Open Models and Maturation: Assessing the Generative AI Market

05/17/24 • 40 min

1 Listener

a16z partners Guido Appenzeller and Matt Bornstein join Derrick Harris to discuss the state of the generative AI market, about 18 months after it really kicked into high gear with the release of ChatGPT — everything from the emergence of powerful open source LLMs to the excitement around AI-generated music.

If there's one major lesson to learn, it's that although we've made some very impressive technological strides and companies are generating meaningful revenue, this is still a a very fluid space. As Matt puts it during the discussion:
"For nearly all AI applications and most model providers, growth is kind of a sawtooth pattern, meaning when there's a big new amazing thing announced, you see very fast growth. And when it's been a while since the last release, growth kind of can flatten off. And you can imagine retention can be all over the place, too . . .

"I think every time we're in a flat period, people start to think, 'Oh, it's mature now, the, the gold rush is over. What happens next?' But then a new spike almost always comes, or at least has over the last 18 months or so. So a lot of this depends on your time horizon, and I think we're still in this period of, like, if you think growth has slowed, wait a month and see it change."

Follow everyone on X:

Guido Appenzeller

Matt Bornstein

Derrick Harris

Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

Follow everyone on X:

Guido Appenzeller

Matt Bornstein

Derrick Harris

Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

Previous Episode

Security Founders Talk Shop About Generative AI

In this bonus episode, recorded live at our San Francisco office, security-startup founders Dean De Beer (Command Zero), Kevin Tian (Doppel), and Travis McPeak (Resourcely) share their thoughts on generative AI, as well as their experiences building with LLMs and dealing with LLM-based threats.

Here's a sample of what Dean had to say about the myriad considerations when choosing, and operating, a large language model:

"The more advanced your use case is, the more requirements you have, the more data you attach to it, the more complex your prompts — ll this is going to change your inference time.

"I liken this to perceived waiting time for an elevator. There's data scientists at places like Otis that actually work on that problem. You know, no one wants to wait 45 seconds for an elevator, but taking the stairs will take them half an hour if they're going to the top floor of . . . something. Same thing here: If I can generate an outcome in 90 seconds, it's still too long from the user's perspective, even if them building out and figuring out the data and building that report [would have] took them four hours . . . two days."

Follow everyone:

Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

Next Episode

ARCHIVE: Open Models (with Arthur Mensch) and Video Models (with Stefano Ermon)

For this holiday weekend (in the United States) episode, we've stitched together two archived episodes from the a16z Podcast, both featuring General Partner Anjney Midha. In the first half, from December, he speaks with Mistral cofounder and CEO Arthur Mensch about the importance of open foundation models, as well as Mistral's approach to building them. In the second half (at 34:40), from February, he speaks with Stanford's Stefano Ermon about the state of the art in video models, including how OpenAI's Sora might work under the hood.

Here's a sample of what Arthur had to say about the debate over how to regulate AI models:

"I think the battle is for the neutrality of the technology. Like a technology, by a sense, is something neutral. You can use it for bad purposes. You can use it for good purposes. If you look at what an LLM does, it's not really different from a programming language. . . .

"So we should regulate the function, the mathematics behind it. But, really, you never use a large language model itself. You always use it in an application, in a way, with a user interface. And so, that's the one thing you want to regulate. And what it means is that companies like us, like foundational model companies, will obviously make the model as controllable as possible so that the applications on top of it can be compliant, can be safe. We'll also build the tools that allow you to measure the compliance and the safety of the application, because that's super useful for the application makers. It's actually needed.

"But there's no point in regulating something that is neutral in itself, that is just a mathematical tool. I think that's the one thing that we've been hammering a lot, which is good, but there's still a lot of effort in making this strong distinction, which is super important to understand what's going on."

Follow everyone on X:

Anjney Midha

Arthur Mensch

Stefano Ermon

Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.