Latest / The Interline Podcast / The Image Trade Is Booming. Can The Trust Economy Keep Up?

Transcript

The Image Trade Is Booming. Can The Trust Economy Keep Up? · The Interline Podcast

• speaker-0: Why is catalog so important? We know it's so important in e-commerce overall, but I think it's important to state that what's happening with agent e-commerce is, and what's new and what's the opportunity for sellers is AI for the buyer is able to match the need from the user with a product that has attributes. And for ChatGPT Gemini to solve your need, like if you're like a quarter of buyers are using ChatGPT Gemini like six months ago. for purchase. It's huge and it's growing very fast and for that to happen you need to have the AI being able to find the attributes. speaker-1: Hey, and welcome back to the Intel'n Podcast. I don't know if you've ever thought about it this way, because I don't think it had quite crystallized in my head in this form either, but shopping online means buying a fiction. What we're actually exchanging money for isn't product, but images, text, and maybe video. And then we're hoping that those things accurately depicted the thing that arrives on our doorsteps the following day. A lot of the time they do, sometimes they don't. Actually, depending on the market segment, there's something missing. in between the story and the actual article a third or maybe even half of the time, hence the industry's sky high average return rates. because those e-commerce images and attributes are all we have to buy from, demand for those is constant. And the bar is also extremely high for accuracy. If the image is the fiction of the product, how closely that corresponds to the real thing is a very real predictor of customer satisfaction, return rates we talked about. Markdowns and plenty more. For a long time now, this has been putting a real squeeze on image supply. How often have you looked at a product that comes in a few different skews, different colors, different materials, but the same platform or silhouette, and then you come away annoyed because you could see video of it in one color, but none of the others. Or maybe you could see it from some angles with some materials and some with others, but you didn't get total lateral coverage across every colorway. I know it's annoyed me over the years. That happens because photographing and videoing final production samples is cost and time intensive. And because in a lot of cases, brands are wrangling different calendars and that means that some samples are shot at different times and some are not shot at all. If this all sounds like I'm making the business case for replacing photography with AI, then you caught me. I mean, kind of. From a pure economics point of view, with a cold and calculating commercial hat on, being able to just not do any of the time or cost intensive stuff and to actually end up with potentially improved image and video coverage as a result. That seems like an easy sell. It seems like something that's hard to argue against. But a fair amount of people don't trust AI images, whether they're labeled or not. And there's also a lot of uncertainty over who should be creating them or enhancing existing photos in some cases and what standard they should be creating them to. If you buy a hoodie from a marketplace or a multi-brand retailer based on generative photography or videography, and then the physical thing doesn't match the images, whose responsibility is that? Who's liable? The retailer or the brand they stocked? The marketplace platform or the seller who sold through it? And as we fast walk into the world that computational photography and image gem models built, where literally anyone can generate a believable looking photo of anything at little or no cost, what does that do to the fiction of buying something on These are all big, heady society-level questions, but they're also the foundations for the roadmaps of a big cohort of generative photography products and applications. It's a hotly contested space in technology, and there seem to be a few notable players emerging there. Photoroom is one of those players. It launched in 2020, literally a couple of weeks before we launched the Interline. And while it was originally centered around homegrown models for quickly removing backgrounds from product shots and other kind of common Shopify seller workflows. The company got ahead of the generative AI explosion and they managed to ride that to a 500 million US dollar valuation by the start of 2024, by which point they were processing around 5 billion images a year. Today, PhotorOOM has clients like Netflix, DoorDash, and Decathlon, as well as a big stable of much smaller users who sell through Shopify, Amazon, and other marketplaces. And it finds itself at, or at least near, the forefront of not just building a product to edit and generate photography, but also understanding where that product fits into the world and wrestling with what trust means in an e-commerce landscape that I think is about to get rocked by some pretty fundamental questions. PhotoRoom recently produced a survey report called The State of Gen.ai in Marketplaces 2026. So I brought on their CEO, Matt Reif to run down some of those findings, but also to build out yours and my understanding of a space that's changing way faster than I think a lot of people realize. Let's see what he had to say. Matt Reif, welcome to the Insight Podcast. Thank you. Pleasure to have you here. Thanks for joining. Now, we always start these shows with two things with every guest. We do a question about their day-to-day lives and we ask them to define something. So first up, what does your typical working day look like? So... I talk to a pretty broad spectrum of people for these shows and some people have really well-defined roles and this is a very short question to answer. For CEOs, there's typically a bit more to wrestle with and you're the CEO of a company that's now I think a hundred people plus. ⁓ So walk me through what your day to day is like. speaker-0: Yeah, the room is a bit more than 120 people. The peculiar thing about the CEO job is like your job is to get yourself out of your job. So the routine kind of changes every over month, every six months. My routine now is mostly into, I'm trying to organize my week into weekly times. Like I have a good and quite senior management team now. The CEO job is to build and organize the team. The team is the product. So like I would like concentrate like meeting with the leadership marketing product sales finance, like two days of the week. There is like a one day of the week of repeating the vision mission, updating the team to make sure everyone has clarity about where we're going. And then my, my passion at the end of the day is, ⁓ you know, AI and photography. So talking to users and talking to agents makes the rest of it. Like always playing with the latest AI and trying to ask users like, why are you using a FOTROM? Like, what would you improve? I would go in the street, every time I see someone using it and having a problem, I try to get deeper and deeper. Yeah. Product interviews, at least like 10, 15 minutes every day overall, like talking to users. love that. speaker-1: Cool. And I did read an article recently that said that the best kind of AI strategies start from the top, i.e. they start from people who are hands-on with the latest models and things like that. So I think it's interesting to note that you're still in that playing around mindset, even though you're managing 100 plus people. speaker-0: Yeah, I mean, I'm working with tons of agents both on the text and image side. Every morning I actually start with reviewing emails that my agent is writing for me, for customers, for leadership. so if you want to build the future, you have to live in the future and you have to be using AI all the time. speaker-1: a good attitude to have. It's kind of similar to the one that I have, although I spend more time experimenting with it from a writing and kind of understanding the model's point of view so that we can then confidently help analyze and talk about them afterwards. Right, now let's do our definition. And it's a question that sounds simple, but it was probably a bit to unpack. So I'll do the unpacking. And the question is, what is a seller? So you build photo rubbers being designed for everybody from side hustlers, people who sell. just as a hobby or as a side project to Amazon. And there's a kernel in there that I think is really important context for everything else we're going to talk about. The codification of e-commerce and transactional platforms, things like Shopify, full service partners who do inventory, warehousing and distribution for you. That's all really opened up the flood gates. So historically you needed to be a good sized company with a lot of infrastructure, insurance, everything else to sell online in a way that looked professional. Now basically anybody can do the same thing. from all the way from product detail page, PDP to fulfillment. ⁓ Selling is a very democratized activity. Paint that picture for me, what you define as a seller and tell me if you feel any tension when you're building a product for an audience across a spectrum as big as that. speaker-0: Yeah, that's, I mean, it's a question we are in the definition we always have at Fodrum. I'll tell you what's a bit unique about it. So for us, what really differs is your, is someone, a seller is someone selling physical goods online. And so it goes really from the side hustler and we call them like aspiring sellers or aspiring entrepreneurs or side hustlers. And really they might like their testing waters, their they have this idea of a business is just getting started or they just want to have a good impact on the environment, give a second hand to the Amazon or DoorDash, which are also customers of us, which are actually sellers, but also selling platform kind of. And so really the primitive of the seller is like someone selling a physical ⁓ good online. And the common with that is like today, ⁓ people buy pictures and they receive a product. so for all these people, like ⁓ 95 % of conversion is like seeing a picture of what you're buying and then receiving something after online. Some might have like a physical like a moment box shop, a brick and mortar, but common ground is they're selling on internet and they're selling physical goods. speaker-1: think that's the, that's important framing. And I think the idea that the kernel that you got out there was that we buy on the basis of images and then we judge satisfaction and quality and everything else on how closely the thing that lands on our doorstep corresponds to the image that we saw. speaker-0: Exactly. You want to shed the best light on the product. I don't know if it's makeup, but really looking good. The nightmare of a seller is the return cost of ⁓ physically having to return and on all the returns and the trust that you might break with your buyer if you lie. Commerce is about trust. So if there is like a huge gap between this picture and the product, it has a huge cost. It's all about trust. speaker-1: Absolutely. And that's something we're going to get into, I think, as we go as you want to showcase the product in the best light, but not an unrealistic light. ⁓ because all that does is get you a sale and then that sale translates into a return as it turns out that the product doesn't match the original kind of visual depiction. staying within that context then, understanding what a seller is, let's talk about why images matter so much. ⁓ we've just talked, we've just said people buy on image, they receive something physical. You worked on a report recently, ⁓ state of gen AI in marketplaces. And as part of the survey portion of that, you found that 90 % of shoppers put product visuals at the top of the decision-making pyramid. So images go way before, and text, brand loyalty, everything else. That seems right. Seems logical. ⁓ We're a visually geared species and it seems pretty clear that pre-AI at least, The demand for images outstripped the supply because there was such a vital decision-making criterion for people. They wanted more images. And in a lot of cases, I imagine sellers were not able to provide those images for logistical reasons. So, you know, I'm going off personal evidence here, but it's been a pretty common experience for me to check out a product that comes in multiple colorways or materials. Let's pick a sneaker, for example, right? And you find that there's photography and videography of some of the colorways from some angles and not others. Or there's videography, but only for one version. have somebody doing a walk on and a walk off with the shoes, but not in the color that you're interested in. It's very clear that sellers can't or couldn't pre-AI produce images in the volume and the accuracy that the buyers demanded. So give me some grounding in the scope and the scale of the problem. People clearly want images and they're the most important decision-making criterion. Give me some. I'm really branding that. speaker-0: Yeah, of course. As you say, 87 % of shoppers say visuals are the most important and the complexity of it is like post-money. know, like one day of studio, like minimal, cheap photo shooting for companies around like between five and 10K. That's kind of how low you can get before in traditional photography. Not only it's expensive, we see lot of brands that would... Some brands have the money to do it, but it's also slow. You need to get the apparel or the product that you want to photograph. You can't compress the time that it's going to take. And we do see, in terms of numbers, we do see numbers as like 40, 50 % of more sales of an item in a catalog if you do have the right images. ⁓ not, not having the right color, for instance, it's like, it's a huge, huge problem. It's really money you leave on the table if you're selling something. So that's, that's a huge problem. And, and then it's also limiting it. Like you mentioned the color side and sometimes like, you know, you have a logistic issue, you don't have this color anymore. The brand that the fashion has changed. there's so many things that can get in there. And then you have this incompressible time of, ⁓ of well taking traditional photography path to get the image out there. so like teams just drop, like they don't have the resources. It's too many, I think work too much organization and they just don't make it happen. And as you say, like missing colorway is, ⁓ is even worse in fashion. Like we're covering all the verticals, but we have a specific focus on, on, on, on fashion. And like we just released, I think recently, like a few, three, four months ago, color. change color specific tool. we saw huge engagement on that. Having a way to have the different variation of your product has been a huge, huge unlock for our users. So that's what's the problem of the supply side. It's very, very difficult. Why does it matter on the demand side is your brain is processing images 10x faster? than text, like our brain is hard-coded. ⁓ Human brain is, I mean, animal brain overall is hard-coded to process images faster, faces, ⁓ and it's much more like, so it is processed much faster. So you make decision like ⁓ in a snap, like really like almost non-rational decision on images where like you involve more of the thinking brain when you go over like a description. So all of that, if you can connect like faster decision, with higher conversion and then creating images is like 10x, 100x cheaper, then it's a huge unlock for all the sellers. So this is like kind of setting the pace and the setting the environment of what's happening right now in ⁓ a fashion industry. speaker-1: Perfect. That's a good summary. ⁓ Getting a bit more specific, what does the marketplace demand for visual content look like and how does that differ from the own channel demand and where should that content come from? So I went back to your report again. I think you found that like 60 % of people lose trust in a marketplace if they see inconsistent images and more than 70 % of people believe it's actually the marketplace's responsibility, not the seller's to enforce content parity and quality. Weirdly to me, that's the opposite of what I think I would have heard if I'd walked into the boardroom at a multi-brand retail or marketplace a year or so ago. I'm not done any selling myself. We've done a bunch of work over the years with marketplaces, with the companies that sell through them. And I know that those companies, the marketplace owners have set really high bars for the brands they're willing to stock. And those bars have transferred basically all the asset and data provision responsibility, i.e. images, written information. clients information and so on, onto the sellers and the providers. Because otherwise, if you're a marketplace brand intake is just an infinitely scaling challenge. Every new brand you bring on board, you're shouldering a huge burden. It sounds like from a customer point of view, at least that script has flipped and people believe it's the marketplace's ownership of that. Do you think marketplaces and multi-brand retailers see it the same way that customers do? Do they, do they see it as their responsibility? ⁓ and is it the case that. AI has basically obliterated a pre-existing bottleneck so that marketplaces can then create that content and have infinite headroom to do it. Because historically they would have said, no, you supply me with the images because otherwise this is a huge burden and a huge job for me. Now it feels like they could change that framing. speaker-0: Yeah, I totally see talking to Marketplace directly that there is like Marketplace do believe, most Marketplaces do believe that there is, the work should rely, should be on the brand themselves, on the sellers to provide the images. And they actually, sometimes it don't feel like they have the right, you know, to kind of ⁓ change some of the assets for bigger brands. Well, the stats is clear from the consumer side. If they believe that 70%, they believe that it should come from a marketplace responsibility, it's this idea of breaking trust. If a seller is lying with the images, on the marketplace, you're not breaking the trust of this specific seller in the marketplace. You're much more damaging the trust that you have in this specific marketplace. So that's where the marketplace owner and people running this should be. really conscious about that. speaker-1: Yeah. mean, if if you buy, if you buy from Amazon, your transactional relationship is with Amazon, not with the person, not with the company selling through Amazon. speaker-0: Yeah, exactly. The guarantee. And so that's why like so far, like in the past, there was like this, this kind of guarantee and like you remove and you have a high bar on the images to like remove the sellers that were not, they were not playing the game. The challenge and what everyone is saying, especially the marketplace is because they want to increase their, their, their GMV and the inventory is that images are often the bottleneck. So now with AI, like you can really get a lot more sellers on your platform to play with your guidelines. What I do believe is, we do see a huge uplift, like Rappi and the food space has been increasing by 20, 30 % by getting more involved in the images. So just you get more business. So what I do see is the marketplace are a bit more dynamic. They are closer sometimes to the customer. And so they do see customer going to other places and becoming a bit... well, becoming irrelevant if you don't act on what's possible. Like if the inventory gets bigger, you have more liquidity and then the best marketplace is your competitor. So there's this huge challenge of like staying in the game and really doing what's best and what's possible to increase the inventory. What I do think is like with AI, you do want like, can have customized images for everyone. And so what does make sense for me is that the marketplace can brand as much as a seller, the imagery to give the best UX user experience to the buyer. And so I believe, and we are doing that with multiple marketplaces, you have the seller providing some images and some guidelines of how they want to communicate and also images or information. And the marketplace also customizing them. You don't want to see the same images on one marketplace and its competitor. So actually, it's more like as the cost of imagery is going down, the marketplace ⁓ and the sellers will build a combination of what's going to sell. Because the marketplace knows the seller better, they know what they're looking for. They want something that looks ⁓ shape, specialized for our vertical. In sports, could be someone specialized for our skiing, for instance. So you do want to have this customization that comes to the need and the segment that you have as a marketplace. So it's on both sides. speaker-1: Yeah, I understand that. think as a marketplace then I would be asking myself a question, would be, do I want to do that image enhancement generation, iteration myself as, as, as a job, or do I want to offer it as a service to my sellers? Kind of white label or something along those lines. Do you see that kind of dual pronged approach or do you think most marketplaces are just saying, well, this is, this is representative of me. My transactional relationship is with the consumer. Therefore, this is my job. I'll take the seller's recommendations and stuff as intake, but it's my, my taste, my curation and everything that puts out. Or do you think there's a place for the platform approach as well? speaker-0: ⁓ I do think it can be an additional service that some sellers are willing to pay. At the end of the day, matters is are you converting or are increasing your GMV? The numbers speak. Today, think the problem with the premium services that is sold that we've seen is less number and it's more difficult to adapt. the most successful marketplace we've seen or rolling out as an option to all their sellers and as a strong brand approach and a strong innovative approach to say, do the kind of the easy, you know, minimum, like have 100 % coverage of our images. We know that if we have five images for every SKU, we're going to sell more. So let's do that kind of the basis for everyone. 100 % coverage, five, six images for every SKU. We know it is going to increase their GMV and it's a win for everyone, so let's do that now. Now we might have some premium sellers that we can reward or help with a better service and communicate some tips so they can sell even more. It's kind of a mix of a reward and help to boost yourself or your best sellers. And this is applied sometimes as a second step. like, I can give this service, I can build some... ⁓ retention for this like top selling ⁓ providers, sellers on my marketplace by giving them extra service or that maybe they have retail media in the platform. And so I can do also ads as a different visual and I can provide that sometimes it's part of a premium subscription. Sometimes it's not. It's kind of what we're seeing. speaker-1: make sense because the marketplace again, as the owner of the relationship with the customer, if it fits their business model and if it's in their commercial interests, can do client telling. They can start to build in what they know about the consumer into a premium service, into ways to give their sellers an edge selling through them as opposed to selling through a competitor. speaker-0: Yeah. the more you, I mean, it's actually the more you're branded and unique to the marketplace, the less you can actually advertise. Like you create good images and if your seller are starting to use them and you manage to like, yeah, get your brand guidelines into as a marketplace in that if people like really, it's a way to advertise in case the seller is using you outside of the marketplace. it like people get caught, like the marketplace can be very smart about how to use that as a marketing service too. speaker-1: It makes sense. You have and you have photo room users on both sides of the equation there. Marketplace users and individual seller users. speaker-0: Well, to back to your question, what's interesting is like initially the marketplaces came to Fotturum that was like, because the best sellers of the marketplace were using Fotturum. And so that gave the marketplace say, it's working for my best sellers. So I might as well get all the benefit for everyone. And let's look at the economics and often the economics work super well. So they roll out to everyone. And then the innovation comes from the sellers that are using the new AI feature like virtual trial that would be in Fotturum. And so there's this loop of always increasing, elevating the bar for visuals for sellers. speaker-1: Yeah. And I think it's very clear across this conversation that the unit economics of generative imagery versus traditional photography will, will continue to make sense. If you look at this through a commercial lens, right. If that's your sole lens, then that's definitely why you would make that decision. Aside from visual content, what about AI when it comes to product attributes, tagging and catalog building and so on. So for fashion, when I talk about attributes, I mean, you know, material, trims, fit, and so on. ⁓ maybe sustainability profiles, certifications. That's another place where I think marketplaces and multi-brand retailers, department stores have shifted most of the burden onto their sellers. but it's also an area where a lot of them are struggled, which is where multi-brand retailers are struggling right now. Because a lot of the data that they get given from a brand level is a mess to begin with, or it's all kind of consolidated into one blob, as opposed to being granular and separated. And. A lot of what marketplaces and retailers want to build. So whether that's semantic, natural language search or personalization, like you just talked about, requires you to have a really granular data foundation and really impeccable governance. How do you see that side of things playing out? speaker-0: Yeah, I think we've seen a strong impact. So we've seen like 90 % reduction in the report for onboarding time, like for a new platform where a seller wants to sell, and a lot more attributes that are being filled, like 37 more additional attributes per product automatically filled. That's what we've seen with Miracle. We are working with them. I think if you take a step back, it's important to go back to... Why is catalog so important? We know it's so important in e-commerce overall, but I think it's important to state that what's happening with agent e-commerce is, and what's new and what's the opportunity for sellers is AI for the buyer is able to match the need from the user with a product that has attributes. And for ChatGPT, Gemini to solve your need, like if you're like a quarter of buyers are using Chagipiti Gemini six months ago for a purchase. It's huge and it's growing very fast. And for that to happen, you need to have the AI being able to find the attributes. And that was not so important before because it was mostly ads that are solving that, or you knew this product or brand. Now the AI can match a need that is ⁓ expressed by a user. And they can match this need with ⁓ well, all the attributes that are made. So this is very, very important now. And I do believe actually on the visual side that some of the attributes are going to be actually provided to the AI with the imagery too. An image is worth a thousand words. So if you want the exact dimension of your product on a fashion marketplace, actually you get five dimensions on your text. But if you want to all the dimension, all the lines you want to sketch, this are in the image and the AI will be able to match that. So it's very important that the information provided both by the attributes and the image are there. It's more true with fashion where you want to have like virtual and see virtual trial and see where how this like a piece of apparel would fit into you. There is another topic of localization of how do you match the attributes for like between like a French 38 is not the same as an Italian 38. So the AI is also helping you when the attributes are not the same. for different marketplaces or for cross-country selling. speaker-1: Yeah, I do, I do have some concerns I have to say around kind of generated images, synthetic data and things when it comes to extrapolating sizing information and points of measure and things like that. think, I think it's a potentially a solvable problem, but it's, it's a solvable technical problem that relies on you having potentially a complimentary and slightly different data input than just the image. but I think maybe time will tell on that one. ⁓ I am, I know people definitely want virtual try on. know that virtual try on is proceeding in parallel sort of trajectory to generative imagery as well. I feel like, I don't know, I feel like there's an opportunity there, but virtual trial has been tried a lot of times before, ⁓ and hasn't kind of seen mass consumer adoption for a bunch of reasons. I'll be keen to see how it plays out this time. speaker-0: Yeah, I think it's a good point. love your, I think you're, well, we do see like we, had like some like small marketplaces trying virtual try-on that see like a three X conversion, right? So it's, it's interesting. What matters is what's your return there. I agree with you that like image alone is not enough to tell you what is the size, but like a multimodal AI is really where the industry is going. And so a mix of like, you know, well, you have the image, you have the imagery. You have some depth data that can come from stereoscopy from your phone. The video is also including a lot of... That's kind of the idea of the world model. That's where all the AI labs are going, you want to understand the physics of the world from the capture. We're not there yet, but this is where the research is heading and making huge progress. So I agree with you that it's not been there, but there is huge scientific leaps that are made right now in labs that might have big impact in the coming six months, 18 months. speaker-1: Yeah, I would agree. I think one of these days we have a separate conversation about world models and the difference between visual representation and simulation. think if you cast your mind back even a year ago, the idea that you could automatically You know, through multimodal AI, you could recognize material types, you could recognize patterns, you could take a good guess at construction techniques and things like that. That all feels viable. That all feels like something you could go after now. You mentioned this earlier. the marketplace kind of feeling that maybe it was not their place to generate imagery on behalf of sellers. That feels like a risk to both parties to some extent. If we think about every product detail page. And let's imagine a hypothetical PDP where ⁓ maybe half the images are either enhanced or generated by the retailer or the marketplace. Maybe some of the text is as well. The attributes are automatically tagged. Does that mean that the brand and seller is ceding a historic amount of control to the intermediary? Right? Cause smaller brands have always had to accept a bit of a devil's bargain when it comes to getting stocked. But even bigger brands, know, Nike and so on have had quite. back and forth relationships with marketplaces because they get distribution, but that distribution comes at the expense of ownership of the channel. And it comes at the expense of brand integrity. some cases, this, to me, this whole space feels like it represents a new risk vector for returns. As we talked about, like if you, if you generate imagery or you enhance imagery in a way that is not going to be. Substantiated by the product that actually arrives, who has that liability? As you know, that's, that's an open question to me and How much of the control over the route to the consumer does the brand and the seller want to give away to the marketplace? There's two questions being baked into that to me. speaker-0: Yes, trust is the most important factor. And if she is like a return that is higher because the generated images are not the same as a product. people buy products and people buy pictures, they receive products and it impacts both the brand and the marketplace. that's, that's a tricky part. think the tricky part that I would add as like extra context on that, when I talk to marketplace is they like some of them, they wish the brands were doing it. I do think as like Marketplace might have like a less, mean, didn't invest as much historically in the brand and they're closer to the customers. They are moving faster. Like they wish the brand were doing it. like we work with Palmanjol as a brand for instance, and they initiated in this case. So you see a few brands that are very proactive. ⁓ Zara is also working on that. So there are some brands that are taking advantage of it and they're taking control of the storytelling of the brand and their visuals. And these are getting the best result. What I do see is the marketplace are acting because they know they're going to sell more and it's how you bring growth and stay relevant in the eye world. And they wish the brands were acting and the brands are a bit slower to move. My advice to, mean, we worked mostly with ⁓ marketplace historically with working with more and more brands like Palmenchal, but My advice to brands is start seeing what's happening in this case because it is a big opportunity in the way. Visual media has always been very innovative from camera 200 years ago to movie 100 years ago. this is just another step. These were very innovative technology by definition. speaker-1: think being proactive is a key part of it. Okay. So I want to talk a bit about photo rooms, a product now, because I think you operate at a pretty interesting juncture of the market. And I think the fact that you just talked about the journey from working with marketplaces to working with brands is part of this. ⁓ What does your user base look like at the individual role level? And do you see it changing? ⁓ You know, I don't, I don't want to describe what you're doing as being like the Canva for AI, because I Canva wants to be that, but it looks to me like the ambition is the same. You want to make generative product photography accessible. to everyone. ⁓ And we just talked about the importance of kind of being proactive from a brand point of view. I'm keen to know what that means for the people who would normally have like held the reins on that side of things. like who your user base is, that would be purely marketing and e-comm and so on. But you're also leaning into something that I'd classes may be closer to product iteration with recolor, which allows users to change garment colors, maybe just components and so on. And those are product development activities, not marketing or comms or e-commerce. Do you see the user base staying marketing and sales and e-commerce, or do you think there is a push for you there into the sort of product creation user base as well? speaker-0: I love this question. So I love Canva. I love to be compared to like the FOTROM is Canva for AI. I think what we really decided to double down on ⁓ is being the visual solution for every e-commerce business in the world. So really doubling down on e-commerce specific solution and workflows and models is ⁓ very important to us. And that means I think it's three layers and you see more and more of these AI companies like ⁓ cursor on the developing side. basically, we're the best at leveraging the labs model for the e-commerce specific use case. We do some pipeline pre-processing, post-processing that is the best for e-commerce. We develop our own AI models like background removal, AI shadows, which give generalized shadows to your product. It's very important for PDP. Recoloring that you mentioned. And then we package that into like an e-commerce specific solution product that is like really making like product fidelity is very important for e-commerce. How do you make that in the user experience very important and make it easier for your user to check that? How do you update your Shopify visuals? So these are the things that we'll, we have a Shopify connector. So really these three aspects of making the most of the AI labs and models, some specific models that we train internally and then building the product around that is how we specialize and our verticals specification is e-commerce. Our users are like from side-osler, small business owners, resellers and marketplaces to some more established brands like Palmenjol that I mentioned, and to Amazon is using us, to World Ash is using us. So the biggest e-commerce platform in the world for building their tool. speaker-1: And I think you, you know, you mentioned it there being, being the best environment for the models because the generative photography is a pretty, can test its space. The generation is the easy part. It's a weird thing to say like a couple of years into the whole generative AI revolution, but anyone can generate an image for free or for some cheap tokens. I, I'm not going to call it a solved problem, but it's not far off. It feels like the differentiation happens in building the application stuff around it. ⁓ big part of the application stuff for this is organization labeling, repeatable workflows, batching and things like that. Maybe like API based programmatic content creation. ⁓ do you see that as kind of the space that you want to spend the most attention? I'm keen to know how big a factor that is in your product building. speaker-0: Yeah, so we have a batch editor where you can edit 250 images at once, 4K. And then we have an API for bigger volumes that is also quite used. We have users using, like, editing millions of images. What I say when e-commerce is kind of a focus on the primitive, if you're selling, the photo is not what the user is, the buyer is seeing. The buyer is seeing five photos, probably a video, and a description. And so we really want to have there some consistency between them. And that's something, for instance, we invest heavily on to be able to see and edit your kind of PDP page visuals all at once and understanding how they fit together. So we do invest a lot in that. I wanted to come back to your question on recolor and product iteration. I think it's very interesting. And I do think what Fertrum does and other AI provider in other vertical is really making some businesses and some product that couldn't exist before. possible to exist. You can recall or put online, sell, even if you haven't printed this item yet. And so this gives life to product that maybe in your catalog where you didn't know if there was demand for it. So that's very important and you'll see a big impact on fashion for that. speaker-1: Yeah, I think there's a very, very weird thing happening at the moment where design and marketing are basically the same activity. when you, you know, historically you could not have sketched something and then instantly seen what it would look like photographed. ⁓ and, and the fact that those things can happen in the same environment and the same workflow changes design. ⁓ and I think that's, that's going to take us a while to unpack what that actually means for design and product development. There's a couple of final stats I want to pick up from the report. The first is that a majority, small majority at 55 % still, of people say that poorly executed AI images decrease their trust in whatever platform is trying to sell them something. And you also describe photo rooms helping to deliver visuals that convert better without looking like AI. What does AI look like now? when it's detectable. don't think we're talking about having six fingers on anyone's hand anymore. There's still some tells that you can spot if you spend enough time on these things. What does AI photography look like? And on the flip side, how often is AI imagery put in front of the average shopper right now? And they don't notice it. speaker-0: Yeah, I mean, every image that you take on your smartphone is AI edited. it's kind of like everything will be AI generated at ⁓ every pixel. Today, it's not six finger. It's not the realism of the photo that matters while you still have some challenging. It's more about the productivity that matters and the model. Like if you're talking about human, how much realism you get on the skin. So it's still a bit glowy. So the texture of the skin is not represented as well. For the product fidelity, you see that in the fine details, the logo that will be on the piece of apparel, ⁓ some part of the texture might have some details that are changing. And that's where you can break the trust to the user. And that's why Photorome is developing special algorithm machine learning models to really understand and check the specific productivity items that are very important. And so we're helping our user to do that. We pick the best model based on that, not based on creativity. This is where is the eye slope, that if you just generate one key image with low context and don't take the time to check, you might see these eye images. speaker-1: Yeah. Particularly if you then upscale them afterwards. Then I think, think upscaling is where a lot of the artifacts and things get introduced and I've noticed. speaker-0: Exactly. And you want to pay attention to the context window, how many images, how do you process the source images, how do you tell the models what is a source image, what is an inspiration image. All of these are very important in the pipeline to get the highest fidelity. Upscaling is not a good solution. speaker-1: Then there's the cultural side of things, right? So your report showed that around 40 % of people are uneasy about AI images in general. And even if you label them as being touched or created by AI, that doesn't really move the needle. That doesn't sort of get people to say, okay, it's labeled, I'm fine with it. How much cultural resistance to generative photography still is there? And if, if transparency, if labeling isn't the answer, what do think it's going to take to wear that resistance down? speaker-0: You know, I mean, one thing that people, when you talk to customers, happening is like you tell them Ikea images have been ⁓ 3D rendered for the past 15 years, and then they're all like surprised about it. But people are really happy to buy on Ikea. I think it's like people, when they see things that are, I mean, the end goal is people understanding the information that is coming from the image and see if it's a good product for them. So as long as you're not, like, as long as you focus on trust and you make it easy for every business to do it, I think people are super happy to, like we do see people super happy to buy from it. They're uneasy because they see like, I mean, it's not on the e-commerce, it's more like what can you do on the political side, how images can be used for the media and manipulation. On the e-commerce, if you really focus on trust and it really reflects the professionalism of the seller, that is important. I had a marketplace ⁓ owner telling me, You know, the problem is you do want to reflect a bit of the care and the craft that was put by the seller, by the images. Like if every image is perfect, then it looks like all my sellers are five stars. And so what's the point of five stars is everyone is five stars. ⁓ But we do see people buying from that. And there is like the opportunity that everyone gets a white glove image that really understand their pain point. Like you want to see this share in your living room, virtual try on if it's possible, it's fantastic. The question is, is it building trust? So it will happen. There are some technical limitations, but I think people will see the benefit at least on the e-commerce side, like very fast. And we're seeing that the uplift. speaker-1: Yeah. And I think it's, to your point, Ikea has also been my reference point for a lot of this. 3D CG was not wrapped up in cultural tension the same way that AI is. nobody was using math, at least at mass scale. Nobody was using 3D modeling and texturing and rendering for mass deception and everything else that the AI is being rolled up in. And I think there comes a point, particularly in the enterprise, particularly in work contexts where You have to be willing and able to separate that wider cultural challenge from, we said, the pure unit economics, the pure value side of things here. speaker-0: Yeah, it's a very powerful tool. So if you use it with trust and you have an IKEA brand that is important, IKEA cannot lie to users in their imagery. So they really try to have the 3D rendered image to be as good to reflect the product is. AI is just a better tool than 3D for that. And it will help a lot of businesses, but they need to build the trust with the user and we're helping them do that. speaker-1: I think it's a better tool than 3D in almost all respects when it comes to visualization, but not necessarily the accuracy side of things, particularly if you think about virtual fit and so on. feel like there's, there's a complimentary role for the two to play there. speaker-0: It's true. Yeah, IKEA is interesting because you need to design in 3D. So you do have the 3D model anyway. I think most businesses don't have like the cost of 3D and I can get as good and we'll get as good as 3D in the future. speaker-1: So finally, we've been talking about all this as though humans are going to be the ones looking at product listings, placing orders and so on. But I think there's reason to suspect that maybe AI agents are going to be on the receiving end of buying if we look a little ways out. Provocative question, are we optimizing a dead end with this? ⁓ we putting a lot of time and effort and model and time and tokens and things into creating images designed for people to see them when in fact what AI agents want is an image free, pure data backend access. Do you think, do you think there's a dual pane future here? Do you think there's a, there's a future where every marketplace has one human designed front end filled with great quality images generated or otherwise, and a machine readable backend or an MCP server that just deals in raw data? Or am I thinking about this wrong? And there are kind of AI native marketplaces that I'm just not understanding yet. speaker-0: Yeah, well, I think it's not a dead end in the sense that clean catalogs, it's like really serves human and AI agent like equally. So you need structured data. We know that if you have like a multiple visuals, if it, even if it's an agent at the end, like if you have visuals, you'll buy more like because at the end of the day, it's still a human clicking buy today and the buy button just makes like make it faster and like reduce some of the selection. I would add that like more and more when we talk to marketplaces, are like people love shopping. So depending on your vertical, like people want to see the images and they enjoy like spending time on it. So it's not at all a dead end. And then I think the most important part is like, you know, an image is worth a thousand words is true both for humans and agents. Like images contain so much information that you can't write in text that I mean, there's no words like that. We're not just a text species. There are lot more information that you can put in an image that it's very difficult to write down. Like dimension, size, all of this is connected to the image side. And so at the end of the day, even the agent will need the images to be able to serve that. And then they will be able to serve this image to the end buyer. if you're, let's say you're looking for outdoor gear. But you know the need from the human is coming from skiing in a specific place. Well, you do want to serve an image that is specific to skiing. So the agent will be able to render the base image into a specific use case and you'll have one image for each transaction. I think this is the future. This is where we optimize and people can buy at first sight because this is the first image that matches exactly their need with all the information. And so I don't think it's a dead end at all. It's going to be even more images that are going to be needed by agents to provide the information to the buyer. speaker-1: like that framing of the agent being doing the shortlisting if you like, and then providing the user with a shortlist and the user is then making a determination based on the full spectrum of written information, visual information, and so on. ⁓ I also think you're going to have more of a technical viewpoint on this than I do. It feels like real time personalized imagery or near time personalized imagery is, is feasible-ish or feasible fairly soon. ⁓ by reducing the amount of time spent on that. Or while you're providing a written answer, be generating a customized personalized visual representation of the product in the backend so that the two can be served at the same time. That's then a completely different surface for generated imagery and a completely different conversation. But I buy your point. The idea that, you know, this is the image matters right now. And I think the image matters as we go into the future as well. speaker-0: Exactly. speaker-1: Perfect. Matt, thank you so much for joining. I've enjoyed this conversation. I hope we get to have it again soon. speaker-0: Me too. Thank you so much for your time. speaker-1: And that's the end of my chat with Matt. You can probably tell there's some real uncharted territory here. And I think this is one of those occasions where the underlying tech and the applications being built on top of it are running way out in front of fashion's ongoing attempts to restructure, to capitalize on them. It's clear there are some pretty interesting elastic products and platforms being built here and being used by everyone from the biggest companies to the smallest sellers. It's also clear that the unit economics are super compelling in isolation. But I don't know if sellers or buyers have really come close to reckoning with just how embedded generative images are about to become in the process of selling things online. I do buy to some extent Matt's argument that the IKEA catalog being staged and rendered and indeed most homewares and furniture photography and automotive advertising and product photography being CG as well. I buy that that's kind of equivalent and that it demonstrates that people will trust synthetic images. But the world also had since the original Toy Story, so 30 plus years to understand what CG was and to become familiar with it. And when it comes to AI, we've had a tenth at that time to adjust to generative or enhanced photography. Maybe less time than that if we consider that image generation has only been properly mature and accurate since NanoBanana launched last August. Needless to say, this is a topic we need to revisit. And I enjoyed having the chance to talk to Matt about it at what feels like a critical juncture. think he was the right person. Next week we'll have something very different, though, so maybe just keep all of this pinned in the back of your head for now, and I'll talk to you again really soon.