The Copilot for Ecommerce with Shopify VP of Core Product Glen Coates

0:05Hi, listeners, and welcome to another episode of No Priors. Today, we're joined by Glenn Coates, the VP of product at Shopify, where he leads the core developer platform, including all of their AI products that we'll get into today. Before he was a leader at Shopify, Glenn was the founder of Handshake, a B2B e-commerce platform that was acquired by Shopify in 2019. We're excited to talk about how AI is changing e-commerce and entrepreneurship, as well as innovation at scale and leading at Shopify. Welcome, Glenn. Thanks for having me. So lots of fun stuff today. let's definitely cover your personal story quickly, since you're also a former founder, one of several at Shopify now.

0:41Can you give us some of your background? I was a comp side grad. I spent the early part of my career in video games. And then I had a weird left step in my career where I moved to San Diego in 2008 to run the US operations of an eco-friendly shopping bag company. You know, like you take your own bags to the store, that whole thing. Anyway, this company did a whole bunch of wholesale. They sold a lot of bags through stores. And through that, I ended up getting to the idea for Handshake. And Handshake was initially a very sales rep focused B2B e-commerce product, but then became both for sales reps and for customers to buy online.

1:20So Handshake eventually became basically a B2B only version of Shopify, like a wholesale only version of Shopify. So it's not hard to join the dots from there to after, you know, building Handshake for better part of nine years here in New York, the opportunity came up to join forces with Shopify. And then I've been at Shopify for almost five, it'll be five years in May. I started out very much focused on B2B and wholesale, which was obviously the point of the acquisition. I spent about a year doing that. I then moved on to focus. I ran a code red on checkout in 2020, which is the first year of the pandemic.

2:01And then since the end of that, I've been leading the kind of core product group at Shopify, which is, you can think of it as all of the built-ins of Shopify, the online store, the checkout, the back office, the developer platform, the App Store. And yeah, that's what I do at Shopify. Lots of good stuff. What's a code red look like? A code red is when Toby sends an email to the entire company saying, this thing is the number one priority. And he means it. From an actual operational perspective, what that really means is, if the team that's working on this code red asks you to help, Please drop what you're doing and help.

2:43This is the number one priority. So that's how they work. But usually a code red is a symptom of some other much more systemic problem that's gotten you to that point. At least in my case, checkout code red in 2020 was, hey, the checkout is failing in, I don't know, three, four, five different ways. And the checkout is a pretty important part of Shopify, obviously. So let's go fix that. So we, me and, you know, somewhere between, I think at its peak, it was probably two, 300 people working on various parts of the problem. So we all like scrambled for a year and like did what it took to fix those issues.

3:22But then at the end of that year, Toby took a step back and said, okay, well, why did that happen? Like, how did we get to the point where those problems even happened in the first place? and then that led to some of the reorganization of the company around less like there used to be 12 to 15 of these kind of fairly small fractured business units and now there is actually only like three or four which is actually truer to what the product is but of course each of those units is bigger than the ones that were before so it actually requires you to lead in a slightly different way once you start grouping that many, much of the product and the people together.

4:04I guess like two related questions from your background. I guess number one is since you were acquired into Shopify, one of the things I've been very impressed with by the company is a degree to which it's been able to retain and grow the roles of founders that it's acquired. Because as far as I understand it, the majority of the management team at this point was acquired in at some point, or at least a very large fraction. And then secondly, as you buy a lot of things, you end up with a lot of disparate products, disparate platforms, disparate integrations and all sorts of things can go right or wrong based on that.

4:31And so I was curious a little bit about the broader concept of integration. Number one, from a team and individual perspective as a founder coming in and why is it such a good home for founders? And then number two, like once you're acquiring the tech and the product, how do you integrate that as well? Yeah, it's really notable. So like when I look at the so the the sort of exact team of Shopify contains a number of founders, some of whom were acquired in and some of whom just were founders. And when I look at like my management team, the people who report to me, who each of these sort of nine or 10 people run R &D orgs that are like a couple hundred people each.

5:09And almost like a very high percentage of those people are also founders, right? And so you ask, okay, well, why are all these people ending up essentially running the main parts of the product of Shopify? And Shopify is a product first company. So that's sort of the same thing as saying running Shopify. I think a lot of this comes from Toby himself, who has extremely strong opinions and has, I think, learned for the better to no longer be shy about like saying, I want to aim in this direction. I think there was a big culture of bottoms-up decision-making, power to the edges, whatever you want to call that school of management philosophy, which is delegate and empower.

5:54I think a lot of former founders are actually coming back to the place where they say, hey, what am I uniquely good at? I'm uniquely good at like having a point of view, being willing to smash my head against a wall and like do whatever it takes to manifest that point of view in the world through building things, building teams, you know, saying the hard thing at the right moment. And I think Toby has kind of re-embraced that, right, in the past few years of saying like, hey, if I'm good at one thing, it's being extremely opinionated. And it seems that in this domain, at least for Toby, in this domain of like the internet and commerce, I'm right more often than I'm not.

6:35Like I have a higher batting average than usual. Therefore, if I can help accelerate this company through like being decisive, that's going to be good. And so he sort of sets the example and a lot of us, you know, see that and we're like, hey, we remember being founders. We remember what it's like to have all bucks stop with you. And if there's a way that we can apply that level of decisiveness here and accelerate teams through like the otherwise like, you know, design by committee bullshit that kills a lot of companies, then like that's a net positive. Right. The other piece of buying in companies is integrating different systems and platforms.

7:18And often you see people do one of two things. They either let things run independently, that was WhatsApp for a long time at Meta, for example, or you integrate in all the infrastructure. And usually that makes things more performant. You can have features that cross over, et cetera. But the flip side of it is sometimes you see these giant projects that just stop a company from functioning. Like at Google, there was a famous identity layer that was built and for a year and a half people just didn't ship things, right, in other areas. And so how do you all think about integrating and acquire companies and acquire technologies?

7:46But more generally, as you build and build and build and build, there's a big munch of stuff. How do you sort of consolidate that down in a performant way that's actually speedy to execute? And then I guess later on, maybe we could talk about whether AI has played a role in that or not. Yeah, it's a really good question. The answer is, it depends, like most problems. Usually when you encounter these problems, you sort of have to look at the stack. You usually don't want duplicates at the same layer of the stack, right? And the funny thing about this is sometimes you actually don't notice that two things are in fact at the same layer of the stack until much later on, right?

8:26So as an example, inside of Shopify, there is the checkout, which takes someone's shopping cart and says, okay, let's take the business rules of the store and convert that to an agreement for a sale with taxes and duties and shipping and all that stuff. So that's one engine. And then at different points in Shopify's history, other things that have been built that are sort of like adjacent to that, but not quite the same thing, right? So like a thing for creating like an invoice or a thing for like even editing an order after it's been placed. And a lot of these things kind of get built because you start with a small problem.

9:10Example, I just need to be able to add a discount to an order after it's already been placed because someone calls up and says, hey, I forgot to put the discount code on. Can you please enter it? Right. And so someone starts a project over here, which is, oh, we're just going to support editing discounts onto orders. And you don't realize that what you're actually doing is creating a second version of the negotiation engine, right? It's just that you started with a very, very small part of it. And then that thing kind of grows for a few years. And you're like, oh, it's not just editing discounts in, it's also editing the shipping costs.

9:43It's also editing another product onto the order that they forgot to add. And before you realize it, you're like, oh my God, we're reinventing the checkout over here, right? And by the way, in the meantime, all the teams that work at Shopify are finding themselves having to build to support both of these things, right? So someone builds a new feature and they're like, I'm going to add, I don't know, international pricing. And then that team is like, oh shit, I have to build it into the checkout, but I also have to build it into that order editing thing. Oh my God. And I also have to build it into that invoicing thing.

10:18And now it's like, to your point, it's like, okay, now every, the thing that should have taken a month to build is now taking six months to build because the layer of the stack that's underneath me has five things where there should just be one, right? And so sometimes it's hard to know what to do, right? Because sometimes the right answer is let's collapse those five things in the stack into a single thing, which is, it sounds like what Google tried to do, but then you're like, oh my God, it's like trying to take the foundations of a house and like rearrange them while the house is still on top.

10:50And like, that's, oh God, this is going to be really hard because I can't just make everyone move out of the house, right? The real answer to how do you solve this problem is one is developing extremely good intuitions for when you are accidentally creating duplicates at the same layer of the stack. So you just never get into the problem in the first place. And then the second part is being willing to actually notice when you have created the duplication and eat the vegetables of saying like, oh shit, whoops, we did it. Okay. Now it's time to eat the vegetables and collapse these things together because if we don't, every team that exists above us in the stack is going to pay this duplication cost in perpetuity.

11:34And the problem is it's not even just your R &D teams that pay the cost. The saddest thing is that usually it's a combination of your R &D teams and your customers paying the cost because the reality is that not everyone will do the thing of building the five versions. They'll probably build the top two or three most important ones. And then all the customers who depend on numbers four and five will go to use the thing and they'll be like, oh, this is super weird. This thing doesn't work. Why is this thing so different to the other thing? I thought they were the same thing. And then they look under the covers and they're like, oh, I know why it's different.

12:09Because actually this part of the stack is all fucked up. Have you approached that from an AI perspective? So I think Shopify was a very early adopter of AI. And in general, whenever there's a really sharp technical founder driving a company, they've been an earlier adopter, right? And Toby obviously fits that mold. But it's an area where I think you kind of have to play around with it, experiment, et cetera, to really understand the capability set. But at the same time, to your point, you want kind of centralized infrastructure approaches. So how have you thought about how to do that adoption as well as how have you thought about that in the context of launches like Sidekick and Shopify Magic and things like that?

12:42I mean, yes, this is obviously the space where everything's moving the fastest, right? Like the world of invoicing is not moving at the pace of the world of AI, right? It depends on what you're invoicing. Yeah,

12:58it's been an extremely interesting last 12 months of looking at where can we apply different models to parts of Shopify. I think the high level idea is, you know, Shopify's real focus and real mission is enabling entrepreneurs, right? And the meta strategy of everything we do is how do you simplify the process of starting and running a business so that more people who would otherwise not get over the hump, get over the hump and have an opportunity to try building whatever that business is. And like the historical strategy for that at Shopify was build really easy to use software. Use software that is super simple when you start and it kind of reveals complexity as you need it.

13:49Like as your business grows and you have harder problems, like the tools sort of like appear at the right time. I mean, the world is littered with the corpses of, you know, the companies that tried to serve SMB and enterprise at the same time. And this is kind of the hard problem of Shopify. How do you be simple, but also powerful and scale up? AI is this really interesting moment where it's like, okay, well, Shopify built its position in the market largely by being the easiest to use thing as you build a business in scale, but in imperative mode, right? It's the, I'm going to give you all these switches and we're going to give you the best switches.

14:26And we're going to show you the switches at the right moment in time. But this is the best switches, right? And AI brings us into the world of like, okay, well, what if we can give you the the driver in the car who knows how all these switches work and can help you along without having to learn all the switches. It's probably one of the most powerful opportunities for more people who would otherwise be daunted by having to learn the switches, even though we try to make the switches as good as possible. There's all these people who can't quite figure out the switches, but they could talk to the, they could talk to the co-pilot and they could explain what switches they want hit, but they just can't do it themselves.

15:09And so I think we have an amazing opportunity to increase the amount of entrepreneurs in the world who actually get a shot. And so that's the kind of headline. And then from there, you go into, okay, well, which models do we apply to which problems? You look at individual problems like, okay, how many people get stumped just writing the descriptions of their products? Because they're not good copywriters. And they're like, oh, this is embarrassing. I can't write a good description for my candle. I'm not going to launch my store, you know? And there's so many instances like this where it's like, okay, well, can you give someone that extra bit of skill that gets them over the hump where they're ready to hit the big green launch button, you know?

15:48Yeah, that's really cool. Glenn, it's really exciting how many obvious places there are to apply that to make it easier for the merchant or the would-be merchant. But I think one of the things that has struck me that you and Toby and the team has done is actually shipped a lot of that quickly when a A lot of very large organizations are like, oh, that makes sense. The outputs are non-deterministic by nature. We have a process for measuring and evaluating quality of our products. Generally, that process does not apply. Like, how did you get to this is good enough and we can ship it? Well, one of the principles that we currently hold, this might change in the future if confidence intervals go high enough.

16:31But one of the principles that we have for all of the Shopify magic features today is that they're allowed to propose changes, but not commit changes, right? So they can like generate text, but they're not going to save it without you actually reading it and saving it. We might suggest a reply for the user that's, you know, someone writes in like, hey, what's your shipping policy? We can like suggest the reply based on us knowing what's in the store and like running that through an LLM, but you have to hit enter, right? And so one of the things that like human is in the loop essentially right now is one of the ways that we're kind of mitigating risk here.

17:06And obviously human in the loop is great because it gives you the feedback cycle. You actually get a three-part signal, you get which suggestions are accepted clean, which suggestions were accepted, but then with minor edits, and then which suggestions were outright rejected. And that's an amazing loop to be able to improve things through. So we're always getting better, but that sort of human in the loop, human must click save thing is a big part of the strategy. Do you think that Shopify has a different risk orientation than other companies of its size? You still need guardrails. And even if they're suggestions, it's not perfect, right?

17:44Correct. Yeah. I think Toby and Kaz and myself, we're all like fairly risk hungry people. I mean, I think that's just a little bit of founder culture is like you want to take risks. Like you don't really want to be safe. You get bored. You get annoyed when things get too safe. But there's always this balance of like, we like taking risks that are risks to us. We don't like taking risks that could hurt, like break someone's business. And so if there's a way to do a thing that's like very risky for us, but the risk is mitigated for the actual merchant, like we're very for that. And I think that's where that kind of human in the loop, human must click save thing is part of that.

18:27Right. And who knows, like maybe a year from now, maybe two years from now, like we get to like, you know, hey, you seem to be accepting our suggestions without edits 99 % of the time. Do you want to just go into like full auto mode? Like maybe we get there eventually. We're not there yet, but like you can sort of see that the hill climbing will eventually take us somewhere close to that, right? Can you just describe for listeners some of the stuff that you think is coolest to add additions, if that's, you know, image gen or semantic search or even any of the foundations work? The foundations work is the thing that I'm most excited about, but I'm a commerce nerd.

19:03So like this is going to seem like very boring to the audience, but like just humor me for a sec. It's a pretty nerdy audience. So it depends. The data model that is the most, most, most central to Shopify is the products data model, right? Like how do you represent the products that you're selling? And Shopify has for, you know, basically like 10, 15 years had the products data model hasn't changed that much. It's like fairly simplistic. It's actually an amazing testament to how big of a company and how big of a, you know, the fact that you can get to like 10 % of all US e-commerce on like a fairly simplistic data model is actually kind of amazing.

19:38But Shopify has been a little bit on the weak side for dealing with very large and complex products. So products that have a lot of options, a lot of colors, a lot of sizes. When it gets complex, we don't do as well. And so we're updating our data model with support for just a much larger number of variants. And again, this is the nerdy part. this is one of these crazy things about tech. The reason this is so hard is because it forces you to go from unpaginated to paginated APIs when you start making the variant counts very large. And it's just a breaking change for the entire app ecosystem. And it's just breaking changes are hard.

20:18So I'm really excited about just the fact that we're unlocking that data model. We've actually also embedded in not just like, hey, you can have more variants and more options, but there's also now a standard product taxonomy that comes with standard categories and standard attributes. So that like when a merchant creates like a t-shirt, we're going to auto recognize, okay, this is in the standard category t-shirts. And that category comes with all of these standard attributes. And we're going to use AI to one, detect what category it should be in to detect what the values should be. So we're going to order to, we're going to try and guess, okay, Seems like the colors here are green and yellow based on the images you uploaded.

21:00Seems like it's made out of cotton based on the description you had. And the great thing about that is when that merchant takes those products out to their own storefront, all of that data is going to make the search experience better. But more importantly, when they take those products out to like Google and Facebook and Amazon and all those places, having that structured metadata around their products makes them way, way, way more discoverable, which is going to lead to them ranking higher in search results and basically making more money. So this is one of those weird things where like getting the data model right and having the data quality be very high actually causes these very like important effects up funnel for the business.

21:38So that's really cool. Um, uh, we are also, uh, releasing, um, the first version of our, uh, image, uh, image editing with AI thing. Um, so you can take product images, you can replace backgrounds. Basically you can take, uh, uh, product imagery and get to a very, very, very professional, uh, standard, uh, on it without having to go through like very expensive photo shoots. So basically a lot of what we do is like, how do you help someone who's like actually just, you know, my mom at home trying to start a business? How do you help that person look like as professional as like the world's largest companies?

22:20And then, yeah, what else am I really excited about in the edition? I think you just mentioned something, Sarah. Now I'm having a mental blank. I mean, I just have like I'm a search nerd. And so I think really, yeah. So here's the crazy thing. So I just learned what LBD means. Like a little black dress. Exactly. I didn't know what LBD meant until recently. But very soon Shopify stores will actually understand the meanings of things. So today's Shopify search is very, very literal keyword searchy. There's a video of me going around internally being like infuriated when I go to, there's a store that I actually buy things from.

22:57And I went on there and searched for... LBD. Not LBD. I searched for sweater. And because that store only lists their thing as sweatshirt, I got like zero search results. I was like, this cannot still be true in 2023. Like this is insane that this is happening. So that's a really easy example. But like, if you go to a store now and they haven't put little black dress in any of their keywords, if you search for LBD, it'll actually do the right thing. You can even do things like, I saw a crazy example from the team yesterday. They were like Christmas themed shoes. And it would actually correctly go and find all the red and green shoes in the store.

23:40So that's kind of an edge case example, but it's like, you know, searching for things like something to wear to a wedding, something to wear to the beach will actually do the right thing. Whereas before would only would never do the right thing unless those keywords happen to be in the product descriptions, which will never happen. So that's, I think, again, that's one of those things where it's like, how can this search experience on someone's tiny little storefront actually be as smart as like what you would get on Google? That's really cool. And are you doing like rag or embeddings or specific technical approaches like that?

24:10Yeah. Yeah. So a lot of what we have to do is figure out what the right embeddings are to use, how to fine tune the models. Like obviously there's categories in Shopify that are more like apparel, fashion, gift, homewares. So spending the time to assemble the data sets and do the fine tunings and pick the right embedding models that are best for this category. But it's also been really interesting with like, especially some of the multimodal models that have emerged in the last, I don't know, two, three months. Experimenting with how much weight to give like the text descriptions versus the images versus the taxonomy attributes and figuring out what mix of those things generates the best results.

25:00I mean, we're still working on it, honestly, but it's a pretty cool and exciting space. That's really cool, yeah, because I guess a lot of people increasingly, I feel, are adopting GPTV or the vision model, which allows you to upload images or understand or interrogate them. And so it seems like there's a lot of information just resident in visual imagery that people aren't really making use of that now you can translate into a textual understanding and therefore tie that into everything else. It's associated with the merchants. That sounds very exciting. And it's an amazing thing for the buyer experience.