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?”

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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.

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From "iConnections" YouTube channel, (link in comment)