The Exchanges

Q I want to come back to some of that a little bit later. I think one of the questions that people want to hear from you is what, what would you say is the biggest difference between the Patrick making decisions today and the Patrick making decisions maybe five years ago in terms of how you actually make those decisions?

A I think there are four big differences. The first is, and I just place more value on decision speed, uh, in that, uh, if you can make, like, if you can make twice as many decisions at half the, kind of, um, half the precision, that's actually often better. Um, uh, and then given the fact that sort of the, uh, rate of improvement of decision making with additional time almost necessarily tends to kind of Flatten out. I think that most people, certainly the Patrick of five years ago, and potentially even the Patrick of today included, um, should be sort of earlier, should be operating, uh, earlier in that curve. Make more decisions with less confidence, um, but in significantly less time, right? Uh, and just recognize that in most cases you, you, you can course correct and, and treat fast decisions as a kind of, uh, Asset and capability in their own right. Uh, and it's quite striking to me how some of the organizations that I hold in the highest regard, uh, tend to do this. The second thing is, um, not treating all decisions kind of, uh, uniformly. Uh, I think the most obvious, um, kind of axes to break them down on are degree of reversibility and magnitude. Uh, and things with low reversibility and, you know, great impact and magnitude, uh, those ones you do want to, you know, really deliberate over, um, and, and, and try to get right. But I think it's very easy, uh, sort of abs…

AI assessment note: “I think there are four big differences. The first is, and I just place more value on decision speed”

Q Outside of Stripe, which company cultures do you admire the most? Not business models, but culture, and why?

A Well, I admire cultures that are strong, first off. Cultures that, when you ask somebody who's in the culture, can you describe it, um, that they will, uh, that they can expound on its merits for more than half an hour. Uh, and in almost every case, describe at some length all the things they don't like about it, right? Um, because if it's strong, they're, I mean, it's improbable that every aspect of it is something that the person, you know, really, uh, agrees with or feels an affinity for. And so whether it's, uh, the New Yorker or the military, uh, a, a shared characteristic of those cultures is that they are strong, right? So I think that's the first order thing, and I don't, I don't think that describes most organizational cultures. I think most organizational cultures are some kind of milk-a-toast averaging, right? So that's number one. Um, the second is cultures of, of perfection. Um, and so both The Economist and Apple have extraordinarily high standards for themselves. Um, and, and really kind of in both cases, the work has a kind of primacy. And so who designed the latest iPhone or who wrote that article? In both cases, that's anonymous, uh, because there's such a belief that the work speaks for itself, right? Uh, and, uh, I have a lot of admiration for that. Um, and then cultures that have longevity, uh, and really sustained success, uh, and so, I think that, um, one…

AI assessment note: “both The Economist and Apple have extraordinarily high standards for themselves.”

Q A thousand employees. What have you learned from scaling the business?

A I think on some level, Scaling a business is both relatively straightforward and extremely hard. I mean, it's relatively straightforward in the sense that it's usually not that difficult to see what the problems are. Um, and to the extent that you don't see what the problems are, it's usually because there's some kind of, uh, uh, subject of blindness rather than it being Actually difficult to see the problem, right? And so it's more sort of a question of what are you oblivious to because of your own biases rather than what is particularly difficult to observe and kind of what are your corrective mechanisms, um, to, to sort of account for that. So it's, I think, straightforward in that sense. Um, and I guess straightforward in the sense that usually solving the problems is not Outlandishly difficult. Um, I mean, it, it's not easy, but you need to hire someone in this role. You need to figure out how to raise this capital. You need to build this system, whatever the case might be. I mean, none of those are easy things, but they're also not sort of scientific breakthroughs. Uh, there are other companies that have done it. Uh, there are generally playbooks that exist, Uh, and while sort of your particular strategy might need some sort of correction, refinement, and you might hit some walls along the way, um, it's, it's rarely unprecedented. And then I think it's extremely difficult…

AI assessment note: “Scaling a business is both relatively straightforward and extremely hard.”

Q How do you decide which speculative projects you take on? Are they based on disrupting your business, or these are things that I want to do, or I want Stripe to do, or?

A I don't know that there's a better answer beyond, given all of the axes of, you know, constraints and returns, which ones seem like a, like a good idea. And I mean, I think it's kind of like investing. When you ask, you know, what, what, what's the, Uh, what are the criteria for investing in a company? It's, well, when you kind of normalize down from the sort of, you know, really high dimensional space of market and founders and idea and, you know, all these things, you normalize all that down into kind of what do you think the return profile looks like while you invest when the return profile looks good enough, right? Uh, I think kind of similarly when you decide, you know, which ideas to pursue, of course, on each axis, there are many things you prefer or you, you know, don't want or, or whatever. And, you know, for example, something that requires less effort rather than more, or entails less downside risk rather than more, whatever, you know, those are all good things. Um, but I think kind of where it all nets out is, well, when you take account of all those factors, which things just, you know, seem like a good bet, right? And so just, you know, to give a concrete example, Atlas, uh, the, the, the service we launched for helping, uh, new founders incorporate companies, and in particular sort of Uh, without the geographic restrictions that tended to exist before, so it's es…

AI assessment note: “given all of the axes of, you know, constraints and returns, which ones seem”

Q So you keep the book, a book that you completely read, that you like. Yep. How often do you come back to that book?

A Um. If I want to make a particular point or be reminded of a particular aspect or, you know, whatever, um, maybe I will, but, but generally speaking, I don't. And I think, you know, part of the value of making the annotations is, of course, to, um, you know, imprint them more firmly in your mind so that you sort of don't need to come back as much in some sense. If it's really good, I don't often do this, but if it's really good, um, I might write a review for friends, uh, And just, you know, share an email or a Google doc or something, uh, or, or just share snippets with friends. Um, and that's valuable both because again, sort of the act of, uh, of summary or summarization, uh, sort of aids the kind of synthesis, uh, and, and, and better recollection. Um, but also of course, uh, it triggers out pointers, uh, and further suggestions, uh, from, from those friends. Uh, and so, you know, if you want to, um, Identify candidates in adjacent, or if you want to perform the clustering and figure out what sort of adjacent candidates might be, you know, interesting for further exploration, uh, writing a review is, you know, a good place to start.

AI assessment note: “maybe I will, but, but generally speaking, I don't.”

Q How would you answer a question about what your personal values are?

A Probably by evading it. Uh, I'm now about to do you think perhaps this disproved that answer, um, um, by actually answering it, but I guess I just think it's so, it feels like too important a question. It's kind of the book question. It feels like too important a question to answer simplistically, uh, and too complicated a question to answer briefly, uh, And thereby perhaps unsuited to something, you know, extemporaneous. Um, and I'm sure whatever answer I gave, you know, when I'm thinking about it in an hour's time, I'll kick myself and realize I'd left out, you know, this critically important dimension to it. And I think I, So I can, I can cite some things I value, but the, the sort of, the sense of giving a complete answer is very oppressive. Uh, I mean, this is of course the value of Twitter, uh, where because of the constraint, um, the, the, there isn't the same, uh, because, because the system chooses when to cut you off rather than you choosing when to stop, uh, that, that, that's quite liberating. Uh, and so maybe if you allowed me 20 seconds to speak about values, I could do that. Um, but I, I, I could blame the constraint on anything I omitted.

AI assessment note: “Probably by evading it.”

Q How would you describe the culture at Stripe? What do you actively try to achieve with that?

A Well, I'll answer that with a caveat, and the caveat is that I'm pretty sure the answer I would have given to this would have differed in some material ways two or three years ago, right? And that's in part because I think we're coming to realize things that we just hadn't really appreciated or sort of seen the significance of two or three years ago, and also in part because literally what it is that we need today is just different to what we needed two or three years ago, right? And so I think there's kind of double contingency in the answer where it's a function of just what we've realized at this point But also sort of what it is that the organization and the company needs, given the sort of challenges that we currently face. With that caveat, I think the things that we really prize and, uh, try to, you know, seek in the people we hire are, um, a kind of rigor and clarity of thought, uh, in that I think so many organizations prize, uh, sort of Smoothness and smoothness of sort of interactions and, uh, trying to reduce, minimize the number of sort of ruffled feathers. Uh, and, and they kind of at least sort of inadvertently, if not deliberately prefer cohesion over correctness. Uh, and we really try to, uh, identify people who, who are seeking Correctness, and who don't mind being wrong, and who are willing to at least contemplate things that seem improbable, or surprising if…

AI assessment note: “the things that we really prize... are, um, a kind of rigor and clarity”

Q You're a huge reader. Um, where did this love of books get started?

A Well, we had crappy internet when I was growing up, um, because our house was so remote. Um, there's so much noise on the phone line, and that we didn't have internet for years, and then we got it was treacle slow, and so on, and you know, I was, I was fortunate. My parents were very willing to pursue all these harebrained schemes, and so we eventually got an ISDN line, which was ferociously expensive, but got that, um, you know, that was sort of the, the, uh, um, fiber of its day, at least as far as I was concerned. Uh, 7.6 K a second was, was majestic. Um, I, I barely keep up with the speed. Um, and, uh, and then we eventually got a satellite, uh, internet connection, which, um, was really a game changer. Um, but, but it effectively meant that for the first, I don't know, fourteen-ish, 15 years of my life, uh, there was no internet, and we lived in a very rural part of Ireland. Um, I was quite distant from even my friends at school, and so all it really was for us to do was to play in the garden, Uh, which we did a lot of, um, and to read. Uh, and you know, it's funny. I often wonder about this in the context of, you know, if I had kids or when I have kids, what's the optimal upbringing for them? And of course you think, well, you kind of want them to grow up in a stimulating environment and have all these, I don't know, experiences and extracurriculars and everything else. U…

AI assessment note: “all it really was for us to do was to play in the garden... and to read.”

Q There's like a perpetual sort of seesaw, if you will, where success sows the seeds of its own destructions. Would you, how would you make an argument right now that San Francisco or Silicon Valley is doing that?

A Oh, I mean, the obvious one. Well, the two obvious ones, I guess. Um, Are, are in culture and in housing, um, and cost in general. I mean, on the ladder, well, on cost, on the ladder, everything is getting more expensive. Um, and And nobody seems to quite understand exactly what's going on, right? And that is this, um, I mean, if you, if you take healthcare again, for example, I mean, the case has been made that this is not in fact a bad thing, that what would you expect? An enlightened society that has solved all of its other material needs to spend its money on, but healthcare, it's kind of, it's, it's the last thing, it's the last frontier. Um, and perhaps we are actually getting sort of commensurate improvements Uh, if you sort of disaggregate appropriately and, you know, analyze the right way, um, you know, or perhaps not, right? Um, how much of this is some kind of Baumol cost disease where some things are getting more efficient and that higher productivity and higher wages, um, are sort of causing cost increases elsewhere to pay for opportunity costs and all the rest. Um, but I think, Sort of specifically in, in, in Silicon Valley, um, and, and, you know, specifically on cost of living and, and housing, you know, Silicon Valley is the sort of, Greatest concentration of wealth creation, uh, that I think has ever existed in the US on a per square mile basis, potentially th…

AI assessment note: “the two obvious ones, I guess. Um, Are, are in culture and in housing”

Q Part of culture is learning from the decisions the organization makes. What do you do at Stripe to make sure that people are learning, and what do you do personally to make sure that you're learning from the decisions that you've made, both positive and perhaps ones that you, in retrospect, would have wished you could make differently?

A I'm inclined to say, I don't know if I actually believe this, but I'm inclined to say in response to that question that decision making in organizations is slightly overrated, uh, in that organizations are not like investment entities, um, or funds or managers, uh, in that organizations, well, with investing, it's fundamentally very binary. There is a moment, uh, at which you You either buy or, or, or don't, or sell, or don't, or whatever. Um, and maybe it's somewhat more continuous in the case of, say, public market investing and so on, but given sort of, um, constraints on just decision-making time, I think you have to treat it as a bit more binary. You assess this stock and you make a buy or, or, or a, um, or not decision. Uh, whereas in organizations, everything is much more fluid and continuous. It's much more about, I think, designing the feedback mechanisms, you know. More biological. Yeah, exactly. And, you know, there's the famous, um, uh, Sort of, um, water model, uh, of the economy, um, uh, you know, with the sort of, uh, circulating fluids, and you can vary the interest rate or the inflation rate or, or whatever, but just kind of try to get a sense for the overall kind of, um, uh, biological apparatus. Uh, and I think an organization is much more like that. And so the, the, I think the things, the things to optimize are the incentive structures and the, uh, Mindsets…

AI assessment note: “in response to that question that decision making in organizations is slightly overrated”

Q Let's geek out a little bit on the, the feedback mechanisms here. What sort of feedback mechanisms do you try to make sure are in place? What point in the process do you try to acknowledge what they are?

A I really think that, uh, and this is not to evade the question, but, um, I really think it's too early to answer that, uh, in the sense that, I mean, I, I can kind of tell you what I think today and the sort of changes we've made over the last year and things like that, but like Stripe has been a thousand person organization for, uh, or has been a more than 500 person organization, uh, for just over a year, right? Um, we're, we're beginners at this. Uh, and, you know, three years ago Stripe was under a hundred people. Uh, and, and I think Either to, uh, opine as if, uh, or to even more problematically believe that we kind of have it figured out would be, would be real hubris. Um, and so, kind of, in what we've been talking about, I think that's maybe some of, uh, you know, where our and my thinking comes from. But, but I don't know what the right answers are yet. Um, and, and we spend a lot of our time Sort of scrutinizing other organizations, trying to find out and kind of reverse engineer what works for them and why. Um, and I think that part of what's interesting at the tech industry is that it's, it's a kind of pure knowledge work that we're still, I think, quite early in sort of figuring out, um, in terms of how to optimally coordinate it, uh, and, and collaborate on it. Uh, in that you can sort of draw a lineage of HP and Intel and Microsoft and Google and Facebook and so…

AI assessment note: “and this is not to evade the question, but, um, I really think it's too early to answer that”

Q Part of culture is learning from the decisions the organization makes. What do you do at Stripe to make sure that people are learning, and what do you do personally to make sure that you're learning from the decisions that you've made, both positive and perhaps ones that you, in retrospect, would have wished you could make differently?

A I'm inclined to say, I don't know if I actually believe this, but I'm inclined to say in response to that question that decision making in organizations is slightly overrated, uh, in that organizations are not like investment entities, um, or funds or managers, uh, in that organizations, well, with investing, it's fundamentally very binary. There is a moment, uh, at which you You either buy or, or, or don't, or sell, or don't, or whatever. Um, and maybe it's somewhat more continuous in the case of, say, public market investing and so on, but given sort of, um, constraints on just decision-making time, I think you have to treat it as a bit more binary. You assess this stock and you make a buy or, or, or a, um, or not decision. Uh, whereas in organizations, everything is much more fluid and continuous. It's much more about, I think, designing the feedback mechanisms, you know.

AI assessment note: “decision making in organizations is slightly overrated”

Q Let's geek out a little bit on the, the feedback mechanisms here. What sort of feedback mechanisms do you try to make sure are in place? What point in the process do you try to acknowledge what they are?

A I really think that, uh, and this is not to evade the question, but, um, I really think it's too early to answer that, uh, in the sense that, I mean, I, I can kind of tell you what I think today and the sort of changes we've made over the last year and things like that, but, like, Stripe has been a thousand person organization for, uh, or has been a more than 500 person organization, uh, for just over a year, right? Um, we're, we're beginners at this. Uh, and, you know, three years ago Stripe was under a hundred people. Uh, and, and I think either to, uh, opine as if Uh, or to even more problematically believe that we kind of have it figured out would be, would be real hubris. Um, and, and so, kind of, in what we've been talking about, I think that's maybe some of, uh, you know, where our and my thinking comes from. But, but I don't know what the right answers are yet. Um, and, and we spend a lot of our time Sort of scrutinizing other organizations, trying to find out and kind of reverse engineer what works for them and why. Um, and I think that part of what's interesting about the tech industry is that it's, it's a kind of pure knowledge work that we're still, I think, quite early in sort of figuring out, um, in, in terms of how to optimally coordinate it, uh, and, and collaborate on it. Uh, in that you can sort of draw a lineage of HP and Intel and Microsoft and Google and Fa…

AI assessment note: “and this is not to evade the question, but... it's too early to answer that”

Q That's awesome. Which book or books would you say have most influenced you?

A So I asked this question on Twitter back, um, a couple weeks ago, and some of the responses I got were really interesting, um, and a lot of people responded, um, like many more than I expected to. I didn't actually, embarrassingly, I feel guilty about this, I didn't post a response myself. Uh, and I thought about it, and it's actually just a very hard question to answer. Like, I actually worry that may not, it may not have been a good question, because, like, it's hard to know, did the book influence you, or did you have an inkling or a leaning, and then you read something that really resonated, but sort of, it's actually not, Like, the book is just the artifact upon which you project the, sort of, the characteristic that had already arisen, or the belief that had already arisen, um, and it's, the book is not actually causal in and of itself, right? Now, maybe it's still interesting to talk about the book as a kind of symbol for the belief, um, but yeah, there's that kind of question. And then also, um, uh, What I've often found is I think the books that perhaps did in fact influence me the most in a causal sense are often not necessarily that good, right? Um, and that maybe I'll read a book that sort of triggers a realization or, or some idea or something, and that, that will kind of jolt me in some direction, and then I'll go read better things about that question. And so it …

AI assessment note: “it's actually just a very hard question to answer.”