Karen Hao

And you throw more data in it, it learns more patterns.

You throw more data, more patterns.

And the other thing is it's particularly useful to companies because there's so much commercial utility in pattern matching.

And so in 2012, Google...

became the first company to recognize this is something that we could commercialize and make money out of.

For example, the most important business model that both Google and Meta use is ad targeting.

And to target a user with the ads that they will click on is a pattern matching problem.

If you have a bunch of data about their interests, you can better predict what the likelihood they might click on an ad about clothing or pets or whatever it is.

And that is the most successful business model that has ever been invented on the Internet.

You know, that makes Google hundreds of billions a year.

And so they recognize that early on.

And in 2012, they then started funneling an enormous amount of money

into machine learning research, which then became deep learning research, which is a derivative of machine learning that uses more powerful software for computing the patterns out of data.

That software is called neural networks.

And that is why all of a sudden there was this massive renaissance in AI research, AI development.

It's because

companies realized that they could profit from it.

Exactly.

commercial imperative despite again all of the kind of highfalutin rhetoric about kind of you know transforming humanity and everything fundamentally at each stage it's about marketing and it's about commercial benefit yeah i mean they they realize that this if you can create the rules of the game in a competitive landscape to correlate with how much data you have then the

actor with the most data is going to win.