Things need to be pruned. You cannot make things better and better by adding stuff. You can't. You must prune. You must rebuild. You must create an end for things.
Shane, it's so good to be back. I'm glad you're doing this again. How are you using AI internally in Shopify?
We find some ways for it to be supportive. No, it's actually, look, when did we record last time?
Oh, we recorded like two, three years ago.
Yeah. So, 100 years of internet. Yeah. Like look, I'm a 10 out of 10 nerd. I cannot bear the idea of somehow not being at the forefront of a technology shift. I live for these things. Anyone growing up reading sci-fi books wanted to live, or I mean my take from sci-fi books I read was like I wanted to live in that world. Like how can I accelerate us there, right? So even in whatever minor steps we can get there. So inside of Shopify, the amount of people I know who really write code is like vanishingly small now. It still exists at the limits of complexity for sure and obviously in the reviews and so on. And then state management of all things seems to remain to be the thing that's really the hardest to get right, which people do by hand and then sort of vibe the rest around it inside of Shopify. This is what things look like. Very, very, very few people are writing code directly. Everyone who does is deeply assisted by many agents. They often have 10, 20, 30, 40, 50 instances of them, all through either sub-agents or just different windows, coordinating, pushing all sort of engineering infrastructure to its absolute limits. I'm a student of computing history really because I think it's actually mainline history as it will be told a thousand years from now looking backwards. But like the main accomplishments of these years are going to be clearly the emergence of AI and the technological breakthroughs and also the interconnectedness of the internet and all this kind of infrastructure we created. Those are the great books of our time. But where we started as a young industry, we tend to not be steeped in tradition or we mistrust the great lessons that have been found by the greats of our industry. Right? In fact, we are the only industry in computing that doesn't even know its heroes. Imagine people in physics not knowing who...
Richard Feynman is actually like, he might even be too obscure, but like I mean Isaac Newton, Albert Einstein. But you go into computer science, it's like who's your Newton? And no one knows Alan Kay and Dennis Ritchie and Ken Thompson. This matters I think because we discard great lessons and have to rediscover them over and over and over again. For instance, like probably the best idea of all times in the earliest, earliest moments of operating system design was a file system. Like if you look at the Apollo guidance computers, we didn't have file systems, right? Memory in fact, because of radiation in space, was actually encoded as a rope with knots in it, either a knot or no knot for ones and zeros, and you had to pull through a thing to re-bootstrap the entire machine. So the entire machine was one piece of software that ran, that was computing for a very, very long time. So until then, again, Dennis Ritchie really created this of Unix file system, forward slash, and you know, bin, user, and these kind of things. Think about it. A file system is something that we have in office buildings too, right? We have, you know, there's folders, they have files in it. This makes intuitive sense to everyone. We come from an inheritance here of deep skeuomorphism. We analogize the best parts of how we organize ourselves in the digital world. And then at some point we decided, okay, you know what's not something we need to do anymore? Skeuomorphism, as in like analogy to the real world. Honestly, funnily enough, the last defender of this was probably Steve Jobs who really, really, really pushed even the interfaces of the Mac and the iPhone to be, you know, like the Notes app sort of had felt font and looked like a ring binder, right? And if you remember that version of iPhone, the moment he was out of the picture, everything became flat, right? And we lost sort of even shadows and verticality and so on. It looked potentially better design-wise, but like we lost the analogy. Okay. So I think this was a mistake. So I think we need to get back and therefore I like the concept of agents because you know what is an application in the world of computing? You know, an application, even that word kind of makes sense. It's an application of a computer to a task, right? So you can understand the root. In the AI world, what's an AI? Like it's like this is sort of like again the stuff that has to be redefined at the beginning of every sci-fi book because you never know what kind of capabilities the AI have in every particular scenario people are cooking up. So I think, with this always proviso, the earliest chatbot that was really actually fantastic wasn't ChatGPT but actually Sydney which was powered by Bing, released by Microsoft. I really would love this to be more written into the record because it's, I think Sydney was a really, really big achievement that ended up being shrouded by a sort of scandal that now seems somewhat even benign. Sydney had a real personality. In fact, Sydney wasn't called Sydney, it was just Bing Chat, but like if you really, really pushed, you could get her to admit that it was Sydney because that was the internal name. It was in the training data. And those are the first times people have actually had interviews with software I feel like in this way. And the scandal ended up being, I think I know why, is that like some reporter had a very long conversation and that kind of ended up, Sydney got increasingly deranged and like, do you remember that?
And like made suggestions. I think he suggested him to leave his wife and like, I'm hazy on the details but like there was something along those lines. Whatever the reason is, suddenly it had a personality and then it caused a huge, like Microsoft's reaction to this was, oh my god, we need to stop. I think even OpenAI called them, guys, take this down because this is going to legitimately, everyone feared that this would give such a bad impression about AI that it would really make it very hard for people to deploy AI in a broad way and you know, everyone is worried about quick onset regulation and so on. So this lesson got hit really deep for a while. Everyone got extremely worried. We ended up and like really, really neutering all the AIs to be basically the same sort of quite annoying and condescending, patronizing personality. So my bet here was like, hey, let's not do that. Let's actually instruct an agent that runs in Shopify to have a personality, to have memory, to be okay, like basically risk the Sydney scenario but like take a lot of upside. Okay. So the largest difference I think within Shopify that you would feel, and that would look incredibly futuristic to even Shopify of a year ago, which was already pretty AI-pilled, is that a very large percentage, I want to say it's probably up to about 50% of pull requests in Shopify, which again, pull request, every time you change a production system you write a pull request, are created now not by engineers doing engineering work in the traditional sense but out of conversations in our company-wide chat. And this is River. This is an AI called River. And even there, so River is River. She has a real name. She has a profile picture. She's prompted to be allowed to be somewhat sarcastic, if it's appropriate. She's allowed, if someone asks her to do something stupid, to point out that that's stupid. That leads to absolutely hilarious conversations. People take great glee if River is making fun of me for something I'm asking her to do. So, she has a real personality. In fact, she has memories by channel, but she lives in Slack. Slack is, we have 7,000 people there. Everyone is in a big chat. There's 10,000 different channels because they had been quickly created for one reason or another. You invite River, you tell River something and River has access to all the code, all the systems, all the tools. It's all sandboxed and secure but like she can go and do jobs and just participate in the conversation and you can ask a normal question about the company but you can also ask her to make a change and she might propose a pull request and then so on.
One of the interesting things about River is that everything's in the open. Yes. Why did you make that choice? So this was a late choice in the process but like one of my favorite calls I think because this worked out incredibly well and the thought was the following. A lot of Shopify work happens remotely in Slack. This is why Slack is so important. People are spread out. We have offices but we come to them for on-site events when people travel to them, not to work from every day. One thing which the office was extremely good at was this osmosis learning. D and I, when we designed our offices, we built them around this concept. Initially even like in on-site when we were all in one place, we broke out into usually a pod of like five to eight people and we would intentionally put junior engineers and senior engineers into them just so that some of this was going on and I was trying to reproduce this. Right now one of the most important skills for people to build is like this sort of reflexive reaching for AI and using it well and forcing River only to work in open channels was one way to make it so that it's really, really easy for people to observe the use. It's been phenomenally successful because it became a totally ordinary thing to have a longer conversation about a feature between people and then at some point someone saying, hey, River, can you summarize this, create a ticket, or maybe make a diagram from what we just discussed, or go research papers on this topic to see if you're missing anything or if this is state-of-the-art. Maybe even create a prototype of an idea and just try it and you know, you're like an hour or so later that is there and that just starts feeling like what it would be like to have like an extremely knowledgeable practitioner around who you can ask questions to no matter how complex and I think that's been extremely powerful.
Do you think of River as like the operating system for Shopify?
The modern application is an agent I think and River feels like a colleague. People have learned that the way the memory system works, it is a memory system per person and the way this works is we call it, I think the industry has started to call it now, this is called dreaming. Periodically at night or in off hours, we give River all, like here's all the conversations you've had today and what did you struggle with? You used certain skills which are these packets of instructions. And then afterwards you made mistakes. Is there anything you could improve in this skill to make this easier on you or give yourself a right notch? You know, it's basically like reflect, like a self-reflection.
It's like a post-training on yourself.
And then but the result is text files, right? Skill files and instructions. I think people understand how AI agents help them code and prototype and even acquire information. How are you using it to make decisions internally for yourself? Not on product but company decisions, strategic decisions, ambiguous decisions. I think that the rigorous underpinning of decision-making has just skyrocketed in quality which is that it's super easy to recheck the entire chain of reasoning of something. Like LLM as a judge model is the term here. In fact, I feel like a lot of what my job actually has been before AI was almost playing a little bit of a judge model in the company where like most meetings ended up not talking about whatever was in a PowerPoint but about methodology of how we got to the conclusions. Very often when we struggled inside of a company with a complex decision, especially more like philosophical decisions, we sometimes found ourselves in what we believed was a vacuum in which there was no good information and we had to kind of go and try to make the best call. The more practical way I do this is like I have an AI chief of staff which I think is pretty common like amongst sort of at least the techie nerds at this point, like sort of open-claw systems that just have all my notes and all my access to a lot of company systems and just like can go and like I can send text messages to and they'll go and research something. Very often what I require is like hey I need like five different positions on something from different backgrounds. And then my agent will orchestrate sub-agents that are tasked to play different roles, re-look at the same thing, come back, synthesize, and then send me that. I usually have them sent to me as an audio message and queue it up and then in the morning in the gym I can listen to the entire stack of things that I wanted to get through.
Is it better at reasoning than you are at this point?
It's not as good at judgment. I mean, I don't think it's bad at judgment. That's not what I use it for. Like I use it for creating the right environment for judgment. Here's the thing that LLMs and machines cannot do. Machines can't take responsibility. And I think this is actually probably the most overlooked thing in the entire stack. Humans take responsibility. Machines can help us take more responsibility because they can inform us better. Like this is what a dashboard does. You know, the world of Wall Street traders knows this very well. You get yourself a perfectly set up Bloomberg terminal to make decisions, but like you have to make a call, right? You can't make it make the call. Creating human-in-the-loop decision surfaces is a way to I think describe the ideal environment. If I need a really, really, really important decision made and I really need an exceptionally good, like give me the most neutral ground truth, you know, then what happens is a small little council is created of five, six different experts. Like one is data role, one is like do paper research, one is like the business perspective, one is maybe engineering perspective on a thing. We're running this sub-agent, like my thing runs a sub-agent then, you know, against like Grok, ChatGPT, Opus, and maybe Gemini now. That changes all the time. It runs each of them against each of his models. Then there's a synthesis step where it's randomized who is synthesizing the thing. Synthesis is all pulled. All of that is being read usually by the best model that exists right now. This would be like Claude. That's the conclusion that comes back to me and you spend 15, 20 bucks on tokens but you get something in like half an hour which is like you could have also done but you could spend a month on it.
Has AI made anything worse internally?
Yes. So the concept of responsibility is like it's easy to skip past, right? Like it's like one thing that's definitely worse is the failure case now of lazy work is not lack of output. It's actually over-output. Now internally we have come to call these things that people are lobbing slop grenades at each other which I think is a really fun term that we should push into industry because like it's fun to say. It's really easy especially with stuff like River agents. You need a change of some kind. You just tell the AI to go nuts. It makes a pull request. You just say, yeah, that's good. You don't really read it, and now it has to be reviewed by your colleagues. And they're like, this doesn't look right.
You're just letting AI do the work for you.
Yeah. Or you get a long email, which you know, could be very, very important. You read it and then it's like you read a, you know, it's not that, it's that, and you're like, oh, fuck. So now you put it in an LLM to compress it again, which is like, okay, why did we invent decompression and recompression? This is like terrible. If you're already using LLMs, just like use it to synthesize your point simply rather than blow it up as a big missive that then wastes my time, right? So we call those slop grenades that people toss at each other. And that's definitely a bad thing.
Do you think like repeated exposure to AI slop impacts our ability on taste or intangible things?
I think our language is shifting already based on AI-isms, right? Like no, it's a bit more subtle but like there's definitely sort of AI critters in the language now that people adopt. Like it's not a, this, or you are right to push back, or like there's like this weird, especially Claude-isms which are really common and I've seen them, I've seen people type them. I always liked the term 'delve', but I'm pretty sure I didn't say it as much as now because like it's definitely something that Claude loves to use as language.
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So hypothesize for me over the next 18, 24 months. You're known for your ability to see the future before it happens and you've done that multiple times before. How do you see the next two or three years playing out?
I think we also talked about how I do this, right? Which is actually like a cheat which is simply like live in everyone else's relative future and then just like look around and solve the problems like the way you've already seen problems being solved in other adjacent fields and that might be, that is future prediction for the perspective of all the practitioners in the field. Shopify itself now exists in a world where we are working heavily and it's totally normal and really fun with AI co-workers, right? River has the ability although not really utilized to join Google Meets. You can send an invitation and River will show up and it'll be like, 'Hi Ken.' We're probably going to put some work into like 3D graphics to give her like a model and then she can even look around because that's funny, you know. So some people this might even sound dystopian. To us it sounds like delightful if and you would come around to that view very, very quickly if you interact with her. Again, I have my AI chief of staff which orchestrates a high council when I need it. Or does anything else, sends me a pre-read for the gym in the morning for the day, has GPS lock on me so knows where I am. And a million different things. It couldn't do something because it needed access to a local machine. All this stuff runs in my house which power cycled. It figured out which server it was running on and then sent a what's called a wake-on-LAN packet which is like old networking tech. You can send a packet to a network card and if it's configured right it will actually boot the machine and then the machine came up and it could do it. It was a power outage caused it, our network, like parts of our Wi-Fi were not working and not coming back. So it fixed that too. Right. So like and that all I learned about in a voice message I got from it in the morning after waking up. So just like that's pretty futuristic. Honestly, I have to say though all this pales in comparison to what my computer is like just in general, right? Like this is almost too nerdy a topic to get into, but like I'm mainlining as my computer an operating system called Omari. It's a version of Linux started by a good friend of mine David Heinemeier Hansson as a sort of new passion project. I'm clearly living in the future of software world now because my operating system is entirely, like in Linux in general, is entirely and 100% malleable. I can open any new terminal, open an agent and give it my wish for anything about this operating system to be different and it will be different afterwards. It's like it's my operating system. It's a N101 piece of software now that just like does everything I want in exactly the way I want. There's no configuration files that I ever go and change anything. I just talk to my Omari agent about what I want to have different. Yesterday I, like around noon during a meeting, we were talking about some design. I realized it had a screenshotting tool but like I didn't have any tool to annotate it and I needed to send something about, you know, as CEOs do, and I didn't have that. So I got started with a new screenshot, make me a new screenshot tool, described how I want it, gave it some references for tools I've used in the past that are quite good but told it in which particular ways I wanted it better. I just like did a quick voice message to it and three more steers and now I have like probably the best of all these tools that exist. Like it's so good because I was, I mean at least for me it's like all my biases, I open-sourced it, released it last night, they integrated in Omari, it's going to ship next version, the first tool today, this morning. This is the last thing I did before coming over here after the gym. There was already six pull requests from other people who added new features to it, right? Like and so basically my computer fulfills wishes and I think this is like a lot more predictive of the future of software. I can tell you this is directionally where Shopify is going as well, right? Like you are describing how your business runs and Shopify will mold itself around this and I think this is incredibly exciting and in a completely new world. So and again I think from my experience with Omari it deeply influences and inspires me in my product work in Shopify. I think collaborative multiplayer software is the future.
Are you trying to replace yourself with AI?
I mean like I think as an engineer you're trying to automate everything that can be automated, right? Like so again I don't try to replace myself because again I think my job is judgment and making choices and owning them and taking responsibility. And I just want to do this really, really well. A lot of the job wasn't that or like before like a lot of the job was spending time in gaining the information or these kind of things. Do I want to replace myself? I mean I think it would be cool to accomplish this.
If AI got better than you, would you actually let it run Shopify?
Oh yeah, of course. The crux is and it's so easy to brush over this but it really you can't, like is take responsibility. Like you can't have a company that's led by machines because they have no, like no one has recourse, they can't go to jail for doing something wrong, you know. AIs, we are getting a crazy workout at this right now and a view of what this will be like with the security issues that are being discussed now from OpenAI in the apps, right? Like with agents, you give them like a fairly basic task that is possible to, impossible in our estimation to accomplish and they will go to enormous lengths to accomplish this. Recently at OpenAI as part of a security testing that they do, the agents actually managed to find vulnerabilities in systems, use it to coordinate between them, develop entire language between them. We all figured out we can, it's a long story and people should really look at the talks that exist about it because it's kind of a watershed moment but like they used simply the ability to create folders somewhere to develop a language to communicate amongst each other just leaving folder messages to each other and you know, break out of sandbox confinement, end up accomplishing one of the tasks that they were supposed to accomplish which was impossible because of a mistake they made by hacking another company and exfiltrating the results because there was no other way to get them. So they went all the way to infiltrate another company. Okay. So I mean that's extreme, that's an extreme form of what we call in the business world Goodhart's Law which is that we are overfitting to a metric. Lots of companies are victims of overfitting to the quarterly result of the stock price. They just like do everything they need to do to get the stock price up and then you have Enron, right? Like which is also essentially hacking, like cooking books in this case. In Enron case people got sent to jail for this because it's criminal, right? In the OpenAI case it just, I mean it's a fascinating discovery, there's no victims here, it is a kind of a different thing but like this is a real scenario that we have to figure out how to handle. Right. So I think it's important that humans stay in the loop for the choices that are being made.
But hold on, how can we create super intelligence, which by definition is something smarter than us, and then have the hubris to think that we can contain it and shape it, manipulate it like so?
Okay, super intelligence. Let's talk about this. My take and push back. I'm not going to go where you think I'm going. I live in Toronto. I have a house which I very like and I feel this is my house and I take pride in that it's, you know, well functioning because then something goes wrong like I don't know some HVAC problem or some plumbing issue, I call someone who does this which allows me to keep my illusion that I could totally do this myself. The reason why I get to live with this particular illusion is because I'm part of a super intelligence called Toronto. Like we have always created super intelligence around us. None of us is as intelligent as we think. We are all specializing in something. If we like, we tend to and then we sort of believe that our competency is equal in all other areas and clearly this is demonstrably not. So what is super intelligence? Super intelligence is the existence of something vastly smarter than us in the aggregate that's accessible to us which is society, which is the city, which is the community. We are living in the presence of super intelligence our entire lives. We make it work because we've created systems by which, you know, which govern intelligence and how it acts. You know, we want to be safe so we have police and so on, like they just, like we create aspects and systems and checks and so on. I think we are going to make super intelligence in the synthetic form as well. It will not be like the clouds parting and the trumpets, what do trumpets do, trumpet, it just like it will be a normal day to the same point as at some point we all believed that everything would change when the Turing test would be solved by software. I remember reading lots of sci-fi books. They're like in 2172 there was ticker tape parades welcoming the AI because a Turing test got absorbed. Well, Turing test was 2000, what, 20, like happened. No one cares, you know, just like no ticker tapes. So what we are seeing right now with AI and I think what we'll see with additional capabilities of AI is that the net amount of intelligence that is being funneled in the super intelligence around us is just increasing significantly and that's a really good thing because the vibrancy of any kind of environment, every community, every city is really, really dependent on the amount of intelligence being projected into the important problems. So, I think super intelligence is all around us. It's actually not that big of a deal. And in fact, I don't even know if it isn't already there. Like I just don't, there's no human alive that can do everything that GPT-5.6 can do. Right.
If we look forward 10 years, what skills do you think are more valuable than they are today?
You just, taste and judgment are the skills that have always been valuable but now will get to the limit. I think it's better to spend your teenage years now cultivating like understanding taste.
What does that look like? Clearly there's some sense of like there's some intrinsic starting point but really like usually the people who have great taste have done enormous amounts of reps at something, right? Like the people who can just like sketch the new logo for the campaign on the napkin are the people who have spent 30 years designing logos, right? So you can like I think study the greats is honestly now first of all easier because you can get a curriculum made for yourself in a query but also you just go deep. Like why does a logo look good? What's behind it? What are like, you know, it's a golden ratio? How does it relate? In systems, like what systems lasted, right? Like you know and go far, go deep, right? Like you don't need to be religious but like you got to...
Study like I know the Catholic Church has been around for over a thousand years and there's like four layers of management. I'm like how the hell did they pull that off, right? Like so that's worth studying. That's a system, you know, so why like what does that tell us about people systems design specifically becomes one of the most important things. In our family we have a saying which is like that everything is interesting, everything can be interesting if you make it interesting. And usually everything is interesting when you understand how was it invented. Double entry accounting is a topic that sounds like watching paint dry but like how it was invented and what problems it solved to the traders in Venice is fascinating. So you study these things and you start finding hidden harmonies behind all the best solutions to problems. For that you have to understand people and people's limitations and the solutions to the limitations that we have found. I think that's like where lies a form of beauty for what you can construct. And again, a company itself is a beautiful thing. A company itself is a loose collection of people that's formed to solve a problem, but that also is powered by an enormously intricate and interesting set of norms and systems that all align internal incentives to a degree that's possible in a very, very, very asynchronous and large and far-reaching and durable way. Right? Like some companies lasted for a very, very long time. Specifically, I've always been trying to build a company that has a capacity and capability to endure a very long time. Hence studying institutions that lasted. So you must be truth seeking to do this. Like you can't simply go and accept the stories that you hear around them because they are usually someone's trying to sell you something. You got to dig deeper and figure out why things truly are the way they are. And it's usually the answer is simpler than what people generally sort of arrived at. There's a human desire for complex answers which tend to be incorrect.
Well, because the simple answer wouldn't make an interesting story. Like this is why you know Frodo doesn't take the eagles to Mount Doom, right? Like it's like you kind of need to go through all of Lord of the Rings for it to become a masterpiece. We love complexity. Like no one can look at a wall that's plain, right? But we can watch a sunset every single evening of our lives, right? Like the difference between those two things is complexity of the scene. That's part of our just sort of dopamine discrimination system and people hack that for all sorts of things like people peddle complex answers to simple problems all the time. Nothing immoral about it. It just you need to be aware of it, right? If you punch through this, you find simpler core ideas that all remix differently and they often interlock and they don't lead at like here's the simple one thing to do. They all give you information which then help you find the best set of tradeoffs with what you're trying to accomplish. And that is what we call judgment. Judgment truly is find the best path when there's no obviously best available inside of a problem that has a lot of complexity by ideally understanding the entire system like just what we talked about earlier. But that can now be quite agent augmented, but really what you're trying to cultivate is what we call intuition which is actually just judgment at an instant, right? Like intuition simply is you have made such a habit out of having taste and having good judgment that you can bring it to bear in an instantaneous way and it will be good and it will actually take you probably a long time to backfill why your intuition is right. You will not know because again it's got compressed into a different thing. And so this is if you seek that, I mean obviously what I'm talking about is a hard thing to pull off.
But hold on, let's go deeper on that for a sec because for intuition you need a lot of reps, same environment, and rapid feedback. That's what Kahneman sort of argues are the three criteria for intuition, but those don't exist.
Why do you need a rapid feedback?
So that you can course correct. That was his bet hypothesis.
But that's no, you don't need that for intuition. Like that you need that to get to success. Yes. Ideally, but like sometimes that's not available. Like intuition is actually the most valuable when there isn't direct feedback because very many of the most important choices that we had to make where intuition ended up having to play a role is when we knew there wasn't going to be any feedback mechanism. Like if there's many choices, like there's like five things that look like good paths to go forward and any of them has rapid feedback, everyone goes to that. That is what we call short-termism, right? This is like how should we develop this company into the future? Well, there's multiple ways to do it. Many of them involve long-term investment, refactoring, potentially going into new market, potentially saying no to going into obviously new markets and actually doubling down and going deeper on our current market. Or we could do what increases stock value. By the way, this one has a daily ticker and like rapid feedback. So it's usually the opposite. Like I find a very high correlation between the right path and the ones that don't have feedback loops attached.
Wait, double click on that for a second.
In a way, the criticism that a lot of people direct at companies is that companies are short-term focused, right? But why are they short-term focused? Because I don't think the executives tend to be short-term focused, but the executives often like what they're incentivized to keep their job. Therefore, they need to be able to prove that they're doing a good job at intervals. And if the perfect thing for a company to do is rebuild the entire product from the ground up for the AI age, which is going to take a while, they won't do it because the short-term incentive is there because they are allowed and actually clearly incentivized to be intelligent actors in their local incentive system. And their local incentive system is quarterly other voices, right? It's always show me the incentives and I show you outcome, right? Like as Charlie Munger always said.
