AI’s hidden 20-year monopoly

Reid and Aria dig into where AI is creating real, durable value—and where they see it heading to next. Reid shares an inside look at his AI-driven drug discovery work at Manas AI, making the case that medicine could be one of the biggest opportunities in the entire AI landscape. They also explore why AI will likely shift toward a mix of specialized agents and “front doors” rather than a single dominant assistant, how enterprise vs. consumer AI revenue will evolve, and what companies like Anthropic signal about where the real business of AI is taking shape. They also touch on the role of governments in delivering AI-powered public goods, before closing with a candid conversation on political corruption, institutional trust, and why civic courage still matters.

• 0:00Speaker 00

  Reed, it is delightful to be here with you in person.

• 0:02Speaker 01

  We always try to do this and being here in person is more fun for us and probably more fun for the people watching, but we try to make it always fun.

• 0:09Speaker 00

  Absolutely. So, no surprise, we're going to talk about AI, we're going to talk about profitability, we're going to end with some politics. But let's start off, for those listeners who don't know, you, in the last year or so, founded a company called Manus AI with Siddhartha Mukherjee, who is the Pulitzer Prize winning writer of Emperor of All Maladies. And the pitch when it launched was ambitious. Take drug discovery from a decade-long process to just a few years. Can you give our listeners an update on where you are right now with Manus AI?

• 0:40Speaker 01

  So, you know, AI compression was actually two years. Right, but yes. So, look, one of the things that we were just reviewing, and you were in the, in the meeting, we were reviewing, kind of like where we've been. And one of the things that was an interesting reflection is, but one can argue, there's a theme in Silicon Valley that like, like the language AI chatbot is basically done with, you know, OpenAI is a leader, followed by, you know, Gemini and Claude and so forth. The coding is basically done with Claude, Copilot, and OpenAI Codex. And, you know, maybe Google and Microsoft will get in the mix, they're going to work to do that. But that's done. But then, actually, in fact, now it's the time for medicine. And what people don't realize is that medicines, like,

• 1:25Speaker 01

  they're all like, like, they're like, actually, I'll use this pun because it's fun, they're googly eyed about the, like, the anthropic revenue thing. And medicines are massively larger TAMS. Like, when you, when you discover an amazing medicine, and what we're doing at Manus is creating a drug discovery factory for monopolies, because drugs are essentially legal 20-year-old, 20-year-long monopolies. And it was like, yep. And, and we've actually already kind of discovered novel chemistry. We've done a whole bunch, like, in, in our building of these models from scratch, and having taken the time to do this, and Ushul Singh has been the absolute perfect co-founder, CTO in doing this. And it's like, yep.

• 2:10Speaker 01

  We're on target and we're going places and we know things that other people don't know. And so that's awesome. And obviously, eventually we'll get to more public statements. I mean, this is obviously a version of public statements, but it's like, no, no, no, we want to have it more locked down and more like as opposed to pre announcing. You announce what you've done versus you announce what you're going to do. But it was, it was a really exciting couple of meetings here that we did, you know, basically kind of two days of, of modest meetings. And as a matter of fact, in the conference room, literally to the right here, here at Greylock, was where Sid and I came up with this because I had flown out to New York to have dinner with them saying, I have this idea that I think AI is native for like medicines and drug discovery and can, can increase the volume of different medicines,

• 2:55Speaker 01

  the accuracy of targeting, the de-risking of them, et cetera, et cetera. And this is why AI, you know, net custom-built technology can do this. And Sid explained to me in thin slicing how drug discovery worked because Sid has brought multiple drugs to market. This is one of the things that people don't realize. He's actually one of those people who has actually brought cancer therapies to market.

• 3:15Speaker 00

  Which is insane.

• 3:16Speaker 01

  Which is like, look, here is how it works from a target and idea and like the work process. And here's the things and here's, here are the loops and cycles that go into it. I was like, okay, AI can do something about that today. We can build something about AI for that today. That we just got to have to go with straight, you know, you know, kind of classic process. And then that's what formed the strategic plan for the, for the company.

• 3:38Speaker 00

  That's awesome. I mean, I think everyone is so excited about the possibility for AI to help cure cancers, cure many diseases. But when you think about the business side, some markets are winner take all or winner take most. When you think of drug discovery and these new sort of AI plus bio companies, is this a winner take all market or there are going to be many companies who are going to be able to come through with blockbuster drugs?

• 4:01Speaker 01

  Great question. Not surprising. The short answer is many, right? The TAM is so large. I mean, think about the number of different providers of GLP-1 drugs, right? There's a number of different providers. All of them are in the tens of billions of revenue, you know, on these with kind of like awesome margin characteristics and all the rest. And by the way, they're all like sold out in terms of how they're operating. So it's very possible to have a monopoly on your drug and have other drugs, even in the same space, and it to be really, really lucrative. But the question is, what other things are there to be discovered? And currently, the current process is you have some room of PhDs who are like,

• 4:45Speaker 01

  we're banging our head against the wall, and we got, Rika, we got one idea. Let's see if we can make that work.

• 4:51Speaker 00

  Right.

• 4:51Speaker 01

  And with AI, it's like, oh, we have 100 ideas.

• 4:54Speaker 00

  Totally.

• 4:54Speaker 01

  And we're working through it. And we're actually like using these super smart PhDs to accelerate, you know, prioritize, etc. Like, one of the things we joke about in the company is, we have CRL. Right? You know, CRL, reinforcement learning, for those people who don't understand what that is. Because they're like, no, no, no. In order to be well, like to identify a good target, to understand if you've got something. Like, part of what we've done is, we've replicated other work that took like a decade in hours. Because we went, oh, okay, try to solve this problem, because we know there's a solution here. Can we come up with other solutions? Can we identify the solution in our outputs, or the solution that has come up? That is already done by these genius researchers? And the answer is, yep, that answer came out in the mix too. And now we're looking at the other ones.

• 5:39Speaker 00

  It just seems like in this in particular, like the generative nature of AI is exactly what we need. We need so many ideas for what are the potential targets? What are the potential drugs? Then we obviously have to test them in a wet lab and make sure. But the more possible high-quality targets we have, the better.

• 5:54Speaker 01

  Yes.

• 5:55Speaker 00

  Let's move directly to AI. So, uh, back in the early to mid 2010s, you used to make an argument that new 100 million user social platforms would keep getting created. You were saying, again, this isn't a winner-take-all market. There's going to be many different types. Perhaps one of the reasons you started LinkedIn, you said people are going to have their social network for friends. They're going to have their social network from a business perspective. They're going to be, they're like, they have different identities. And so, each part of their identity, they might have a different social network. And this, these could be 100 million people, they could be a billion people. Does that same logic apply to AI? When we think about the AI agents that are going to be helping us, are we going to have one to help us, you know, in fitness, one to help us cook, one to help us to child rear,

• 6:39Speaker 00

  one to help us get us medicine? Or are we going to have that one general purpose agent who does everything? Like, what do you think the personal agent landscape is going to look like? Is it going to follow the same properties as social networks?

• 6:52Speaker 01

  So one mod on the social network question side, but that also gives a lens under the eyes. There are multiple winner take most networks. It's not there isn't one, it's that there's multiple of them, right? One really does social, one does, you know, kind of video, one does, you know, da, da, da, da, it's kind of ways of doing this. And there's room for more. By the way, there's still room for more social networks.

• 7:17Speaker 00

  Totally.

• 7:18Speaker 01

  You know, as, as it is, but you have to be technology native to where you're going. You have to have a distribution plan. You have to, you know, like all of them try to be, you know, broad based in what they're doing. And so, you know, is there room for your particular one? And there's a whole bunch of different things. And that's the social network thing.

• 7:35Speaker 00

  Yep.

• 7:35Speaker 01

  So I think there's still a lot of zone for doing stuff there. And so those comments still plus one. And I think the parallel to AI is exactly right, which is there are some people who think, no, no, there's just only going to be one super intelligence model, and that's going to be the one for everything, because that's one that is just better. But there's a bunch of different limitations on that. One is the, as you get to the super intelligence model, it's actually very expensive to run, right? And yes, you'll figure out how to bring down the compute cost. But if you're like, oh, look, the super intelligence model, it costs the equivalent of $10,000 an hour to run. Well, you're going to focus in on like, you know, help me cure cancer or, or help me with fusion or, you know, like other kinds of things. You're not going to say, help me with my email responses.

• 8:20Speaker 01

  Or, or, or, you know, like, hey, if I have this rash, you know, on my baby, is this something that is a normal thing? Something I should be concerned about? Something I should do something about, et cetera, et cetera. Now, part of what will happen from the super intelligence is I think there'll be a bunch of different, you know, kind of essentially distilled models and derived models. This is part of the thing that we're seeing is like people like, like legitimately amazed by some of the things we're seeing in China. But by the way, a bunch of them are efficiency and distillation and all our rest is that not all of them, but a lot of them have that, have that characteristic. And so a bunch of things. But it's not just that there will be a bunch of different models. And distilled from top models. And by the way, currently, it looks like, you know, similar kind of prediction of

• 9:05Speaker 01

  there'll at least be three to five frontier models. And maybe that's going to seven to 10. And we'll see what happens in China and we'll see what happens with various European efforts and a bunch of other things. But as ways of kind of doing this. But there's also different ways of getting distilled models and partnerships. But then there's also different data sources. There's different user affordances. Like, one of the things that people underappreciate Codex as a coding model. And it's actually in fact got some unique strengths that generally speaking, you know, people should pay attention to. Yeah. But right now, when you go around Silicon Valley and you do, you know, you look at social media and it looks like it's only Claude code all the time. And Claude code is an amazing product. There's a reason why there's all that heat.

• 9:50Speaker 01

  It's great. But it's trained on less compute than Codex. And so it's a lesson to all the other, the other, the other kind of lesson, but all the other ability to develop specific AI models is it isn't just, you might have unique data. It isn't just, but if you're actually going after a particular product surface, if you're doing something that you're learning a lot of what that affordance and the interaction works like, that will also allow you to create differential products. So there's multiple zones, not just smaller models, not just compute efficiency, not just, you know, whether it's distillation or new data or other things and all this. There's also the product surfaces and the interaction, what you learn from that and how you get there.

• 10:33Speaker 01

  Then, of course, you get to what's the zones that a person might like have in the interaction of their, of their thing. And the thing is, is yeah, the tendency is I don't want to have to try to remember and go to lots of different AI models. So it's like, I'm gonna have a hundred? Right, you want one front door. Yeah, most people don't do that. That's part of the reason why, you know, like, search becomes synonymous with Google, etc. Because it's like, nah, like a general front door, I will want, really, I'm using one of those. Yeah. Right? And I think there's still contention of that. I think ChatGPT is still the leader in that. Gemini, obviously, trying to come in. Claude, not as much. Right. Right, but is, and you know, obviously, but then it's like, okay, I have a general front door.

• 11:16Speaker 01

  But I have a front door for my enterprise. Well, that might be co-pilot. I have a front door for like this other thing that's kind of much more specific for me and that really important to me. I'm a parent. I mean, I have something that's specific to that. Like, and that's where the social network parallel, the opening of your question actually in fact really applies because it's like, well, actually, in fact, if you can get to a large enough, coherently valuable and growing thing where people go, this really matters to me. I would add another door.

• 11:45Speaker 00

  Right.

• 11:45Speaker 01

  For this.

• 11:46Speaker 00

  Right.

• 11:47Speaker 01

  Then there's a zone there.

• 11:49Speaker 00

  It's willing to be an app on my home screen.

• 11:50Speaker 01

  Yes.

• 11:51Speaker 00

  This is what I'm willing to give up real estate for.

• 11:53Speaker 01

  Yes, because and it's that coming. And by the way, we do remember, like, people are a little harder in remembering, like, okay, going to different URLs and so forth. That's the reason why there tended to be a, a kind of winner takes most in search, plus, you know, AdWords, the backend and paying for distribution in order to do that. But, um, but we actually, most of us have multiple people in our lives. It's like, you know, well, you know, what would I call Aria for? What would I call, you know, David Z for? What would I call, et cetera, et cetera. And so our brains more naturally work in that. So I actually think the narrowing focus to, I just want one front door.

• 12:30Speaker 00

  Yep.

• 12:30Speaker 01

  Even though you tend to get a trust, I think it'll be broader here than the other circumstances because it's like, oh, once I discover the, no, no, no, like, look, ChatGPT is great, but I really get much better legal from this, or I really get much better medical from this. Ah, fine. I'll remember to add it in because it's like, great. I, I have the thing to go to on that. And I, so I think we'll have a broader lens because, um, you know, Aristotle said, we're citizens of polis, which actually doesn't necessarily mean we're political animals, means we're social animals. We remember a bunch of different people and kind of like, I'll have this conversation here, have this conversation here. Agents will naturally slot into that. So I think there will be a broader range of this. And it's one of the reasons I said, there's a huge amount of startup opportunity here. It's not.

• 13:13Speaker 01

  Like the small end frontier models who are extremely expensive to build, the small end frontier models are going to run away with it. I think there's a huge startup zone here.

• 13:26Speaker 00

  We're just at the beginning. And so we're talking about these big companies, you know, whether it's you're talking to ChatGPT or Claude or Gemini. But should some of these agents be public goods? Like you often talk about that everyone should have a tutor in their pocket, or everyone should have a medical assistant. But you'd also assume, like, right now, everyone is afforded a public defender. Should everyone have a legal agent that is provided by the government? Should everyone have a tax helper who helps them file their taxes every year? Should like, should some of the government goods that people are afforded now, they also be afforded an agent in that space? And should that be a public good owned by the government? Like, how do you see that playing out?

• 14:06Speaker 01

  So I'll speak a little bit more bluntly than I've spoken about this before, because as you know, for, you know, maybe coming on eight plus years now, I've been going around to various governments and saying, hey, you should do this. And the reason I've been doing it mostly there and not publicly is because it's like, look, people tend to have an idea they can own, and they can go do something. And it's like, I want it to happen. I don't want credit for it, whatever. I want it to happen.

• 14:33Speaker 00

  And when you say it, you mean a medical assistant?

• 14:35Speaker 01

  Like a medical assistant, a legal assistant, tutor, etc. It's like, this is something you could do that you could be a champion for in providing services for your people, whether your people are, you know, like Californians, Americans, Brits, French, Italians, etc. And I've literally been singing this song sheet for eight plus years. And of course, we see nothing, right? And it's the incompetence of being able to figure out how to deploy tech strategy, what kinds of things to do, not being able to imagine the possibilities in the future, you know, uncertainty, listening to too much press, negative chatter on AI, and other kinds of things, and not being willing to be bold and so on. So look, the short answer is,

• 15:15Speaker 01

  Every government that actually, in fact, really cares about their people should arrange, this doesn't even build themselves, right? This is a part of the thing, because to build themselves, of course, they can't do that. They don't have the competence to do this. For a medical assistant, for a, you know, for a legal assistant, right? Kind of not just public defender, but actually, like, most people, you know, sign rental contracts.

• 15:36Speaker 00

  Absolutely.

• 15:37Speaker 01

  Like, okay, what does that look like? Most people, when they're engaging with medical services, it's not just the thing. It's like, well, what is this thing that I'm agreeing to with my data and all the rest? And how should I navigate that? What are my rights in this, in this, in a number of different circumstances? What are the opportunities for me in a number of different circumstances? Tutor, right? And obviously, sure, it could be kids and all the rest, but also, like, oh, I would like to learn new jobs and new skills, and how do I do all that? And like, and that's just the beginning on all the things. And what's more, like, governments, it's not hard to do. And the reason it's not hard to do, please, like, get out of your own way and make this happen,

• 16:17Speaker 01

  is if you go to any number of these providers, and it's not just the, you know, the Microsofts and the Googles and so forth, but it's also like Anthropic and OpenAI. And by the way, just to substantiate earlier comments I was making, where people said, well, you're worried about antitrust. It's like, look, I said, look, if we were five to seven hyperscalers going to three, I'd be worried. We're five to seven, heading to 10 to 15. And now, like, I was right, right? You have Nvidia, you have OpenAI, you have Anthropic, you have whatever is gonna happen with SpaceX and that random.

• 16:52Speaker 00

  I mean, 10 years ago, people might have laughed at you if you had said there was gonna be these, you know, multi-billion dollar companies.

• 16:58Speaker 01

  Because it's like, no, what I see is these, more of these coming, and they're competing with each other and all these things. And that competition is precisely what creates the speed of good. So how do you create these public goods? Well, you negotiate with them, saying, okay, like, if we, if you provide this, we help you in the following ways. Like, for example, a simple one for a medical assistant is. We will give you the following parameters of safe harbor for providing medical advice.

• 17:27Speaker 00

  Yep.

• 17:28Speaker 01

  If you do that, we want something that runs for free in every phone that's connected on our domestic networks. Right? So, and then we can go to the carriers and say, you must provide this app on every single phone free of charge. Right? For doing that, because then all of our people have this.

• 17:51Speaker 00

  It feels like the future, we're going to have to have these public, private partnerships that is delivering for our citizens. The government cannot build this technology.

• 17:58Speaker 01

  Yes.

• 17:58Speaker 00

  But perhaps the companies aren't going to be delivering the technology in the way we want unless they have these government partnerships.

• 18:04Speaker 01

  If instead what you say is we are beholden to the trial lawyers association for

• 18:09Speaker 00

  Too much risk.

• 18:10Speaker 01

  Lots and lots of suit, we're going to decrement these capabilities and not provide them.

• 18:14Speaker 00

  We're going to be much more conservative.

• 18:16Speaker 01

  Right? Of course. Well, why should we? Because what you want to do is provide, for example, medical advice, legal advice, you want to provide it for free. For free. Right?

• 18:26Speaker 00

  Totally.

• 18:26Speaker 01

  And just like, okay, so like, how do you make the economics that work? Well, you can't be the, oh, well, we're going to make you massively legally liable for this.

• 18:35Speaker 00

  No, absolutely. And so one of the companies you mentioned was Anthropic. And it feels like Anthropic is everywhere in the news these days. But a new reason they're in the news is because they're actually, this is their first ever operating profit this quarter. So they're projecting around 10.9 billion in revenue for the June quarter, up roughly 130% from the previous quarter. And that's $560 million in operating income. And so, so many people talk about these companies and say they are spending, spending, spending. They're spending on compute, they're spending on inference, and they're never going to make a profit. Perhaps Anthropic is proving them wrong.

• 19:14Speaker 01

  Certainly Anthropic is proving them wrong, but keep going.

• 19:17Speaker 00

  Okay, okay. For Anthropic, the growth here is driven by coding tools, enterprise. It's not coming from their consumer chatbot. It's not coming from everyday Americans talking to Claude every day. Does that tell you something about sort of the durability of the AI revenue that's going to come? Does Anthropic, will they look fundamentally different because their revenue is coming from a B to B situation as opposed to a company that's chasing hundreds of million, billions of consumers to interact with their platform?

• 19:49Speaker 01

  So this is standard pattern in technology, which people say, well, there's no revenue here. I remember this from the earliest days of the internet.

• 19:56Speaker 00

  Absolutely.

• 19:57Speaker 01

  Et cetera, right? And then later getting to Google AdWords, which is the best business model they're thus far invented in software technology for doing this. And so they start with no revenue and they say, well, then it's just that. And like, for example, when the internet 1.0 winter boom happened, it was like, you know, bust happened, you know, the internet winter, like, oh, like, the only winners in consumer, and now we're all going to go to enterprise software and clean tech. And that's part of what allowed, you know, Web 2 revolution to happen and all the rest of thing. And then like, oh, no one's ever going to make any money with Facebook, you know, blah, blah, blah. And so it's just, it's a standard pattern to go, it's, it's nothing, then it's only this. What I guarantee is, yes, there's durable revenue in enterprise.

• 20:41Speaker 01

  Yes, there's durable revenue in code. Those will continue. And there will be other major revenue streams, right? That was part of the modest gesture earlier. It's like, look, all of the stuff with, like, if we can get the drug factory going, which seems very early, promising and awesome, like, that kind of thing, then there's a huge amount of revenue there. There's going to be lots of different revenue things, including new, new models. And by the way, of course, there's going to be revenue in consumer chatbots. Like, I can think of three easy ways in order which to do that.

• 21:12Speaker 00

  But do those companies look different? Are you fundamentally a different company if you're going for enterprise?

• 21:17Speaker 01

  Yeah, well. Almost for sure, right? That's again, when you have the platform shift tsunami, lots of things change. So stating everything with a hundred percent probability is always a little cautious. But like, for example, there's a lot of different rules between enterprise and consumer. And they tend to fork for particularly good reasons. And companies that tend to be good at one tend to be not good at the other, even when they have some things that allow them to do that. And it's usually good strategy to go, especially as a startup, but even as, you know, scale companies to go, we're gonna really prioritize this. And the other thing is just an option that we're gonna kind of try to play at. And so, and so, yes, and I think they will look different.

• 22:00Speaker 01

  But I think that one of the things is not only are we kind of early days in this where like people say, well, is it gonna, is all the enterprise AI stuff gonna be, you know, Microsoft and Anthropic? And the answer is, I guarantee you, there's gonna be others. Right? As you know, is all of the use of AI for consumer and search gonna be OpenAI and Google? And I guarantee you, there'll be others. Right? So it's just like, now, which prediction? What do we do? That's part of the venture gig we do here at Greylock. We're sitting in the Greylock offices. That's one of the things we try to do and, you know, make smart bets on. And then, by the way, there will be others too. Right?

• 22:43Speaker 01

  That's part of the reason why, like, you know, I guess it's now three years ago. I first went, oh, drug discovery for this stuff. Right? And we've got other things, as you know, that we're cooking on this stuff as well. So it's, it's, I think that, and then like the most interesting mind boggling is, what are the different ways the business models come together? Because this is one of the things about AdWords. It didn't start in the computer revolution of the PC revolution of the eighties. And it took a while in the internet to get to it. And in, and it, you know, like when part of the reason why People willing to hand search distribution to Google was because they went, oh, this is the wrong business model because the right business model is a media business model, not an AdWords business model.

• 23:27Speaker 01

  And it was that discovery of the AdWords business model, which by the way, wasn't initially invented by Google, it was by, you know, kind of go to and overture and so forth, but then massively improved and, and kind of put it in a loop with buying distribution and all the rest as part of how that, how that played out. And by the way, you know, people said, well, no one will ever pay for the kind of, the, the kind of ads you see on Meta. Well, well, actually take a look at the revenue stream, etc. So, so you, we, we build new business models in this. And one of the things that I am always paying attention to in AI is what are the new business models, right? Because yes, what is the new distribution? What are the new engagement patterns? What are the ones that co-opt existing ones?

• 24:09Speaker 01

  But also what are the ones that create new ones and create new business models?

• 24:14Speaker 00

  So we're going to end with some politics. Uh, this is, I have a lot on this sheet of paper to give people context because there's been so much, dare I say, corruption happening in our government this week. So back in January, President Trump sued the IRS for $10 billion over a leak of his tax returns. And that was just settled, I believe with Trump on both sides. And he wants to build a $1.7 billion fund to give essentially restitution for those who were prosecuted and then pardoned during January 6th. So he wants to take $1.7 billion and give it to people who were arrested, who are criminals, who fought cops, who did

• 24:59Speaker 00

  violence in our state capital, in our country's capital. But there's also other things. He got out of a $100 million IRS fine. He secured tax immunity for himself, for his family. Like there are so many things happening this week and a lot of, there's a lot of outcry about all of this. But there's also outcry about the lack of outcry. It seems that no one cares. It seems that when Trump is asked about it, he doesn't even pretend to give a reason why this is happening. When you ask Republican officials, they don't even try to defend it because we can see this is sort of blatant corruption. And so I think when you're a regular person, you feel if he doesn't have to pay his taxes, why do I? If they're gonna

• 25:40Speaker 00

  be corrupt and get ahead, like, why do I have to follow the laws? So you often talk about the rule of law. When we have sort of blatant corruption or not following the rules or stealing money, how does that translate to sort of a breakdown in the institutions and our society? And what should these, like, what should these institutions be doing to safeguard the US economy so it doesn't tumble into something we don't want?

• 26:08Speaker 01

  Well, let's see. So obviously, this is one of the reasons why, you know, I put a lot of energy to try to not have Trump reelected. And I think that the degradation of the rule of law and corruption is one of the things that I think will be felt by generations of Americans for, you know, frankly, generations to come.

• 26:30Speaker 00

  Yeah.

• 26:31Speaker 01

  Right? It's, uh, it'll be collapse in economic prosperity. It'll be collapse in ability to, to trust and operate the systems you're working in. It'll be, you know, kind of, you know, more towards, you know, running us as a, as the bad forum of a South American country or an African country where everything works less well because of all of this corruption.

• 26:54Speaker 00

  Yep.

• 26:55Speaker 01

  And, you know, I think that, you know, basically, you know, anyone who doesn't speak out against it is basically either I don't care, I am fearful, or I, uh, or I'm trying to get me some of my own.

• 27:11Speaker 00

  Mm-hmm.

• 27:11Speaker 01

  Right? And I don't think there is any moral principled or otherwise intellectual defense. And I think it's part of the thing that I find that the people who are, you know, kind of articulating praise for the Trump administration to be essentially morally corrupt on. And so I think it's, it's, it's really important to say, speak up here. And if you say, look, I'm not speaking up here because I'm fearful, then I think the question comes down to, which a lot of people are, right? It's like, well, if you don't speak up because you're fearful, Doesn't that mean that you should be? And doesn't that mean that, you know, kind of like, kind of, can you discover your own courage?

• 27:57Speaker 01

  Right? Like it's, like it's precisely when you feel fear is, is a time where you go, okay. And by the way, the people who should be holding themselves more accountable are people who have, you know, positions of power, strength, wealthy people, other kinds of things because it is, and you'll tell yourself, it's, oh, I have a responsibility to my business, or da, da, da, da. And it's like, look, when you're feeling fear of speaking up, your responsibility to your business might be to speak up. Right? As a, as a kind of function of this. And like, this kind of corruption is precisely the kind of thing that has been the basis of American prosperity for, or anti-corruption.

• 28:42Speaker 00

  Yes, this lack of corruption.

• 28:43Speaker 01

  Lack of corruption is the basis of American prosperity for many decades and a centuries. And that's like a really important thing. We're all equal under the law. We're all having the law operate, you know, for us, right? And, and, you know, by the way, the, the, the question is, is like, like I haven't, like, so far, what I've seen in the operation of the judicial system, like, for example, throwing out essentially fraudulent indictments. It maintains some strength and vigor. But then it's like, oh, let's go do all the other things. Let's create a, you know, kind of a 1.7 whatever billion dollar payoff fund for the people who stormed the Capitol.

• 29:28Speaker 01

  You could say, look, at the very minimum, the crime was like breaking and entering. But there's a lot of evidence of, we're trying to get in there to kill, you know, Vice President Pence, there's people trying to do that. You know, the, there's violence on police officers who were trying to execute their, their job. You go, okay, this is what you're going to then say, hey, guys, in my third world dictatorship. You should feel free to go do more of that because not only will I pardon you, but I'll get you paid off.

• 29:59Speaker 00

  Absolutely.

• 29:59Speaker 01

  Right? And who, and by the way, who will pay you off? Hardworking American citizens who are paying their taxes.

• 30:04Speaker 00

  Absolutely.

• 30:05Speaker 01

  That's who's gonna pay you off, right? Like, it's like, okay, like, you know, the, the nausea coefficient of this is,

• 30:12Speaker 00

  is, is infinite, right?

• 30:14Speaker 01

  And so it's like, look, and I think part of the outcry is people go, well, I don't like, maybe the one I should add in addition to fear. And I was, I don't know what I can do, right? And it's like, well, what you can do is vote.

• 30:25Speaker 00

  Right. People are feeling helpless and it's like, we need to, right now.

• 30:28Speaker 01

  Vote and, and tell, like, make this a referendum on what you think about this administration.

• 30:35Speaker 00

  Absolutely.

• 30:35Speaker 01

  Right? And I think that's what to go do. Get your friends in, do that. That's what matters.

• 30:39Speaker 00

  Yeah. I mean, we have the midterms coming up. I mean, so you sort of point to the fact Derek Thompson was saying that, again, the real problem wasn't even the corruption. It was sort of the lack of discussion around it. It was that no one even tried to defend it. He said that we're in a world after virtue. Do you buy that? Do you think the US is living in a world after virtue or, or can we, can we come back from this?

• 31:00Speaker 01

  Well, I think we can definitely come back. I mean, look, we've had, we've had very bad things before.

• 31:06Speaker 00

  Absolutely.

• 31:07Speaker 01

  Slavery, right? Like this, like, so it is, it, it just hard, it takes a lot of work to do it. It's harder to do.

• 31:15Speaker 00

  Yep.

• 31:15Speaker 01

  And we need to, uh, like, have belief and hope in civic virtue. And the mistake is, well, is it perfect? It's never perfect, right? Like, no leaders are perfect, but then saying all leaders are bad is like, no, we don't know how to work without leaders. Leaders can be imperfect and it's like, okay, which imperfections are fine and which imperfections are not? And that's one of the things that I actually think corruption, where you're paying yourself off, corruption, where you're paying off people to do illegal behavior for you that involves violence and other kinds of things is, is not fine, et cetera. And it's like, go draw the line. And I think right now, like in this administration, we are living in that world after virtue, but that doesn't mean we can't get back to virtue.

• 32:00Speaker 00

  Absolutely. Well, let's, let's end on that. If, if we don't, if we don't think there's anything to do, we're gonna end up in a bad place. So we need to vote, we need to have hope, we need to speak up when we can, we need to get our, you know, fellow citizens, communities involved. But like you said, they're, we're in a bad place right now, but we can sort of crawl, crawl our way out of it.

• 32:19Speaker 01

  And the other thing to remember, because you know, Earlier generations have been in bad times. World War II, wasn't clear we were going to win.

• 32:27Speaker 00

  Sure.

• 32:28Speaker 01

  You know, think about what Britain had to go through in World War II. And, and one of the kind of poetic lines that's important to remember in this is, "It's darkest before the dawn." Doesn't mean the dawn's right away, but it's like, just because it's dark now, doesn't mean it'll be dark forever.

• 32:43Speaker 00

  Amen. Reed, thank you so much.

• 32:45Speaker 01

  Pleasure. Possible is produced by Palette Media. It's hosted by Ari Finger and me, Reid Hoffman. Our showrunner is Sean Young. Possible is produced by Tenasi Delos, Katie Sanders, Spencer Strasmore, Imo Zhu, Amon Suri, Lexi Kevin, Danny Garrison, Trent Barboza, and Tifadzwa Nimerundwe.

• 33:04Speaker 00

  Special thanks to Surya, Yala Manchili, Sayida Sabieva, Ian Alice, Greg Beato, Parth Patel, and Ben Ralas.