Making the Most of Open Source in AI

**Percy Liang** (0:00)
As technologies, we do have to realize that the technology we build is still used. People can use it for a lot of good things, and people can also misuse it. And it's the same with every single piece of technology that has been developed with the internet, with code, with encryption, and all of these things.
Now the question is, what action do you take?

**Derrick Harris** (0:22)
Hi, I'm Derek, and you're listening to the a16z AI podcast. We're weeding into all things artificial intelligence with our in-house team of experts, as well as the founders, engineers, and researchers working at the state of the art. In this episode, we're talking open source. So get ready to hear from some seasoned vets about what the AI community can learn from previous battles over the benefits and bogeymen of developing in the open. As a reminder, please note that the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16z fund. For more details, please see a16z.com/disclosures.
There are few terms in the world of AI, if any, that invoke more of a reaction than a simple four letter word, open.
Whether it's industry debates over business models and the actual definition of open, or the US government actively discussing how to regulate open models, seemingly everyone has an opinion of what it means for AI models to be open, the good, the bad, and the ugly. But to be fair, there's a good reason for this. In a world where many developers have come to expect open source tools at every level of the stack, the idea of powerful models locked behind enterprise licenses and corporate ethics can be disconcerting. Especially for a technology as game-changing as AI promises to be.
It's a matter of who has the ability to innovate in the space and whose release schedules and guardrails they're beholden to.
This is why, back in February, a16z convened a panel of experts to discuss the state and future of open source AI models. Led by a16z general partner Anjney Midha, the discussion featured three panelists who've thought a lot about this topic and have battle scars from decades working in open source. They discuss how today's AI moment compares with previous debates over open source, share their definitions of open, and offer advice on how the AI community can best ensure an open future.
Anjney kicks off the discussion and here are some quick introductions to the panelists and the order they speak. First up is Percy Liang. Percy is an associate professor at Stanford and an instrumental member of the AI community. He's involved with numerous AI related groups at the university, including the Human Centered Artificial Intelligence Lab and is director of the Center for Research on Foundation Models. Percy and his team at CRFM maintain the popular Helm Foundation Model benchmark and he's a co-founder of Together AI, which is building open and transparent AI systems. Up next is Mitchell Baker. Mitchell is the co-founder of the Mozilla Project, executive chair of the Mozilla Corporation and chair of the Mozilla Foundation. She has received numerous awards and accolades over the years for her work on Mozilla and the open web overall, including induction into the Internet Hall of Fame. Among other things, Mitchell is now focusing her efforts on Mozilla's product and AI strategy. In October, Mozilla authored a letter in support of open source models that was signed by more than 1800 people, including many well-known AI builders and researchers. And finally, we have Jim Zemlin. Jim is executive director of the Linux Foundation, which probably needs no introduction with regard to open source software. It might be worth noting, however, that the Linux Foundation also manages a variety of projects and groups beyond the Linux operating system, including the Cloud Native Computing Foundation, RISC-V and various initiatives around AI. These now include PyTorch, as well as the LF AI and Data Foundation that houses dozens of individual projects.

**Anjney Midha** (4:12)
Thank you so much for being here.
Before we jumped into questions, I just wanted to go around and ask you why you got into open source. I think it's easy to go Google you guys and read up on what you've done, but I think it'd be fun to start off with just why. You know, what is it that drew you to this space?

**Percy Liang** (4:28)
So I've been an AI researcher for 20 years, and for most of that time, we were just things that didn't work. And most of the energy was spent on design models and algorithms to make things work. And I think in the last three years that I started thinking a lot more about, you know, the social impact of AI technologies.