a16z on Protecting Little Tech: The Techno-Optimist AI Policy Agenda with Matt Perault, Head of AI Policy

In this episode, Matt Perault, Head of AI Policy at a16z, discusses their approach to AI regulation focused on protecting "little tech" startups from regulatory capture that could entrench big tech incumbents. The conversation covers a16z's core principle of regulating harmful AI use rather than the development process, exploring key policy initiatives like the Raise Act and California's SB 813. Perault addresses critical challenges including setting appropriate regulatory thresholds, transparency requirements, and designing dynamic frameworks that balance innovation with safety. The discussion examines both areas of agreement and disagreement within the AI policy landscape, particularly around scaling laws, regulatory timing, and the concentration of AI capabilities. Disclaimer: This information is for general educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product. Turpentine is an acquisition of a16z Holdings, L. L. C., and is not a bank, investment adviser, or broker-dealer. This podcast may include paid promotional advertisements, individuals and companies featured or advertised during this podcast are not endorsing AH Capital or any of its affiliates (including, but not limited to, a16z Perennial Management L. P.). Similarly, Turpentine is not endorsing affiliates, individuals, or any entities featured on this podcast. All investments involve risk, including the possible loss of capital. Past performance is no guarantee of future results and the opinions presented cannot be viewed as an indicator of future performance. Before making decisions with legal, tax, or accounting effects, you should consult appropriate professionals. Information is from sources deemed reliable on the date of publication, but Turpentine does not guarantee its accuracy. SPONSORS: Oracle Cloud Infrastructure: Oracle Cloud Infrastructure (OCI) is the next-generation cloud platform that delivers better, cheaper, and faster solutions for your infrastructure, database, application development, and AI needs. Experience up to 50% savings on compute, 70% on storage, and 80% on networking with OCI's high-performance environment—try it for free with zero commitment at https://oracle.com/cognitive The AGNTCY: The AGNTCY is an open-source collective dedicated to building the Internet of Agents, enabling AI agents to communicate and collaborate seamlessly across frameworks. Join a community of engineers focused on high-quality multi-agent software and support the initiative at https://agntcy.org NetSuite by Oracle: NetSuite by Oracle is the AI-powered business management suite trusted by over 41,000 businesses, offering a unified platform for accounting, financial management, inventory, and HR. Gain total visibility and control to make quick decisions and automate everyday tasks—download the free ebook, Navigating Global Trade: Three Insights for Leaders, at https://netsuite.com/cognitive PRODUCED BY: https://aipodcast.ing

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Nathan Labenz?

  Hello and welcome back to The Cognitive Revolution. Today, I'm pleased to have Matt Peralta, Head of AI Policy at Andreessen Horowitz, here to discuss A16Z's approach to AI regulation, including their advocacy for AI policies that support experimentation and innovation by little tech startups, and their efforts to avoid common pitfalls like regulatory capture that could further entrench big tech incumbents. We cover a wide range of important topics, starting with A16Z's core principle that regulating harmful AI use is a better approach than regulating the research and development process itself. We also cover the RACE Act, sponsored by New York State Assembly Member and recent

• 0:40Nathan Labenz?

  guest Alex Boris, and the challenges of setting appropriate thresholds for regulation, transparency requirements and what information companies should and shouldn't be required to disclose, and the pros and cons of California's SB 813, which could create a novel public-private regulatory framework that offers liability shields to companies in exchange for opt-in compliance. Overall, I was again struck by how much common ground exists between the A16Z worldview and that of many in the AI safety community, myself included. Our techno-optimist credentials are perhaps best proven by the fact that we began to take both the upside and risks of AI seriously years before the technology itself really started to work.

• 1:22Nathan Labenz?

  And many of us share A16Z's generally libertarian politics, veneration of entrepreneurship, and fear of concentration of power. With that shared foundation in mind, I think it is clearly good that someone is working to ensure that AI capabilities don't become unduly concentrated in just a handful of companies. And Matt does raise some important considerations in this conversation, including the possibility that SB 813 might create an unfair advantage for incumbents by setting the compliance cost for liability protection so high that only the biggest companies are able to participate. I've not yet studied this issue in depth, but it does strike me as a worthwhile red teaming of the bill, and I definitely plan to raise that concern with some of the people who are advocating for SB 813 in an upcoming episode.

• 2:08Nathan Labenz?

  Meanwhile, where important disagreements persist, and you will hear a few, it seems to me that they largely stem from disagreement on empirical questions, such as whether scaling laws do or don't imply that only a small number of hyperscalers will push the frontiers of AI forward over the next couple of years, and more importantly, differing expectations about just how powerful AI could become and how soon that might happen. With those differences in mind, I am definitely more concerned than Matt is about the risks of secretive R and D processes, particularly if companies are allowed to double down on AI-powered machine learning research, to execute large-scale YOLO training runs without careful testing throughout the training process, and to continue deploying models internally

• 2:50Nathan Labenz?

  without the same safety standards that have become industry best practice for public deployments. All this, to me, does seem to create the potential for surprising and potentially even irreversible harms, for which prevention is really the only viable option.

• 3:05Nathan Labenz?

  That said, I still came away from this conversation feeling very optimistic that the overwhelming majority of non-ideological people, who simply want what's best for everyone, can converge on a shared understanding and smart policy ideas as more evidence comes in. I personally would love to see a research breakthrough that solves core AI safety and control issues on a technological level, and I would happily update my policy positions if that happens. On the other hand, it seems that mounting evidence of autonomous bad behavior from AI systems also has the potential to change at least some accelerationist minds. Timelines, of course, might be quite short, so it's critical to keep tracking all these developments in real time, and I look forward to continuing to bring people of very different

• 3:47Nathan Labenz?

  perspectives together, to grapple with the latest evidence in good faith as we move through this critical period.

• 3:55Nathan Labenz?

  As always, if you're finding value in the show, we'd appreciate it if you'd share it with friends, post about it online, or leave a review on Apple Podcasts or Spotify. We welcome your feedback too, either via our website, cognitiverevolution.ai, or by DMing me on your favorite social network. Finally, for now, a couple of quick disclaimers from me and from A16Z, which, as you may know, recently acquired the Turpentine network. First, from me. While I have lots of help in the production process and the business side of the show, for which I'm very grateful, I personally am solely responsible for all content on The Cognitive Revolution, including guest and topic selection, the questions I ask, and the editorial commentary that I offer. For this episode, with Matt in particular, I was under no pressure and I received no

• 4:39Nathan Labenz?

  consideration to have him on the show. And while we did follow our usual practice of sharing questions in advance and allowing our guests to review our edit and request any additional cuts, no topics were off the table and nothing meaningful was cut in the editing process. And second, this is directly from A16Z. This information is for general educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product. Turpentine is an acquisition of A16Z Holdings LLC and is not a bank, investment advisor, or broker-dealer. This podcast may include promotional advertisements. Individuals and companies featured or advertised during this podcast are not endorsing AH Capital

• 5:19Nathan Labenz?

  or any of its affiliates, including but not limited to A16Z Perennial Management LP. Similarly, Turpentine is not endorsing affiliates, individuals, or any entities featured on this podcast. All investments involve risk, including the possible loss of capital. Past performance is no guarantee of future results, and the opinions presented cannot be viewed as an indicator of future performance. Before making decisions with legal, tax, or accounting effects, you should consult appropriate professionals. Information is from sources deemed reliable on the date of publication, but Turpentine does not guarantee its accuracy. With that, I hope you enjoy this exploration of AI policy from the techno-optimist, little tech perspective, with Matt Perault, Head of AI Policy at A16Z.

• 6:04Nathan Labenz?

  Matt Perault, Head of AI Policy at A16Z. Welcome to The Cognitive Revolution.

• 6:10Speaker 01

  Thanks so much for having me on.

• 6:12Nathan Labenz?

  I'm excited for this conversation. Obviously, AI policy is a hot topic and I've got a bunch of different angles that I want to get into it with you on. But maybe just for starters, because I think we'll actually have a lot in common here. I noticed in my prep that you are a fellow at the Abundance Institute. And so I wanted to just take a minute to give you the floor to kind of share your dreams for the AI future of abundance and get a sense for kind of what you envision that looking like.

• 6:41Speaker 01

  Yeah, so my primary affiliation is as Head of AI Policy at Andreessen Horowitz, but I have a couple of side things that I had started as an academic before this. I was at UNC Chapel Hill running a center on technology policy. And so as a fellow at Abundance, that's one thing I've been able to continue. I'm also a fellow at the center that I used to direct, which is now at NYU. And those affiliations are awesome because they enable me to kind of continue working with certain communities that I really enjoyed working with in the past. And the Abundance Institute is a great organization. Christopher Kugman is the lead for it and it's really focused on a regulatory agenda that can help unlock abundance. And I've learned a lot from that group. They've got like a great group of policy professionals, people oriented around like

• 7:25Speaker 01

  economic policy and stuff. They do a lot in energy, which has been interesting and obviously relevant to my current job at Andreessen Horowitz. So I've really enjoyed maintaining a close relationship with that group. The focus for me in the question that you ask, I think is going to be like a little bit disappointing for you. Like people always ask tech people, regardless of where they sit in tech, to sort of forecast the future and what does the future of technology look like? I would say that's something that I've never been particularly good at. I'm not an engineer, I'm not a computer scientist. So the people who are really building the tools tend to sit on a different side of the house. I've previously worked at then Facebook, now Meta. I was on the policy team, not on the product side. At Andreessen Horowitz, we've got a lot of people who really understand the technology really deeply.

• 8:09Speaker 01

  That's obviously an important part of my job is like doing it as well as possible. But my focus is on the public policy side. So my orientation to the question is really like, what is the right policy agenda to enable other people to unlock abundance? Like, how do you think about ensuring that the people who can create the tools are able to do that? And that doesn't mean just... In total deregulation, that doesn't mean being able to do whatever an engineer wants to do, but it does mean trying to avoid regulatory models where the cost of those models, particularly for startups, for little tech companies outweigh the benefits. And so that's really the day to day focus of my work.

• 8:48Nathan Labenz?

  So I hear you on all that. Do you, you gotta have some dreams though, right? I mean, the future of like, for me, it's, you know, medical advances, equal access for everybody around the world to the sort of AI doctor, the robot, maybe one day that's in my home that's like doing my dishes and picking up my kids' messes and stuff like that. Maybe it's one day like people don't have to work if they don't want to, or if they don't have, you know, work that they find intrinsically motivating or fulfilling. I don't know, I've always interested in it here, especially because you're so invested in this. I would love to hear sort of what animates you.

• 9:22Speaker 01

  All those things sound cool. I mean, both of my parents and my sister are all mental health professionals. And I think like, so when you talk about AI and medical care, I sort of think about breaking down barriers to access to mental health treatment. I mean, that's been something that I think I've had a conversation with about my parents since I was really little and they were talking to me about the value of having mental health support in your life. And that's hard for people to do for a bunch of different reasons. It can be cost prohibitive, it can be hard to identify a mental health care professional, it can carry a stigma. And so, and so I think some of the advances there are really exciting and interesting. Obviously it's important that that be done in a way that is consistent with providing a high quality level of care, but I think breaking down access barriers is a really key thing.

• 10:07Speaker 01

  I've been excited about it as someone who spends a lot of time writing and reading the benefits that AI can bring to the analytical process and to the writing process. It's a scary one because I think it's coming off, it's coming after my business model. Like my ability to have some level of writing competence has kind of always been a part of my skill set that has been important in my academic life, like as a student and then as a professor, and been important to my professional life too. And I think the ability of AI tools to develop thoughtful, clear,

• 10:41Speaker 01

  Pros is something that sort of corrodes. You have to be a differentiator on top of that. Another way to say it is, I, at one point when I was working at Facebook, I said to a boss, I understand my job now is to deliver a bad first draft to you. And his response was sort of funny. He was like, yeah, that's great. That's great. That's great. And then he was like, but every now and then, could you develop a good first draft? And to the extent that like the jobs that some people in these fields have is to produce that first draft that enables people to provide feedback and critique and then massage and hopefully turn into like a really good final. You know, now we have tools that are able to, I think, do first, bad first drafts and maybe at some point do very good first drafts.

• 11:21Speaker 01

  So I think the fact that it's coming after a thing that has been kind of important to my professional life, I think is like a really interesting, exciting opportunity and also somewhat of a scary one. But one that I think is exciting because I think it will again, like play an important role in breaking down access barriers.

• 11:38Nathan Labenz?

  Yeah, I feel that very much myself. Coding and even podcasting, I mean, Notebook LM, I feel like in many cases competes pretty effectively with what I'm able to do. So yeah, it's coming for all of us.

• 11:50Speaker 01

  Find that like, what is the balance for you on fear versus exciting motivator? Like I, like as a writer, I think if there's something exciting to feel like there's, usually it's a person. Now it's a machine and people who are kind of like chomping at your heels. And I think that's like, that can result in being more creative and in being more like focused and devoted to parts of your craft in certain ways. It's also scary to feel like people are like potentially coming for your work if you don't perform particularly well. I haven't thought about it as a podcaster, like how do you, what's the like fear versus excitement balance?

• 12:28Nathan Labenz?

  I describe myself as classically ambivalent, meaning the dictionary definition of ambivalent is like. contrasting or even contradictory but strong feelings. And that's always been very natural to me with AI. Like the upside is legitimately thrilling. And I get new models and new products come out like I'm always rushing to go try them and get hands on, experience it. You know, today, this morning, it's been Claude for coding up an app and operator for doing some web tasks. And I do genuinely feel a thrill from these new product experiences. And then I'm less concerned honestly about it coming for my work, not because it's not, but because I just kind of worry about even bigger things like what does gain of function AI research look like?

• 13:10Nathan Labenz?

  And do we really have the wherewithal to control this phenomenon in like a macro sense? I'm less worried about meaning or jobs or sort of what are we going to do with our time? I would say I'm like an optimist when it comes to people being able to like figure out how to use their time and have a good life, given resources, space and opportunity to do that. But I'm a little concerned about just like, boy, we don't really know how these things work. We don't really know why they do what they do. And they do seem to be demonstrating more and more of the sort of, sort of negatively thrilling behaviors. Like it's as much as it's thrilling to see these things go about these tasks and learn to overcome obstacles and whatever.

• 13:51Nathan Labenz?

  I also can't help but see in that, that paired with like deceptive sort of behaviors and scheming sort of behaviors that we are kind of seeing emerge. It's like, geez, I don't know, you know, the dynamics of that just get to be really weird and really hard to predict. But really, the only thing that I kind of don't expect is like a business as usual future. So I try to, and you know, obviously I'm a small player in history. I sometimes call myself the Forrest Gump of AI because I always find myself in these like notable scenes, but usually as an extra, I just try to make whatever little nudge I can in a positive direction. And I kind of feel like whatever those odds are, it's less for me about like handicapping

• 14:32Nathan Labenz?

  and more about can I put a little bit of shoulder into shifting the odds of the radically positive future? Just a bit, and hopefully, hopefully, we can all make a little impact like that.

• 14:43Speaker 01

  That that seems right to me, and I think that feels like more of the direction of travel. I mean, you probably track the macro conversation more closely than I do. I had sort of thought initially when in the like post chat GPT release moment, that there was a lot of focus, like not on the immediate, not on the kind of like what's exactly in front of you, but on the kind of infinite future, like robots taking over the world. What happens in a world where like people have less autonomy? What does that mean philosophically, like those big picture stuff, big picture things? And then over time, it seems like as the technology has like improved and improved, more models have become more available to more people. People have a better sense of how to use them. Our like digital AI literacy, I think, has improved. People compare notes with their friends about how they use them,

• 15:28Speaker 01

  and then they integrate some of those use cases in their own lives. That the gaze has turned much closer to people. So like kind of exactly as you're describing at the end, like more about kind of marginal value creation as opposed to like, I'm creating a robot that is going to take over my life. And my sense at least is like when the focus is on that, but the marginal value creation that you get has connected people much more closely to a value proposition that's much less scary. And so the question is less like robots taking over the world and more like, I need ideas for recipes or like, how do I deal with a kid who's not going to sleep? Or I'm looking for activities to do on a family vacation in this place or like whatever it is.

• 16:12Speaker 01

  Or like, I'm struggling with this, like how to get this piece off the ground. Like, how can I structure the ideas? Or like, you know, I've been using AI sometimes for like, I don't like writing conclusions. AI writes very good conclusions, or at least gives you like a bad first draft of a conclusion. It can be helpful for that kind of use case. And I, that to me has like shifted the conversation. And then also, I think shifted importantly for, given the nature of my work, like how people think about public policy, because you want public policy, I think, to like, to help us avoid harms, like long, long-term catastrophic harms. But I think you also want policy to give you room to unlock and experience the value. And that thinking that through, what are the right models?

• 16:57Speaker 01

  I think changes is more people have like a closer connection to the value proposition.

• 17:05Nathan Labenz?

  Maybe that's a perfect segue to jump it into some policy questions. I think folks will know A16Z as obviously a prolific investor, super successful investor led by techno optimist thought leaders. And a huge theme of everything that I have read of your work in the firm more broadly is making sure that there's room for little tech as opposed to big tech to experiment, innovate, bring things to market, develop new use cases, et cetera, et cetera. With all that in mind, you know, you said it's like, it's not about opposing all of regulation. But what is the sort of first order articulation of the policy agenda that you would advocate for today?

• 17:50Speaker 01

  Yeah, so, so I can even like frame, I think, what's kind of baked into your question even slightly more explicitly, which is like, a lot of people think that we are extremely focused on being as deregulatory as possible. Like that I would have done my job effectively if we saw no AI policy. That's not the case. And it's not the case, I think, because it's connected in a really important way to the economics of the firm. So the life cycle of an Andreessen Horowitz fund is 10 years. That's actually significantly longer than like the life cycle of a tech vesting, of a vesting cycle at like a public tech company. So like when I was at Facebook, it was a four year vesting cycle. I think it's a longer time horizon than you typically see in private equity.

• 18:35Speaker 01

  Like, you know, we're not looking to juice stock prices tomorrow or looking to like produce massive returns tomorrow. We're looking to create a healthy, interesting, exciting, vibrant, abundance oriented, big dream oriented, to get back to your first question, like view of the world and a reality in AI technology. Over a longer period of time. And if we, I think that's important for a bunch of reasons. One other, one additional way that you could think about it is if like people were really into AI tomorrow, really, really excited about it. And there was an explosion of interest in AI and explosion of AI products. And then the market cratered a year later because of lots of examples of harms in our society.

• 19:18Speaker 01

  We're not going to get the economic returns that we're aiming to get. Like, we're trying to build healthy, long-run, strong companies over a period of time. And when you're trying to do that, that means that you need to allow startups to get companies off the ground. So you were getting at this in your question, like, we are not focused on the health of big tech companies. I mean, sometimes big tech interests overlap with ours and are aligned with ours. Sometimes they're not. That's really sort of beside the point. Our focus is for little tech companies, how do they have room to grow? But they have to, we have to have an ecosystem that's also providing a set of tools that people like to use and feel safe and secure when they're using, when they're using them.

• 20:00Speaker 01

  And so the kind of core of our policy agenda is focused on trying to encourage policymakers to regulate harmful use, not to regulate AI development. And in our view, that leaves room to little tech companies to build and grow. It's typically how technology regulation has historically been approached. Like, if you think about software development, there's not a lot of, there's not a lot of regulatory intervention in the building of software. But if you create software that's harmful for the world, depending on the specifics of what it is, but in most cases, there's going to be law that's going to be able to take account of that harm and prosecute you and deter you from future activity where you're building something that's harmful. So that's our orientation, is like, try to leave the process of development available to little tech.

• 20:45Speaker 01

  Give them room to build. But then importantly, the second component is regulating harmful use.

• 20:53Speaker 02

  Hey, we'll continue our interview in a moment after a word from our sponsors.

• 21:00Nathan Labenz?

  Yeah, so let's dig on in on those maybe one at a time. I mean, on the

• 21:05Nathan Labenz?

  development side, obviously, development can mean, you know, many things, right? It can mean me sitting down and, you know, fine tuning a model and just tweaking it a little bit for my

• 21:15Speaker 01

  Yeah.

• 21:16Nathan Labenz?

  purposes and baking it into an app. On the very high end, it means pushing the frontier of AI capabilities. And while I'm definitely not a big analogy guy, I do sometimes think of this as being akin to more like biological research when I think of the sort of, obviously, the canonical example, without coming down firmly on exactly the origin of COVID.

• 21:42Speaker 01

  Yeah.

• 21:42Nathan Labenz?

  Obviously, there are these institutes out there in Wuhan elsewhere that are doing this sort of research where they're like, well, geez, what would it happen if we made a virus do this? What if we could be? And I think they have good intentions, to be clear. Like, I understand the ideas. We're gonna make these things so we can figure out how to treat them so that if it ever does happen, we'll be prepared. But then we do have this like history of lab leaks. So I sort of think at the very frontier, it does seem like there is a very material risk that is analogous, pretty analogous to the sort of, you know, biological research thing where you don't necessarily have to deploy it. You don't even have to intend to deploy it. If you're developing something that's sufficiently powerful,

• 22:25Nathan Labenz?

  there is kind of this risk that it might get out of control. And so I wonder how you feel about that risk, which I think for most people in the AI safety community is like, honestly, the number one concern.

• 22:40Speaker 01

  Yeah, so I don't know enough about the regulation of biological research to know whether it's a good analogy or not. It feels at least like, at least with my superficial understanding, to be a good one, we give broad latitude, I think, to researchers to research, including things that could be very harmful if they're released in a way that violates the law. And the reason that we do that is there's a lot that you can discover and a lot that can be really beneficial to society from engaging in the research process. The research process itself isn't really good or bad. The typically, again, I think we want to like,

• 23:14Speaker 01

  Allow researchers a lot of room to explore and not be super prescriptive. There are any number of different things that researchers can do that are problematic. Like one that's closer to a thing that I've worked on is I was at Facebook when the Cambridge Analytica incident occurred. And that was data going from Facebook to an academic researcher, right? And so like that transfer, at least under Facebook's terms, was legitimate. And then the researcher misused the data, but it was actually going, like the initial transfer to a researcher was consistent with Facebook's terms. And I think in some ways, most ways maybe, you would think that that initial transfer is desirable. We want academic researchers. Typically, the research community is asking for more access to data, not less. That is probably a positive thing.

• 23:58Speaker 01

  The abuse, misuse of data, once you have it in your control, is really a problem. And we would want to make sure, I think, that we have stringent rules in place to penalize misuse. And so again, in your COVID analogy, like it's not okay to develop a virus that could cause a global pandemic and then release it into the world so as to cause a global pandemic. That I don't believe, I don't know, again, I don't know of a legal reason, but my assumption is that that is unlawful and therefore should be penalized as such. The research, I think, is a different question. And again, mapping it back on to AI development, you know, the development itself is just science, it's math. And so the concern that our firm has had is that when you look to criminalize the math,

• 24:40Speaker 01

  when you look to make it harder for scientists to do science, that just has the impact of slowing development. It actually doesn't really do much to address potential harmful uses. And so if we're concerned about harms, we should try to make sure that they're punished. I think the concern that I have, and maybe it's why so many people think we're purely deregulatory, is I think like, I think people spend a lot of time hearing, thinking, absorbing, don't regulate development. And then when we say and regulate harmful use, it's like then the ears close up because I think maybe the idea is like there it doesn't feel like there's a clear pathway there like is it, you know, maybe the view is like it's just a diversionary tactic. We're trying to get people away from regulating AI development so that they do nothing.

• 25:25Speaker 01

  But actually, we think regulated harmful use is like, there's a lot for lawmakers to do there. Lawmakers and enforcers that is really significant and meaningful. When I was in law school, I was going to summer at the criminal section of the civil rights division in the justice department and they, that was an interesting experience for a bunch of reasons. Like primarily people go there because they want to litigate cases, I think. And when you're there, like every day over the course of the summer, what you see is like, especially like as an intern, you're not traveling around the country, going to different courtrooms. You're like, you're in the offices in the civil rights division, watching what the attorneys are doing when they're not in court and what they're doing is building cases. And that's really hard to do. Like you don't, it cases don't just come with a clear fact pattern

• 26:07Speaker 01

  where you can like really understand exactly what's going on and where a violation occurs. They don't come to you with that clarity. You have to build the case and you have to identify the violation. I think the same thing will be true in AI. Like we're not going to know when AI is used in a violation of anti discrimination law. That's not going to be like, or I should say it this way, like, it's not going to necessarily be obvious. You will need to build cases. You need to identify the harm that's occurring and AI tool being used for that harm. And that I think requires work and investment and and probably lawmaking in some cases. Like you need to have enough people to do the enforcement. You need to have, those people need to have the resources that they need in order to do the enforcement. They need to have the technical

• 26:50Speaker 01

  understanding of the technology in order to understand when laws are being violated. There may be cases where existing law isn't sufficient to take account for harms that are created by the use of AI. In those cases, we may need to like, think about tweaks to existing law to make sure that they, you can take them into account. So when we say, don't regulate development, that means something. And then when we say and regulate harmful use, like we consider that second component to be a really active part of the policy agenda.

• 27:23Nathan Labenz?

  Let me do one more beat on the development and then we'll circle back to the harmful use. On the, you know, obviously a big kind of pattern of bills that have been put forward, which I think the proponents would say is their effort to

• 27:40Nathan Labenz?

  meet folks like you in the middle on this or even be quite compromising. And also I would, I think that they would say reflects like a genuine shared concern that like they don't want to squash frontier research, certainly unintentionally. Yeah, Hatteran has kind of been recently, and this is now happening again in New York with a bill that I just did an episode on there, the RAZE Act, which I'm sure you're tracking. Basically, the idea is like, we'll have some threshold, and I know your thoughts on thresholds. And if you are above that threshold, and typically you're trying to design the thresholds so that it's like a high single digit, maybe low double digit numbers of companies, you know, would be caught by these thresholds, then you have to

• 28:26Nathan Labenz?

  develop a safety plan, publish that safety plan, share it with a regulator, maybe publish it publicly. Those little details vary. Then you have to follow it. And then there's some variance on the provisions of like, do you have to have an audit or do you just self-report or whatever exactly? Right. But I, my impression is that you don't favor those. But it does seem like that is pretty consistent with the general principle of like, giving people pretty wide latitude, because most of these proposals, at least the ones that seem to be getting the most traction, are like, the companies get to define their own safety plan, and they're basically grading their own homework for the most part. We just want to make sure, speaking on behalf of the sponsors of the bills, that they're actually doing it in a serious way, because we do think

• 29:09Nathan Labenz?

  this stuff is pretty serious. And like, yeah, we're not going to tell every biologist exactly what experiments they can and can't run. But there is like, a security level four type concept where if you're not implementing certain standards, then, you know, we look very unkindly on that. So how do you feel about the, we can bring in thresholds here, but also just this kind of very top tier, grade your own homework, but like, we really want to at least know that you're doing it kind of vibe?

• 29:34Speaker 01

  Yeah. So just as a threshold matter, you agree that those regulations are targeting AI development, right? Not harmful use?

• 29:43Nathan Labenz?

  Yeah, I think so. Yeah, I would say frontier development is usually the phrase that I

• 29:47Speaker 01

  Yeah, so there are like a bunch of ways to get into this. I mean, one, just at the kind of, I guess, like top tier thing is, what is the problem with targeting harms? If what we're concerned about are harms, why don't we put the emphasis on ensuring that we can address harms when they occur? In terms of the development side, which I know is what you want to focus on, I mean, so I think there are a couple of different aspects of it. One is like, who do the bills cover? And then the second is like, for the people, for the organizations it covers, what does it ask you to do? And is that like, your words were reasonable, you know, is that, I would say maybe not, I wouldn't use the term reasonable. I think I would say like, is that going to encourage the kind of AI ecosystem that we want to see?

• 30:30Speaker 01

  On the who side, I think you actually like, even in your question, you said the thing, it sounds, as you framed it, it sounds like a compromise, it sounds totally reasonable, and it's the thing we fear most, which is, this is just going to apply to a couple of companies. Five, 10, 15 companies is just going to apply to a couple, a small number. That's the thing that we're worried about, because the other way to say it is, this is going to be a concentrated market, and we're going to have a regulatory model that is not only going to, like, be okay with a concentrated market, we're actually going to have an approach to regulation that is going to create concentration. Like, you can only build at the front, like, there are only going to be a small number of companies that are able to build at the frontier. That's not what we want. We want to ensure that startups can build at the frontier, and that when,

• 31:15Speaker 01

  if you're a founder and an engineer, or like, a founder and five engineers, founder and 10 engineers, those kinds of companies are able to have the ambition of building at the frontier. So when you said in your opening, you know, like, what's the dream of abundance? And I said, in sort of a roundabout way, like, the way that I think about that is, what is the regulatory model that allows smart, creative, ambitious, skilled entrepreneurs to build great businesses? This is what I'm talking about. The capacity, the ability to build at the frontier.

• 31:51Speaker 01

  It's just going to be five to 10 companies line. I thought before I started at Andreessen Horowitz, I kind of thought that was an exagger like was a story that I'd heard people tell, but I didn't think it was a real thing. And now it, I mean, I've heard it a couple of times. I heard it on your previous podcast. I heard it in a conversation with a member of the European Union, someone who's working with them on their AI regulation. And so I don't think it's an exaggeration. I think it's a sort of widely held view that like, look, this kind of development is just going to be a small number of companies. And that is, I think for a lot of people, they'll hear that is very reasonable. And I think it's really important to, whether or not you agree with us, to see it as very

• 32:32Speaker 01

  strongly opposed to the vision that we would have of what a healthy AI ecosystem would look like.

• 32:38Speaker 02

  Hey, we'll continue our interview in a moment after a word from our sponsors.

• 32:46Nathan Labenz?

  Okay, I, in some ways, am very sympathetic to the idea that like, concentration of power generally does not appeal to me. So I do find this, for what it's worth, to be the case among many, you know, people that would call themselves AI safety minded people. Broadly speaking, they are like techno optimists libertarians throughout their lives until this kind of one thing. I think the thing that is sort of makes it different for people that answers the question around like, what's wrong with just regulating harms is, there may be some things where by the time the harm is done, it's just kind of too late, right? I mean, the COVID pandemic is sort of an instance of that where it's like, and

• 33:26Nathan Labenz?

  again, I don't take a position on where COVID came from exactly, but if it did come from a lab, and now we have 10 plus million people dead globally, it's really hard to go to that lab and say, now you have to pay a fine and act like that's sort of.

• 33:44Speaker 01

  I think it's a perfect example. How many constraints have we put on biological research in the wake of COVID?

• 33:54Speaker 01

  Because there's

• 33:54Nathan Labenz?

  As far as I know, the gain of function research continues, right? I mean, I don't think any actual like lines of research have really been shut down, have they?

• 34:02Speaker 01

  But I'm not What I mean is like, what if the response to that, instead of focusing on all the problematic incentives and their like geopolitical issues and stuff related to how COVID was released into the world? The response was, we're going to make it really hard for every single researcher working on biological research to do their research.

• 34:25Nathan Labenz?

  Well, that would be, I mean, you'd hope to be more targeted and that does obviously connect to the number of companies. But like, there is something to be said, I think, for like, I'd love to fix the world. I'd love to have more wastewater monitoring and I'd love to have air scrubbers installed in all the schools. And when there's so many things that seem like honestly, pretty obvious kind of hardening. And certainly this is a theme in the AI safety discourse too, is like, what can we do to sort of harden society against these potential future assaults? But there is also the notion of choke points and you can say, okay, there's one thing that seems to be like a really bad problem. And that is if you create new viruses that don't exist in nature, that have like super transmissibility or super lethality. Like that seems really bad, or at least potentially really bad.

• 35:11Nathan Labenz?

  And it doesn't seem crazy for me for society to come in and say, and I don't think we have, but I would support it if society got together and said,

• 35:18Speaker 01

  Don't create pandemic and we haven't, we haven't, we lived through the pandemic. We didn't create civil or criminal liability for what you're describing. I'm not a biological researcher, so I have no idea. I don't have a sense of why we didn't do that and like, why we as a society might think that risk is one worth bearing. But my guess is because that kind of research on some of the most like virulent, potentially problematic viruses is the same thing that will like prevent those viruses from spreading over time. Like you, you have to do research on them, I assume, in order to figure out how to combat them. That doesn't mean it's okay to release them into the world and it doesn't mean that they're like, you know, I think, I think there are like lots of reasonable things that you could do in that situation.

• 36:00Speaker 01

  To try to ensure that the harmful use is that there's a deterrent for the harmful use, but we don't take that kind of research off the table because of the upside. And I think that like when we were talking about AI use cases, like there's just a there are an enormous number of positive cases from the like mundane to the revolutionary. I mean, mundane might be it helps me write a conclusion, but like dramatically dramatically lowering barriers to medical access or mental health treatment or making it much easier for more developers to build more products that are valuable to people, whatever the like explosively positive use cases. I think that just like the biological research, the reason we preserve a fair amount of ability

• 36:44Speaker 01

  to do the research itself is because of those possibilities of really positive explosive use cases. And I think if what we want is abundance, and I think there are a lot of people who are skeptical of abundance, but if that is a thing that we want, then we need to have a regulatory environment that is conducive to it. And putting the significant constraints handcuffs on the development process, I think is not going to get us there. And there are a few different components of it that feel to me to be like particularly off base. One is just going to apply to a couple of companies. That is another way. I mean, for us, that means like we can't invest in startups to grow. I also think it means that's one like part of the innovation angle. The other part of the innovation angle is like the reason, one of the reasons that

• 37:29Speaker 01

  our laws disfavor monopoly is that monopolists tend to not innovate rapidly. And so if you have a super concentrated market, it's likely that you're going to see fewer of those like most interesting use cases be pushed and be pushed aggressively. The other thing which I think is really important is it would be, I think it would be a different story if we said, okay, we're going to make it a lot harder to build AI models, but we're going to, the impact of that is going to be that we avoid a significant number of safety incidents and we really can ensure that we are going to dramatically reduce harm in the world. And I don't think that's the case. Like, I don't think that publishing. A document with safety protocols, that doesn't mean that you're going to not have

• 38:11Speaker 01

  safety incidents. It doesn't even, I think there are probably some number of safety incidents that wouldn't occur, that might otherwise occur, but I think it's conceivable that like, that there are going to be very few of those, that it's actually, that it actually has like no direct correlation to actual safety of the performance of your product. And if that's the case, like if there's pretty limited efficacy in what you're doing, then you're simply burger in development without a lot of upside. And that again is like, why we think the idea isn't, don't regulate. The idea is put resources and emphasis on the harmful use side, both because it lives, leaves room for development, but also because it actually, I think, would be more effective in addressing potential misuse.

• 38:53Nathan Labenz?

  I do agree with you that the proposals that have been put forward of this sort of like, everybody has that, not everybody, of course, some number, which we could debate exactly how big that number might grow over time, but that these companies above a certain threshold have to develop a safety plan and follow it and disclose safety incidents. I would agree, that's like, not that strong of a barrier against certain, whatever possible bad things happening. I think it is more meant to try to make sure that we are not flying blind. And that is honestly one of my bigger worries. I mean, I wonder if a reframing of

• 39:32Nathan Labenz?

  some of these requirements around like transparency is appealing at all? One of my worries is that, and I think we are seeing signs of this from OpenAI and others, and frankly Anthropic, who has, at least in some corners of the safety community, been the sort of darling frontier developer. They seem to be going for some sort of self-improving feedback loop, some sort of recursive dynamic, where it's like, AI is going to get so good at coding and ML research that it's going to do the improvement. And then literally somebody from Anthropic, as they launched Claude 4, said, "We want Claude to take over all the ML research so we can all go to the beach." And people were like, "Well, you can't put it much plainer than that, right?"

• 40:12Nathan Labenz?

  I do wonder what starts to happen behind those closed doors. And again, these are all, it's all very speculative right now because we're just in unprecedented territory all over. But I also think Daniel Cocotello, who is one of the lead authors on the AI 2027 projection or forecast or scenario, I guess might be the best word for it. One of the things that he says is that we should expect right now, we have like a very narrow gap between frontier AI capabilities and what is deployed for you and me to use. But he expects that gap to grow much wider over the next couple of years as the companies realize that, and I think they maybe already know it, but that they're in this sort of race for supremacy, who can create the most powerful AI model first that dominates the

• 40:57Nathan Labenz?

  market, dominates their rivals, what have you, and that they just aren't going to want to show that to anyone. And right now, there's not really any rule that would require them to do that. So I wonder if you would support something that's like transparency requirements, just even if only around like what capabilities the AIs have, you know, OpenAI, Anthropic, Google, you have to tell us what is it that your AIs can do. So we at least know like what they are capable of in your labs. What do you think about something like that?

• 41:24Speaker 01

  Yeah, so we published a blog that I'd encourage anyone who's interested to read because it's a little hard to run through it quickly. But the basic idea is that a lot of the transparency proposals we think are probably unlawful, they probably violate the First Amendment and also aren't particularly useful for consumers. So I don't think some of the things that have been proposed, some of the transparency mandates around like certain kinds of assessments of safety risk are actually things that will provide consumers with information that's useful for them, that's really informative and shapes how they interact with the model or which models they choose to interact with. And in our view, the

• 42:03Speaker 01

  Way to get at a disclosure regime that's lawful, that is useful for consumers and that is also not unduly burdensome for little tech, is something that we call AI model facts. And so it would basically be a basic fact sheet that would run through different information. They would be helpful for users. And this is sort of a fun project to work on because it was interesting to see like in current disclosure mandate proposals, what are the things that might sort of map on to this? Like what would actually be, if the criteria is not unduly burdensome for startups, useful, not unlawful, legal? What are like, are there elements from current proposals? And there weren't that many actually that I'd say would meet that criteria.

• 42:45Speaker 01

  Like the kind of stuff that we thought of were things like knowledge cutoff date, which I think is actually like extremely useful for consumers. So if you ask, if you ask an LLM like to give you information on who won the basketball game last night, like that's not in its training set. So having a sense of exactly when that cutoff date is will enable you to ask, A, to ask better questions that are actually like within the corpus of data. But B, if you were to ask it, who won the basketball game last night? And it gave you an answer, the likelihood that that's a hallucination or like information that you shouldn't trust should be high. So I think that's the kind of information that actually is like useful for consumers. The courts have been pretty clear that it's very hard to survive legal scrutiny

• 43:30Speaker 01

  if you require information that's either controversial or not factual or burdensome for companies. And so I think some of the types of information that they're typically seeing in transparency proposals, like speculating about potential long run harm or like forecasting potential safety risks. I think it's going to be really challenging for those things to survive First Amendment scrutiny.

• 43:55Nathan Labenz?

  One more quick bit on the thresholds. You know, I, again, share your, without going on too long, share your concern about like regulatory capture and the sort of structural oligarchy of various markets that we could find ourselves in via mechanisms like that. I think the people have, who have developed these proposals around thresholds have come to them because they see the scaling law phenomenon as a good indicator that we're kind of headed there regardless, and certainly it seems like we basically are there today, right? Like, I do a live players analysis every so often where I'll ask people like, who do

• 44:35Nathan Labenz?

  you think, you know, should count as the top tier AI companies in the world today? And the list is like, sometimes as short as three that I get back and like, never longer than 10. And that seems to be like, at least so far, a structural reality of just like the kinds of inputs that these things require. And yes, the costs are definitely coming down, and so people have sort of proposed like naturally escalating thresholds, whether it be like a certain amount of money or even could be revenue, which I know you're a little bit more favorable toward than other concepts. But I guess, do you see a, the notion that there's going to be like a lot of companies

• 45:16Nathan Labenz?

  at the frontier of AI seems to run contrary to what increasingly seems like it is kind of a natural law, that there is just sort of a scaling law reality and like, power, as you know, as a venture investor, right? Power laws rule everything around us. So like, is it so unrealistic to think that it could stay a small number of companies caught by these thresholds?

• 45:39Speaker 01

  Well, so, so first of all, I think if what you're trying to do is really just capture large companies who are able to bear compliance costs, revenue thresholds are clearly the what, the policy vehicle that gets you there. And the reason is, once you're taking like hundreds of millions of dollars in the door, we can like figure out what the threshold should be. Like, I've seen proposals like $100 billion thresholds, I've seen higher revenue thresholds, I've seen lower ones. But whatever it is, at some point of taking revenue in the door, you are able to devote some percentage of that to compliance costs, right? Like, it's hard to argue if you, if your annual revenue is $500 billion that you can't devote some percentage of that to.

• 46:23Speaker 01

  Whatever kinds of exercises and transparency disclosures regulators want you to do. Compute and training cost thresholds are different because we think startups can, you know, can build at the frontier. They do, they are able to build models with significant compute power. And I had a conversation with someone on our technical team about a hundred million dollar training cost threshold because I was starting to see that in more pieces of legislation. And I said, oh, is that a good way to carve out little tech companies? And he said, and I trust him on this. I don't have the technical chops to critique it, but he said every model costs a hundred million dollars to build. And so I think those thresholds are, are not going to be successful in separating out little tech from big tech. And the technology

• 47:08Speaker 01

  will outpace the ability of regulators to update them over time. One thing I noticed in the RAES Act and as part of the discussion that you had about it is that there are a couple of different ways you can hit the large developer threshold. You have to hit a hundred million dollar training cost cap, but I don't think there's anything that says that that couldn't be cumulative, which means that like if you built a large number, you know, 25 billion dollar models, you'd be captured. And right now, that might seem like very far in the future for a startup, but you might have startup that's building those models, you know, a couple of year. I don't know exactly what's like the highest rate of model development might be, but like more than one a year over a period of

• 47:50Speaker 01

  time. And at some point, that would mean every startup is a large developer. Right? And so those kinds of thresholds are focused on the development layer, which is where we think regulation is really much more fraught. You know, if it's cost or compute, that's focused on the science of building the tool and really doesn't have anything to do with the ability of a company to handle the kind of regulatory complexity that some of these regulatory models require. Another way to get at your question, I think, too, is I think you're right that there are these natural barriers to entry in AI. So cost of compute, and we have some ideas actually about different ways to try to provide broader compute resources to more entities, access to data.

• 48:35Speaker 01

  We have some ideas there about trying to ensure that more startups have more data that they can train on. The question for policymakers is, do you want to add an additional significant hurdle? Do you want regulatory barriers to make it even harder? And if that's the case, then we end up in the world that I think you're describing, which is we have a small number of developers who can build at the frontier. Everyone else can have the rest of the ecosystem, you know, less capable models not building at the frontier. But we have, we essentially have a regulated monopoly. That looks more like what we've had in telecommunications, for instance, you know, a very small number of companies with stringent level of regulatory oversight. At one point, even like an explicit government grant of a monopoly to one telecommunications provider.

• 49:20Speaker 01

  And I think there are people who would say, that's the right model. That's exactly what we should, that's exactly the direction of travel that we should pursue. I think from our standpoint, that is a level of monopolization that we would think of as problematic. It, I think, suggests a lower level of innovation than we would think is desirable and reduces the ability of startups to compete at the frontier in a way that we think is going to unlock a lot of value for people.

• 49:51Nathan Labenz?

  One thought I had earlier on the 10-year time horizon of an A16Z fund is that not only is that longer than vesting schedules, it's also longer than most people's AGI timelines these days. And so I wonder, you know, something I've pitched many times and it never seems to get any traction, but there is sort of a version of it right now that's under consideration in California, if you squint at it, with SB 813, is some ability to sort of...

• 50:18Nathan Labenz?

  Move in and out of different regulatory regimes, like the simplest possible thing might be like, have a sunset clause on a lot of these things like, okay, for the next three to five years or whatever, we think we're in a critical period. Let's put this in place, but then have it sunset at three years. And if it turns out some of this stuff was hype, or we got certain thresholds wrong, then they can just sort of disappear. With SB eight thirteen, there's kind of a similar concept where it's like a trade is able to be made between an AI company that wants to have a developed liability shield. If they opt into a certain regulatory scheme that's approved by the state, but like administered by a private organization, then they can have that liability shield. But then they could also potentially opt out of it at a future point in time.

• 51:00Nathan Labenz?

  If it's like not a trade that's working for them for whatever reason anymore. So I guess two questions there. One is like, what do you think about SB eight thirteen specifically? And do you have any other creative ideas for ways that we can put some, some measures in place, whether they be guardrails or transparency or whatever, that don't like lock us in long term, because nobody wants like the GDPR of AI. But like definitely some of us are nervous about not, you know, Tim.

• 51:26Speaker 01

  Quite a lawmakers who seem to want to, there are a lot of lawmakers who seem to want to GDPR AI. Cause I think the proposals that they are moving forward with suggests that, I mean, one sort of interesting thing is I saw a story and I don't know how accurate it is, but I saw a story that the EU is considering pausing implementation of the EU AI Act. So the EU is even having concerns about its own AI oriented version of GDPR and starting to pull back. The only state that's actually enacted legislation to govern AI comprehensively is Colorado. And the governor of Colorado was, came out in support of the federal AI moratorium, which would have made it impossible for him to essentially enforce his own law. So policymakers who have actually enacted these approaches are, I think,

• 52:11Speaker 01

  expressing some rightful concern about how they'll be implemented in practice. Again, that doesn't from our standpoint, mean the policymakers shouldn't take action. You know, we think lawmakers should focus on actively regulating harmful use. We think there's a role for states and the federal government to play in that. Regulating harmful use is actually pretty consistent with how states historically, and according to the constitution, have gone about lawmaking. So they can't unduly burden interstate commerce, but they can police harmful conduct within their jurisdictions. And some areas of law are disproportionately occupied by state governments. So like, most of criminal law is at the state level. So if you're looking to tighten criminal law to address harmful criminal use of AI, most of that is really for state lawmakers to do.

• 52:55Speaker 01

  Investments in workforce development and AI literacy, there's a lot for states and the federal government to do there. Transparency, we've talked a little bit about a model fax, AI model fax model. So I think there's a ton of stuff that lawmakers and enforcers can do to try to arm us for a world where AI is more prevalent. That doesn't mean that there's like, an infinite, you know, infinite road. The way that you described it, I think of like, hey, is there, could we just do some things for a little, a short period of time? And if it's not the right approach, we can pivot. I like that kind of experimentation generally. But I do think, depending on the model, like if it's a really stringent regulatory model, it's kind of like saying, we want to run this race. We want to run it as quickly as possible, but you're going to run, you're going to

• 53:40Speaker 01

  have to wear a 20-pound backpack for the first mile. And I don't think, again, different people have different views of like, how important it is to run that race at a certain pace. But there, it's not cost-free, even to do it for the first mile, to require someone to wear a heavy weight.

• 53:58Nathan Labenz?

  Yeah. So where does that leave you on SB 813 and the sort of quasi-private, at least like more dynamic regulatory idea there?

• 54:08Speaker 01

  Yeah, I think, I mean, it's something that we're still exploring. I think there are, like, lots of promising things about it. The idea that lawmakers will have to wrestle with tort liability at some point, I think, is important. I think that's kind of baked into the fundamental premise of the bill. And I haven't seen that many proposals that really seek to do that. And I think if we want to see people have even very mundane uses of AI in their lives that will like, improve their lives in some... Marginal, but also like level that is meaningful. You have to think about tort law, I think. And so I think the fact that Bill does it is a positive thing. I think the question is whether there's a regime there that works for little tech.

• 54:52Speaker 01

  And I think there are some things that people have said about it that don't fully wrestle with that component. So like, some people say, well, this is just voluntary. But if you, but immunity from tort law, or like some level of immunity, some protection from tort law is a massive benefit. And so if, I think if you said, look, you can get immunity from tort law, but you have to pay $20 billion. Most people would say, that doesn't sound fair. Like, that doesn't sound like a good, that doesn't sound workable for little tech. It means every large company is just going to pay to get immunity from tort law, and every small company is going to have to bear that legal risk. That's not really voluntary. You're saying there's this very valuable benefit, and you're making it prohibitive for startups.

• 55:33Speaker 01

  So I think the question is the regulatory regime in A13, one that's workable for startups. And I think there are elements of it that are, but there are elements of it that aren't. My hope is that over time, as that bill is examined, that it comes into a form that's more workable for little tech. And if it's workable for little tech, that's the direction of travel that we want to see. That gets us closer to support.

• 55:54Nathan Labenz?

  Do you want to leave the audience with any other thoughts, which could be, you know, any maybe specific things that you're tracking and supporting right now, or just other priorities or ideas that we have a touch on and want to make sure people are aware of?

• 56:06Speaker 01

  I mean, the, the one thing I was going to flag is like the Trump administration's releasing this National AI Action Plan at some point in June, July. So that's like a thing to look out for.

• 56:14Nathan Labenz?

  Yeah. Well, there, I definitely expect that there will be many more developments in this story before we get any sort of stable resolution or stable policy regime, let alone, I mean, even stable technology regime. So I appreciate this. You know, I think a good constructive conversation, and I hope we can do it again, because

• 56:32Speaker 01

  Yeah.

• 56:32Nathan Labenz?

  if anything, all the time intervals are getting compressed, because I suspect all these things are kind of tightening and going to be coming at us with a level of intensity that If I'm generally right about the direction of where the technology is headed, is going to require all of us to be coming together and really trying to synthesize all the different perspectives into the best possible plan. So come back and let's do that again before too long. How's that sound? Sounds great. Thanks a lot. Cool. Love it. Matt Perault, Head of AI Policy at A16Z. Thank you for being part of the cognitive revolution. Awesome. Great. Thanks for having me on. It is both energizing and enlightening to hear why people listen and learn what they value about the show. So please, don't hesitate to reach out via email at [email protected] or you can DM

• 57:17Nathan Labenz?

  me on the social media platform of your choice.