Patrick Collison (Stripe CEO) - Craft, Beauty, & The Future of Payments

OK.

Today, I have the pleasure of speaking with Patrick Collison, CEO of Stripe. Patrick, first question.
You have an excellent compilation of advice on your blog for people 10 to 20. And you say there that once you turn 35, you'll write some for people in their 20s.
What advice do you have for us now, the people in our 20s now? Wait, when's it coming? I haven't really thought about that. The one I've been wondering about recently is, you know, I said for that advice for people in their teens, they should go to San Francisco.
And I wonder for people in their 20s if they like shouldn't go to San Francisco. And I mean, glib, and I think there's a significant set of people

who should in fact go to San Francisco. But the thing that I wonder about is, um, for, there is a set of career paths that I think some set of people, um, you know, ought to pursue and would derive most fulfillment from pursuing and that are really valuable for the world if pursued, that require accumulating a lot of expertise and really studying a domain in tremendous depth.
And I think San Francisco valorizes, and look, this is also San Francisco's great virtue. San Francisco valorizes a kind of striking out on your own, iconoclastically dismissing the sort of received wisdom and the founding archetypes and lore of the Steve Jobs and the Bill Gates and all the rest.
And I'm way less successful than those people, but to some extent, Stripe, in as much as it fits a pattern, is an instance of that pattern. And look, that's great.
And I'm kind of happy that that phenomenon exists in the world. But I don't think that...
The world needs lots of other things, right? And I don't think San Francisco particularly, I mean, I'm again using San Francisco as a kind of metatum for a cultural orientation, but I think that San Francisco doesn't really encourage, yeah, the pursuit of really deep technical depth. And we're recording this in South San Francisco and, you, and South San Francisco is most noteworthy in the corporate world for, of course, being the headquarters of Genentech.
And Genentech was co-founded by Bob Swanson and Herb Boyer, and they produced cheap insulin for the first time with recombinant DNA. Herb Boyer couldn't have done that at age 23.
Herb Boyer first had to accumulate all of the knowledge and the skills required to be able to invent that over the course of a multi-decade career. And then, I don't know what age he was when he finally went and invented it, but he was not in his 20s.
And I feel San Francisco perhaps doesn't culturally encourage one to become her boyer. Or yesterday, at the time of recording this podcast, Patrick Hsu, one of the co-founders of ARK, which maybe we'll speak about later in the show.
This is a biomedical research organization we started a few years ago. He announced this new phenomenon of bridge editing, which is a new recombinase where you can insert DNA into a genome.
And it's pretty early, but it might turn out to be quite consequential. And in order to do something like that, you have to study for a long time and just acquire a lot of basic and not so basic technical skills.
And so anyway, the thing, and I don't quite know how to synthesize it yet, but as I think about advice for people in their 20s, look, I'm not going to normatively pretend or presume to know kind of in which direction one should go in one's life. And obviously, there are successful examples of basically every strategy.
And I'm really glad you're doing what you're doing at what age? 23. 23.
So that's... I mean,, I think, um, uh, information dissemination, um, is a, is a really valuable thing in the world.
Um, and causes like the, the, the guy who, um, who last I heard, um, was, uh, in the, in the lead for Nat's scroll price. Uh, uh, uh, told me, learned about the Scroll Prize, you know, listening to your podcast, right? So I think kind of increasing the catalytic surface area of, you know, certain kinds of information is a valuable thing in the world.
So I'm very glad, you know, you're presenting the podcast. Anyway, I don't presume to know what people should do with their lives, obviously, but I wonder, in as much as I was trying to give advice, and especially maybe if they're reading my advice and not someone else's advice, maybe they're kind of thinking about career paths that look sort of directionally like mine.
I think my advice might be, I mean, maybe you should do something like what I did or I'm trying to do, but there are other paths as well. And I think a lot of really important invention in the world and a lot of the things that I'm most happy are happening actually require a very different trajectory.
And I think there were counterfactual versions of my life where I pursued that path and who knows how well it would have worked. And anyway, last point in this.
And San Francisco is just very status-oriented, I feel, in this way. I mean, maybe status-oriented is, everything is status-oriented, so that's kind of tautological, but maybe really what I'm saying is I feel San Francisco, the entrepreneurs are held in excessively high regard, in my view.
And look, I guess I like entrepreneurs. And, you know, I think, look, entrepreneurs as an aggregate group in the world, you know, all the companies built on Stripe, you know, I think are great.
But there's just a strange version of it in San Francisco that I think should not be people's only fixation. Yeah.
I mean, what I like about this and what I like about you is just like you have this sort of a sense of like contrarianism of like the things people are expecting to hear from you in an even moment. You just like really want to just tell them the opposite.
I don't even know, like when, I feel like when EA was a little bit more popular, you were like, here's the problems, here's why progress studies are important. And when it was down in its depths, like, hey, guys, pay attention.
But on this particular piece of advice. Michael Nielsen says that, you know, every field in science has, you know, way too many adherents or way too few.
But like, you know, the market is almost never in sort of the right equilibrium. And I think something like that might be, I mean, I think the reflexive, in a contrarian way, I would say that I think reflexive contrarianism for the sake of it is also tired.
And, you know, if you're just contrarian to the prevailing mood, then, you know, you're just following the prevailing mood, but, you know, with a sign bit inversion or something. So I don't endorse that either.
But I think that the herd is a really powerful phenomenon. Actually, one of the learnings of my adult life has been that everyone knows and kind of says or frequently hears that you should be very wary of following the prevailing tides and moods and whims and everything.
But it's freaking hard to do in practice. Yeah.
So what practically does that look like to hone your craft in any of these disciplines that take a long time? I mean, you, you know, you've, you've spoken and tweeted about like some of the problems with modern universities. Is it still, is that still the de facto path? If you want to be the great biologist that arch hires or something? Yeah.
I mean, well, I, you know, in many domains, I don't know. Right.
Uh, so in, in like hardware, which is not a small domain, uh, most, most things in the world involve stuff and things. And I just have no facility with or experience with doing things in hardware.
And so if you wanted to become a super skilled practitioner there, what's the best career path? I don't know. Maybe it's to drop out and join SpaceX or something.
I'm not necessarily endorsing just pursuing the most establishment and credential-oriented path. I think people should try to find the gradient of maximal learning in whatever it is they care most about.
And the question that is for, for, for biology. Look, not that I'm a biologist, but it is very clear that in order to do really good work, there are a lot of bench skills and, and well, there are a lot of bench skills one has to acquire.
and then there just is a lot of actual specific knowledge um where you know the body well

any kind of life, you know, wasn't designed with, you know, neat fundamental principles the way that maybe physics was. You know, a lot of it is obviously evolved and contingent and messy and complicated and all the rest.
And so there is a lot of just specific factual stuff to learn. And I think for those two reasons, I think there are very few, there are very few successful, pure autodidacts in biology, where you at some point, in virtually every case that I'm aware of, have to have had direct experience with, you know, in and with a top lab where you're seeing how people actually do it in practice.
And actually, maybe this also ties back to what we were discussing previously, where your question about the founders and what they learn from each other and so on. I think there's an interesting book, Apprentice to Genius, that follows three or four.
It's three generations of scientists. So someone who mentored somebody else, who in turn mentored another scientist.
And they were all extremely successful. And the book is kind of this reflection on, I mean, this description of what they all did, but also this kind of reflection on, well, like, what is it that was transferred? And, you know, for example, one thing it describes is, well, one of the most important and subtle questions in science is problem selection.
Like, just how do you choose what to work on, right? No one tells you what to do. And you do have to answer this question multiple times.
With a company, in some sense, you just have to decide once. And then maybe it's an iterative process from there.
Whereas in science, you're frequently pursuing completely new problems. And of course, you need to choose something that's sufficiently important and hard that would be important if you succeeded, but also that it's not so intractable that you can't actually make any progress.
And so the book describes how this is part of what the mentees describe that they learn from their mentors. Another thing they talk about is just learning about high standards and what high standards actually are.
And when I talk to people in other domains, this is so frequently the thing that I hear from them, that when they worked with X person or Y organization or in Z environment or whatever, that they learned what great actually is. And that just permanently changed their sense for what their own standard

for their work ought to be. And so maybe one version of what people in their 20s should do is

get some ideas to domains you're interested in or care about, but then figure out where can you

learn the highest standards? Where are the highest standards embodied? And where can you go and experience that firsthand? Before we get back to Stripe and Arc Institute and everything, I want to touch on the Parker study stuff for a second. There's a view that says, listen, if we improve the NIH 10% or whatever percent, are we really making a dent in the fact that ideas are getting harder to find over time? And how much of a difference do institutions make anyways? If it's just about a number of researchers and how many people in your society you can put into research.
It's not like Singapore can have a much more effective scientific institution that lets it compete with America in science or something like that. What's wrong with that intuition? Noah Smith and others have talked about, I can't remember the term he used, something like moneyism.
He had a funny phrase. But sort of this idea that we assume there is some kind of constant elasticity between investment in some particular outcome, like building a semiconductor factory in Arizona or a new bridge or whatever, and the outcome of the factory or the bridge.
And one, the conversion rate between those inputs and the output is not a cosmological constant. Maybe any of these things could be done for a half or a tenth or whatever of the cost.
But two, there are even deeper questions as to, is it possible at all? Or what else would have to change for it to be possible? And what are the other constraints? By just talking about these things in funding and dollar terms, you're kind of making the implicit assumption that the only relevant constraint is the financial

one, where in practice, maybe it's permits or it's labor shortages or it's other things.

Anyway, in the context of the NIH and science and R&D, I'm really skeptical of this same

approach being brought to bear where we can just talk about the amount that we're spending

I'm not sure what's happening. of this same approach being brought to bear where we can just talk about the amount that we're spending on R&D and think that that's implicitly a useful measure of the output.
And to a fairly close approximation, there were around 1% as many practicing professional scientists in the. pre-World War II as post-World War II,

or say even 1950. And the other epiphenomena in papers or patents and so forth, it tends to

follow pretty similar ratios. And we got a lot of pretty good stuff in the first half of the century.
And, you know, despite increasing the amount that we spend by, you know, between two and maybe, I mean, slightly more than two orders of magnitude, not quite three, it's not clear to me that there is like a linear relationship. And so when analyzing the NIH or how we should pursue any of this stuff, I'm inclined to try to get way more, I guess, concrete and tactile and try to think, okay, what would success here look like at, well, what is happening today at the micro scale? And what are the actual problems? And then what could success look like at the micro scale? And then what might it look like to scale that up? And just to give one kind of pointed example of that, we ran a survey of the FastGrants grant recipients after FastGrants, asking about their normal work and not about anything to do with FAST grants itself.
And we asked them if they had flexible funding. That is to say, if they could spend their research dollars, their current research dollars, whoever they wanted.
So not if they had more research dollars, just if they could direct their current dollars however they wanted, how much their associated research program would change. And we gave them three options, not much, a little, and a lot.
79% said a lot. So four out of five said that their research agenda would change a lot if this constraint was removed.
And so should the NIH funding level be X or 1.1X or 1.2X or whatever? That seems to me like a bad way to

analyze this question as compared to, for example, perhaps, how bound and constrained should an NIH grantee be in choosing their research agenda? Maybe, I mean, if their judgment was way better than that of the committee's, I'm not saying it is, but maybe it is, who knows? Maybe there's a 5x improvement to be generated just by making that one switch.

So yeah, I'm not saying it is, but maybe it is. Who knows? And maybe there's a 5x improvement, you know, to be generated just by making that one switch.
So, yeah, I'm just, I'm very skeptical of these, of these financially oriented frameworks. I mean, maybe the financial is not the right word for it, but just like trying to map inputs to outputs is the framing which you're using to compare the pre-World War II inputs to what's happening now.
And if it was particular to the scientific institutions, you'd expect, for example, that things that are disconnected from the NIH-specific structures, obviously you've talked a lot about the RIA is getting harder to find paper. And sector through sector, it's not like NIH is running Moore's Law progress, right? But even there you see you need exponentially more researchers to keep up the same level of progress.
So, it does seem important to have these level effects that are one time in the case of something like COVID, where like, yeah, we need that level effect right now. But if we're framing it in terms of like hundreds of years from now, this is going to be the thing that increases growth rates, which is a sort of framing that is also supplied when talking about these progress or these things.
Does that make sense in that context, when all these sectors are seeing these slowdowns, which seem consistent with how the economy and science progresses over time? I don't know is a short answer. I think it's really puzzling.
I think the constancy of U.S. GDP I think, just one of the weirdest things.
I forgot explanation for it. But also, I don't think that it's ...
Or, sorry. An obvious thing to do would be to shrug and say, OK, well, it's overdetermined or something, and that's just how countries work.
But you can look at other countries, where it's obviously manifestly not the case. And so, what is it that's weird and special about the US? The thing that I wonder about in a lot of these cases is you could get many of the observed system phenomenon characteristics if we weren't actually adding productive capacity.
That's a simple way to explain a lot of it. And that if you're just adding exponentially more unproductive capacity, then on a stylized level, a lot of this stuff would just fall out of it.
Now, I'm not saying that we're necessarily doing that. But it could be that, you know, maybe we're making them.
um well there's lots of ways where where that could be the um that could be what's effectively

going on even if it's not the case that the marginal people or things or organizations themselves are bad. It's just kind of somehow how the components interact.
But the fact that you could get these exponentially diminishing returns through the addition of ever more nonproductive capacity makes me not persuaded that the low-hanging case is necessarily true and give some weight to the prospect that, yeah, it's fundamentally structural, cultural, or organizational. And, you know, just to give a sort of, you know, a micro example there, and it's a very, you know, basic and an obvious one, but, you know, I think it's interesting to compare the SpaceX R&D budget and the NASA R&D budget and, like, look at those two time series together.
And, you know, we're just returning to the kind of the financial point again, but it seems pretty clear that the trajectory of NASA's efficacy has not fully followed the trajectory of its inputs. Yeah, yeah.
Although the point about the marginal inputs we've put into science have not been as highly effectively used or as high quality as what was before. Like the 1X is a much higher quality 1X than 100X.
It's not clear what you do to fix that. Like if it's just a case there's a limited amount of John von Neumann's in your society that are part of the pre-World War II 1X, it's not like we can just put 100X more John von Neumann type physicists into science.
If the binding constraint is the number of John von Neumann's, then yes. That's bad news, I guess.
There's not a lot kind of do on the margin. But I'm not sure that it is.
Like, I think the, I guess I keep going back to the, the, the cultural and the sociological point where, so Gertie and Carl Corey, they, they run a lab at the University of Washington, St. Louis, and six of their students, if I recall correctly, went on to win Nobel Prizes.
And, you know, they had a well and they got good students. But they weren't the most prestigious lab in the world.
It's not like they got to cherry pick every year the single most promising person. And so something was going on there.
And there's a book about it, and it tries to get into this a little bit, and I don't know that I can figure out quite what it was.

And there was also some good fortune where they got into molecular biology at a good time.

But I think there were these kind of hopeful data points where, you know, again, they were obviously extremely brilliant people.

But I think that the thing that distinguished them and their students was not that they were these, you know, seven sigma Martians.

I think rather that they found organizational structures and cultural practices that really worked. I think those are at least in principle more replicable.
Now, you might still say, okay, fine, in theory, but how do you actually do that? And I think that's the big open question. Okay, I think that's a great point to talk about our institute Um, so yeah, I mean, I think you just kind of answered this question basically, but, um, uh, it's not exactly like biology research is, uh, uh, it's something that society has neglected.
So what's the theory of change here? Is it just a story similar to Stripe in that if you get the right people, even though, you know, there's like tens of billions of dollars of biology funding, getting the right people and the right culture and right dedication is what it takes. Even though there are lots of scientists and lots of universities, there's a lot of homogeneity today in how science, and in particular how biomedical science, is pursued, and kind of basic research in an academic context, before there's any commercialization or prospect of it in sight.
And I don't know that the model is necessarily a bad one. Certainly, we're not particularly claiming that it's a bad one, but sort of the construct of universities, labs, PI, a principal investigator running the lab, that person applies for grants primarily to the NIH, maybe supplemented by other sources, and grants reviewed by committees with pretty study sections, as they call them, with pretty rigid scoring criteria and so on.
That's the structure. And it just seems suboptimal to me.
I mean, homogeneity is bad in basically any ecosystem, especially ecosystems where you're, you know, where you're producing, or excuse me, where you're seeking tail outcomes. And we thought that for a variety of reasons, like, well, from first principles that other models should be possible, and that like, we had specific ideas as to how, you know, one particular model might be a good idea and complementary to the status quo.
In very short terms, what's different about ARC is, one, scientists are funded themselves to pursue whatever they want. So it's curiosity during research, whereas NIH grants are given for projects.
Second, we build a lot of in-house infrastructure so that scientists can draw upon other platforms and other capabilities that they don't have to go and build and maintain themselves. Whereas, again, in the standard university academic context, scientists would virtually always have to do that in-house.
And because of the natural skill constraints on any given lab, that effectively circumscribes the ambition of a possible research program.

And then thirdly, we try to provide career paths for people to remain in science, even if they don't want to become principal investigators, where the university structure kind of commingles the training purpose of academia with the execution, where the people who are doing the work are typically the grad students and the postdocs who are both themselves, at least nominally, on the career path of themselves eventually becoming principal investigators. And there are lots of people who, for all sorts of different, very valid reasons, love science and love the pursuit of research, but don't want to be a manager running a lab, choosing their own research programs and dealing with all of the overhead and typically grant applications that are concomitant with that.
And so with ARC, we have a real emphasis on hiring scientists who have finished their postdocs, finished grad school, and just like that's what they want to do in their lives. And there, again, isn't really a career path for them today.
And one of the things that's actually really exciting about the discovery that we mentioned that came out yesterday, this new bridge editing technology, is that work was led by one of these senior scientists who had finished his postdoc.

And, you know, it's not clear to me that he wanted to go on to become a PI, but he loved science and he's an amazing researcher, clearly. And so he's able to go on to, you know, have that career at ARC.
And, you know, prospect of these mobile elements being usable in this way for this genomic insertion, whatever, that's a pretty speculative out there thing. And had he applied to the NIH to go and pursue that? I mean, he didn't, so I don't know what the outcome would have been.
But Jennifer Doudna's work was, if I recall correctly, funded by DARPA because her CRISPR NIH applications were rejected. And of course, Carolyn Currico's NIH applications for mRNA vaccine work were famously rejected uh so um so it at least seems very plausible that it wouldn't have uh it wouldn't

have worked out. And so, look, all these things are random, and I can't make any definitive claims about what would have counterfactually happened.
But it seems plausible to me that this thing announced yesterday wouldn't have happened or would were even less likely to happen in a different

environment.

When we think forward 10 years or 20 years, the specific line of research where

you understand the effects of the genetic architecture on different traits, and also

you can edit, invert, insert, whatever, the DNA arbitrarily.

You know, you saw a little cell anemia. You've done the obvious things.
What does that lead to? What are you excited about? Well, the thing that I think is really interesting about it is using it as a new kind of telescope, by which I mean, you know, when people hear about CRISPR, there's an obvious excitement and a legitimate excitement around, you know, using this to cure things, you know things directly in the body, using it as a kind of therapeutic. But you can also use CRISPR to try to figure out what's going on in cells and in cell cultures in a kind of structured way.
And so the body is interesting in that it has this switchboard of like the DJs, I guess, at those fancy mixing sets of 20,000 genes. And with CRISPR, you can systematically go and sort of perturb each gene one by one, like mashing all the keys in sequence and try to figure out, well, you know, what the effects of perturbing this versus that are.
And, you know, if you do that in a cell culture where, you know, you can subject the cells to some stressor or some treatment or, you know, whatever, you can kind of see differentially how different perturbations affect different cell outcomes. Or you can just kind of use it for synthetic data generation more broadly, where, you know, you could perform all these perturbations and then sequence and kind of see what's happening in the cells and so forth.
And, you know, single cell sequencing has come a long way. Anyway, point is, there's a lot you can do with all this gene editing stuff for discovery and for data generation in the broadest sense.
And, you know, because for a lot of diseases, they're complex in the sort of fields jargon, meaning, I mean, yes, they're complex in the colloquial sense, but they're specifically complex in that they're not infectious. They're not just like some pathogen getting into you, and they're not monogenic, like, you know, Huntington's where it's like one specific mutation.
Instead, it's like some combination of environmental factors, but like maybe some genetic factors as well. And, you know, it's somewhere in between.
And by figuring out, and that includes most autoimmune diseases, most cancers, to some extent, cardiovascular disease and neurodegenerative disease, like the big ones we haven't yet solved. And so then coming back to these functional genomics technologies, what's interesting, I think, is trying to figure out how it is that the genetic component of those diseases happens and works and so on.
And even if that's only a small contributor, it can potentially shine light on just like what the general pathway is. And so the question would be, and look, this is speculative, none of this has actually happened, but by figuring out the genetic interactions between genes and say Alzheimer's, can you figure out how Alzheimer's arises, which we don't understand today?

And then once you understand how Alzheimer's arises, maybe you can use kind of conventional

technologies and targeting to figure out how to inhibit that or to sort of modulate those

pathways.

And so, yeah, that's what we're really excited about from a functional genomics standpoint. And there's kind of an AI angle as well that we could talk about if you want.
How do you think about the dual-use possibilities of biotech? I am sympathetic with the idea that if you think of prior technologies, just like Google Search or even just the computer itself, you could forecast in advance, like, oh, this has all this dual-use stuff. But for some reason, just history has been kind to us, and maybe we should just...
Chester's meta-fence here is keep doing science. But with biotech, we don't have to do specifics here, but there's specific things you can think of with this specific technology where you can imagine some nefarious things.
How do you think about... Why not just focus, let's say, on ameliorating the risks first or something like that? Well, I don't think the binding constraint on harmful use of biotechnology or bioweapons today is pure biological capabilities.
Like if some set of incredibly capable, intelligent people wanted to cause tremendous harm with, well, presumably with pathogens, but with something biological, they wouldn't necessarily need to invent anything new. They would just need to apply currently known techniques in kind of a malevolently directed fashion.
I think there are some concerns and some risks there with respect to things that don't invent new technologies, but do make them more accessible. And so, I mean, I think the question is, you know, how would the effect on the world be if there was a sufficiently sophisticated LLM, uh, that, uh, you know, it could, it could help anybody, you know, synthesize and disperse smallpox.
Um, like, I don't know that the laws of physics, uh, prohibit such an LLM existing. I presume they don't.
Um, and would the world be fine if such an LLM was, you know, widely distributed? Like maybe, but you know, maybe not. Right.
So I think, I think there is that kind of threat vector, but my point is, I don't think, um, knowledge at the frontier of biology is the, is the relevant margin here. And if we take seriously what, um, what, um, you know, that, that this is, I mean, we don't need crazy AI risk to motivate this, you know, where the world is perfectly capable of originating, you know, really severe pandemics and pathogens itself, plus all the other diseases that are not pathogenic.
So, you know, we got other problems. But, you know, whether we care about the possible, you know, kind of dual use harms you just mentioned, or we just care about things that already exist, to ameliorate both of those, we do need enhancement of our capabilities.
There are a lot of biological problems that we don't today know how to solve. And so I think in that respect, if one were to do what you're proposing and try to advance the defensive side of this, I don't know that what one would do would necessarily be that different.
Because there are just fundamental capabilities that we would presumably need to have that we don't today have. And by trying to solve current human diseases, I think you're probably also pursuing something pretty close to the best steps to solve the potential diseases that, you know, malicious actors could cause in the future.
That makes sense. I mean, zooming out from bio-risk in particular, just like how are you thinking about AI these days? Well, you know, I think everyone has to be sort of high perplexity in the sense that, I mean, the verdict that one might have given at the beginning, you know, we're recording this here pretty close to the beginning of 2024, the verdict one might have given at the beginning of, you know, 23, 22, 21, you know, back, say, the last eight years, those would all, I think, have looked pretty different.
I mean, maybe Guern might have scored the best from 2019 or something onwards. But broadly speaking, it's been pretty difficult, I think, to forecast.
And so I think the basic position to a first order has to be one of some degree of humility. I think, as your blog post identifies, the big question right now is to what degree scaling laws hold.
And I guess if they hold, then, you know, what exactly is it that we're, well, asymptoting is maybe a presumptuous word, because it's not an asymptote, but like, what is it that we're approaching? You know, it's not, we don't necessarily know the shape of that thing, whatever it is. And yeah think how one should feel needs to be or ought to be very sensitive to the exact parameters of those curves.
And I just don't think anyone knows what the true value of those parameters actually are. So it's clearly going to be important, is already important today.
And it has a pretty central bearing on both Stripe and Arc. And we'll see.
Yeah. I wonder if the meta lesson here, and I totally agree with that sort of general sentiment, but I wonder if the meta lesson that we got from COVID, for example, and with things like fast grants was you obviously can't predict these things in advance.
But the most important thing, even in addition to these specific sort of countermeasures trying to come up in advance, is like when the thing is happening, having competent individuals who can synthesize and organize information and also having these like new initiatives and institutions to get the right thing done. Yes, the adaptability premium is probably going to go way up over the next decade.
Yeah. And with that in mind, and I know you have already a couple of day jobs, but yeah, I feel like something like Fast Grants, like when the time comes down to it, like, I don't know, you like, it should be like, you know, you'd be like one of the top people you could think of in terms of having expertise and respect in a wide range of domains and competency as a leader.
I don't know, just keep it in the back of your mind or maybe in the middle of your mind, given how far we are into the transition. Well, Fast Grants was three beloved squirrels in a trench coat, or I guess, well, I was one of the squirrels, so I don't know.
I'm self-ful of it. But it was also with Tyler Cohen, who's an amazing person and a great friend.
And then my wife, who's also one of ARK's co-founders. And so Fastcrans was not this giant, impressive edifice that would qualify me for anything at all.
But it doesn't have to be giant, right? To have that kind of big impact? Yeah, I guess as an objective matter, that's true. I mean, look, John and I try to be very self-aware of the limits of our expertise, which are very proximate to us.
And I'm sure if something like that was necessary, there'd be. I mean, look at Operation Warp Speed.
They chose a super effective domain expert, Moncef Slaoui, to run that. And it was just monstrously successful, truly remarkable.
And I don't know who the Moncef Slaoui of, I guess it would depend on whatever the problem in question is, but I think my recommendation would be, figure out who MANSEF is and go hire MANSEF.

And I think it is extremely unlikely. I think anybody who deemed me the MANSEF of that thing is probably mistaken.
I don't think you're being too humble. But just staying on FAST grants, now we have the retrospective of how effective the FAST grants recipients were compared to the other grants that were given out by, let's say, the NIH or NSF.
To your knowledge, what has been the reaction of these institutions to the discrepancy between the speed and effectiveness of FAST grants? Have they analyzed their protocols and what happened during COVID? Is there any sort of retrospective there on their part? Not to my knowledge, but I don't want that to sound like an indictment. Like maybe they've done a lot of reflection and, uh, and you know, I just don't know about it.
Like, I don't think I would know about it even if it had happened. Um, so, um, I don't know.
Um, uh, I mean, look, most, um, well, I don't know anything about the response at CDC or FDA or NIH or NSF

or any of the relevant organizations or their international equivalents.

And so none of what I'm saying should be taken as like specifically, not only not critical of them,

but not even a comment to them.

I just like don't know what they did.

But in general, organizations are not awesome at self-reflection. And I think, I assume as a default prior that some of the dynamics we discussed at the beginning of this are rooted there where none of the people who started those organizations are there today.
And so, you know, what exactly are the incentives of those leaders? And, you know, I haven't, yeah, it's not clear to me who would have the incentive to, you know, really take stock in a fully objective and self-critical way to figure out what was done well and what was done poorly. I promise not to be too myopic about AI, but one more question.
Long term, we can forecast. Maybe even medium term, we can't.
But near term, it looks like we might have things that look like AI agents and they might need to trade. What does a financial infrastructure for AI agents look like?

Yeah, I think that's a really interesting question. And I think automated or autonomous transactions, I mean, they already exist to some extent today.
I mean, lots of services have

usage-based billing, right? And a lot of the expenses being incurred are, you know, autonomously incurred. Like, no human is pushing a button when Stripe does most of what it does, you know, with cloud computing and incurs, you know, some costs with some cloud service.
So it's in some kind of extremely primitive way happening today. And I assume it will follow some gradient where some of those decisions are either directly or indirectly being made by an LLM or some LLM equivalent or whatever.
And I think there'll be some almost unnoticeably smooth continuum up to very considerable degrees of autonomy. But it's not that we're going to wake up some month to be like, oh my god, suddenly the bots have been unleashed.

And very considerable degrees of autonomy, but it's not that we're going to like wake up some month and be like, oh my God, you know, suddenly the bots have been unleashed. And I think there'll be interesting questions there around, I mean, this will now sound very kind of parochial and kind of maybe getting excessively tactical or something, but I think there'll be very interesting questions around the legality of those in terms of like, are these treated as the responsibility of the owner? Or is there any degree of kind of independence granted? How does liability work? Which rails are best suited? What kind of transaction velocities are we talking about here? Because if it's a billion transactions a second, then the properties of that system should look very different to one giant clearing transaction every day.
And again, if we just use the analogy of the usage-based services, those tend to incur liabilities in tiny increments, but then to settle on a monthly basis when you pay your bill. So maybe these agent transactions will have that character.

So I think there were a lot of practical applied questions,

but I think what you're saying around these autonomous transactions

conceivably being an important dimension is very true and real

and is one of the interesting ways in which the economy might change and expand over the next decade. And I think it's possible that the crypto plays some role here, where, you know, it's, if, you know, we take KYC and AML very seriously for humans, and we want to know the human that is associated with some particular financial activity.
Obviously, that's a murkier question in the context of some AI agent. And if we, in some blurry sense, look at crypto as the part of financial services that is de facto exempt from AML by design, then maybe that plays a role.
How long before Stripe was founded, do you think a product like Stripe could have been invented? That's a good question. Well, depending on what exactly you define Stripe as being, I think conceivably decades earlier in that, I mean, on some level, PayPal is a kind of stripe.
And there were many payments companies before PayPal, and you could go all the way back to cash registers or something, right? So, it depends on these definitional questions. I mean, the particular secular tailwinds that we benefited

from around the rise of app stores and the on-demand economy and maybe the startup boom post YC and after the financial crisis, those particular tailwinds were idiosyncratic and specific to Stripe. And I guess the GFC was 08.09 and Stripe was founded in 2010.
And so, So in as much as you define those as being core, then not that much earlier.

But most... GFC was 08.09, and Stripe was founded in 2010.
As much as you define those as being core, then not that much earlier. But mostly, my story of Stripe is one of market inefficiency, and I do wonder why much of this didn't happen sooner.
Yeah. I always find it really interesting when there's these cases where it wasn't even the case that, well, it could have been started sooner, but there was nobody in the market.
There were like many people in the market. And they weren't just like random people.
There were technology companies headquartered in San Francisco who were in the market. Do you have some explanation for why it didn't occur to them? I'm hesitant to generalize too much because, well, I only have maybe N equals one experience.
And so I think it's dangerous to over extrapolate from that. Maybe N equals two now with ARK as a very different kind of organization, but an organization nonetheless.
Or if you include all the features of Stripe, N equals like 10, 20 something. So yes, depending on your definition, maybe there's some kind of samples out there.
I guess my general view is most products and most businesses, things can just be done much better. And I think moats are typically kind of overrated.
And I mean, the payment's a great example of a domain where on a logical basis, you would say that there are so many sources of defensibility where there's the network effects of the account holders and there's the data network effects slash economy of scale for fraud and so forth, and there are regulatory modes and barriers, and, and, and. And yet, not only does Stripe exist, but there are lots of other...
I mean, there's a whole fintech ecosystem today, right? So, yeah, I think it gets down to kind of deep questions of what's the binding constraint on just the number of effective organizations that exist in the world? And for any given sector, why is it that number of companies rather than twice that number of companies and so on? I think it's about motivation and ideas and people's willingness and determination to organize talent and so forth. But these kinds of more sociocultural explanations, rather than ...
I mean, Hamilton Helmer is probably the leading scholar of some of the sources of defensibility for businesses. He has this niche, but very well-known in the niche book called Seven Powers.
It tends to disaggregate all the various sources of market power in this respect. And I think that is true and important insofar as it goes.
But nonetheless, it's kind of strange to me that nobody had done Stripe before Stripe. When you think about the fact that most are overrated and just doing the thing is underratedrated, does that like what is Stripes mode in that context? Does that make you, you know, think differently about Stripes mode? Yes, one.
And I guess I do think that one can have organizational and cultural moats. Maybe this contradicts what I was just saying, or maybe it's consistent with it in the sense that it's a kind of cultural explanation.
I think that in as much as we have a moat, it's because we have a very good understanding of our domain and a set of people who actually care about solving the problems and who are continually paranoid at the prospect that we might be forgetting something important instead of trying to figure out what the important thing that could supplant Stripe's approaches is and making sure that we build those first and so forth. I think organizations that are ...
I mean, you're familiar with Conquest's Laws and there's Conquest's third law, I mean, there's, you're familiar with conquest laws, and there's conquest third law, I guess, which is that one should model organizations as if they're run by a cabal of their enemies. And, you know, obviously it's, or presumably it's tongue in cheek, but, you know, it's interesting to try to think about like, well, kind of what is the kernel of truth in that, and why would it be there? And I think what's going on is that I think most organizations, when they start out, are actually trying to achieve their stated goals.
Like somebody started the organization for a reason and probably it was for the stated reason. But then over time, you know, that person and that set of people who initially populate the organization depart and some set of new people come to take their place, and there's multiple versions of that, there's generational turnover on a continuous basis.
But say for the fifth generation, why are they there, and to what degree do their particular specific local incentives align with the nominal, originally stated goals of the organization? I think there can be a lot of misalignment there, right, where they're following a local path and conceivably even the leader of the organization, not even through any fault of their own per se, necessarily. Just they're a human and they have their own incentives.
And again, the original kind of constitutional incentives of the organization might be quite different. And so I think this phenomenon is kind of a fact of life.
And I think these kinds of explanations for me are much more explanatory in trying to figure out sort of why some of these things either happen or don't. And to your question, like in as much as Stripe has a moat, what is it? I think it's that, I mean, others can judge to what degree it's actually manifested and rooted in practice.
I think it is, but I'm a biased observer. But I think it would be that people at Stripe really care about solving the problems that we say we are trying to solve.
Yeah, the point about the misalignment over generations or over time is interesting. And actually, do you have examples of institutions which have for decades or hundreds of years managed to keep their original, not only mission statement, but the organizational competence? Because you think of tech companies, even the oldest tech companies have not been around that long, right? And they're some of the biggest tech companies in the world.
And the median age of the corporation is famously low. What are good examples here? I think some of the explanations around the effects of shareholder capitalism and sort of the idea that shareholder capitalism as a mechanism does, in fact, have some consequence with respect to the incentives of organizations and their long-term fates.
I think those theories have some credibility, and I think it is very plausible that shareholder capitalism even attenuates the durations of these organizations. I'm not saying that's definitively true, but I find it incredible, the idea that it is.
It's not clear to me that that's necessarily bad, even if it is true, right? In that, are we on the side of the humans or of the kind of aggregate innovation in the world or on the side of like the corporation's, you know, qua legal entities? And, you know, yeah, it's not clear to me the answer. It should be the third.
At the same time, or maybe, in fact, consistent with that, you know, if you look at, say, Europe or some other places, like in Denmark, for reasons related to the tax code there, a lot of organizations are either controlled by or very substantially held by non-profit foundations. And so Novo Nordisk, for example, the GLP company, but Maersk, the shipping company, I believe also Lego, a lot of these corporations are controlled by, and again, usually have a lot of their stock held by foundations.
That has the secondary effect in many cases where they actually do embed in a legally binding constitution their mission. And so, you know, I'm not an expert on Novodordisk, but I happened to get a book about it over Thanksgiving.
And actually, there's also a book on the Danish Industrial Foundations. But it's enshrined in their constitution that they have to make insulin, you know, broadly available really cheaply, or at least cheaply in Scandinavian countries.
And I think they're allowed to charge market prices elsewhere. And then the rest of their profits, they're, again, legally obligated to reinvest in R&D.
Is that somehow causal in the fact that they then invented one of the most remarkable pharmacological discoveries of the last 20 years in these GLP-1 agonists, I mean, plausibly. And so I think these questions around why it is that the median age of organizations and corporations is what it is are definitely interesting.
I suspect it's a somewhat contingent aspect of how we've chosen to organize large corporations in the U.S. today.
The thing you were mentioning about this firm seems very similar to the export-led growth in Asian. Totally, 100%.
You have tariffs. This one company, you're tasked with making the cars, but you better make the cars good.
You have no competition, but you had to invent the best car in the world. Yes, yes, yes.
And I think, I mean, you know, we are all fans of, you know, Smith and Ricardo and, you know, all these characters. And, you know, even they, I think, are sort of less dogmatically attached to free trade than perhaps, you know, people today interpret them as being.
But I think people like, you know, Friedrich List and, you know, those other, not quite contemporaries, but quasi-contemporaries, are maybe on a relative basis underrated. And I do think, I mean, in as much as you believe the kind of sociological, cultural skill, whatever, even vague alignment, not in the AI sense, but in the more interpersonal sense, in as much as you think these are important and explanatory, then I think you end up thinking about some of the things you just raised.
That's really interesting to hear you say that, because if you think about Stripe's mission, it's to facilitate global trade, to make sure that some firm from India can compete with any firm in Nigeria or whatever. So the room for you to have this learning curve where you're less efficient than the global competition should be less if Stripe exists, right? Isn't Stripe the anti-list company? Well, it depends which version of lists.
And to be clear, I'm not specifically endorsing these tariffs and trade barriers. I think the history associated with them is checkered at best.
Look, I think it's possible that if you have a specific sector where you have clear goals and a credible path to actually achieving some substantial degree of success there, and probably some, and, probably some more conjoined propositions, then maybe some degree of activist trade policy might be on net the beneficial thing to do. I don't think that describes most sectors in most countries at most times.
And yeah. That's so interesting.
I think there's an interesting thread here in how it relates to Stripe Climate in that you're, I don't know, subsidizing these learning curves that these East Asian countries did for their own internal companies. I mean, you haven't picked out a specific company that's going to necessarily be the key of carbon sequestration.
But yeah, how do you think about this? Well, maybe a way to unify the two points, and I'll speak about Stripe time in a second, is that I think, I guess it's Sayy's law about demand creating supply. And in as much as Stripe aggregates more and more global demand, I guess part of the, I don't know, it seems too self-aggrandizing to call it the theory of Stripe, but some vague hunch in Stripe is that that aggregation of demand can have important expansionary effects with respect to the ensuing supply.
And yes, Stripe Climate is some version of this hypothesis applied on a much smaller scale than Stripe itself, but still real and, well, we'll see, maybe important. And the basic idea, just for folks who aren't familiar, which I assume is most of your audience.
So we observed in 2018, I guess, that everyone seems to agree that carbon removal will be very important. And even if we decarbonize the economy on the kind of timescale that optimistic people, on the most optimistic timeframes, there'll still be an accumulated stock of carbon that is a problem.
It sounded pretty weird. There were virtually no carbon removal companies in the world in 2018.
Maybe there were two or three or something. No companies had ever purchased from a carbon removal company.
These were really science projects. And so we thought, well, somebody's got to start,

and it might be valuable to not only transfer some dollars, but to confer some credibility on this sector. Not that Stripe is the world's most credible company, but it's better than nothing.
And so we started contracting with some of these carbon removal companies. That went pretty well, and they seemed appreciative of us.
And so we thought somewhat more about this. And we then in 2021 formed Frontier, which is an AMC, an advanced market commitment.
So, inspired by the first AMC, which was a pre-commitment to purchase vaccines for developing world countries for diseases that, I mean, well, either were kind of market failures where pharma companies hadn't pursued the vaccines or were

just the profits weren't sufficient to pay for the program. So, we said to this for carbon removal,

we raised a billion dollars. Stripe was the first investor.
We're not actually investing,

we're just buying. So, they're the first company to commit.
But then we're joined by Shopify and

Alphabet and Meta and JP Morgan and a bunch of other companies.

And now there's like a fairly active sector of carbon removal companies. I think Frontier has contracted between 40 and 50 companies, the overwhelming majority of which didn't exist when we started out with this.
And actually, we ran an anonymous survey back at the end of last year.

And we asked them to what degree was the existence of Frontier somewhat causal in their starting the company in the first place. Again, there's an anonymous survey.
I think it was 74% of the companies said that Frontier played a causal role in their starting the So yeah, I think these inducement effects can be pretty significant. Yeah, that's huge.
Well, what are other ideas you've come across where an AMC would be an effective instrument of moving forward with the tech? Hmm, that's a good question. We've actually been having some of that discussion internally.
It's not that we plan on doing it ourselves necessarily, but I'm just wondering, are there people we should share our technology with? Not that it's even technology per se, but share our experience with or something and try to help along. I mean, there's still, I think there's still a lot of stuff in the biomedical field.
And I mean, patents are pretty useful insofar as they go, but there's a lot of innovation that seems like it would be socially beneficial that patents don't provide a way to cover the cost of. And so, you know, there was some excitement a few years ago about mannose, which is, it's a sugar.
And there was one paper that, maybe a few papers, I can't remember, that suggests that maybe tumors will selectively take up mannose rather than glucose, but they can't actually metabolize it properly until they just die. And so, you know, maybe this could be an effective, you know, onco-treatment of some sort.
But mannose is like, it's a generic sugar. It's been, you know, understood for, I guess, more than a century, and you couldn't patent it, importantly.
And so it's not clear who has the incentive to even fund the work to test whether or not this would actually work in practice. And this is not an endorsement of Manos, but just there are things of this shape where there's something where you can clearly see, wow, that might be very beneficial, but it's not totally clear how the kind of economic structure of the market can make it possible.
So I think there are still a lot of those across the biomedical landscape. I mean, look, there are still a lot of vaccines that, you know, could in principle exist that don't.
I mean, Lyme disease, you know, there's no, there's one vaccine that was withdrawn from the market over some safety concerns that I think were misplaced, but, you know, it's still no vaccine. Not even that well understood, right?

People have chronic Lyme disease.

We don't know if it's legit or not.

Exactly.

Yes, yes, yes.

It's a good question.

Maybe some of your listeners will know.

We'll have ideas for fields where we sorely need an AMC.

I want to go back to Stripe for a second.

You're famously appreciative of craft and beauty, but also you appreciate the power of scale and growth. Is there a type of craft? And speed.
Oh, interesting. Okay, yeah.
But is there a type of craft that is just not amenable to speed, growth, scale? If you think of a Japanese chef, he's learning to cook rice for a decade, and then he can move on to the sushi or something. Is that just not competitive in the modern world? Craft, scale and speed.
I don't know, they're strictly necessarily intention in every case, but they're definitely frequently intention. So just yes, I think is kind of one short answer to that.
At the same time, a lot of the most successful companies are those that I think are distinguished by the extent to which they exhibit appreciation for and skill in realizing craft and beauty. And so, LVMH is one of the largest companies in the world, and their business.
I mean, I think Tesla is pretty good at this. I mean, they're good at many things, but including this, obviously there's Apple.
I mean, TSMC is a kind of, you know, it's not the Japanese sushi chef you mentioned, but it's the TSMC chip sushi chef in Taiwan. And so much, again, tacit knowledge and difficult to transfer skills.
So I think it might be the case that craft and the pursuit of it is as important as it's ever been. And certainly as Stripe has gotten larger, I think we ourselves have come to greater conviction in this where I think part of what's interesting about these aesthetic qualities is they're generally speaking unquantifiable.
I don't know if they're intrinsically unquantifiable. Maybe you could train a model to do so or something.
But today, they're, broadly speaking, unquantifiable. And yet, they influence people in significant ways.
People very demonstrably care about aesthetics. If they're a company, they care about the aesthetic characteristics of the products that, you know, they produce just like an intuitive level.
People know that that's true, but you know, yeah, it's, it's, it's difficult to manage that at an organizational level where there isn't a P and L associated with it. And if you're screwing it up, you don't see a neat time series decline.
But over the 14 years of Stripe, we have, I guess, through a kind of, not exactly trial and error, but just by studying cases where things worked well at Stripe and cases where things worked less well and what customers responded well to and so on, it really seems clear to us that even in a domain like ours, where we are selling primarily to businesses, that this is something that's truly important. And also that in as much as, you know, getting back to what we were discussing previously, you want, in as much as the sociology and the kind of cultural explanations of defensibility are real, the best people, themselves craftspeople in their domain.
And they really, above almost all else, want to work with the best other people. And so I think it may almost be true that even if, from a customer-facing standpoint, craft was not valued by the market, you actually might still want to build an organization that indexes very heavily on this because you just want the best people for other reasons.
And now, as it happens, I think customers do, in fact, value it. And I think the evidence is broadly consistent with that.
But yeah, I think it's very hard to assemble groups of the best people if you don't take the practice of the work super seriously.

What kind of beauty or craft or simplicity is more important?

Interface or implementation?

There's famously that essay that Unix is successful because the implementation is simple and not the interface.

Yeah, I guess the interface is kind of simple, but there's a lot of asterisks and caveats and edge cases that I guess Unix doesn't handle for you. But Stripe does, right? Presumably, it depends what you're building, right? For TikTok, it's probably more important that their interface is simple.
And even if their implementation is a mess, that's probably OK. I'm not saying it is.
I have no idea. Whereas for Stripe, yeah, people are on some level purchasing our architecture or purchasing their ability to do certain things and some set of things rather than some different set of things because of what our architecture makes easy and makes possible.
Now, I don't think, I mean, if by interface you mean the visual GUI interface, then maybe we can draw some separation there. But I guess we don't really draw that distinction.
We think of the interface to Stripe as being the architecture. We're selling, no one else seems to agree with me, but I often think of Stripe as similar to Mathematica, where we're selling kind of a self-contained universe to model whatever it is of interest to you and that you care about.
And we're providing some primitives and some, yes, kind of interfaces and tools and so forth to enable your modeling. But fundamentally, we're helping you do something in your own terms.
And in that sense, I don't think the architecture and the interface are necessarily that separable. That's a really interesting analogy.
Although, I mean, if you think of Mathematica, the entry that that's giving you two is just like the platonic objects of math. Whereas for you guys, it's like the entry is that to like Visa error codes, right? Right, right, right.
Like the end object is not the platonic. That's true, though.
In both cases, yeah, I think, yes. So the analogy falls down in a few respects.
But look, I mean, the idea of a transaction is pretty fundamental and is roughly as old as the quadratic equation or something. I guess the transaction's older.
And Mathematica, especially today, now supports all kinds of, I mean, to a very impressive extent, supports all kinds of crazy arcane stuff. If you go through the more obscure packages in Mathematica, you can definitely find things that are, I think, much less broadly employed and understood even than visa error codes or something.
But yes, look, these are not the same. It's more just I find it to be kind of an interesting source of intuition.
And I think what Wolfram has done with Mathematica is pretty amazing. Yeah.
Another way in which I'm curious how you think about this, one way in which Mathematica maybe differs is if they had to make a change in Mathematica, deal somebody has to learn new syntax if you make a change you know it's like uh billions of dollars of uh um yeah uh transactions don't happen right uh like what how does that change the way you think about the initial architecture and just the stakes yeah um it's a good question um well actually first the point on just with respect to architecture, then I'll answer that one. So just as a side note, I guess, I think it's interesting that API design in general doesn't get more study as a discipline and as a practice.
I think it plays a significant role in the fate of platforms or can. I'm not saying it's always the determinative thing.
And if you get it right, there can be compounding positive benefits and the converse. And I think it's really striking that with, say, with mobile app development, which was one of the most dynamic and fast-moving ecosystems of the past 10 or 15 years, that so many of the objects in the classes, say in iOS development, are prefixed with NS, less so now with Swift, but for much of the iPhone's history.
And the NS, of course, refers to the next step, back from next in the 90s. But when you get API design and architecture right, it can be so enduring over literally multiple decades.

And, you know, even in the face of what are otherwise kind of frenzied evolutions in everything around it.

And Unix, of course, is kind of another example of this where, yes, Unix has tons of shortcomings.

But like the architecture has basically worked for now, you know now more than, I guess, around half a century. And so we're all trying to impress upon people at Stripe the importance of multi-decadal abstractions.
And I think people sometimes respond to that thinking that that's some insanely lofty, kind of implausibly ambitious, I don't know, hyperbola. But no, I think that's actually just what happens when you get this stuff right.
And if in fact you get it right, you can just reap these, or really the people building on your platform can reap these incredible benefits for a very long time. To the Mathematica point, they, I know, take backwards compatibility really seriously, where you can run programs written 20 years ago unchanged in today's Mathematica.
That really raises the stakes in API design for sort of obvious reasons. And we have that same problem ourselves, where when we think about introducing something new, it's not just, does this exigently address the particular need that's motivating it today? But do we think we can stand behind this in 2044? And how do we think the world might evolve around us such that it all remains coherent? And we certainly don't always get that right, but that's on some of what we're trying to do.
Is Visa net an example of this? And one might even say that one of the downsides of being able to use implementation for many decades in the future is even if it's self-sustainable and you have this ecosystem and equilibrium set around it, if you can't modify it just because of people's local incentives, you get stuck in this equilibrium that's worse than it could be otherwise. I see.
I see. I think the card networks generally, Visa and MasterCard, are pretty good equilibrium.
It's easy to judge today with the world as it exists in 2024, but I think you have to look at the world as it was when they started out and the particular problems that they're solving. I think when you compare the financial landscape in the US or in the Western world to those in other places, it's certainly not clear to me that the US has gotten a bad hand, so to speak, or is somehow stuck in any meaningful way.

So, the Cardinal Works do a couple of things. Originally, they were designed to replace a store credit.
I mean, it was a credit card originally, not a debit card, right? And that was important.

And the availability of structured consumer credit, I think, is actually a pretty big deal and pretty beneficial,

and especially beneficial typically for lower-income people.

And then with the advent, I guess, of jet travel and mass market tourism and so forth, then they helped supply into traveler's checks and and various worse alternatives like carrying cash around in your little bag. And then with the internet, they were substantially involved in enabling online transactions.
And I think that the fact that they got the architecture so right that so much of this, so many of these different use cases were able to be addressed by their core design is just really impressive. And the guy who designed all this, D-Hawk, I think is, I mean, he was just, he was a remarkable person.
And even, I mean, people complain about interchange. And I mean, lest I sound like a defender of the card ecosystem, I mean, like Stripe is on the, well, it depends, you could look at multiple ways, but many people would consider Stripe to be on the wrong side of the interchange cost equation in the sense that we're giving away the interchange revenue to other companies.
And so I don't think I'm structurally biased in favor of interchange. And yet, I will say, I think it's pretty interesting what Interchange made possible, where it's a distribution

incentive fee, where you're paying other entities to go and

do the work of recruiting these customers and convincing them

to get a card, and getting them to maintain the card,

and to pay it off at the end of the month, and all this stuff.

So you're paying for that, just the pure distribution.

There's a person at the end of the flight telling you, hey,

sign up for the United Credit Card, or whatever.

But that's what Interchange is paying for. That guy annoys me.
We'll get to the counterfactuals in a second. So there's that, there's paying for the actual credit issuance itself, and then there's the customer support and all the ancillary things around the dispute

handling and so forth.

And then I think it is interesting to look at the cases where, for whatever contingent

reason, the card networks didn't arise.

So Germany is one of the classic ones.

And from our vantage point, dealing with the online economy in Germany as compared to the

US is so much worse.

If Strike could push a button and have really broadly adopted cards in Germany, a la the US, we would push the hell out of that button, right? You can look at China, which on the one hand does have Alipay and WePay, or WeChat payments, are really ubiquitous. And so in that sense, they're very digitally enabled from a transactional standpoint.
But those products don't tend to be as sophisticated with consumer credit. And so, yes, the transaction fees for transferring money that you, in fact, already have, that's super cheap.
But I think you need to look at it on a fully loaded basis where, okay, but what about the cost of actually getting the credit to make the purchase in the first place as a credit card would enable? And I think as you look at these other counterfactuals in other places, one feels a kind of gratitude for what it is that DHOC and Visa and MasterCard and the card networks made possible. And look, I'm not saying they're perfect or anything, but I think that I'm most interested in critiques.
I'm not saying, again, that one can't make them, but just I'm most interested in critiques from people who've really studied the ecosystems of other countries, because I think it's easy to underestimate what we got in their invention. Maybe there's a sort of test and expense kind of thing going on here.
If you had to design payments from first principles now, does it make sense that, you know, all these things you mentioned, taking on credit risk, the chance of fraud, dispute adjudication, should that cost like 2-3% of each transaction that happens in the economy? What would payments look like if you had to design that from first principles? Well, we're seeing a live version of this experiment play out for the first time in many years in a number of countries today where central banks are becoming more active in designing national payment schemes. And so PIX in Brazil launched in late 2020, I think.
But I'm sure you've heard of UPI, the central bank. UPI was kind of the instigator here where it's the central bank payment system in India.
And it was tied up with ADHAR and their national identity system and so on. But that inspired a lot of central bankers in other countries to go and build their own UPIs.
And so, yeah, PIX in Brazil launched in 2020. And now a significant majority of all Brazilian adults are like weekly active users of PIX.
Again, even though it launched in 2020. And now a significant majority of all Brazilian adults are weekly active users of PIX.
Again, even though it launched in 2020, so it just had this incredibly rapid adoption curve. You have Swiss in Sweden.
Across East Asia, Japan, Thailand, Switzerland, central bank after central bank are deciding, hey, we should have our version of this. And so this is a kind of reinvention of the payment system from scratch.
For kind of hard to understand reasons, yeah, things typically seem like once you layer in the customer support and the consumer protection and the fraud prevention and the anti-money laundering controls and the credit, you know, the things just for some weird reason seemed asymptoted around, you know, two or three percent. It's important to also note that a lot of the two or three percent, you know, beyond just covering the costs, much of the surplus ends up getting remitted to consumers in the form of rewards, not in every country, but in many countries.
And if you look at the public reports from various banks in the US, their interchange revenue where they're getting these delicious fees on every transaction, as you put it, a lot of that is going straight back out the door to the consumers themselves and so on. So it's not clear how exactly one should think about the economics.
If it's going back to the consumer, should you include that as a transaction tax, or is it just like a weird circular relationship? I've not seen any evidence to suggest that the 2% or thereabouts is massively inefficient in the scheme of things. I'm not saying it's the optimal level.
maybe 1% would be better, but within some range of 1% to 3%, it's probably reasonable. As we think about some of these ad valorem fees and figures, I think the place where there's, um, even more change at the moment that I, that we find ourselves thinking more about is actually the changing structure of global tax, uh, where, um, you know, the, the idea of, you know, there's been a reasonable amount of innovation, uh, I guess in the, in the tax domain over the last, you know, then we had value-added taxes and so on.
The new thing, at least in the online context, is jurisdictions imposing sales taxes on businesses that don't kind of locus in the jurisdiction in question. So you're a, you know, you're a podcaster in the Bay Area and, you know, the Dworkesh merch store, you know, will have to pay, you know, the town of Uppsala in Sweden will have a special tax on baseball caps.

And you will need to know about that particular tax on baseball caps. And any baseball caps that you are selling to the Uppsalans, you'll have to collect that amount from the buyer, report to Uppsala, and then eventually figure out how you're going to get that money to Uppsala.
And obviously, it's this combinatoric problem of buyer jurisdictions and product types, and then all the different jurisdictions that you have to remit the money to. And those amounts, we're not talking three basis points.
The taxes in question are often 5% or 10% or something. So, it not trivial.
And so just as I think about the funds flows on the internet and how all that's evolving and unfolding, I think changes in tax law are actually a much bigger deal than anything about the transactional economics. Yeah, yeah.
But by the way, it's not the Barcash Podcast. It's Lunar Society Podcast LLC.
Registered on Stripe Atlas. So any merchandise I sell in the future, Stripe will take care of that.
Okay, well, if there's ever any Stripe complaints, we will write. It's great.
It's been super useful, honestly. It would have been much more difficult to get business operations going.
Do you think, sorry, I know you're supposed to be interviewing me about that, but did Stripe play any, like even on the margins, counterfactual role in you charging for anything? Because this is the thing we're always interested in. When we talk about growing the GDP of the internet, it's not like, get the existing GDP onto our rails.
It's where on the margin can we cause there to be economic activity that isn't already occurring? So yeah, you did, in fact, start the podcast before incorporating, but, you know, were we, you know,

causal in any fashion in like the merch

or anything of that nature?

I, like, to the extent that like Substack

would not be like a convenient place

to get payments from to begin with.

That's like definitely everything.

And also, you know, if I do...

You wouldn't charge for the newsletter

if Substack hadn't made it super easy.

Yeah, and also if I do like an ad or something, just like I wouldn't even know how to begin with getting the money if I didn't already have an LLC through Stripe with an associated bank account that I can get money through. So yeah, probably kind of fractured responsible for a lot of the monetization.
That's cool. Yeah, yeah, yeah.
Appreciate it.

So what are some unexpected complements to payment processing you see in the future? So, you know, all this stuff, Atlas, identity fraud detection. You know, in retrospect, it might not have been obvious.
Back then there was a good complement. Now it does seem that way.
What would be like this in five, 10 years? Honestly, our problem ends up being that too many things, more things that we can possibly pursue look like compliments, right? In that every business, almost by definition, has revenue. And so we obviously want to help them generate and accept and manage and orchestrate everything pertaining to that revenue.
But once you're in that flow and you kind of just go through the steps of running a business, yeah, a lot else looks relevant and somehow connects quite directly. you know, we're not, I mean, when Stripe started out, Stripe seemed like it definitely wasn't cool.
It was sort of the opposite for just a couple of us and we thought that we could make this superior payments API. And for the vast majority of its history, Stripe has, I think, attracted people who are drawn to unglamorous infrastructure challenges and problems.
And, you know, we're not a company that specializes in making beautiful cars. We make roads.
and I bring all of that up because I think it's relevant to this kind of compliment question where in our discussions internally, a lot of it, and probably the significant majority of it, is still about, okay, where are there actual practical shortcomings and limitations in even our core bread and butter? And that's not, I mean, payment processing might be a slightly too limited term to use for it. Maybe it's more about just global programmable money orchestration, which yes, is consumer to business payments, the sort that we were just discussing in say the context of your sub stack, but it's also business to business payments.
It's also payments with those

credit or lending involved. It's also how you hold money.
It's how you convert money between different currencies. It's how you represent money that's held by different legal entities and how we make it possible for even individuals or small businesses to act as kind of micro multinationals and all this kind of stuff.

But those problems that we just skimmed over are all, even though they all directly pertain to the movement of money, they're not small. And if we could just solve those really, really effectively, then Stripe will be a very consequential organization and I think force force in the world.
And I think the counterfactual importance of building some of this stuff as we go to newer markets that are on a relative basis more poorly served is actually increasing rather than shrinking. Like in the US, there were payments companies before Stripe, and maybe if Stripe had never done its thing, eventually you'd have found some way to monetize a newsletter or something like that.
But if you're in Albania, the set of options available to you is far more restricted. And so I think that the marginal impact as we expand globally increases quite a bit.
So that's all to say that even though we are interested in and do today pursue some of these direct adjacencies, I think that the core problem of global money orchestration remains a really big and unsolved problem. Does that look like being a better interface for all these complexities and glossing them over under the seven lines of code? Or does that look like actually replacing the rails and the infrastructure to make all this more efficient and effective? The former.
The former. Like, it's just not that useful to build financial ecosystems that are self-contained, right? A financial island is not that helpful.
It's much more valuable to build, I don't know, a financial, this is mixing metaphors, but a financial air network or something. But I think we would much prefer that Stripe plugged into every existing system in rail and domestic organization rather than that we tried to come along and supplant them.
And this has been Stripe's strategy very deliberately from the beginning, where there were lots of companies when Stripe started out that were trying to do their own thing and go their own way. where, whereas our belief was you got these, I mean, it's classic, I guess, Metcalfe law stuff of, you know, by enhancing the capabilities of an existing ecosystem, you create quite a bit more value.
Okay, let's go back to Stripe. Is Stripe a writing culture for the benefit of the writer or the reader? It can be both.
But which one's the more so? I think there are actually really considerable benefits on both sides. Because for the reader, it's not just that it's maybe more efficient to communicate stuff through text, though in many cases it is, but also there's this intertemporal benefit where future readers can try to understand the through line and the thought process that led us to this point.
And I think that's very considerable. But it's also true that I think that, I mean, I write things, and lots of people write things in order to organize one's own thoughts.
And if that ability was taken away from me, I think I'd be meaningfully less effective. So how exactly those bounce out is hard to say.
Maybe the, I mean, they're not actually separable. That's my answer.
Like, literate cultures are just a different thing. And I don't mean literate in some kind of faux intellectual way.
I just mean textual cultures is a better term here, where, you know, Bruno Latour spoke about how, you know, he thinks part of how the printing revolution, like Gutenberg's, caused the scientific revolution was by making knowledge more rigid, where before, if some observation didn't match, you know, some claim, you can always kind of shrug and be like, well, I guess the person who transcribed that thing, you know, just like made a mistake or whatever. And so by making things more rigid, it's easier to break them.
And, you know, then you can notice discrepancies between, I guess, the theory or the claim or whatever, and, you know, the actual reality. And I think there's some version of that organizationally where, I mean, I'm not drawing that precise parallel, but there are analogous dynamics where the nature of oral cultures and textual cultures are just quite different.
And so the kinds of collaboration that are possible and the kinds of consistency that can be achieved, like it is just fundamentally different. And, you know, um, is it, is it, is the, uh, um, is the, you know, front or rear wheel of the bicycle more valuable? Um, I guess theoretically you can view any cycle, but like as a, as a practical matter, you do just need both.
Um, I said, I know I said no more AI questions, but on this particular point, it actually seems very legitimate to me that you might expect firms that have a lot of writing to be the first to experience the productivity gains of AI, because there's all this context that the model doesn't have available readily. I don't know if that's something you anticipate.
I think that's probably true. Yeah.
I don't know. And if the model is really good, maybe you should be able to pick stuff up quickly.
But I think most organizations are not recording all of their meetings for a variety of reasons. And if they're not, then yeah, there is this question of what is the corpus? How do you get up to speed? So yeah, my guess is that'll be true.
Tell me about the internal LLM you built. Oh, we didn't build an internal LLM.
We built an internal LLM tool for making it very easy for people to integrate LLMs into production services, but also into their regular workflows as humans. So the ability to work directly, I guess, with the LLM as a standard chat agent, as lots of people have built.
But then also to integrate that with some of our tools for querying and accessing data, or maybe most interestingly, with sharing prompts across different people. And so somebody might discover.
I mean, one of my favorite examples actually is somebody put together a prompt for optimizing SQL queries. And no, it doesn't always work, but sometimes it does.
And it's very cheap to ask us, got any ideas for optimizing the SQL query? And, you know, sometimes it'll come up with some good stuff. And so, yeah, the collaborative abilities there have proven surprisingly kind of high return.
And then having just, I mean, lots of organizations have this. We're not claiming that it's very novel or anything, but having kind of a central bus to, through which to route all access to these LLMs, such that we can, you know, experiment with different models and have, you know, some degree of, you know, observability into the respective performance trends and the usage of different cases and so forth.
Like that, we have found building a fairly significant amount of production infrastructure around LLMs to be valuable. And now, given the proliferation of LLMs themselves, with all of the obvious contenders, this is proving quite valuable because we're able to try to figure out for different use cases which models, self-worthed models, who knows, are most effective.
And I don't know what the total number of invocations is, but I think we're making millions of invocations per day now. There are just dozens of dozens of actual production use cases across Stripe and all sorts of really...
I mean, the financial services ecosystem is in some way a giant analog to digital exercise because like humans are analog and intentions and identities and all these things have, you know, there's always some degree of kind of uncertainty around them and some noise. But then transactions are digital, right? And we often find in these analog to digital conversions that LLMs can be a surprisingly interesting augmenting tool.
And actually on that point about the, I don't know, the flexibility and the edge cases in the way humans interact with these systems. I mean, in some sense, Stripe is like a really high stakes bug bounty program, right? If somebody hacks it, not only the financial services, obviously, like money's in play, but, you know, if there's like reliability issues, not just because of a hack, but because you deployed the wrong way, a significant percentage of world GDP would grind to a halt, at least while it's down.
How do you deal with that kind of responsibility? How do you keep the uptime and keep the reliability while deploying fast? Yeah, this is one of the things we've spent the most time on. And I mean, back to this point about wanting to be the place with the best people.

And if you and, you, and the value of focusing on craft so that you can have the best people, in the context of software development, one of the things that developers really hate is, well, actually two things developers hate, slow development cycles, and it'll ship in the next release in a month

and that kind of thinking.

Developers also hate being paged at 2 a.m. for incidents.

And so, yeah, given the criticality

of the businesses that we serve,

which is in rough terms 1% of the global economy.

I mean, that's, it's not totally clear how to measure this

because, you know, we're measuring, we're not measuring,

like GDP is defined as final goods

and Stripe is not only selling final goods.

And so like, in theory,

there could be a bit of double counting,

but Stripe is mostly selling final goods.

Like we're not used for, by and large, by and large, for giant supply chain shipments. So I think maybe there's a mismeasurement of 10% or 20% or something.
But long story short, I think it works out to about 1% of global GDP. It's about $1 trillion a year.
As you say, that then makes us really terrified of outages. And so we work so hard to enable fast iteration and development cycles without having outages.
And just to kind of put some numbers on it, we deploy production services that are in the core charge flow around a thousand times a day. Most of these services are automatically deployed.
So when anybody makes any production-ready change, it just goes into production. And it's kind of meticulously and carefully orchestrated so that it first is just running some small sliver of traffic and then incrementally more traffic until it's everything.
So about 1,000 deploys per day at roughly or somewhat in excess of 5.5 nines, like 99.995% reliability, which works out to about, I think, about 100 and, yeah, two, two and a half minutes of unavailability per year. It's not that we have, obviously, two and a half contiguous minutes of unavailability, but that's what it kind of approximates, even though it tends to happen, you know, as kind of background radiation throughout the year.
And getting to that point, yeah, just takes a huge amount of investment in, and then there's security properties that are less readily measured, but, you know, analogous to those figures. And I guess Silicon Valley doesn't tend to, I'm perhaps being now unfair and kind of attributing things to Silicon Valley, but maybe a lot of the tech industry doesn't place a lot of value on process and operational excellence.
We kind of culturally value the spontaneous and the creative and the iconoclastic and the path-breaking, but building mechanisms that can enable really reliable, enable the very reliable provision of important services at scale and, you know, removing the, you know, sources of variability that can, you know, really cause a bad day for a very large number of people. I don't think they get quite as much cultural credit.
But yeah, we have found that we've adopted all sorts of, you know, for example, we found that there's kind, there's kind of a core feedback loop around, you know, none of this sounds like rocket science, but, you know, defining what it is that we care about and then like building automated measuring systems to obviously measure to what degree it's actually happening in practice. And then to sort of try to figure out, well, in the cases where we're not living up to that, like, what is the reason? And then to, you know, to actually intervene and to improve the system so that, you know, that's not happening.
And then importantly, to build kind of secondary controls that detect, you know, instances of deviation long before they actually cause like a production problem or anything, but just, you know, where we understand the behavior of the system in sufficient detail

that we can instrument it in some upstream way. Most of what I said there, I think, was well understood by production engineers in 1930.
So again, I'm not claiming that it's any kind of radical breakthrough, but we have found that the adoption of these practices in really kind of tenacious like multi-year form and is um and just yields really high returns. And there may be other organizations that both ship at that rate and maintain that developer velocity at this kind of combination of scale and reliability and security.
But I don't think there are that many. And I think it's a real testament to, yeah, the remarkable folks at Stripe who made it happen.
Last day I point, but like actually the fact that you have this huge internal tooling and testing is like once you get the AI engineers, they can just like push the commits and you have the infrastructure set up that it can be readily evaluated, you know? Yeah. Across the board, I think so much comes back to what has to be true for us actually to be able to build and to kind of take seriously this goal of building, you know, the best software.
And it's easy to say that as some, you know, lofty, vague, you know, hand wavy aspirational statement. But if you sort of take that seriously as a goal and if you think, well, what would you have to measure if, you know, you're actually going to, you know, pursue it in earnest? And what are the characteristics of organizations that do produce it? I mean, you get down to, well, customers have to really like your stuff.
And so, OK, well, how can we measure that? And how can we systematize the process of making sure that there aren't progressions there? And so we have this concept of experience journeys, which are sort of pathways through Stripe, that we really care are always implemented at a really high quality level. And it has to be true that developers can iterate very quickly.
And we just kind of spoke about how to make that happen, you know, and, and, and. And so I feel like maybe kind of a theme through everything we've talked about is actually taking the goal seriously.
And I feel like a lot of what we do at Stripe is, again, I dislaim any genius in it. I think it's just the very earnest, repeated, serious, and long-term application of taking the gold seriously.
A few more Stripe questions. 1% of global GDP is such a staggering number.
When you think about where further growth for Stripe comes from,

does it come from the internet economy expanding, or does it come from Stripe becoming a larger share of the internet economy? And to the extent that Stripe is growing faster than the internet, if we consider that the beta in your case, where is that alpha coming from? That's a good question.

I, well, the customers that Stripe serves are outgrowing the internet economy as a whole, like an aggregate. Now, at some point, those have to converge for kind of obvious mathematical reasons.
But we're 14 years in, and they haven't converged yet. So I think there's a lot of headroom there.
And, you know, say Stripe is handing around a trillion dollars a year. When Stripe started out, the global economy was 60 to 70 trillion-ish.
The global economy is now around 100 trillion. And so, you know, we still have quite a bit of headroom before the, like, you know, the amount of activity that is coming out to Stripe is, like, really butting up against the ceiling of global economic growth.
And of course, there's no ceiling on global economic growth. And for all sorts of reasons, it could be vastly higher than it is.
And I don't even mean new technologies or AIs or whatever, but just obviously all the basic per capita math you can do around, what if everybody had an income on par with the US? And, you know, I think it is. And like one of the reasons I am so interested in working on Stripe is I think it's, you know, it's the old line, the Lucas line about how when you start thinking about differential rates of development in countries, like it's hard to think about anything else.
You know, why does Brazil have the particular income and GDP level that it does? Why does Poland have the level that it does? Why does, why did Ireland have the trajectory that it did, where we went from being the kind of the sick man of Europe to now one of the wealthiest countries there? And I feel like Stripe is some applied version of this question in practice, where you're kind of building software products, but in some sense connected to or, you know, touching upon these questions of, well, why aren't there more countries? Excuse me. Why aren't there more countries? Why aren't there more companies? And what determines the growth rate of a company? Like why, you know, when you start the merch store, like why does it have, you know, X level of buyers rather than, you know, 2X? And I actually think those questions, you know, I think those remain fruitful questions.
We actually haven't optimized the meta system of business to any particularly great extent. For the vast majority of business, businesses have been offline, inefficient, analog, everything.
And it's really only over the last one to two decades that a significant share of this has been meaningfully digitized. And the prospects for efficiency gains and optimizations there are still pretty significantly underexplored.
And we find incredibly basic things like just extending capital to businesses. I mean, the reason we do that is not to generate profit from the loans, but because we find that the businesses when we extend the capital then just grow faster on a sort of persistent subsequent basis.
Or, you know, trying to figure out how does a business decide which countries it sells in? And you'll find even from the smallest business through to some of the largest businesses in the world, that these are very kind of ad hoc and not particularly deeply thought through questions. Like, you know, why don't you sell in Mexico and Brazil or whatever? It's like, well, it seemed kind of complicated and so we didn't quite get around to it and so forth.
And so I think there's, to your question about, like, where does the growth come from? I think that there's still an awful lot of low-hanging fruit in just asking some of these incredibly basic questions. So when we think about the way in which Stripe will continue to grow in the future, in some sense, it'll obviously involve a lot of big businesses.
And you're now processing a significant amount of Amazon volume. There's these other businesses you're doing deals with.
First, tell me how you think, it kind of makes sense how an exponentially growing startup

would contribute to exponentially growing growth for a strife. How does the strife keep growing

in the same trajectory when it's existing big businesses that you're partnering with? And

just second, like also like the case for why these startups matter is like so compelling,

right? Like a new thing is coming into this world that we should really support and make sure it

happens. Why is it compelling that like Amazon can fulfill orders more efficiently or something? Yeah.
Those are very good questions. So on the first one, you're right.
It's, you know, Stripe is doomed to eventually grow at the rate of the economy.

And there is just a question of, you know, how long it takes to get that right.

Now, the good news is I think it can be a very long time because there is, as we just discussed, there is so much low-hanging fruit around different optimizations and improvements that are possible.

And so I think it would be many decades before that happens, but it's true. That will eventually occur.
On the second question about, yeah, what's the, like, it's obviously virtuous or compelling or exciting to foster all these nascent startups and to kind of be an anti-incumbency force. But what's the case for supporting established businesses? I think people misunderstand where a small business typically, not in every case, but at least in the cases where we denote them startups, there's usually an embedded innovation.
And the innovation is kind of all that the company is. Like they have a new idea and they're going to do something better or different or, you know, whatever.
And so, you know, generally speaking, we like innovation. And so we've, you know, positive sentiments towards that startup.
But there's a lot of innovation that comes from large established businesses. That's not all they do.
You know, there's also just running the existing thing. And so maybe it's a smaller share, but the aggregate fraction of innovation that comes from established businesses is really large.
And we have to be cognizant of the cognitive bias of the startups perhaps being somewhat more conspicuous and maybe on a relative basis, the improvements in turbine technology or in fab technology or in insulation technology that come from established businesses or to choose any sector of the economy. And a significant fraction of the important inventions that occurred over the last 10 or 20 years will have come from the incumbents.
And so I think as a general class, and Tyler, of course, wrote a book on this, I think big business is underrated. And if you look at the survey data, people tend to have very positive sentiments, not only towards startups, but towards small business as a class.
Whereas even though, even though they have negative sentiments or relatively negative sentiments towards big business, not that bad on an absolute basis, but not as favorable. I think it's true that established businesses tend to pay better.
They tend to be more efficient. More of the innovation in our economy comes from them.
And they produce a lot of consumer surplus. I think the specific case for Stripe working with them is typically they're coming to us not because they want to take the thing that they're already doing and just go to all the work of transposing it to Stripe, but because either they want to do a new thing that they're just not doing today.
And so it's associated with some new business line or some new innovation or invention or, you know, whatever. Or they've spotted the opportunity to, I guess, to maybe not produce a new product, but to meaningfully change how they provide an existing one in a fashion that, again, yields consumer surplus.
And that sounds very abstract and theoretical, but in practice, what it tends to mean is they want to take this thing they're selling in this market and sell it in many more markets. Or they've realized that they're selling it in this kind of modality and they should sell it in other more convenient ways, like they should sell it on mobile or something.
And each of those, if it's successful, if people actually buy it in any significant numbers, I guess we're getting this decentralized signal from the economy that there's now something of value being provided that wasn't here to four. And as I take stock of the businesses, like the enterprises that are in the process of migrating to Stripe or that did so over the last year, whether it's the large retailers or the large global manufacturing firms or shipping companies, things like this, it typically has one of those two patterns, new product or current product sold to people who weren't buying it before.
Yeah, yeah. I mean, if you think about just like the big trends in this society that are needed to solve our big problems, right? Like Moore's Law or the cost of solar or something.
These are just, you have marginal improvements over many decades that, you know, the big tech or big companies are just able to invest a lot of money into doing the R&D. We're aligned to say the root of improvement, yes, is underrated.
Can I ask about John for a second? Sure. So you guys recently published Poor Charlie's Almanac, and subsequently Charlie Munger has passed away.
Did Munger ever comment on your relationship and if or whether it reminded him of his and Buffett's? Not to me, but he knew John better, and so it's possible that he did to John. Yeah, I don't know.
What have you learned about marriage from John? I mean, this sort of like co-equal, intense, lengthy partnership is like the closest thing to that you have is marriage, right? Well, I'm relatively new to the practice of marriage. So, you know, maybe in a decade, I'll be able to kind of extract the generalizable commonalities.
I suppose the general thing I'd say is I think working with people you're close to is underrated. And I'm doing ARK with Patrick Su and Sylvana.
Fast Grants was with Tyler and Sylvana.

Stripe is obviously with John.

And actually, John was also, I should mention,

instrumentally involved in ARC's formation.

It would not have happened without John.

And I could give more examples,

but I feel like for all the ventures of any significance in my life, they've not only been with others, but been with other people that I'm very close to and where I had and would like to have an enduring relationship that outlives them. And sometimes one hears the advice that you shouldn't work with friends or maybe you shouldn't work with your partner or something like that.
And look, all these things are idiosyncratic and there are instances of every possible permutation. But for me, it's been a really rewarding experience.
And yeah, I think John and I can work together for, you never know life, but I think we'll probably work together for decades. And for us, it's been a really both an important source of just meaning and again fulfillment, but also I think there's a real complementarity.
And I think that Stripe would be a less effective company without either of us. I don't know what you mean from a bandwidth standpoint or something, but I think we both bring different things to bear.
Patrick, I think that's a great place to leave it. Thank you so much for coming on the podcast.
Thank you. Hey, everybody.
I hope you enjoyed that episode. As always, the most helpful thing you can do is to share the podcast.

Send it to people you think might enjoy it. Put it in Twitter, your group chats, etc.
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the world. Appreciate you listening.
I'll see you next time. Cheers.