How Should AI Be Regulated? Use vs. Development

**Martin Casado** (0:00)
Open source is always a critical part of the innovation ecosystem, because while it's not the number one business driver, like the proprietary models is, it's what's used by hobbyists, it's what's used by academics, it's what's used by startups. And that tends to be the future. And so this uncertainty in the regulatory environment is keeping US companies from releasing open source models that are strong. And as a result, the next generation, the hobbyists and the academics are using Chinese models. And I think that's actually a very dangerous situation for the United States to be in.

**Jai Ramaswamy** (0:29)
To claim that at the outset of the internet, you could have foreseen how social media would develop, be used and misused is kind of a fairy tale. Like that couldn't have happened back then. It can only happen once the risks emerge and are known. And then you can figure out what the bad things are that you want to regulate.

**Martin Casado** (0:47)
We focus on development and we don't focus on use. You end up introducing tremendous loopholes because it requires you to describe the system that's being developed. And right now, there actually is no single definition for AI. And everyone we've used now looks totally silly because it's evolving so quickly. So if actually the lawmakers want to have effective policy, the only area that you can actually specify is the use of these things.

**SPEAKER_3** (1:13)
In this episode, a16z's Jai Ramaswamy, Chief Legal and Policy Officer, Matt Perault, Head of AI Policy and Martin Casado, General Partner, take a first principles look at AI regulation, arguing that if policy makers want an effective way of protecting people from AI-related harms, they should focus on targeting those harms directly rather than model development until AI's marginal risks are better understood. Drawing on decades of software governance debates from encryption to cybersecurity, they explain why development-level rules are difficult to define, easy to loophole, and likely to become obsolete in a fast-moving field where even the definition of AI remains unstable. The conversation also examines how regulatory uncertainty is already shaping US competitiveness by chilling open-source research, advantaging incumbents over startups, and pushing the next generation of builders towards Chinese open models, making the case for evidence-based technology neutral policy that protects against bad behavior without stifling innovation.

**Matt Perault** (2:13)
This is a fun conversation for me because I get to ask Martin and Jai some questions about how you guys were thinking about AI policy before I joined the firm. A couple of years ago, the scene was really different than it is today. Sam Altman is testifying in Congress, Brad Smith at Microsoft is talking about things like licensing regimes for AI, an international regulatory agency that would regulate AI just like international nuclear regulatory agencies do. Jai, can you just start with telling us a little bit about how the firm reacted to that? How did we put that in context in terms of what AI policy might look like and what we were concerned about?

**Jai Ramaswamy** (2:51)
Yeah, I think that for us the big eye-opener was the Biden executive order that came out at the tail end of the Biden administration. And that order did two things that I think seemed very, very different to us than what had come before in the regulation of software, of computing.
The first thing is it sort of made a nod in the direction of wanting to regulate fundamental math and computing power through kind of restrictions on the types of models that would use certain amounts of computing power, right? Flop thresholds, I think it came to be called. And the second one was, for the first time, a questioning of the value of open source software, or as they called it, I think, models with open weights. And the reason that that was such a shock to, I think, many people who had been involved, you know, Martina is amongst them, but I think Mark as well, was involved in earlier debates around the regulation of the internet, regulation of software, regulation of encryption. And what I think was new here was a skepticism, or at least a perceived skepticism, that the way that we had regulated software before, which was really to focus on regulating use cases, as opposed to regulating underlying software development. And in the case of AI, that's, you know, regulating model layer development. And that's one of the reasons we became so actively involved, because that distinction that had served the country so well in terms of regulating uses, it has a long history in kind of regulatory law. We have typically regulated behaviors, human behaviors, and bad behaviors typically, as opposed to regulating invention, creation, and the sort of development of things.