Tobi Lütke x Jess Hertz: Invisible Work

Have you seen the X threads on how there are going to be four jobs left? No, which? Have you seen it? No. Production engineering, security, hot people, and adults. I can't believe you haven't seen this. I haven't seen this. This is amazing. Wow. Okay, so I'm disqualified from adults. Right off the bat... So that's a no-go. By the way, I definitely want to be in the hot people category. Yeah, yeah. I aspire, but I think I might have to give up on that. But it goes to my, I've spent much of my career in the invisible side of work, which is now, of course, becoming more visible and decision-made. It's going great. Yes, yes. Welcome to Context. Welcome to Context, everybody. Jess, welcome to Context. Very excited. You are COO. Newly minted. Can I still say this? Yes. You joined us as general counsel. I did. I think that's a... Four and a half years ago. Yeah. Kicking ass and telling us to go for gold and, you know, go win things and so on ever since.

Should we dive into, like, your story here? Because I feel like we should. it. Let's do it. It's such a word. And so, you know, I think a lot of context is about, like, in a way we talked about this earlier, but, like, it's like, you know, how did fix happen? like matters, like what were the inputs, what organizations were important ones. And it's such an interesting time. We're in and we'll get into all this, but like - Yes, where did we come from? All right, well, how far back should we go? - What's Jess all about? - Well, I think it's, you know, it's not what you expect. I think that's always been the interesting part of my journey. I have had lots of different experiences, and I think that makes me a different kind of creature in a lot of ways. Certainly, I think you and I share a very similar way of thinking about things. - The non-standard path. And I'm a deeply unsentimental human. And I actually think that has really led to great things 'cause I think about things in all sort of ways that are fairly detached, and I can think about things in lots of different kinds of first principles ways. And I don't really get attached to an idea or an outcome or something that becomes- - Have you always been like this? Or it has the law study. - Always been like that. - I've always been like this. that. - Is it that, do you think people become, because the role of at least the comic book sketch of an attorney is like that someone who can, passionately defend any side, which I think is an enormously valuable skill. But I wonder, is it that people who can do this become lawyers? Good question. Or is it the other way around? I don't know the answer to that. I didn't innately want to become a lawyer. My dad's a physician, and so I grew up in a very doctor-centered household, as you did, too. And so I liked the sort of critical thinking aspects of things. and how to think about things always drew my attention. So I think I sort of naturally led down the path of law. And then I studied at law school with a behavioral economist. Oh, yeah. And so I did a lot of behavioral economist work. I then went to government, and then I sort of followed that path a bit. The nudge unit times. The nudge unit times, yeah. So I did a lot of work on nudge and other kinds of work. And that, I think, shaped quite a bit of kind of how I think about things. Who did you? Is that a big name? Cass Sunstein. Oh. Yeah. So, and he was a professor of mine and sort of deeply influenced how I think about things. And, well, very well-known behavioral economist, libertarian, and went into government, did a lot of work with him, thought about how you set things up. I wasn't a lawyer. I mean, I was a lawyer, but I didn't, I wasn't really practicing law at that time. And I've practiced law. I was in politics, as you know, for a while. really don't know what got into you. I me either. But the common standard is really like how you think about things, right? And how you develop skills to be on your feet and really think. And this kind of goes to my theory of how the type of talent's density is really changing. I've always been a generalist and I've always been someone who thinks about lots of different things, who likes learning lots of different new spaces. And that part has always been exciting to me, learning new challenges and taking on new things that I know nothing about. As evidenced by my path here, it has grown fairly organically over the last couple of years.

One of my favorite early Jess memories is I think when you came in. So I'm like, how do you deal with this transition? And your response was, not even close to the hardest transition I had to deal with in my career. Meaning the White House transition. White House transition was a big one. Which one? The last one was from President Trump to President Biden. And that experience of being able to think about how would I set up things? And how do I get to create this like day one of this new administration and what we're going to do and what decisions are we going to make and what we're going to prioritize? It's actually a fairly amazing thing. Wow, you just made being in a government sound interesting. That literally never happened to me. I would never go back. But for a moment in time, it was this amazing way of thinking about like, oh, OK, like actually, how do I set up this information architecture of like how decisions get made? - Actually, can you like, so like literally, how do you do it? Like, because at some point you're like, okay, I mean, first you have to figure out what's true, then what to do about it. Like, I mean, this is one of the funny things in life is like, if you can just do that, you're outperforming most people, frankly, because that's a good starting point. But like, you know, like in an infinite possibility space, how do you figure out what to do? - Yeah, it's not easy. And I think there are things that have to get done. And then there are things that are like sort of discretionary choices. - Because there's like a schedule, you have to like, I mean, at some point, there's gonna be, I guess a state of union. At this point, really, you have to figure out your messaging. - Exactly. And then the question is like, how do you actually, the coolest part, right, is how do you actually get all the information from where you need to get it? How do you synthesize it? And then how do you force a decision to get made? Which is obviously a vast simplification of what that process looks like, but it's taking a massive amount of information. And basically, like my job was to go to each of the different policy members or cabinet members or whomever to sort of say, you guys aren't talking to each other. It's basically my job now in a different way. - I was like, yeah, that sounds familiar. - Have you actually talked to this person because from where I sit, and it was cool, right? 'Cause I get to see everything. And so part of that role is to say what's actually important. Like, where do you pick up signal and what does that actually mean? And then how do you connect the dots? And then how do you actually make good decisions quickly? And one of the kind of superpowers is really to synthesize that information in a super simple way to be able to say, this is what's important. This is who's disagreeing or not disagreeing. And this is how you actually sort of think about a problem. So not all that dissimilar, but in a very different context. - It is funny. Yeah, like again, it's really, it's funny how fractal of Audrey is. - It is, yeah. And I do think people, I think politics and tech are actually much more similar than people give it credit for. There's a lot of pace. different kinds of flavors of trying to figure out problems in different ways. But I actually think it's not as dissimilar as we all might. This is probably why tech and politics get so famously well along. Maybe, maybe. Oh, boy. - Yeah.

- I do think though, I'd love your take on, I know at Shopify we've said there are T-shaped people. - Yeah. - I even remember when I was interviewed, it was very like part of my interviewing process was sort of- - We probably talked about it. I think we did talk about it. And so I'm curious for your take on, is that changing? My theory is maybe we're morphing from a T-shape to an X-shape because X-shape has two different T's in it. - Craft, you know, craft kind of still remains. - Yeah, explain that. That's fascinating. - Maybe we'll see if it sticks. But craft still remains obviously useful and obviously an important part, but it's no longer quite as singular, right? So you no longer have the vertical T and your ability to do different kinds of things, your ceiling is quite open. And so the sort of expansion up becomes even more possible. And I think part of the really special part about Shopify is we've had so many leaders in different parts of the company and jungle gyming and that kind of multidisciplinary aspect is so important at Shopify and valued at Shopify. I think we might be progressing. - Yeah, I haven't thought about this in a while, but I love, I think you're right that this is the kind of thing that's like, you know, again, as much as you want to, as much as I want to anyway, like you can't rethink everything all the time, It's just like possible. And so, but what is so good is, again, not having just the deference to the prior decisions all the time, going to rethink this.

I agree with you, but clearly there's so much happened with the tools we get to use, with the way information now can be acquired. So the relative value between fluid intelligence that you bring to make a good decision given everything you've got or crystallized intelligence, which is the knowledge you have accumulated. Right. You know, the world has shifted enormously towards fluid intelligence again, because knowledge in many cases is a little bit... Well, I honestly don't want to dismiss it. It's still massively important. Like, oh, this is another one of those is like the most, probably most valuable thing anyone can say when something goes wrong, because then you have seen the movie before and you know what to do. But acquiring of knowledge is quite much easier. So it's really like, what it really should do is that people are more ready to step out of their roles. I really, I think this whole thing about roles is going to be not, I feel they'll come, actually, not the extreme case, but it will be much more muted. I think people are going to, like I think the entire world is going to turn into more jungle gym. If you've proven that you can do something at an excellent level in one area, you should think that you can do excellent work in most areas after. does that mean, but does that apply to high agency people? Or do you think that's going to apply everywhere? I think the high agency thing is the thing we've always looked for, right? Like this has just been like the life story is really like find things that ideally we find a story where something went wrong. and then we dig into what did you do about that? Like, were you part of a, you know, that's a lot of what the life story does. We didn't have a words for it, but like we knew that people who had a good story about their particular role, then that something happened good or bad, or generally is the world happening to them or are they making a dent is, we always thought was deeply correlated with doing well at Shopify. And so this is like why this has always been good. We've been stacked up with the kinds of people who want to do this and then hopefully give them the room to actually act on this. And I just feel like it's as Shopify systems have become more mature and code bases have like just more, you know, more age, like somewhere like, do I really want to edit something that clearly has been not been a problem for 10 years in this like do you want to change this code because yeah afterwards you're taking some responsibility for it i i get that but like um and i think the incentives are to not make a new module replace the thing or whatever like work around it i like the beautiful thing is now we can just like hey let's actually improve the thing and so i think high agency people have like the tools again to be high agency at much, much larger scale. Yes, yes.

Well, one of the things I think about too, which is, and I have a question for you in this, which is most companies call what we call talent HR, right? Which is so interesting in this moment in time because the whole point of HR is that it's human resources. Yes. And now everything is possible in a different kind of way. We, of course, call it talent. Was that a conscious choice? Or how did, I mean, because of course the beauty of it is that everything can get rethought in this moment in time. So I'm curious where that distinction came from. I'm actually not aware of that. It's not common in the industry. mean, I don't know, but it would be surprised because it seems pretty, like human resources always sounded wrong to me. It just, I don't know. It sounds too much like seeing a state. bureaucratic legibility. Some areas of a company have to be named after an activity. But where you can drive to an outcome, you want to name it after this. I think there's... Often these things don't matter, but I think it matters if you're... The difference... If you only have marketing teams, then you clearly get a lot of units of marketing performed in a company. But that's not an outcome. like growth is what you want, right? So like either you have none, no growth team, or you have a growth team or you only have a growth team. Those are all valid decisions you can make. But like, I think most companies don't even are way of a choice and you do get different behaviors there. So I think talent density is the outcome that we want. And I think it's like shorting it to talent is good. But also, I think it set us up well to rethink things. I do too. I think it set us up extremely well.

- I talk sometimes with people that were boomerangs and come back and maybe have done one or two reps in other companies. And funnily enough, even the boomerang thing is accelerating. People are coming back much faster. We've now have the first people boomerang from after we created the FlexWallet system and they're coming back and they're like, I don't think many people think too much about Reflex wallet compensation system we have, but like the boomerangs are saying, holy shit, this is much better. It's so much easier to reason about and all these kind of things. Yes, I just had a boomerang come back after we changed the sales incentive comp that day. I had someone email me and they start next week. So another boomerang coming back. That's awesome. Very good. On the sales comp. Yeah. So as we kind of went about changing that system and externally, it makes a difference too. So it's great. - Yeah, I mean, I love this. I maybe make, you can call it company engineering. Like, let's do things differently to see if something works better and be okay admitting if it didn't end up, like sometimes that happens too, action cause information. But we've like worked on a bunch of things. Like, I mean, man, Whenever I'm talking with another CEO about the Flex wallet system, the fact that people can dial it in every quarter, they're like, how did you do this as a company with international employees? It's like, I'm like... We just made it work. Like somehow the team made it work. Yeah, we did. We made it work. I have no idea how you did that, but like, yes.

Can't have been easy. So how do you think about the kind of combinations of building systems and teams? I think these are incredibly symbiotic, but like where's the line? It's really like, again, I did, like I read biographies of people who did great work. And just like you'll see the patterns. It's very, very common that you read about the same thing. The people who biographies have written about clearly are people of significant aptitude. It's usually like winding roads to get there, picking up skills along the way. And then somehow, either having a physical or mental space for it, like a room, like an attic. It's sometimes physical, sometimes it's like a domain they are responsible for. And they take ownership to try within this area, within this thing, within this box. I'm going to do my best work to solve the problems I care about or create the books I care about. I think these things in every one of the cases have been created by a structure around them. There's always a support around it. These are not naturally occurring environments. Leonardo da Vinci had his addict, but only because of the organizational structure of patronage. Maybe this goes back to our invisible, visible distinction. And so I think they can both, like what is a team? What is the roles in a team? The size of it? Like those you can optimize for what's, you know, how to get the best out of a particular metaphorical box that is the, you know, owns the problem set. But like an organization structure and holds this up. I agree, yeah. And so this is how they've worked together. But it matters so much what those choices are. And I have found, and even within a team, I think kind of understanding people's strengths and understanding where you need to fill gaps and understanding different people that work well together. And so saying, okay, well, this pair of people, I can drop into any particular problem and they're going to do well, or this foursome is going to do well. It really, I find, changes the output of the work. And so the combination of that underlying organizational design and that team structure and how to get leverage out of that team is something that, you know, I spend a lot of time thinking about.

Me too. There's a lot of unaddressed things in this area. But I think we can make some progress on now if again we're getting better tools. And also I think the feasibility of a small team has massively increased in an AI-agentic world. because the world sort of over-specialized quite a bit because all the skills are hard to acquire, so maybe by necessity. But the time of a project where you need a front-end engineer, to take a random example, is a period of time. But what we know about teams is that the best teams have done reps together. Like individuals are atoms. what you want a team to be is like a stable, durable molecule. I mean, this is partly why we sometimes do talent acquisitions, because you can bring an entire molecule into a company, which is quite wonderful. How do you allow teams to be stable? Well, again, if everyone can do more of the jobs, again, AI assisted, that means it starts being more about because of capabilities, creativities, and offer individuals and you're not bound to have to grow the thing just by necessity of at some point you need front-end design. You just know that someone has done the front-end design work at least as good as they can and then maybe you can go into refinement and closer to shipping. We can always bring people in instead of starting every team with a minimum of six people because you need six different discipline skills to just even get started. So I think that's a really, really powerful thing. But really the way organization happens in companies is that there's like areas because institutional knowledge about an area, the payments team isn't payments, like this sort of institutional knowledge about the area, you know, the code base, you know, just so much of a lingo. Like, again, I'm talking about very much engineering, but I think you can apply the same thing to absolutely everything. I'm pretty bullish on that might actually, we finally don't need that so much anymore because that knowledge about like, where's the code base, where do you go and what does this module do? It's like something you can again. That's the context point, right? Like that gets compressed and that's easier to access and it just changes the calculus. That's right. So now it becomes more about what is your skills, taste, decision-making and just talent here in an area.

And I think it will hopefully allow to create these stable teams so people really appreciate it, they work really well together and have them roam over the inside of a company more, because I think that actually is also more fun. I do too. - And so, you know, there's definitely something there. - I do too. I really like this concept and I've thought a lot about how does this actually kind of play out? And I see it playing out inside the company where you can see these kind of mini teams, these people that pair so exceptionally well together. It's often multidisciplinary. It's often, you know, people solving problems together, repeatability and scalability. And so the question is, how does that kind of get, and how do we think about repeating that inside the company? - It's actually really interesting to think of it. We never gave people the ability to sort of fuse into a molecule sort of in the vault. I wonder if that's like a thing that we should just like- - Swati if you're listening. - Yeah. This is like, yeah, this is prompt engineering through context episode, just like, just perfect. But like, you know, again, I don't know what this exactly would look like, but I feel like it's often making the concept explicit. I mean, this is, I am, this is where. We do some of this in mastery too. Yeah, mastery is amazing of this. Yes, the talent tools are insanely good. They're so good. Like every time. They keep getting better too. Yeah. And the team has done an exceptional job at progressing this. Yeah, yeah. But I've often thought about this. Do we build in some sort of social coefficient in some way, right? So you can sort of see exactly kind of who you're working with most. It actually sort of underpins this idea. A little bit of my old meta days coming through. Yeah, exactly. It's like graph proximity. Exactly. So that's another direction we could go in it eventually. That's interesting. Yeah, I mean, I'm sure we could whip up a quick page to like, who is your sort of virtual team here? And probably we have all the information we could do that. Yes. But again, I think that's so exciting is this sort of like, again, I just feel company building is such an unfinished project. All companies that exist are bad. Some are less bad than others from a perspective of what we eventually will deem to be the right way to organize companies. It's one of the most fun projects to figure out how to arc the systems of Shopify or like of a company towards being more grounded in reality of how great books truly happens rather than how sort of textbooks described it somewhere in the 30s. I think it's a special part of this company that is, and we don't talk about it. We talk about it, of course, but not maybe enough.

And are you seeing other companies out there that you look at to say they're doing this exceptionally well or what parts do we want to emulate? Yeah, I mean, I think there's lots of companies that do, well, no, there's a few companies. Most companies seem pretty dead, sadly. I'm not dead in this sort of business sense, so I should clarify. There's an old essay that I read, and I always estimate how much it's sort of obscure, and then I reference it, and everyone just looks at me, called Dead and Alive Players. And I actually always sort of apply this more to companies. And do they do something? Like, is there any kind of thing happening there? Like, that's like not the sort of most expected. Like, could an LLM predict the next move of a company reliably every time? Because that means it sort of like works by an operating system that is also gleanable by just training like a floating point matrix on the sum total of human observable knowledge. So what is actually the decision that happens there? And it's remarkable how many companies just kind of perform like this. Then there's some companies that are unpredictable in a great way. And I think unpredictability, you know, is a virtue in many ways. It's also very often quite unpopular. So there's actually counterincentives to being unpredictable. Trust me, Wall Street loves predictability, right? Yes. So it's, you know, but like companies that are unpredictable do something different axiomatically than everyone else. Now, they might be doing worse, but they could be doing better. And so the set of companies that bring novel ideas in is a small one, but there's lots of interesting things going on. You know, the path by which people took to AI emergence is one where really most companies did nothing and people just used their private accounts sometimes. and then some companies went all the way to banning it, and some companies went deeply to embracing it. And when you find out which one is the right path, I think in this one, I think we are probably the far top right of a scatterplot company, which is neat. I really like companies doing their own thing. I think there needs to be diversity of companies, and I think people should then choose companies based on how much are they willing to deal with experimental designs and so on and you know i think

all companies are different yeah so we have our own challenges to face yeah and you do like again it's also like first you need to figure out what is true when to do about it like i mean at some point it was true that people are just like you know everyone knows lms are expensive and so there was like you know some people got a lot of value other people were like I don't know if I can, really should use it a lot. So then we're not the only ones that said, okay, well, let's do just one way to confirm that we are actually okay with this. It's just like make it okay to talk about how much money costs and demonstrate that people do not get in trouble for this. So then we went all the way to high score tables. I think they were even called that. And I mean, okay, so of course, everyone's allowed to be intelligent actor in a local incentive system. If a company literally puts me on top of a high scale table for us, then some people will be incentivized to do something very, very silly, predictably. And it's like, okay, I mean, I wish people wouldn't, but let's find a better way to communicate the case than maybe just... So I think we found our equilibrium here. But I think yesterday there was an article about meta that just went also to the top list, which I may have actually suggested. Maybe I didn't update them on the conclusions of the book. Anyway, so seeing the same problems and maybe having gone a bit further. Anyway, it's kind of funny to see the space being explored. I think the exploration is healthy, and I think it's good. And I think the pendulum will swing in lots of different ways and it will steady. But it is another, you know, show me the incentives, I'll show you the outcome. Yeah, exactly. And, you know, as a company designer, you want to create, you want to make a company as intuitive as possible in such a way that like if you just like do what you think seems to be. Like people shouldn't have to spend a lot of brain cycles on having to change the behavior just because a company wants them to, right? Like that's a bad thing. We want to create a very large sort of pit of success for everyone. But like, hey, if you just like do the things that are celebrated around here, then the right things happen. Right. Like this is like it should be not like it's like software in a way, like software, like you want your software to be simple to use. And so. Yeah.

So how do you approach a new challenge? Me? Yeah. I. It's different today, I think, than probably it was a year ago. - But I think it's-- - Maybe not that different. I don't know. - You know what, some things are always the same and then some things are, I think, I want to make as different as possible because that's interesting. It usually involves a lot of writing to myself. That's been sort of, I try to get, I've got notes. - Yeah, you're really good at the notes. - What I've been really good at is automated, circumstantial data gathering and keeping. Not losing data has been good. And then sort of doing the random screenshot stuff and so on. I just like data. The early proponent of his exoment life or actually quantified self, I think he coined it as Tim Ferriss. And we are friends. And so I think he kind of got that bug in my ear. so just keeping data and seeing what I can learn from it. And of course, that ended up being the best decision I ever made given now. modern capabilities because you can go back and... It's a little bit of, you know, your relationship with data probably is misunderstood from a company level. Interesting. That's my suspicion. Why? What do you say? Well, maybe not because I think perhaps more specifically kind of the idea of metrics and how we use think about, you know, how we think about metrics role in the company and how that's evolving. and kind of how we see metrics as something perhaps not to chase, but also to be mindful of. Yeah, I think that's fair. Because I mean, I have to play the role the company needs me to play. Shopify's problem has been, like has never been to not look at data. Shopify's problem sometimes is that we over rotate on data. So I had to always play the role of like, hey, don't do what the metrics say. Use the metrics to inform. I think that's a really good clarification. And I think a lot of people have misunderstood this as like the metrics don't matter. That's like anyone who actually knows me like knows like I use metrics for everything. I just don't let them dictate. Like I let them be the major input in my understanding of an area. But then I want to leave room for making good choice that is sometimes contradictory to the metrics because the metrics will only illuminate the quantifiable part of a product. But there's so much more. That's so interesting, too, because I think in some of the spaces that I've recently gone into, I mean, certainly this was the case in talent and kind of in the commercial space, too, where we haven't really actually had enough structured data, clean data to be making decisions. And it is super important. I remember when I took on the talent team, we took on the recruiting team to say, okay, what is our funnel health? How do you create metrics? data that actually helps guide the work? Same thing kind of on the commercial side to say, on the sales performance side, what are we looking at? How is that structured? But it has been a progression, certainly, of work here. And I think this is why I also know you are right about my relationship to data having been misunderstood because people have probably for years only seen me on the side of like trying to modulate people's desire to just like, because I mean, it's really fun to like have a perfect proxy data for success because everything becomes immediate. Like, I mean, I'm a race car driver. I get a lap time every lap, right? So I'm like, I love that. Like it's like there's a perfect lap time out there And my actual lap time is the sum total of all my inadequacies as a race car driver. And like, this is like, and then the next lap can do a better job in every corner and exceed real time. It's great. Wherever that exists, holy shit, let go. Like, but that is, again, think about what an incredible metric for lap time is. It's like the composite metric of a million things that could possibly be true. It's like even like your tire degradation and how much you degradated your tires over all the other laps you have done is summed into this one. And so, you know, most things in the world are not like that, but you can go and make a perfect composite metric. Like this is like, so people mistake that and then they pick a lesser metric and then they over fit to it and really bad or good. Like if you're a machine learning person, It's quite overfitting if you're talking about company engineering, it's called Goodhart's Law. Same thing. Overfitting to a heuristic metric that is just a proxy for a sub part of a business leads to very bad outcome. And again, all these dead and alive companies out there, live ones are all different, the dead ones are all the same. They overfit to very likely the quarterly call if they're a public company or something else entirely. So yeah, let's get lots of data, how to make decisions. I look at the data, I let it inform me, especially I try to form hypothesis and then I try to use data to disprove my hypothesis. Which is easier now with all the tools we have. All of this is like much, much easier. And I mean, this is why I'm having such a good time with it all, frankly. This is where I write myself. Writing about something is kind of honest because you just immediately know when. You don't know it. I know. Right. So you can't hide. It's true. You can. I've been told I'm really hard to write for. So I think I have the same problem. But I also do the same thing where if I write a lot to actually test how much I know and how much I can compress ideas work with them and how facile I am in terms of kind of the edges and where to see that, which is a unique process. At the end of the day, my job is like making great decisions for the company tilted towards the long term, which sometimes means we have to sacrifice short term things. But like, I mean, I literally wrote that in the thing I included with our, and even public with F1 IPO note for me, like says that. And so that's basically the way I try to do

it. Consistency has been a hallmark of this company for a long time. And I think as we enter this new age, what's balancing that consistency with being able to relook at things and find new ways is going to be an exciting part of the next journey. I think this is such an interesting thing because it's conceptual and philosophical consistency, I think, is very underappreciated, I think. Especially because it's overshadowed by a story of much more veering and exploration. If you look at Shopify through a very short-term lens, the company can be all over the place. Like this is really well documented even in the public. It's like, hey, let's go very, very heavily into fulfillment. And then like AI comes on the scene and it's very clear what Shopify really, really will have to do over next years. And there was enough like setbacks that we didn't in the fulfillment world, that we didn't necessarily have like... I mean, Sankar's fallacy was mostly in the form of losing face over having misallocated some resource potentially, or like, I would get criticism, I don't care. Right? Like, it's like, it's, I'm going to be judged as a founder CEO of like, how much, like what the shop ultimately become. So I can absorb that part. And I like, I don't mind looking like an idiot. Like, I know I'm not an idiot, therefore, I allow people to think incorrectly about me. And that's nothing to me. Well, I suspect one interesting thing also to explore is the position of the company itself is changing with AI, right? As we talk about a lot, right? Like the layers are all changing. AI is verticalizing many companies. maybe let's unpack a little bit how we see the position of the company with this sort of shifting of the layers. Yeah. You mean from how we work or from what we do? I think from what we do, actually. The most valuable thing that Shopify delivers to our amazing customers for incredible people reaching for independence is the ability to continue doing that. And ideally in such a way that it works, they're more successful than they maybe even hoped. And, you know, that they can build larger companies, more international, more global, more, you know, complex without needing to absorb all that complexity into, you know, their own life, distracting them from a thing that they actually want to do, which is create the products. So it's like we are fundamentally a company that actually takes all the stuff I talked about earlier about creating a box and an organizational structure to create a Da Vinci's attic for our own, for entrepreneurs that use Shopify and the businesses. Like you bring the products, we do everything else. That in a changing world is like way more important again. Every single time the word gets more complex, Shopify becomes more valuable because we can absorb more of that complexity into our systems and therefore not expose you 100% to that. The way we will do this is going to be completely different because it's going to be involving looking a lot more like a teammate in your company.

Everyone should expect Shopify, maybe Sidekick, to literally join Google Meet within the next years. also be on iMessage and Telegram or whatever you want. And you have a very different relationship with it that you can always go and inspect the guts if you want. But it's increasingly going to be andromorphized in this way. And I think that process is already starting. And the world's not quite ready for that. There's nothing to speed run there because there's a sort of sufficient acceptable level of entomorphization in the world that we have to wait for. Yeah. But we're ready. I've been there forever. I'm like, let's go. I have clankers and claws in every aspect of my life. Yes, you and Fiona have a joint. We have a group chat. You have a group Our open clause have a group chat and they have a very lively discussion, which we find is absolutely incredible. We recently added a new clause that is for the home automation system, like an expert in home automation that just joined the group. And now this morning, we have this complex smart home house kind of thing. And there's like this annoying button right at the garage, which like, like house off. And it's just like, when someone hits it, like, oh, let's go. And like, like it's almost always by accident. Yes. You know, and like these days we go in a group chat, like I did this on a drive this morning after Fraser, my youngest did a joke push to like, and then three bathrooms were like, oh, turn the light on. So it's just like, hey, Cresto, because we use Crestone, sadly. Crestone, can you make it so that you have to push the all off button three times before it actually does something? And like, it just like goes and packs the Crestone system. And it just like all, like the other two bots say, oh, that's a really good idea. And like, yeah, that Fraser is like, it's trouble. And you're like, this is so delightful. - It's always the last kid. - Right, right. So, last kids, man. - Last kids. Like, so, yeah. But I'm excited to be here and in the moment. And what a ride these next six, 18 months are going to be. Buckle up. Yeah, I love that you're here. Such a delight to work with you. Likewise. It's a journey. But a fun one. Yeah. Thanks for joining. Thank you. All right, team. I don't know if you got into anything you wanted. to get into.