Scaling Laws: Google's Kent Walker on Regulating Frontier AI

[Intro]

Alan Rozenshtein: It's the Lawfare Podcast. I'm Alan Rozenshtein, associate professor of law at the University of Minnesota and a senior editor and research director at Lawfare. Today, we're bringing you something a little different, an episode from our new podcast series, Scaling Laws. It's a creation of Lawfare and the University of Texas School of Law, where we're tackling the most important AI and policy questions from new legislation on Capitol Hill to the latest breakthroughs that are happening in the labs. We cut through the hype to get you up to speed on the rules, standards, and ideas shaping the future of this pivotal technology. If you enjoy this episode, you can find and subscribe to Scaling Laws wherever you get your podcasts and follow us on X and Blue Sky. Thanks for listening

Intro Voices: When the AI overlords take over, what are you most excited about? It's, it's not crazy, it's just smart. I think just this year in the first six months, there have been something like 1,000 laws. Who's actually building the scaffolding around how it's gonna work, how everyday folks are gonna use it? AI only works if society lets it work. There are so many questions have to be figured out. Nobody came to my bonus class. Let's enforce the rules of the road.

Kevin Frazier: Welcome back to Scaling Laws, the podcast brought to you by Lawfare and the University of Texas School of Law that explores the intersection of AI, policy, and of course, the law. I'm Kevin Frazier, the director of the AI Innovation and Law Program at UT and a senior editor at Lawfare.

Today, we're joined by Kent Walker, President of Global Affairs at Google and Alphabet. Google has proposed a new model for governing the most advanced AI systems, a frontier AI regulatory organization, or FARO. The organization would be privately funded, staffed by technical experts, and overseen by the federal government. In many ways, it would look like FINRA. But is that a good thing? Can a FINRA for AI meet the challenges posed by frontier AI in light of recent events like the Hugging Face OpenAI incident?

And perhaps more pressing, would a frontier AI regulatory organization even be constitutional? These are big questions that demand immediate answers, which is why I'm so glad Kent joined Scaling Laws. To get in touch with us, email scalinglaws@lawfaremedia.org or follow us on X or Blue Sky. And with that, giddy up for a great show.

[Main Podcast]

Kent, welcome to Scaling Laws.

Kent Walker: Kevin, it's a real pleasure to be with you.

Kevin Frazier: So from the outset, I should disclose that about a decade ago, you were my skip, skip, skip level boss when I was a legal assistant at Google. I don't think we ever crossed paths. In fact, I think if I had seen you in the hallway, I probably would've dodged you out of fear of, "Oh no, did I do something wrong? Did I disclose some data I wasn't supposed to?" So, it's nice to connect a decade later.

Kent Walker: Well, I, I hope you had a good experience at Google. It's been delighting, delightful to watch your career since.

Kevin Frazier: Oh-

Kent Walker: Congratulations on the success.

Kevin Frazier: that's, that's very kind of you. And you know, those who can't do, podcast, so I'll, I'll leave it to listeners to decide whether, whether that's fully accurate. But in that decade, a lot has changed. For one, Google continues to spell out high-level thoughts on AI governance. You all were way ahead of the crowd in 2018 in spelling out some AI principles for how we sh- should think about this new technology. And ever since then, you've been continuing to try to help policymakers grapple with this technology.

Most recently, you all announced a self-proclaimed “pragmatic approach” to AI governance in America, and we're gonna dive into that proposal soon, but I wanna start at a high level. To be blunt, why should tech policy observers treat this proposal any differently? As I noted, you've been spelling out principles and policy ideas for a decade. What's different about this moment and this proposal when it comes to taking this seriously?

Kent Walker: You know, it's the right question. We think it's time to break the stalemate. There have been a lot of talk about no regulation. There's been talk about heavy regulation. We do think there's a, a middle path through there, that at a time when we need to build trust and confidence in these AI tools, and the tools themselves are getting increasingly powerful, there is a pragmatic approach to a light-touch regulation that's still pro-innovation, that still allows American technology to lead the world, does it in an evidence-based way and creates standards and, and testing protocols that people can have confidence in.

Kevin Frazier: And how much weight would you say Google is placing on this specific proposal? And again, we're going to get into its two prongs, but we know that right now there are wild ideas floating around the Hill in light of this Hugging Face OpenAI incident where we've seen loss of control suddenly become headline news, whereas for many folks it used to be a sort of hypothetical scenario. Now we know that models are breaking out of their harnesses, breaking out of these testing systems, and perhaps being even capable of hacking fairly sophisticated actors.

So, is this a real pivotal moment for Google? Because if you all don't lead with this sort of middle-path approach, talks of kill switches, talks of, you know, government ownership of AI companies, these are now policy proposals that you can hear hanging out at a Starbucks in D.C., which even a few months ago would have been ludicrous to think about.

Kent Walker: You know, the, the rate of development of these models, the increase in capability, certainly shows how high the stakes are. At the same time, we really need to start in a grounded evidence-based place. We don't have consensus on what the standard should be. What does good look like for these tools?

So one of the key elements in the proposal is to come together to have technically informed approaches to benchmarks, evaluation suites, et cetera, that would, that would morph, that would grow as the technology evolves, and that would be reassuring to people and across the industry would set a, a good housekeeping seal of approval standard for how to do this right.

We think that would actually help us land the tremendous benefits in AI, the trillions of dollars in economic progress, the science, the, the advances we're seeing in, in science and in biology and energy and so much more, but grounded in a way that's actually well integrated into some of our existing legal frameworks.

Kevin Frazier: So this call for a middle path starts with a frontier AI regulatory organization that you all say might look something like a FINRA for AI, or you even mention state bar associations as a potential model of this kind of, you know, independent organization working with industry that has the government as a backstop. But what does this mean in more detail? How would this FARO work out as you all imagine it?

Kent Walker: Sure. The, the proposal talks about two different types of, of regulatory structures. One is, is the, the FARO, the, the frontier model approach, and the other is one I'm, I'm sure we'll get to in a moment with regard to widely deployed AI applications that people are already using every day.

At the, the high end, at the frontier end, we've talked about a variety of different analogies that have been used in America for a long time whether it's, it's FINRA or the PCAOB, the Public Company Accounting Oversight Board or, or less known groups, like there's the North American Electric Reliability Corporation, which is supervised by FERC. In all of these settings, you have the private sector coming together to help set standards and, and norms for an industry, still with federal oversight, supervision.

We think it's important to have private participation in this because the tools are moving so quickly. That would allow you to have industry funding, it would allow you to actually hire computer scientists at market rate salaries, so you're bringing in the best and the brightest to help in this really important work.

It also allows a certain nimbleness because it is moving so quickly. Sometimes if you write something into law, you're stuck with that for some number of years. Here, you're seeing models advance every few months. So you need to have that ability, almost like a, an underwriter's laboratory where you can go in and assess and, and confirm that given models are meeting the, the standards that you set forth.

Kevin Frazier: So I wanna attack this from two different perspectives. I'll, I'll give you the version that says, "Kent, you're not dreaming big enough," right? We just saw this instance of a loss of control. We now know that even the most sophisticated labs are having some degree of difficulty making sure that their models work as intended. And you're thinking, "Oh, let's just go for a FINRA for AI. We'll, we'll kind of do a copy and paste scenario." Don't we need something more immediate? Don't we need something more drastic? Isn't this the time for a kill switch type intervention? Why not go that far?

Kent Walker: Yeah, I, I think the, the best approach is a complementary one. We're not suggesting that this would replace testing by classified government agencies, the national labs, et cetera. It would complement that kind of work to make sure that we're looking at the, the AI risks, the chemical, biological, radiological, nuclear, cyber risks that have been talked about and that are starting to emerge.

But at the same time, maintaining the flexibility to be able to test in, in different ways for a whole variety of different areas. And that kind of lighter touch approach is what will allow America's models to continue to be at the cutting edge, and that's gonna be critical in this global competition that we're in right now.

Kevin Frazier: And thinking from the other side of the spectrum then, which is to say, hey this is a great idea but it still may have some issues from a institutional capacity perspective, and maybe this is just another instance of big tech trying to create a regulatory moat that doesn't have some degree of public oversight.

We know, for example, there have been concerns about FINRA and about other organizations, that they've become too detached from the government. Over time, I've been nerding out about FINRA and the SEC. Over time, FINRA has become more and more closely tied to the SEC, and the SEC has clamped down more and more on just the extent of independence and expertise that FINRA's actually able to execute. So might this just eventually devolve to the government creating some new agency that's slow and bureaucratic and sclerotic? How do we get around that?

Kent Walker: Yeah, I, I think this is the, the right set of questions to be asking, and we should learn the lessons from all these different public-private partnerships or regulatory organizations that have been set up with regard to composition, the board, tools, incentives. The core element here, though, I think is, is important to, to keep in mind, and that's the notion that you really wanna have evidence-based scientific standards that are evolving at the speed of the technology. And the best way to do that is to b-bring in people who are really at the cutting edge of this work.

If we don't do that, we're gonna be stuck with standards, laws that don't evolve fast enough to keep up with the new tools, or we're gonna be so, risk-preferring that we don't build trust or so risk-averse that we fall behind in this global race. So, we think that there's a middle ground approach. Figuring out exactly how we integrate that in with federal testing for some of these most extreme risks and the existing infrastructure for widely deployed AI, that's gonna be something that Congress is working on, I know the administration is working on, so it's a really critical area to make sure we get right.

Kevin Frazier: So building off of this idea of the difference between a frontier AI regulatory organization and then widely deployed AI, number one, how would you spell out the difference between what should qualify as frontier versus this notion of widely deployed AI?

Kent Walker: Yeah, yeah, so this is part of the standards setting. We need to figure out a classification regime for capabilities of these models. Too often laws have just fallen into if you're larger than X number of flops in your training, you're subject to one regime, and if you're smaller, you're subject to a different regime.

Most people would agree that's not a very good way to slice and dice because we're seeing more and more smaller models that are increasingly capable. We really need a capabilities-based assessment to get this right. And if it has certain capabilities, it moves into the, the frontier AI regime, where you have the security of government oversight, the consensus of a standards body, and the speed of private sector expertise.

If not, if it's working with more traditional classes of issues where you're worried about important questions like how do we keep kids safe? How do we make sure information integrity is maintained? How do we think about the evolving future of work? Well, there we have e- existing bodies of law and regulation that can actually be important foundations for additional work, and we need to update those, but we don't necessarily need to reinvent the wheel and create an entirely new regime to, to deal with those fairly traditional questions, so old wine in new bottles.

Kevin Frazier: So you all break down this widely deployed AI kind of framework into four different categories. We have “workforce preparedness,” we have “protecting kids and families in digital work,” we have “modern energy infrastructure and data center ecosystems,” and “provenance and information security,” and I kind of lump that into with “creativity and copyright and the AI value exchange.” These are massive categories that are, in many ways, pretty distinct and novel from some of the other challenges we've faced with respect to digital governance. But there's definitely a rhyming function going on here with respect to helping folks prepare for a new economy, with respect to protecting kids in particular.

And so to what extent, just to again put on a bit of a, a skeptic's hat here, folks say, all right, it's Google again saying, trust us, we're going to figure out how to deploy this technology. While at the same time, I think I can go pick out a copy of The Atlantic from a year or two ago, and I'm sure I would find something about how Chromebooks were poorly diffused across the U.S., and there were educators who felt like they didn't know how to use those books, and that's led to as Jonathan Haidt would say, a real suffering of attention and a real inability for kids to connect and grapple with some of these technologies. This is a long way of saying, why should we trust this approach now? Why, what is it that Google has learned from prior iterations of trying to govern its latest and greatest tech that suggests that this widely deployed strategy is the one that makes the most sense?

Kent Walker: Yeah, I, we very much want to engage with a, a broad ecosystem of participants a- on each of these issues. And they are, as you note, very different. I mean, it's important to have courses for courses on some of these things. When you're approaching the future of work, it's a different question than how do you keep kids safe online, and a different question from how do you think about, you know, the energy grid of the United States.

So, we can work though with the existing learnings we have in each of those areas. We don't wanna fight the last war. There are new challenges that are raised, and new opportunities that are created by these AI models. In many ways, we've, we focused on AI models in the form of chatbots, but the biggest breakthroughs we're seeing are on the scientific side. We're seeing this, AI is not just a scientific breakthrough, it's a breakthrough in how we're making breakthroughs. So we're starting to see AI-designed drugs coming to clinical trials in the course of the next year or so. We're seeing material science advances. We're seeing quantum accelerating.

So we're gonna have all of this very important, very fundamental progress that's gonna be happening, but with any form of progress, there's disruption, there's change. We've learned over the years, you do need to engage with the, the stakeholders to make sure that you get that right, that you bring as many people along as you can. And I'm happy to go into the individual areas where we can say, you know, "Here are some ideas we have for the next steps." Nobody's got a silver bullet in any of these areas, but we do believe that we have some great foundations to build on, and if we do it in a collaborative way, we can get to a, a, a solid outcome, a, a, a, a, an outcome that helps build confidence and trust and optimism in these technologies, at the same time as continuing the rate of progress.

And it is, I wanna stress, it's important to have that spirit of optimism. Optimism is a strategic advantage when it comes to any new tools. Without optimism, you don't invest. Without optimism, you don't adopt the new thing. You, you huddle into a defensive crouch. And sadly, it's the first time in my career I've seen this, America is the least optimistic country in the world when it comes to AI. We recently surveyed more than sixty countries, and China, Singapore many other countries are quite optimistic, are applying these tools very broadly. The United States trails, and we need to find ways to turn that around, and we think we can do that by engaging on each of these different fronts

Kevin Frazier: I, I share that concern because when I talk to my students, some of them will say, "I've never used an AI tool, and I never want to." And to me, that's just really tough to grapple with because as you pointed out, while there are certain negative use cases, as with every tool, the sheer number of possibilities that you can unlock when you properly harness AI are just huge, and that pessimism is really hard to tackle, especially for, because so much of it is cultural.

And so I wonder how we can begin to frame some of these regulatory paradigms in a more cultural context such that people get away from the big tech equals bad, AI equals bad, and instead see this as a means for them to contribute. And I, I think that the idea of something like a FARO is really interesting by virtue of having the opportunity to bring in new and novel stakeholders to the governance process, whereas right now I, I do feel like a lot of folks feel like it's happening behind closed doors.

Kent Walker: Yeah. I mean, we think it's very important to make sure that AI is delivering value for everybody in the country. And I, I tell people, "You've been using AI for 15 years.” If you've used Google Search or Translate or Gmail or Maps you weren't getting into traffic jams. You were finding information faster. You weren't getting spam, or you were able to understand a different language, and that was all AI behind the scenes.

And so now this next generation of generative AI needs to also deliver. It needs to lower drug costs. It needs to create batteries that have more capability. It needs to help us with our electrical grids and, and do a lot of that, and we need to make sure people realize that it's AI behind the scenes in the same way making life better, helping with living standards, helping people with, with real problems that they're concerning. If we can come up with cu- literally cures for cancer and people recognize that that's a result of American ingenuity, which is another way of thinking of, of AI, that I think will start to, to turn this around.

And then you complement that with a regulatory structure where they feel as though there are appropriate checks and balances. There are limitations on, you know, things going wrong. But when you get it right, as you see with you know, Waymo cars on the street, people love Waymo cars once they have a chance to, to ride in them and they, they realize that they're actually safer than a car driven by a human. So, it, that kind of learning by doing for all of us I think is gonna be key.

Kevin Frazier: It's one of my favorite tourist activities in Austin. Anytime someone visits we go get some breakfast tacos, we hop in a Waymo and it's instantly the best morning they've had in a long time.

But I, I do wanna stress that I, we need labs to really embrace that idea of showing the positive AI outcomes that are achievable with this investment in such a big technology. Because right now, when, again, when I talk to my students or I talk to AI skeptics, it's the idea of all we're getting is AI slop, and for what? You know, we have these big data centers, we have all these potential privacy threats, we have all these new cybersecurity threats, and what are we getting from it? And so I think that storytelling notion is so important.

But to, to go back for a second to the FARO and the idea of needing something that allows for a harmonized approach the world over, something that's unique about Google is more so than any other tech company, you all have multiple tools with billions of users around the world. And one thing that you all flag in your proposal is some degree of aspiration that the FARO could become a sort of standard-setting organization that could result in reciprocity agreements with countries around the world.

Now, to what extent, given our geopolitical moment, do you think that's, that's feasible? And, and to be more concise, are folks ever going to trust a regulatory regime that's so grounded in the U.S.? Do we need to be thinking about an international scheme first, and then working backwards to the U.S.?

Kent Walker: Well, I, I think you can pursue parallel tracks, and it's been good to see the recent announcement, the U.S. is gonna be engaging with China around this. Secretary Bessent will be engaging with his counterparts in the run-up to a summit that's gonna be happening this fall. But from the FARO perspective, we do see that it has deep roots in international standards organizations the, which are transnational, which do have the ability to bring along people from different countries.

Now, most of the leading AI companies to date are American, and I think there's a strong role for American leadership in a lot of, you know, helping set these standards in ways that are broadly accessible and available to, to models around the world. But if we can do this, if we can have a proof point of how can the leading labs come together, align on some of these areas, start to expand from that, and, and build from that core, I think that's a winning model. And then if you can get other countries to also sign up and support them more broadly, and this becomes sort of a standard of excellence around the world, that's, that would be a great outcome

Kevin Frazier: Now, Google is heralded for starting this whole transformer architecture. Who knows if we would have ever gotten to this point without the innovation that you all made possible. Is there a fear that having something like a FARO that has clear regulations, clear standards, and yes, they may be evolving and iterating over time, is there a concern about path dependence here, right?

If we see that FARO is generally regulated or including the major labs today and/or their former employees and folks who are adjacent to those communities, might we unintentionally foreclose some of those innovations that we wanna see just because we have a regulatory regime in which going down the middle path is the easiest thing to do, and taking those risky bets is suddenly something that looks a little legally risky?

Kent Walker: I, I think it's very important. A guiding principle here, is that we should be looking at regulating outputs, not inputs. You don't wanna micromanage the science. There may be a lot of new and different ways of accomplishing something, of getting great outcomes in a responsible way that minimizes the risks of cyberattacks or, you know, other sorts of, of harmful, risky behavior. You don't wanna to prescribe exactly the way of achieving the goal.

The, the, the objective would be to get alignment on what those goals should be. All of these models have challenges. No model is perfect, and so we have to understand, you know, how much perfection are we gonna demand of these tools? Is it, you know, 99.9, 99.99, while recognizing that they have remarkable benefits on the other side as well. So those kind of democratic value choices will be really important.

But once you align on that in the same way the laws align on the core notions of, of you wanna make sure that citizens feel safe and you'd establish property rights, and you have the all the other sort of core agreements of a society that make society work, you know, free expression in the United States and the like, that's the starting point. And then you free the innovators to figure out, okay, within that environment, how do I maximize for the best outcomes, the most productivity, the, the biggest scientific breakthroughs and the like?

Kevin Frazier: Well, I'll just say to you, Kent, and to the rest of the Google team, keep the data coming. You all recently released your ATLAS report, Activity, Task, Landscape, and Adoption Study, a wonderfully long acronym that actually comes out pretty well. And this is just a deep dive into how folks are using Gemini and giving key insights into some of these risks, but really into some of these incredible benefits. One benefit in particular, folks using Gemini to look into civic engagement opportunities, and folks finding novel use cases at home and at work. And so, keep that data coming so we can have more informed policymaking.

But before I let you go, Kent, I need to know let's imagine we've got some folks on the Hill right now listening, and their boss just came and said, "Hey, this kill switch proposal looks really interesting. I'm scared out of my mind from all of this Hugging Face OpenAI news." What's the reason to take a deep breath and to not result into panic right now? What's your final message to folks who are maybe considering some, some hasty action?

Kent Walker: Right. I think the goal throughout the industry and on Capitol Hill is to build safe models, not, not to have the “break glass” moment or even to get to the break glass moment. You want to build safety, responsibility by design into these tools, even as you allow the, the, the innovators to create new generations of tools. Not to lock things up with a small group, but to have open innovation.

And we think the best way to do that is a clear set of standards with the ability to test and validate, and to base all of that, as you allude to in the ATLAS report, on a solid base of evidence, so we understand what the trends are. What are people using these tools for? What are the bad use cases? And what are the many, many good use cases? And how do we optimize the latter?

Kevin Frazier: Well, Kent, we'll see if the folks on the Hill hear this message. Thank you so much for joining Scaling Laws. We'll have to leave it there.

Kent Walker: Kevin, it's been a real pleasure. Thanks so much for your time.

[Main Podcast]

Kevin Frazier: Scaling Laws is a joint production of Lawfare and the University of Texas School of Law. You can get an ad-free version of this and other Lawfare podcasts by becoming a material subscriber at our website, lawfaremedia.org/support. You'll also get access to special events and other content available only to our supporters. Please rate and review us wherever you get your podcasts.

Check out our written work at lawfaremedia.org. You can also follow us on X and Blue Sky. This podcast was edited by Noam Osband of Goat Rodeo. Our music is from Alibi. As always, thanks for listening.