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Raphael Thys
If you've been closely following the progress of Open AI, the company run by Sam Altman whose neural nets can now write original text and create original pictures with astonishing ease and speed, you might just skip this piece.
If, on the other hand, you've only been vaguely paying attention to the company's progress and the increasing traction that other so-called "generative" AI companies are suddenly gaining and want to better understand why, you might benefit from this interview with James Currier, a five-time founder and now venture investor who co-founded the firm NFX five years ago with several of his serial founder friends.
Currier falls into the camp of people following the progress closely -- so closely that NFX has made numerous related investments in "generative tech" as he describes it, and it's garnering more of the team's attention every month. In fact, Currier doesn't think the buzz about this new wrinkle on AI is hype so much as a realization that the broader startup world is suddenly facing a very big opportunity for the first time in a long time. "Every 14 years," says Currier, "we get one of these Cambrian explosions. We had one around the internet in '94. We had one around mobile phones in 2008. Now we're having another one in 2022."
In retrospect, this editor wishes she'd asked better questions, but I'm learning here, too. Excerpts from our chat follow, edited for length and clarity. You can listen to our longer conversation here.
TC: There's a lot of confusion about generative AI, including how new exactly it is, or whether it's just become the latest buzzword.
JC: I think what happened to the AI world in general is that we had a sense that we could have deterministic AI, which would help us identify the truth of something. For example, is that a broken piece on the manufacturing line? Is that an appropriate meeting to have? It's where you're determining something using AI in the same way that a human determines something. That's largely what AI has been for the last 10 to 15 years.
It didn't change suddenly, it just changed gradually until the quality of its generation got to where it was meaningful for us. So the answer is generally no, the algorithms have been very similar. In these diffusion algorithms, they have gotten somewhat better. But really, it's about the processing power. Then, about two years ago, the [powerful language model] GPT came out, which was an on-premise type of calculation, then GPT3 came out where [the AI company ] would do [the calculation] for you in the cloud; because the data models were so much bigger, they needed to do it on their own servers. You just can't afford to do it [on your own]. And at that point, things really took a jump up.
We know because we invested in a doing AI-based generative games, including "AI Dungeon," and I think the vast majority of all GPT-3's computation was coming through "AI Dungeon" at one point.
But the other big change is that Open AI wasn't really open. They generated this amazing thing, but then it wasn't open and was very expensive. So groups got together like and other folks, and they said, "Let's just make open source versions of this." And at that point, the cost dropped by 100x, just in the last two or three months.