the reason for that is the one part that he hasn't considered: I believe every single enterprise software company will also be a value-added reseller of Anthropic's tokens. And they’re going to get this logarithmic expansion. Their go-to-market is going to expand tremendously this year." --- From @theallinpod YT channel (link in comment)
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Chamath: AI advantage may come less from models than from private inputs. "When labs can build similar models, the real win comes from one unique ingredient in order to monetize it well. Here is a basic thing about machine learning that is worth knowing: if you take 1,000 of the same inputs and give them to Facebook, Microsoft, Google, and Amazon, they will all come up with the same machine learning model. But if you have one extra thing, one little ingredient that all of those other companies do not have, your output can be markedly different. It is like giving two great chefs three ingredients, but giving the third chef one extra ingredient. That person has the ability to do something very special. Right now, we are in a world where everybody is crawling the open web. We are going to move to a world where, as everybody gets sophisticated enough and information is widely available, somebody is going to say, “You know what? This site, I am not going to allow anybody else to access. It is only for me, only for my models.” Those models will become better. So we have to let that play out a little bit. It is going to be a really interesting arms race. The next wave of M&A, for example, could be companies like Google, Microsoft, and Facebook looking at these companies and saying, “Can they be viable inputs to my large language models or to my other machine learning and AI models?” --- A company with unique workflows, transactions, medical records, industrial logs, legal archives, design files, or user behavior can turn boring private data into a compounding advantage. Some startups may never become great public companies on their own, yet still become valuable because they own a data stream that makes a larger AI system sharper, more differentiated, or harder to copy. That turns acquisition strategy upside down: the buyer may not be purchasing revenue, brand, or even software, but a private ingredient for intelligence. ---- From "iConnections" YouTube channel, (link in comment)
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