If you followed the progress carefully Open artificial intelligencea company run by Sam Altman whose neural networks can now write original text and create original images with amazing ease and speed, you can just skip this part.

On the other hand, if you’ve only been vaguely aware of the company’s progress and the growing traction that other so-called “generative” AI companies are suddenly gaining, and want to better understand why, you might find this interview with James Currier helpful , a five-time founder and now venture capitalist turned co-founder of the firm NFX five years ago with a few of the founding friends of the series.

Currier falls into the camp of people who are following progress closely—so closely that NFX has made a lot of relevant investments in “generative technologies,” as he describes them, and they’re getting more of the team’s attention each month. In fact, Currier doesn’t think the hype about this new wrinkle in AI is less hype than the realization that the wider 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 online in ’94. In 2008, we had one dedicated to mobile phones. Now we have another one in 2022.”

In retrospect, this editor wishes she had asked better questions, but I’m also learning here. The following are excerpts from our chat, edited for length and clarity. You can listen to our longer conversation here.

TC: There’s a lot of confusion about generative artificial intelligence, including how new it is or whether it’s just the latest buzzword.

JC: I think what happened with the AI ​​world in general is that we felt like we could have a deterministic AI that would help us determine the truth of something. For example, is it a broken piece on the production line? Is this an appropriate meeting? Here you define something with the help of artificial intelligence in the same way as a human defines something. This is basically what artificial intelligence has been for the last 10-15 years.

Other sets of algorithms in artificial intelligence were more of the propagation algorithms that were designed to look at huge arrays of content and generate something new out of it, saying, “Here are 10,000 examples.” Can we create a 10,001st similar example?’

They were pretty fragile, pretty fragile up until about a year and a half ago. [Now] algorithms have become better. But more importantly, the corpus of content we’re looking at has grown because we simply have more processing power. So what happened is these algorithms are governed by Moore’s Law – [with vastly improved] storage, bandwidth, computing speed – and suddenly become able to create something that looks very much like something a human would make. This means that the face value of the text it will write and the face value of the drawing it will draw are very similar to what a human would do. And all this happened in the last two years. So it’s not a new idea, but it’s new on this threshold. That’s why everyone looks at it and says, “Wow, that’s magic.”

So it was computing power that suddenly changed the game, rather than some previously missing piece of technical infrastructure?

It didn’t change suddenly, it just changed gradually until the quality of its generation reached that value for us. So the answer is generally no, the algorithms are very similar. In these diffusion algorithms, they became somewhat better. But it’s really about processing power. Then, about two years ago, art [powerful language model] GPT appeared, which was a local computing type, then GPT3 came out where [the AI company Open AI] would do [the calculation] for you in the cloud; because the data models were so much larger, they had to do it on their own servers. You simply cannot afford it [on your own]. And that’s when things really picked up.

We know because we have invested in a company has created generative AI games, including “AI Dungeon”, and I think the vast majority of all GPT-3 computations have gone through “AI Dungeon” at some point.

Does “AI Dungeon” require a smaller team than another game developer?

This is definitely one of the big advantages. They don’t have to spend all that money to host all that data, and they can, with a small group of people, create dozens of gaming experiences for everyone to enjoy. [In fact] The idea is that you’re going to add generative AI to older games so that your non-player characters can actually say something more interesting than they do today, although you’ll have a fundamentally different gaming experience​​​​ from artificial intelligence to the game , compared to adding artificial intelligence to existing games.

So, a big change in quality? Will this technology plateau at some point?

No, it will always get progressively better. It’s just that the differences in increments will be less over time because they’re already getting pretty good,

But another big change is that Open AI wasn’t really open. They created this amazing thing, but back then it wasn’t open and it was very expensive. In this way, the bands gathered, as it were Stability of AI and other people, and they said, “Let’s just make open source versions of this.” And at this point, the price has dropped 100 times in just the last two or three months.

These are not branches of Open AI.

All of this generative technology won’t be built on the Open AI GPT-3 model alone; it was only the first. Now the open source community has replicated a lot of their work, and they’re probably eight months behind, six months behind in terms of quality. But it will have to. And because the open source versions are a third, a fifth, or a twentieth of the cost of Open AI, you’re going to see a lot of price competition, and you’re going to see the proliferation of these models that compete with Open AI. . And you’re probably going to end up with five, or six, or eight, or maybe, maybe 100 of them.

Then, on their basis, unique models of artificial intelligence will be created. So you could have an AI model that really does poetry, or an AI model that really looks at how you create visuals of dogs and dog hair, or you could have a model that really specializes in writing emails about sales. You’ll have a whole layer of these specialized AI models that will then be custom built. Then on top of that you’re going to have all the generative technologies that are going to be about how do you get people to use the product? How do you get people to pay for the product? How do I get people to log in? How do you get people to share it? How do you create network effects?

Who makes money here?

The application level of where people are going to go after distribution and network effects is where you are going to make money.

How about large companies that will be able to incorporate this technology into their networks. Wouldn’t it be very difficult for a company that doesn’t have that advantage to come out of nowhere and make money?

I think you’re looking at something like Twitch where YouTube could have integrated this into their model, but they didn’t. And Twitch created a new platform and a valuable new part of culture and value for investors and founders, even if it was difficult. So you’re going to have great founders who are going to use this technology to give themselves an edge. And this will create a seam in the market. And while the big guys are doing other things, they will be able to create billion dollar companies.

The New York Times printed a a piece recently featuring several creatives who said the generative AI applications they use in their fields are tools in a larger toolbox. Are people here naive? Are they at risk of being replaced by this technology? As you mentioned, the team working on “AI Dungeon” is smaller. That’s good for the company, but potentially bad for the developers who could have worked on the game differently.

I think with most technology, people feel some discomfort [for example] robots replacing auto factory jobs. When the Internet came along, many direct mailers felt threatened that companies would be able to sell directly and not use the services of paper advertising. But [after] they’ve adopted digital marketing or digital communication through email, they’ve probably had huge bumps in their careers, their productivity has increased, their speed and efficiency have increased. The same thing happened with online credit cards. We didn’t feel comfortable putting credit cards online until maybe 2002. But those who accepted [this wave in] From 2000 to 2003 it was better.

I think that’s what’s happening now. Writers, designers, and architects who think ahead and use these tools to increase productivity by 2, 3, or 5 times will do incredibly well. I think the whole world is going to see productivity growth in the next 10 years. This is a huge opportunity for 90% of people to just do more, be more, do more, communicate more.

NFX has a lot more on their site generative AI it’s worth reading, by the way; you can find it here.