The Edit is our weekly show, where Social Editor Grace Robinson quizzes editor-in-chief Ben Hanson on some of the most significant fashion and technology stories from the past seven days.
This week on The Edit, Grace and Ben discuss Google’s use of AI at New York Fashion Week, Daydream’s move into on-device AI for shopping, Raspberry AI’s expansion into product development, Lectra’s launch of Apogy, and what AI’s threats to infrastructure could mean for fashion.
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Grace Robinson: So, welcome to The Edit from The Interline, the show where we run a quickfire analysis on the pick of the most important fashion and beauty technology stories from the last seven days. I’m Grace, the social editor, and I’m joined by Ben, the editor-in-chief. Together, we have around 25 minutes to give you our analysis on the stories that really matter.
Ben Hanson: Hey, Grace. Good to see you again. I’m going to apologise for both of us in advance. It’s an AI-heavy week this week, but you go where the headlines lead you.
Grace Robinson: Yeah, exactly. It is, but they all are slightly different in a way, so I still think it’s going to be super interesting.
Ben Hanson: I think every conversation is an AI-heavy conversation, particularly this week. So without further ado, let’s do it. Hit me.
Google brings AI into fashion week
Grace Robinson: So the first story is about Google’s push into New York Fashion Week. Google partnered with two emerging designers. One is called Jane Wade and the other is called Sergio Hudson. They both used Google’s AI Flow creative suite, but they created slightly different tools.
Jane Wade created, firstly, a visualisation tool. It helped her to think about the styling of her runway looks prior to doing the show, and it helped her to make colour and fabric decisions. She also worked with Google to create a kind of simpler version of Adobe’s Illustrator. This is going to help her team do tasks quicker, and it also means that they don’t have to have Adobe skills necessarily.
Sergio Hudson worked with Google to create a more spatial visualisation tool. He used it to create a visualisation of what his set design would be for his runway. This meant that he could visualise it before committing to the costly build of the runway.
Now, Google has said that they’ve intentionally wanted to partner with creatives to develop these tools because they really want to champion the creativity within all of it. But do you think this is a different strategy from Google? Do you think it’s going to have a big impact on the industry? What do you make of this first story?
Ben Hanson: So Google have so many different AI products that I struggle to reconcile them and to understand which ones are which. I think people within Google struggle to reconcile these things as well.
The visual generation side of AI is one of the two areas in which respondents to our AI survey told us they perceive AI to be the most mature: generating images in general. The other is the analytical and market trend analysis side of things.
Now, generating visuals, your mind tends to instantly go to coming up with concepts for new products. That’s what most people mean by generating images in the creative stage. The same way that historically you would open a Miro board or what have you, and you’d pull together some material swatches and reference images and things. Being able to do that kind of pre-visualisation is what I think most people think of as being the image and concept generation side of fashion.
Then you have it all downstream. You have all of the product detail page photography, campaign photography and so on. There’s a big generative push there. They’re all basically the same thing, which is generating visual representations of something that doesn’t physically exist. Either you can’t have that thing because it’s not here yet, or it’s impractical or costly to build it, prototype it and photograph it.
This story is interesting to me because you can take the same principle there, and you can just apply it to a range of other different use cases. So pre-vis for show looks has always been, I think, a pretty intense creative process, but one that also involves a lot of non-value-add tasks: literally moving clothing around, layering it, experimenting with it, having models in one place.
I think this is one of those areas where the unit economic side of it makes a bunch of sense, the same way that it does in product detail page photography. If it’s faster, cheaper, more flexible, more agile and more creative to be able to do some of that pre-vis stuff for a show with AI, I think it’s logical, and it’s a decent fit.
Then the other side of things is if you want to do architectural visualisation, which is what runway builds, actually building out a runway in an environment and stuff around it, technically is. Historically, you would have done that in 3D CAD, or you would have done it with some 2D sketches and some artistic licence. Again, that’s something that you can do faster, easier, cheaper with AI, and in a way that I don’t think is taking away anyone’s job. It’s not taking away any creative potential.
That’s true of both of these use cases, really. You have teams or individuals who previously would have spent a lot of time doing something manually, who can now visualise it in a way that gets them closer to the end result and that cuts time and cost.
So does AI make sense to fit into fashion weeks and into runway shows? Yeah. It seems like it does. It seems like a pretty clear fit. Is this a product for Google? Is this something you’re actually going to go and productise and bring to market and give it a proper name, and do anything beyond this kind of halo experimentation? Who knows?
It seems like something that’s destined to fall into Google’s pile of, “We have this AI idea, and we tried it, and isn’t it cool?” And it’s somewhere in this scattered assortment of products that we have. But you don’t need to partner with Google to do this. If you’re putting together your own runway show, no matter how big of a designer you are, you can do exactly the same things here with other off-the-shelf models. You don’t need a formal partnership for it.
When you put this one across my desk, I was initially a bit like, “Okay, AI involved in fashion week. Here we go. I wonder what that’s going to look like.” Actually, deeply practical, deeply useful. I just don’t think it’s necessarily going to turn into a formal product.
Daydream and the potential of on-device AI
Grace Robinson: The next story is about a development from Daydream, the AI shopping discovery application. They’ve just built two new features using the new iOS 27, Apple’s new iOS.
Essentially, the first one uses Apple’s image-context capabilities. That means that any image that is in the camera roll on your iPhone, Daydream can analyse that image and then make the image shoppable. For instance, if I had a picture of myself wearing an outfit, or if I’d screenshot a picture from Pinterest or from Instagram that I wanted to use as outfit inspiration, Daydream will essentially analyse each piece of the outfit. Then you can click on it and you’ll be able to shop it. I think Daydream is also recommending different pieces that could go with the outfit.
On top of this, they’ve also just built in this Siri system for the app. So now a user doesn’t even have to open the application on their phone. They can simply grab their phone, use Siri and ask Daydream to find a new product for them. I think the example they gave was someone needed a new blazer for a board meeting. They could just ask Siri and then Daydream would find it for them.
I also think Daydream is now incorporating shopping passports. I know we spoke about this a couple of weeks ago with EcomID shopping passports. So I wanted to get your take on this. Do you think it’s a novel new experience and a new feature of the application, or do you think it’s a good insight into how we’re going to be shopping in the not-too-distant future?
Ben Hanson: I do have some thoughts, but I’m keen to get your perspective on this one. Two questions. One, have you updated to the latest iOS? And two, is this something that you think you would use?
Grace Robinson: I haven’t updated to the latest iOS yet. I will be doing it. I think I would use something like this, but only because it’s becoming more and more spoken about and agentic shopping is getting bigger and bigger. So it does seem like it has value, but I don’t think I would have been an early adopter of something like this.
Ben Hanson: Okay, fine. So I’ve also updated to the latest iOS, although weirdly, I’m on the waitlist for Siri AI, just professional curiosity more than anything. So I can’t test this myself for a bunch of reasons.
Now, what you’re describing with the camera roll stuff is, ironically, based on what we just talked about, another product that Google had, and I think they’ve probably since forgotten that they had. It was building the wardrobe quantification stuff from your Google Photos library, saying, “Oh, look, these are the outfits that you already have. These are the outfits that you own.” It runs on the same fundamental thing: identifying garments and pieces in pictures and then surfacing that to the end user.
The history of smartphones, not to get way off topic into general tech, is very much that Android is a grab bag of features and places that Google and its OEM partners and people just try stuff. Some of it works, some of it doesn’t. It doesn’t tend to break into the zeitgeist the same way that it does when Apple does things.
iOS, by contrast, and Apple devices are the ones that attract the most attention. They’re the ones that have the largest footprint in the way that we think about technology. I know people who are Android users get endlessly frustrated by this because it’s a case of saying, “Well, what do you mean? This has existed on Android for a long time.” It probably hasn’t existed on Android for a long time in a way that breaks into the public consciousness, which is what I think you have Daydream trying to get ahead of here.
Now, there’s two bits to it. Access to stuff on your camera roll and using Apple’s own image-context capabilities: great. You’re tapping into something that exists and is broadly available and adds value to your application.
The second part of it, though, is the most interesting part to me, which is that a lot of this is on-device. A lot of the Siri AI side of things is done locally. Not all of it. There is an opaque process where Apple hands off things from on-device to what they call Private Cloud Compute.
If I remember correctly, everything from an iPhone 15 Pro and upwards is able to run the more complex on-device models. If you think of that as the future target for these kinds of applications that leverage new stuff that your phone can do, this reminds me of the early days of the App Store as much as anything.
I’m old enough to say that felt very much like an experimental wild frontier, where if you had an early iPhone, you had that app where you could pretend that you were drinking a beer, because it used the gyroscope to simulate that side of things. Seemed completely useless, but then before you know it, the App Store completely upended application distribution, social media, Apple’s entire service revenues and everything else.
This, I think, is a good and prescient use case for that. It’s Daydream, I think, getting ahead of these kinds of on-device capabilities. Just because it’s Apple, and because of the ubiquity and the way it breaks into the public discourse, they have a chance of becoming the next kind of App Store, potentially, if AI really takes off the way that we think it will. And if it does, I think you’ll see more people building on top of these capabilities within iOS rather than doing it just in fragmented other applications.
Can Raspberry AI turn images into real products?
Grace Robinson: For the next story, we’re talking about AI coming into the product development process. Raspberry AI has announced that they’ve developed a new system of AI agents that are essentially unifying every stage of product development: design, marketing, merchandising, all the way to e-commerce and actually selling the products.
Now, this new pathway is said to speed up the product development process nearly two to five times, and big brands like SKIMS and Alo Yoga and Oscar de la Renta are already using this. Raspberry AI has obviously digitised parts of the product development process in the past, but now AI is really unifying all of those fragmented teams and elements.
So I wanted to get your take on this generally, and also ask if you think that this is going to be the start of mass adoption of on-demand processes.
Ben Hanson: Okay, so first things first, I don’t know anybody at Raspberry. Never been able to successfully establish a line of communication with the company. If anyone from Raspberry is listening to this, we’d like to chat to you. We’d like to learn a little bit about how this works.
What we have here is a pretty widespread trend at the moment, which is companies that got their start in generative image workflows. Now, I don’t necessarily mean campaign photography or even product detail photography, just generative workflows and workspaces that have a primarily or exclusively visual output. There’s quite a lot of those. Raspberry is by no means the only one. There’s at least five or six that I can think of off the top of my head.
On my weekly interview show, I interview the CEO of FLORA, Weber Wong. Again, not fashion-specific, although they have grown more in the way of fashion capabilities, but a big node-based generative workspace. There’s a lot of those. They’ve all been playing on the visual representation end of things.
I think they’re all starting to realise that when you sell to product-centric industries, industries that make physical things and the images need to correspond to the characteristics of those physical things, images only get you so far.
If you’re doing it for initial concepting purposes, they get you to a point of, “Okay, cool, this was faster, this was more effective than traditional moodboarding.” If you’re using them to sell in to internal audiences, trying to secure adoption, build assortments, line review, that kind of stuff, again, great. If you’re trying to use it downstream for marketing and advertising and content creation, again, wonderful, very well suited to that.
Our data from the AI Report tells us that the market, based on our survey, perceives real production to be one of the weakest areas of AI. That is what they are talking about here. When you talk about product development, when you talk about merchandising, patternmaking, tech pack or technical specification generation, you’re talking about real goods. You’re talking about turning images into real products.
So I think there’s clearly a gap here that companies are going after. They’re recognising that the capabilities they have only get them so far and that they need to build new capabilities. I don’t think those capabilities are easy to build. Again, I have not seen the solution here, so I can’t say one way or the other.
I will say that if you’re used to playing in the image space, the quickest analogue I could use would be Photoshop or Illustrator or InDesign, an application or a suite that’s focused on visual creation. That’s not the same thing as making a product. If we think about fashion workflows, for example, Adobe Illustrator is where sketches get made. Photoshop is where final images get retouched and polished and so on. The real work is not done in those suites. The real work is done in separate, very big solutions that have been developed over the course of decades: your PLM, your ERP, your supply chain management and so on.
I don’t know if it’s a bit of hubris. I don’t know if it’s going to work, but every AI company is converging on the same idea, which is, “How far do we go? We generate images for fashion. Why can’t we do the rest of it? How hard can it be to do the rest of it?” I think the answer is quite hard, and I’m going to reserve judgement until I’ve had a chance to sit down and spend some more time looking at these capabilities.
Again, Raspberry by no means the only ones doing it, but it’s very clear that this is something the industry is lacking and that the technology vendors are trying to build. I just don’t know if it’s as easy as people present it as being.
Lectra brings production experience to the AI race
Grace Robinson: It’s funny you say that this is becoming a trend in the industry because Lectra has also just announced that they’ve launched Apogy. Apogy is a cloud-based platform that uses agentic AI to address all of the complex product development workflows. Essentially, its aim is to centralise all of the critical product data to bring together designers, manufacturers and suppliers all into one digital workspace.
Key features of Apogy include concept generation from prompts and also from sketches or images, real-time collaborative editing, and prototyping through 3D simulations and photorealistic renderings. So I’m sure you have similar thoughts with this one as well, but I wanted to get your take on Lectra’s approach to this and if you think it’s any different to the previous story.
Ben Hanson: Okay, so it is different, is the simplest answer I can give you. I was at the press launch for Apogy in Paris at the start of this week, on Tuesday, so I do have some additional insights that are not contained in the press release. I might also have some other additional insights that will reveal themselves over time as well.
It is fundamentally different, and it’s fundamentally different for the reasons that I just described in the previous one. If, as a company, your product starts with the image, you then need to walk backwards, or backwards or forwards depending on where you’re generating the images, to actually creating real products. Real products made of materials, pattern pieces, sewing operations and so on. I cannot emphasise enough how fundamentally different that is from image generation. Just because an image superficially represents or resembles the output of a real product development process doesn’t mean that it instinctively contains any understanding of that product development process.
Lectra as a company has existed for more than 50 years. I think most people know them for their hardware rather than their software, although I think they were a software business first, then hardware, then software has become a bigger component of their operations.
That hardware is all in manufacturing. That hardware is in spreading, plotting, cutting, marker making, fabric estimation, all core product development and production work, right? Which means that something like Apogy starts the opposite way. It starts from real pattern development, real cutting, real production, and it walks into the visual representations of that and the agentic side of that.
The other bet, the opposite bet from the companies we just talked about, is that you can go from the image backwards into the complex product development side of things. History, based on the journey that 3D and digital product creation have been on, would suggest that starting with the foundation and building upwards is easier, more effective, more complete and more robust than starting with the visual representation and trying to reverse-engineer it from there.
I’m not saying history will necessarily play out the same way. Again, I have not sat down and experienced any of these solutions, but my bet at this point in time would be that you will get a more complete, useful and accurate version of agentic or AI-native product development from a solution and a company that’s previously done a lot of work in product development, rather than from a company that’s previously done a lot of work in image generation.
Again, no guarantees. I don’t know if things are going to necessarily play out the same way, but I do see these things as fundamentally different. So even if everyone seems to be converging on this idea of agentic product development, generative tech packs, generative specifications, generative pattern development, all this kind of stuff, the different ways into that are, I think, going to mean that it plays out very differently for those companies over time. We will be here to observe, and I think you’ll hear some more about that in the future.
How AI disruption could become fashion’s problem
Grace Robinson: So the final story is looking at AI in a bit more of a scary light. It seems that more and more there are people who are leaving the big AI companies and then coming out with all of these statements that AI is going to actually kill us in the near future.
One of the most recent examples is from Jacob Coxon. He went viral last week when he quit his job as an AI researcher at Anthropic, and then he made a viral post talking about all of the dangers about AI. This comes just after his ex-boss, Dario Amodei, recently made a call through an open letter for all of the AI developments to slow down. This is something that Sam Altman at OpenAI agrees with, and even Elon Musk at xAI as well. They’ve both agreed that the proposal to really slow down and regulate AI is very, very important.
So I wanted to get your take on this. Do you think we should all be really worried about this? And also, do you think it’s going to impact fashion in any way as well?
Ben Hanson: Yeah, so this one is super weird. This was the front-page story on the BBC when I was in Paris earlier this week. Even though I was looking at the French version of the BBC, it was ahead of Celine Dion’s return concert in Paris, which ironically was at the hotel that I was staying at.
Now, we’re not a general tech publication, so I’m going to stop short of having a real opinion on AI development in general, on the social side of things. I don’t think it’s going to kill us all. I think the mechanism by which that would happen is incredibly convoluted and unlikely. What I think is more likely is that we see more AI-incited or AI-related disruption to infrastructure.
How big that infrastructure is, and how essential it is to either a particular country’s operations or to the fashion industry’s operations, that remains to be seen. But it doesn’t require a big leap of faith to go from, “Okay, you can have an agent swarm from an unreleased model going through testing and evals that breaks into an alternative AI lab’s systems.” It’s a short hop from that to the same swarms and the same ideas breaking into alternative systems. That seems like a far more likely outcome for me.
Alternative systems can mean anything. Alternative systems can mean businesses. They can mean critical civilian infrastructure and things like that.
Now, the week before last, I flew from France to Germany. I was chairing the CLO Summit in the EU. I was lucky I was flying from France to Germany rather than flying from the UK to Germany, because I think my flight would have been grounded because of the air traffic control disruption which happened here in the UK.
I’m threading two things together here, but I think a far more likely outcome is that you have ungovernable AI that causes that kind of interruption. That’s sci-fi and scary and dystopian in its own right, saying, “Okay, all flights are grounded because we’re fighting off an AI botnet.” But it’s not impossible to conceive of. In fact, I think that’s something that Dario Amodei specifically cited: the idea that the whole internet could be taken over in six to twelve months with a persistent botnet.
Probably too much, but it’s not a far leap from that to disrupting shipping lanes, to disrupting the energy grid in a particular region. If that region is relevant to a fashion brand, either as a consumption market or a production market, if that shipping lane is relevant, then all of a sudden it becomes a big fashion concern.
This sounds alarmist, but the example I would go to for this would be Stuxnet. There’s a good Alex Gibney documentary about it. There’s a couple of good books about it. Stuxnet was the cyberattack that the US staged on Iran a decade or more ago, which used software to disrupt the programmable logic controllers in the centrifuges that were apparently being used to refine uranium for either nuclear energy or nuclear weapons development. That’s an example of how cyberattacks can manifest themselves quite quickly in the physical world and can cause actual damage to a piece of infrastructure or a piece of scientific development or so on.
So I’m not ducking and covering and expecting AI to release novel bioweapons on us any time in the near future, but I also wouldn’t be shocked if we see more in the way of, “Oh, crap. We can’t source. This particular shipping line is closed, or this country’s energy grid is under attack.” Not just within my lifetime, but within the next five years or so. That seems like something that is feasible and possible, and that’s scarier to me than the sci-fi stuff because it feels at least plausible and realistic. I don’t know.
Final thing, how about you? Are you expecting AI to kill us all, or are you a bit more pragmatic about it?
Grace Robinson: I really want to believe it won’t, but I think it is quite alarming when all of these big people are saying it. But I don’t think there’s any way to truly know the truth of what all their intentions are behind the scenes. Hopefully not.
Ben Hanson: Very true. I think these people do have vested interests in emphasising the capabilities of their solutions. But for our business-to-business audience listening to this podcast, for fashion enterprises, the big AI labs are trying to sell tools to them. I don’t think you sell tools to enterprises very well if you sell them on the basis that they might go and attack another enterprise or somebody else’s infrastructure.
So I do get the argument that maybe this is a bit grandstanding on the part of the AI labs. But it’s almost counter to what they’re actually trying to do, which is to IPO and to become big business software providers. So there’s a kernel of truth in this. I just think it’s incredibly weird, again, that this was a top headline on the BBC. I think that’s more representative of how intense and distilled the AI discourse has become than it is of anything else.