David Rosenthal: commitment

Transcript context

…We’ve gotten to hire the people that we want. We’ve built these great teams. There’s a money spigot pointed at our face. Not only are we getting paid just ungodly amounts of money, but we get to work directly with the best AI researchers in the field. If we were still at academic institutions—say you’re at the University of Washington, an amazing academic institution for computer science, one of the top in the world, or the University of Toronto where these guys came from—you’re still in a fragmented market. If you go to Google or you go to Facebook, you’re with everybody. So the answer is no from basically everybody. Except there’s one person who’s intrigued by Elon and Sam’s pitch. To quote an amazing Wired article from the time by Cade Metz that we will link to in our sources, “The trouble was, so many of the people most qualified to solve all these AI problems were already working for Google and Facebook, and no one at the dinner was quite sure that these thinkers could be lured to a new startup, even if Musk and Altman were behind it. But one key player was at least open to the idea of jumping ship,” and then they have a quote from that key player. “I felt there were risks involved, but I also felt it would be a very interesting thing to try.” That key player was Ilya Sutskever. After the dinner, Ilya leaves Google and signs up to become, as we said, co-founder and chief scientist of a new independent AI non-profit research lab backed by Elon and Sam, OpenAI. Okay, so David, OpenAI is formed, it’s 2015. Here we are eight years later, and we have ChatGPT. Super linear path from there to here, right? It turns out, no. As we were talking about a little bit, AI at this point in time, super good for narrow use cases. Looks nothing like GPT-4 today. The capabilities that it had were pretty limited. One of the big reasons was that the amount of data that you could practically train these models on was pretty limited. The AlexNet example you’re talking about 14 million images. In the grand scheme of the Internet, 14 million images is a drop in the bucket. And this was both a hardware and a software constraint. On the software side, we just didn’t actually have the algorithms to suppose that we could be so bold to train one single foundational model on the whole internet. It wasn’t a thing.…

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