Julie Bornstein: Building the Future of Fashion with AI
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Hey, everyone, and welcome to Generative Now. I am Michael Mignano, a partner at Lightspeed. And this week, I'm talking to a true pioneer at the crossroads of fashion, AI, and e-commerce, Julie Bornstein. Julie has led digital transformation at iconic brands like Nordstrom, Sephora, and Stitch Fix.

and then went on to found The Yes, an AI-powered shopping platform that was later acquired by Pinterest. Now she's back with her latest company, Daydream, a bold new startup that's looking to reinvent how we discover fashion using AI. We talk about the lessons she's learned in her many years in the e-commerce and fashion spaces, what it's like being a second-time founder, and her hopes for Daydream. So let's get into it. Let's do it.

Hey, Julie. Hi, Mike. Thanks so much for doing this. Really, really appreciate it. I've been looking forward to it. Me too. So I have so much I want to talk to you about, and I'm sure we're going to go back, jump back and forth between what you're doing now and your very sort of amazing experience, storied experience. But before we do anything, tell us about Daydream. Like, give us the high-level pitch for Daydream. Daydream is basically a fashion search engine that leverages...

AI to be able to interact as you would if you were in a store. So I think we've all, everyone who's been shopping online for the last 20 years very well knows the difference between being able to go and talk to someone and find what you're looking for versus in a store versus needing to be very specific around the taxonomy of the brand site and knowing how to find something on the

the web. And so with the advent of LLMs, we now have the ability to have a sort of real language interaction with consumers and understand what their general need is to find what they're looking for. So we are working with all the fashion brands in the world that are real brands. The consumer can come on and

Ask a question, tell us what they're looking for, use regular language. We got one recent request that said, I'm looking for a revenge dress for a wedding in Paris in the vibe of Saltburn. And so, yeah, you can, you know, you can ask anything you want. The agent will help you find what you're looking for. Once you find it, you can save it into a collection. You can share it with friends, get their advice, etc.

and feedback and click out to buy on the brand or retailer site. Amazing. You know, when I saw this company get announced, you know, when I understood, or at least at a high level, what I believed it did, immediately I was like, okay, this makes so much sense for all the reasons you just mentioned. But the other thing that just made so much sense to me was there's a very common through line between your previous experiences and

And this, you know, I think about Stitch Fix where you were COO. And at the time I remember thinking, wow, because I remember being a customer of Stitch Fix at some point. I'm like, wow, this is simplifying and making it easier and more possible for me to shop without, you know, wasting my time in stores or navigating online stores. And then, you know, same thing with the Yes app.

AI for helping people discover and help shop. It feels like there's a theme here. Maybe, you know, say a little bit about like kind of those experiences and how they helped inspire what you're now doing with Daydream. So when I was a kid growing up in the 80s, I would get Seventeen magazine, find a product and go into the mall and try and find it.

So I feel like I've been working on search for a very long time. When Amazon first launched, I remember literally like watching the site for the first time. It was the summer of 1996.

And I was like, oh, this is the future. This is going to make shopping for fashion so much easier. And so I actually spent about six months trying to convince Dan Nordstrom to hire me at Nordstrom.com because I was in Seattle. My husband was at Amazon, actually. And so we were sort of there for a while. And it was they had announced that they were going to launch this big web business. And so

You know, I would say everything that I've worked on has definitely been leading up to this. And whether it was at Nordstrom building the first generation of e-commerce and then going, I was at Urban Outfitters, at Sephora, and then Stitch Fix, all of those were sort of steps towards what I ultimately wanted to be able to create. And in each case, I was sort of

And chasing technology, like as it emerged to be able to do things that made shopping easier. And so the idea for Daydream was an idea I had really when I was back at Nordstrom.com in like, you know, the year 2000. And we just were so far apart.

technology-wise from being able to build this. But I dreamed of not having to shoot samples of products, of not having to show everyone the exact same results based on what they searched, of being able to use natural language search. So we've finally gotten there, and this is very exciting for someone whose obsession is online shopping. When I was thinking about your trajectory and your career, it does seem like

All of the key experiences and, you know, especially, you know, being being a technology investor, focusing a bit more, I would say, on Stitch Fix and the yes, like it seemed like those two moves and, of course, Daydream, as you just said,

sort of were chasing a technology. Like, talk us through the technology that Stitch Fix was chasing and that Yes was chasing and how that compares to Daydream. Yeah, so I'll go back just even a little bit before then because, so at Nordstrom, you know, we were trying to figure out how to sell things online. So it was...

it was the early era. You know, now the problem is overwhelm and there's too much online. And so the problem we're trying to solve is helping you find the right thing. But, you know, so we were focused on sort of very basic things like how do you get basic search to work? It was all keyword search. How do you create categories that people understand? And how do you shoot, you know, things and get them in a warehouse and get them set? By

By the time I got to Sephora, there were, you know, I think the big technology changes where mobile became a thing, social became a thing. Interestingly, we were doing reviews with Bizarre Voice and we really wanted to have real time Q&A about a product and they didn't have that capability. They were like, we don't know what you're talking about. That doesn't exist. So we ended up working with a gaming company called

or a company that worked with a lot of gaming companies called Lithium to create sort of our first conversational platform. So we created something called B&B Talk, and that was kind of based on what we had seen happening on Facebook. People would ask questions and then other people, we thought we were going to have to be the moderator to answer them, but we realized we didn't.

then what happened is the question would come and go and you couldn't get back to it. And we really wanted to create a repository of all the questions you might have around the beauty space and products, whether they were sold at Sephora or not. And so, you know, that I would say in that era that I was at Sephora, which was 07 to 2015, it was sort of the,

really getting mobile as this bridge to, you know, web and store and getting sort of these social interactions working. At Stitch Fix, what I was really interested in was this idea of using data science. And we had some people who were at Netflix doing some early things, a guy named Eric Colson, who's great, and had built some of the early algorithms, rhythms at Netflix. And so, yeah,

It was really how do you sort of understand a consumer and serve them up, you know, the right relevant product for them. But because the model wasn't real time, it was, you know, we basically had a profile on each user and then the stylist packed a box and it was shipped to the consumer. But I loved learning about how to use...

AI in the early days for, and it's really, you know, sort of a combination of machine learning, AI,

and computer vision to figure out what the right recommendations were. But the model was somewhat limited because we owned our own inventory, and so you could only serve people well who happened to map to that inventory. And then, so sort of through the early AI and ML days, we decided to build the Yes because we wanted to build something that was

really geared towards more of a fashion customer. So Stitch Fix really served the non-fashion customer quite well. It was a treat to get a box of items that look good and fit you and tended to be people who didn't know brands and weren't super picky about what they wanted from a fashion perspective. And so Stitch

I started the yes after that because I felt like I really want to build a fashion recommendation engine for people who are engaged in the category. And so we were, you know, that was the sort of genesis of that. And, you know, I would say then we were acquired by Pinterest. We'll go back to that later. But

And during that time, ChatGPT launched. And the sort of understanding of an LLM, a large language model, and what it could do kind of really transformed the way I started thinking about shopping and what was possible and led to starting Daydream. Yeah, that's super, super interesting. Some of the other things that come to mind for me, I don't know if there were things that

you know, were important to you at the time. But with Stitch Fix, you know, it almost seems like a lot of the innovations in sort of like supply chain of e-commerce and sort of efficiency of sort of global shipping and operations, that must have been a bit of a tailwind for you. And then with the yes, the thing that jumps out at me is,

It feels like when you started it, and I want to get into the timing of this a little bit, it almost feels like the personalization wave, right? Spotify, personalization, TikTok algorithm, like that sort of thing, which I think, per your point, is much different than, say, LLMs, but also very, very important and very, very...

relevant for the time at which these products existed. Were those important factors for these two businesses? Yeah, super important. Yeah. And I think that, I mean, that was the whole idea behind Stitch Fix was personalization. And it's funny because we had done a lot of interesting things at Sephora around understanding the user and targeting marketing based on their past purchases and their brand affinity.

But what I loved about Stitch Fix is we were using that information real time to make purchase decisions or product decisions. And so to me, it was really exciting to go from sort of analyzing data and using that data for marketing purposes to real time use of the data to help make recommendations for the user. And so that really helped.

I would say Spotify and Pandora, we had employees who had worked at both of those places at Stitch Fix. And I always loved hearing how they built and what their journey looked like from sort of their first version of the product, where they basically had like, you know, 25 professional musicians who were

keywording every song so that they understood the dimensions of a song and could make recommendations based on that to sort of using different data points over time for their recommendations. Yeah, that must have been super cool. One of the things going back to the yes really quickly, one of the things I noticed, and not that this has anything to do with AI, but it must have been really fascinating nonetheless, is this company was started just before and was acquired just after the pandemic.

And, you know, I was operating during that time and through that time and obviously know so many other founders and companies that were. And that was just a really, really crazy time for for company building. I'm curious just to hear that perspective of what it was like to operate and exit throughout such a challenging moment for startups. We started.

in May of 2018. Yes. And our plan was to launch in March of 2020. So our launch date was literally set for March 20th, 2020. And, you know, so March 12th came and everyone's like, what's happening? And of course, as most entrepreneurs, I'm like the ultimate optimist. And so I'm like, oh, this is going to pass in three weeks. Let's just hang tight. And we'll, you know, see, we'll talk next week and we'll talk the week after and we'll decide.

So obviously, a few weeks in, we realized this wasn't going to go away anytime soon. And we realized that we had to launch during COVID. And it felt so weird because, you know, it just like fashion was the last thing people were worried about. I mean, we were in this like global crisis and it just felt really awkward.

odd. And then I think that what happened was, you know, by kind of like late April, early May, we were all kind of like, this thing isn't going away anytime soon. And we do need a little, you know, play in our lives. And so let's lean into the fact that this is a fun way to pass time as we think about going to launch. Yeah. And so sort of like, you know, we

the yes was not dissimilar from Daydream, my new company, and that you can sort of save the things that you like and help to train the model to get to know you better. And so we found that a lot of people were just enjoying yesing and knowing products and getting their sort of style better understood. And so we kind of leaned into that and we ended up launching in May. We got lucky because we

launched about, I think, a week or two before the George Floyd incident, at which point everything kind of went dark again. I don't know how much you remember that, but like, you know, no companies were doing any advertising for weeks after that. There was just this like darkness that sort of and heaviness that came over everything. But we sort of happened to like get out in the little window before that. And then it was just, you know, I would say we had no

business we had to comp. So starting in kind of a slow time wasn't a bad thing for us, and it gave us time to sort of improve and learn on the product. And then, you know, I think as everyone readjusted to the fact that this was not going away,

soon and they needed to, any purchases they wanted to make needed to go online, you know, it gave, became an opportunity for the online business to really sort of grow and scale across all businesses.

So that was kind of the start. It was definitely hard to go from being all in the office to everyone being remote. But I felt lucky that we had about 45 people on the team. And I looked at all my friends who were in former businesses I was in with hundreds and thousands of employees. And I really felt for them having to manage and motivate huge teams was

remotely, I think that would have been much trickier than having a small, tight team that could stay connected. Yeah, that makes total sense. And, you know, my startup journey was similar-ish timing. We sold to Spotify right before the pandemic. And, you know, the main thing that I remember being really challenging, and I'm curious if you had a similar experience with the yes, was the integration, right? You obviously, during this period, you sold to Pinterest. I think that was in 2022. Yeah. You know, the pandemic was...

guess you're sort of coming out of it at that point but what's that integration like yeah at that moment where everyone is like trying to figure out how to work in this sort of post pandemic world yeah yeah well so the story with pinterest is that um that the team had reached out to me they i had known ben silberman who was the founder and ceo at the time of pinterest and

He was sort of watching our development and he always felt like Pinterest should be an e-commerce and had sort of struggled to find the right team to build that. He had a lot of people who came from the ads world. And so his idea and his vision was bring the team over, use the technology and start to use that as the foundation to build Pinterest.

um this shopping experience on pinterest um and so when we the acquisition process was a little bit of a nail biter because the market was kind of in a bit of a free fall from during that time so we started talking in february of 2022 and the deal closed in june of 2022. um but ben was amazing and you know very clear on his vision and really stuck to um

you know, what he wanted to do. And we got the deal done and it was very exciting for the team. It was a big win for everyone. And then as happens in this world, Ben announced that he was stepping down a few weeks or a few months, sorry, into the timeline of our integration. I tried to be as helpful as I could. We sort of figured out the right roles for the team within Pinterest. And I stayed for a while to help with that. And then eventually,

um as i looked up to think about what was next chat gpt had launched and sort of all the possibilities uh were really exciting how soon after that do you say to yourself okay i have something else i want to do obviously that thing turns out to be daydream so

So I stayed on as an advisor to Pinterest until the summer of 23. And I would say that spring, I started to talk with some people about Daydream and really just get a sense for the idea and if there was interest for some co-founders to join me and started to talk to some investors. And then we basically ended up raising that fall. So the fall of 23. And building pretty quickly a group

good alpha product that we started testing in the market in the beginning of 24. So obviously LLM is the sort of big inspiring thing, but as we talked about earlier in the conversation, so many elements from Stitch Fix, and I'm assuming the Yes, probably went into that initial idea. Like, you know, talk to us about sort of the inspiration and how that all led to that initial moment of sort of inception for Daydream. Yeah, I would say that the thing that

I had been interested in my whole career from-- was search and more sort of specifically natural language search. And it was really hard-- even if you built the tools to be able to do it, consumers were not trained to search that way. And so at the ES, we did some testing. You know, if we tell you you can put anything into the search bar,

you know, we can understand it and react to it. It just the consumer wasn't trained to do that. And search and fashion in particular has always been really bad. And so the idea that now suddenly because of ChatGPT, there's this opening of this opportunity for consumers to be retrained and to understand how to interact with

with an agent and ask for something in natural language became really important because it's really hard to change consumer behavior when it's so embedded in the way people interact. And so you need something as big and broad as like ChatGPT coming on the market to sort of say there's a different way that you can start to search and ask questions of the web and be able to get information back. So I think that was kind of the first aha for me is

Not only do we now have this model that we can leverage to build an application, but we also have this training ground that they're doing with consumers that help them understand you can kind of ask for anything you want. And we know how to translate that into a good search. Yeah, so it almost sounds like the model providers, OpenAI, Anthropic, Google, to some extent, it sounds like what you're saying is they're almost training the consumer

how to use Daydream or how to use products like Daydream. And I imagine that's going to be a huge tailwind for the product. You know, we haven't seen the product yet. It's pre-launch. But I guess maybe help us imagine a little bit. Like I'm picturing natural language search, obviously, you know, before we started recording, you gave some examples of like things people, or maybe it was in the beginning of the interview, you gave some examples of some of the things people are searching for. Give

give us a little more flavor of what we can expect from the product. I think the UI interaction is going to change over the next few years, and I'm really excited to experiment and play with it. I would say what we've built is a bridge to whatever the future is.

So you can start with a query. I do think that having a search box versus like a conversation box, that small change actually really helps the consumer figure out, oh, this is more than just a one-line search.

I can tell you things. We also show examples of what other people are asking for. It's really fun to see what other prompts people are using, and it also just helps give you examples of what you can prompt with. You can also start by uploading a photo or by voicing your request in. And it's funny, when people start to voice, they get much more conversational.

And so there's a lot more detail. So, you know, you may, if you're typing it, you might say like a red dress with long sleeves, three quarter length, you know, and, you know, for Valentine's Day party. Whereas when you're talking, you're like, I'm going to a Valentine's Day party. I need a dress. It could be this. It could be that.

Any of that works, but we just have this multimodal entry point. And a lot of people either see a photo, they either see someone they want to photograph or they see stuff on Instagram. And so you can also start with a photo. And then the great thing, really what we've built and spent a lot of time on is understanding the relationship between items in the fashion world. So a lot of times you'll see something you like, but it's too low cut.

or it's the wrong color, or it's short sleeves and you want long sleeves. And so you can basically pivot what you're looking for based on this combination of image and text. So you can either upload a photo or you can describe what you want. And then when you find something that's similar to it, you can say, yes, I like this, but show me in green or whatever it is. You can also just ask for more like a specific item. You can look for something that's higher end or, you know,

better priced. And so we've built in a lot of functionality that just is very innate to when you're in the sort of shopping moment and you have these nuances of things you want to be able to express that you haven't had the ability to express in the past.

And so, you know, what we know to be true is that words can express some things. But then once you start showing images, the ability to use the images to help you refine to what you're looking for is quite helpful. And so that's what we've built. And then you get images.

you know, the search results that are both relevant to the query and relevant to you. We've asked you a bunch of questions up front, and if you skip that, we'll ask you the questions kind of in the context of your search. We store that information and every user gets their own style passport.

And so that passport is actually explicit. There's a place you can go see it. You can update it. You can change it. You can add dimensions to it. But it's sort of the information that you've shared with us and we've gathered from you that helps us predict the things that you'll be likely to want the most as you're going about your different searches.

What's really cool about all that, I mean, there's a lot cool about it, but I think the thing that jumped out to me, which resonates most, is this sort of almost like iterative shopping experience. Like, oh, I want something green. Oh, not that green, actually this green. And I want the sleeves to be a little bit longer. Like that sounds really cool. We haven't really seen that before. I imagine that's like one of the,

key sort of differentiators for why people enjoy this. I'm guessing. I think that's why I will like it. Yes. And it's sort of a, it's almost like a personal shopper-like experience, which again, reminds me of some of the earlier products you've worked on. Yeah. I think the other benefit is that we bring together all the brands in one place. So most of us, I mean, if you don't, if Google Shopping doesn't work for you because the ads that they

you know, are basically the ad section, which is what they're focused on, doesn't get you what you need. The results are very messy and very inaccurate and very unpersonalized. And so, you know, I think for us,

So many shoppers have like 10 tabs open and they're trying to check at this store and this store. They want to just make sure they've seen everything. And so that's the other benefit is we bring sort of all the retailers and the brands together in one place. We're just doing new products. We're not doing secondhand yet. It just is nice to have kind of this comprehensive view that I don't have to go to all these different sites to check.

Yeah, it makes total sense. And how do you accomplish this? Is this through your own models? Are you training your own models? Are you taking, you know, some of the bigger models from OpenAI and Anthropic and fine-tuning them? Like, how do you actually technically accomplish this? Yeah, it's interesting because it's actually changed in the last year. You know, I think all of us who are sort of working on building applications on top of these LLMs

started by really using the LLM directly. So we were working directly with OpenAI. We always test sort of Anthropic and Gemini as well and sort of just make sure we're paying attention to the latest models and what's doing the best. And they all have their sort of strengths

Now, what we've realized is it's very hard to rely on a large model for a couple of reasons. One is that the answers tend not to be sort of consistent. And so you might get a good answer one time and not a good answer another time.

The other is that there's real latency. And so if you are trying to build a consumer-facing experience, the lag time in going to the model, getting the information, retrieving it, and bringing it back is just too long to ask a consumer to wait. And so...

I think where the world is moving, especially for consumer applications, is this world of ensemble of models, of small models, basically, that learn from, gather information from these big models, and then are much more responsive and targeted at what you need in your zone. So for us in fashion, we are building many models on sort of all dimensions of fashion. It just allows us to certainly leverage

open AI to train models and then to use that information to build our own mini models that are much more responsive. And over time, what we're doing is we're also adding. So as a

catalog of a brand comes into our system. We work directly with brands. We bring in a feed from the brands. And so we're able to kind of augment the information around the product itself. And so we also use AI to understand everything that might be relevant to that product,

you know, in the context of how people are shopping. So occasion-based or body type-based or those kinds of things. Weather-based, you know, this is good for these seasons. So we have, we've built sort of this deep knowledge base around the product catalog and then this deep knowledge base around the user. And then, you know, the models kind of sit underneath the

large model to understand the query, quickly serve up the best results for them and re-rank them based on your sort of personal preferences.

And that's kind of how the system works. Very, very cool. Makes a lot of sense. Speaking of the large model providers, I'm sure you've been asked this question a hundred times over the past, I don't know, month or so. But obviously OpenAI recently released a shopping experience. I believe Perplexity has a shopping experience. It seems kind of obvious that all of these providers are going to have baked in shopping experiences.

How do you view that as either maybe a tailwind or a headwind for Daydream? And I guess, where can you remain defensible versus the large language model providers? Yeah, and two days ago, actually, Google announced a bunch of shopping tools as well. So, yep, they're all doing it. I think it's very valid. I think it's very validating, actually, and I think it's going to be helpful to...

you know, understand what Daydream is, I think will become kind of a part of the conversation around how shopping is evolving and how, you know, search is moving over to like, you know, a prompt-based, agent-based shopping experience. Google is, you know, they're testing this try-on model. They're testing agentic checkout methods.

But the underlying issues are still there, that they have a very, very large, messy catalog that they're dealing with, and they don't really understand the nuances of fashion and shopping and what you need to know about both the product and the consumer in order to make a great recommendation. And so I do believe there's going to be, you know, sort of vertically focused discussions

experiences that leverage the capabilities of LLMs, but do a much better job in especially taste-based categories. You know, I just think that if you go now and you search on perplexity or chat GPT for a dress for some event, like you get three results, they're super random. They have nothing to do with you. And, you know,

You know, I think that you need a lot of love and care of this space to build something that really works when you're trying to buy something. And so, you know, I think that's where we're focused. And I think our defensibility will be the deep understanding of the domain and understanding the user as it pertains to this domain.

Yeah. I mean, I think that makes a lot of sense. I mean, these companies are, they're being very, very horizontal trying to do kind of everything. There's clearly an opportunity and sort of verticalization and specialization. And as you say, like the more and more users use this, the more it's going to know about their personal tastes, which is obviously going to just strengthen the product and what it delivers. So I think that makes a ton of sense. The other trend that jumped out at me, and I'm wondering if

anything like this has a role at Daydream is sort of the trend of TikTok shop and

and sort of, you know, live streaming, you know, shopping over live streaming, like platforms like Whatnot. I mean, is this a trend that is relevant to what you want to do or do you view it as a completely different behavior? No, I think that, so video shopping is not something I'm a huge believer in in the U.S. market for fashion. And I think it's a small, it will play a small role.

But TikTok and Instagram are huge and are super relevant and important. And if you don't think about how they integrate in, then you're just, you know, missing a huge opportunity. So we the way our product will work ultimately is you can screenshot something you see or you can interact with Daydream within those channels. And so we have a bunch of

cool features that we're building that integrate within those channels and make it easy to find the item that you like on the model to buy. Assuming it's, you know, not an ad from a brand, but something that you see an influencer wearing or just, you know, see someone wearing that you like the idea of. And, you know, we're going to build it in such a way that you can, you know, snapshot and just find it on the site or you can interact with us within those environments. I think that

They're a huge source of inspiration. And so we want to be able to leverage that and leverage how great they are for that. Yeah, that makes total sense. And I agree, like it has to factor in in some way. As you said, it is a trend. So the other thing I really wanted to ask you about coming into this conversation was it feels like the fabric of the web is shifting. Yeah.

And it's changing sort of underneath our feet right now. And that is we're coming from a model where so much of the behavior and the discovery of content has come through traditional search, where we as humans are searching for things, you know, we're doing the searching and we're moving to a world where a lot of that search and discovery is going to be happening through agents. And obviously this, I'm

I'm sure this plays, factors in heavily into how you think about Daydream. But I think it also factors into kind of like the business model of both the internet and then also products that are sort of built on top of AI and models, you know, to drive discovery of your clothing brand in, you know, 2010 or 2015, you know, you're leaning heavily into search and paid search and, you know, hopefully capturing the attention and the eyeballs of humans

But now you have to think about agents and how sort of agents are interacting with the world. Do you think about this at all? And sort of how does it factor into the strategy for Daydream? Agents will sort of perform tasks for consumers. And they're, you know, I think we will see agents in many places. And the question is,

is like, you know, how much can a single agent do? And does it have like, is it multifaceted? Or are you going, you know, is it sort of their multiple set of agents that are all rolling up into one agent? Does everybody have one agent? Do they have multiple agents for different things? I think the way it's, it's hard to know how it's going to play out. But I do think that is what's already happening. Like when someone comes in and makes a prompt to us,

it's our agent is basically saying, okay, I got this. I'm going to translate this into a search result so that I can show you the products that match the search result. And I'm going to come back and ask you more questions so that I can answer anything else you might want. And ultimately, you know, maybe they can say, and I can check out for you if that's what you want. So I think this sort of role of agentic kind of actions will be

become the default. I just think it's going to take some time both for the agent foundation to be built across everything. And I don't think the average user understands what an agent is or how it works. It's kind of like we actually tested in some of our marketing leading up to our launch a

sort of your fashion agent. And people didn't really understand what that meant. Didn't register. Yeah, they were like, like a real estate agent or, you know, like an entertainment agent. So I think the notion of an agent is still unfamiliar to most consumers. But I think the role of the agent in taking actions for you across the web is going to become very commonplace. Right.

where, how it sort of evolves? Yeah. Yeah. It's, it's, it's tough to say. And, and, you know, like I said earlier, I think it's also going to have kind of like a profound impact on the business model of the internet, right? Again, everything today is driven by attention and clicks.

But what happens when human attention and human clicking like factors less and less into the equation? It's really hard to say. I do wonder how it factors into something that you mentioned earlier, which is the interface. You know, I think you said you made a comment on how you're sort of building for the interface that people expect today and sort of where it might go in the future. How does your team think about that? What might the future look like? Obviously, we don't know, but like, what are some of your hypotheses? Well, I think

that as more things get done by voice, you know, there's less of a, you know, we all think about building the web experience and even the mobile experience for sort of this, you

tactile, you know, interaction. And so, obviously, if you're shopping, you want to see images, and you might want to see the item or outfit on you. And so, you know, what I could see over time is as we think about building for sort of a mobile-first and voice-first interaction, that, you know, it's very simple.

but in a way that allows you to feel confident that you're seeing the best options. I think it's very hard when you're searching for an item to buy and wear, right?

That if you see three options, one of those three are the right option. Right. You need to be very, very either what you're looking for is very narrow or you need to know everything you could possibly know about the person and what they're looking for in that moment. And so the truth is right now there's still a grid and you still like sift through lots of product, but we make it easier for you to narrow down what you want.

I think in the future, you know, we have this vision of being your fashion agent and basically knowing what you have coming up and being able to suggest things to you, remind you of things as well as basically take your input. And, you know, it may be a simple voice interaction with then kind of a few items served up and then it's not overwhelming feedback.

We know enough about you that we can give you really good options in a sea of truly millions and millions of SKUs. And then you can sort of interact from there. And so I think it's going to be less about sort of the UI and more about all of the stuff that's happening is behind the scenes. And the interface is very simple. Yeah, I think that makes a lot of sense. One thing I wanted to ask about, you know, this is your, I mean, you've had a major

amazing career and we've talked about a number of really, really key roles and experiences you've had. What's it like

being a second or third time founder? Like, how are you building Daydream in a way that's different relative to maybe how you built the Yes based on, you know, such incredible experiences? I was very excited about doing this first. So I'm not the founder of Stitch Fix. That's Katrina Lake. So I joined the board early on and got to see and be a part of that company. But the Yes was the first company that I started. And so it

I thought as I was starting Daydream, oh, this is great. I can sort of fix all the mistakes I made first time around. It's going to be amazing. And then, of course, I made all sorts of new mistakes. So, you know, I would say it's been humbling. I would say that I'm less nervous and stressed about everything than I was the first time around. And I feel a little bit less

I remember starting the YES. I just felt so vulnerable, and it made me think about music artists and authors who, like, put themselves out there. They wrote a book. They, you know, wrote a song. Everyone's sort of commenting on it. And I kind of felt that way with the YES. I felt like this is the reflection of me, and if people don't like it, how is it gonna be? Also, I found the fundraising very stressful, and I had never done it before myself. And so, you know, I would say those things,

have been easier this time around, just knowing how it works and having done it before and sort of taking my ego out of it a little bit. And I would say the other thing is that, you know, with so much of that matters with startups is the people. And if you know, it's so hard to find the right people. And when you do, it's amazing. And when you don't, it's pretty devastating.

especially if they're in big roles. And so I would say, you know, I, the first person that we brought on to sort of build out the technology just wasn't the right person. And so it set us back. And I would say that it took me longer to find the right person, but I knew that what we needed was someone who really understood the space and

as well as really understood the tech. And so it took a little bit longer, but I knew to hold out because it matters so much. And it's been amazing because we have the best CTO on the planet for this role and job. And it's been such a delight to work with her. And she's taught me so much. And I think the other thing that's happening is

Unlike when during the yes, like we're in a different technology world. The, as you sort of said before, the, you know, ground is not stable. Like this is such a fast evolving space and there's so many hypotheses about what's going to happen. There's so much new compute power that is, you know, sort of changing everything. And so at this moment in time, it feels different than it did when we were doing the yes, because it feels like, you know, who

knows where things are going and you just need to be very active and following everything that's happening and experimental and be able to adjust as you go. And so I think that requires, you know, a specific type of technical team to be set up to do that. And

So that's been a bigger focus for us this time. Does that require also like a different type of leadership or different sort of like values as a company to operate that way? I think so. Does everyone just get it like out of the box? No, everyone does not get it out of the box. I think some engineers are much more sort of wed to the way they've always done things and the way they've written code. And, you know, they're very proud of that and they're slower to adapt new techniques. But

I think if you have a leader who's really into it and experimental, then they can set the tone. And Maria loves to, you know, every new tool that comes out, she's playing with and she goes very deep on and she helps the team to figure out how to use these tools to, you know, speed up their development process. And I would say the majority of our engineers and all of our non-engineers are using the

AI in interesting ways to make their jobs more efficient. We're just at the beginning of this. So I think that's going to continue to change and having sort of the mindset of, you know, growth mindset of like, great, let's see what's next. Let's try that. You know, I think being experimental around using those tools is really important as you're building a company, because in theory, we should be able to hire fewer people because we have this huge advantage of this technology to help us.

Totally. Last but not least, tell us about the launch. I believe it's coming up. Am I right about that? It's coming up, yes. I think by the time this airs, we will have launched. And, you know, I think what's really hard for me, to be honest, is that the product is not where I want it to be. And I would wait another six months probably to launch, but that's just not the reality of this world. I think if we waited until we felt perfect about it, we would have waited too long. So for, you know, I think the product, when we launch...

will be better than anything out there to start, but we are just getting going. And so we have so many improvements that will come around search, will come around agent interaction, will come around personalization. So those sort of paths will sort of always be walking down and

building and improving. And then we have a bunch of additional new features that we'll bring to market after we launch that we're really excited about. So it's going to be a journey and I hope customers like what they see at first and are excited enough to stay with us and be a part of the journey too, because we're going to build some amazing tools that really transform the way you make shopping decisions and just make you feel much more confident, find the right thing you love, return things last

discover new brands. And so, yeah, we're really excited. Awesome. Where can we get it? Daydream.ing. Awesome. Julie, this has been amazing. I learned so much. I'm sure everyone listening and watching have as well. So thank you so much for your time and congrats on the launch. Thank you. Great to talk to you. Thank you.

Thank you for listening to Generative Now. If you liked this episode, please rate and review the show. And of course, subscribe. It really does help. And if you want to learn more, follow Lightspeed at LightspeedVP on X, YouTube, or LinkedIn. Generative Now is produced by Lightspeed in partnership with Pod People. I am Michael McNano, and we will be back next week. See you then.