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Blend uses generative AI to give you a personalized clothing guide

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Shopping for clothing online has liberated us from the need to brave the endless aisles, fluorescent lights and sale-hungry crowds of the brick-and-mortar retail inferno. But anyone who has found themselves two hours deep into a fashion rabbit hole, with nothing to show for it but 15 open tabs, four full shopping carts, an earful of YouTube clothing haul reviews and the gnawing anxiety of the overwhelmed, shopping online can feel like a chore.

Enter Blend, a U.K.-based startup that is using AI to cut through the noise and help shoppers find personalized product recommendations to suit their style, budget and size.

“The vast majority of retailers do absolutely no personalization, and in the instances when they do, they only personalize according to historic purchase data,” Blend co-founder Jemima Bunbury told TechCrunch. “When trends are changing relatively quickly, and people’s style does change over the course of their lives, it doesn’t stay relevant for a user to have such historic recommendations.”

Blend participated in TechCrunch Disrupt 2023 as one of the Startup Battlefield 200 companies. At the event, the startup launched its MVP — an app that will slowly open to the 2,000 users on Blend’s waiting list. After raising angel investment in April, Blend is now on the hunt to secure investors for its seed round. The startup will use those funds to build out additional features on the app and push for a full-scale launch.

Blend has already signed on over 250 retailers, including Net-a-Porter, a luxury retailer. The startup’s go-to-market strategy targets users aged 18 to 34, “very digital, native mobile-first shoppers” who are starting to define their personal style as they accrue disposable income. Blend is launching in the U.K. first and then hopes to move into the U.S. market.

“We hope that by attracting first the very fashion-forward, trendsetting crowd, we can then move more mainstream from there, but it’s much more difficult to go the other way round,” said Bunbury. “Ultimately, the vision is really to be the front door for every online shopping experience, and therefore, to be the largest-scale retailer because of that ability to personalize and only present people with the 1% of the internet that is most relevant to them.”

Generative AI we can get behind

Blend co-founders Bella Levin (left), Jemima Bunbury (middle) and Eva Piskova (right). Image Credits: Blend

The fashion industry has tapped the generative AI frenzy in a range of ways. Some companies are using natural language processing algorithms to improve the customer service experience. Others are using image generation to create new designs. There are also applications in production improvement, trend forecasting, inventory management and virtual try-ons.

Blend’s approach centers around transformer technology and recommendation algorithms, powered in large part by user interaction data. Transformer technology, which makes up the tech stack of popular generative AI models like ChatGPT, is a model for teaching computers how to understand and generate human language. In the world of fashion, this means it can better understand user preferences and make tailored clothing recommendations.

“The main thing that’s always important when it comes to AI is what data you are actually putting into [the model],” said Bunbury, noting that the founding team decided on an app rather than a web page in part because it’s easier to track a user’s data that way.

When the user opens the app, they’ll scroll through a feed that is a mix of product imagery and descriptions that have been pulled from different retail and e-commerce sites. Their feed will also feature short-form videos and product curations from influencers who can earn an affiliate commission on any sales they generate.

As the user scrolls, Blend collects data on how they interact with the app, whether they’re liking products, saving them, sharing with a friend, “or simply how long you’re looking at one product,” according to Bunbury. Blend uses all of that data to form a picture of the user, who has already pre-set preferences to size and budget. The more a user interacts with the app, the more personalized their recommendations will become.

On the back end, Blend is comparing products and users to get a statistical picture of which products will be right for which users. So, for example, let’s say there are two users who were actively using the app three months ago. User A pauses engagement with the app, while User B continues to engage regularly, and sees her feed adjusted according to new trends. Rather than let User A’s recommendations stagnate, Blend will use User B’s data to inform recommendations to User A.

“By tracking those cultural trends and how different people’s styles are similar or different, we can use that data to inform other people’s recommendations,” said Bunbury. “So the personalization gets more powerful the more users we have on the platform to base those off of and create cohorts.”

The AI model behind the app is impressive not only because it can recommend you the right outfit today, but also tomorrow, next week, next year. It’s dynamic, and it tracks how a user’s style changes over time.

Blend also helps users find the right fit for their body type, something retailers who have to go through expensive returns cycles appreciate, as well. Part of getting this right is allowing the user to set their preferences for what their size is for different body parts and determine what their body type is. But that information isn’t always reliable — brand sizing charts can differ wildly and most of us aren’t good at classifying our own body shapes.

That’s where the user-generated content enabled by the app kicks in again. The hope is that users will take photos of themselves in their new clothes and post them on the app, giving Blend’s AI engine and other users a diverse representation of what specific products look like on different frames. Down the line, Blend hopes to incorporate reviews and a voting system to help users better determine the right size for them.

Business model

The more a user engages with their Blend feed, the better the personalized recommendations become. Image Credits: Blend

The three moving parts in Blend’s business model are: 1) Shoppers; 2) Influencers; and 3) Brands.

Blend is predominantly trying to solve a user problem, but to do that, it needs to partner with influencers and brands, both of which stand to gain, as well. By partnering with Blend, both influencers and brands can diversify revenue streams and appear across multiple different channels in a very light-touch way.

For brands specifically, Blend could present as a powerful market marketing platform.

“For most brands, the key difficulty is getting your products in front of the right audience and having a risk-free way of advertising,” said Bunbury. “With social media advertising, yes, you can target fairly well according to demographics and user group, but even then it isn’t necessarily based on what their style is. Whereas we should have this incredibly granular style-specific dataset that will allow us to put the right brands in front of the right users when they are actively looking to be buying.”

Blend wins by taking a commission on sales from partner brands and retailers, which can vary depending on retailer, according to Bunbury.

The first version of the app will link out to a brand’s website to complete the transaction there. Future versions will allow users to get to the point of sale within the app for a more seamless user experience.

“There’s huge growth potential just in that, but we’re also aware that with the dataset we have and with our ability to put brands in front of users, there are also lots of B2B revenue lines in the future,” said Bunbury. “Things like advertising, data and analytics on trends, being able to forecast what sorts of products will be selling and at what quantities.”

On the consumer side, Blend says it might launch a subscription service in the future for additional premium features, like end-of-stock alerts, discount alerts or early access to brand products.



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Affirm launches in the UK, as ‘buy now, pay later’ market faces regulatory overhaul

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Buy now, pay later (BNPL) giant Affirm is launching in the U.K., its first market outside North America.

Its long-anticipated arrival comes as U.K. lawmakers mull new rules to bring BNPL firms into line with other traditional consumer credit services, though such laws aren’t expected to come into effect until at least 2026 — long enough for Affirm to build traction, and curry favor with consumers and regulators alike.

Founded in 2012, Affirm emerged from a startup incubator called HVF, setup by PayPal co-founder Max Levchin (pictured above) who eventually took the reins at Affirm in 2014 to drive its commercial push. The company expanded beyond the U.S. and into Canada in 2022, and it has struck lucrative partnerships with major ecommerce companies through the years — Affirm has been Shopify’s major financing partner for close to a decade, not to mention Walmart, and Amazon, which tapped Affirm as Amazon Pay’s first BNPL partner in the U.S. last year. More recently, Affirm also secured the mighty Apple as a customer.

‘Normalizing debt’

The BNPL model is simple: customers are invited to purchase goods on credit, repaying the debt in several interest-free instalments, with the BNPL provider monetizing through merchant fees. Or, where the customer may require a longer repayment period, the loan may include interest, too.

The BNPL market has long been on the U.K. regulatory radar, with incumbents such as Klarna and Clearpay often criticized for encouraging impulse buying and normalizing debt. The U.K.’s Financial Conduct Authority (FCA) has hitherto had some power to keep BNPL providers in check, but there are key exemptions, such as services that involve interest-free credit, where fixed-sum agreements stipulate that debts be repaid within 12 months.

But new rules in the works could bring BNPL companies fully in line with other consumer credit companies. The Labour government last month announced a fresh BNPL consultation, with plans to introduce regulation to “ensure people using BNPL products receive clear information, avoid unaffordable borrowing, and have strong rights when issues arise.”

It’s clear that Affirm is already pushing to position itself favorably both with patrons and the the powers-that be. Indeed, the company notes for the U.K. launch that its interest-bearing payment options won’t involve compound interest — instead, interest will be fixed, and calculated entirely on the original amount borrowed.

It’s also worth noting that Klarna started charging late fees in the U.K. last year, and this is one area where Affirm is setting out to differentiate — it says it won’t be charging late fees or any other “hidden charges.”

Head-to-head

It has been a bumpy few years for the BNPL sector. Klarna was valued at more than $45 billion in 2021, a figure that swiftly plummeted by 85% to $6.5 billion following the great post-pandemic “correction” many companies endured — however, news emerged last week that Klarna’s valuation has risen again to $14.6 billion. It has been a similar turbulent time for Affirm, whose ups and downs have followed a trajectory reminiscent of its European rival.

Following its 2021 IPO, Affirm saw its market cap hit the giddy heights of $47 billion, but its stock took a giant hit, with its market capitalization dropping below $3 billion last year. However, Affirm’s shares have surged to more than $13 billion in 2024, with the NASDAQ-listed company recently reporting a Q4 year-on-year revenue jump of 48%, and losses dropping from $206 million to $45 million. Levchin also predicted profitability in 2025.

We’ve known for some time that the U.K. was likely going to be Affirm’s next port-of-call outside the U.S. and Canada, with the firm’s chief revenue officer Wayne Pommen going on record to say that it would be targeting markets where some of its largest existing partners already have a presence.

For its U.K. launch, there aren’t any of the same big-name brands it has domestically, but the fact that it counts the likes of Amazon, Shopify, and Apple as customers in the U.S. means that it wouldn’t be a huge stretch to expand such commercial partnerships to the U.K. For now, though, Affirm is going to market with the like of flight booking site Alternative Airlines and payments processor Fexco, with “additional UK and international brands expected to follow.”

In the build up to today’s launch, Affirm told TechCrunch that it has already hired in the region of 30 employees, including Ruth Spratt who’s leading the local charge, while it’s also looking to add to its headcount through the remainder of the year. And similar to its remote-first ethos elsewhere, workers aren’t tethered to a particular physical hub.

The company wouldn’t confirm its next plans for growth in Europe or elsewhere, though it said that it would be “taking the same disciplined approach” that it has always done to any future expansion.



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OpenAI has hired the co-founder of Twitter challenger Pebble

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Gabor Cselle, the former CEO and co-founder of X challenger Pebble, has joined OpenAI to work on a secretive project.

Cselle, who according to LinkedIn has been employed at OpenAI since October, announced the news in a post on X yesterday. “Will share more about what I’m working on in due time,” he wrote. “Learning a lot already.”

Cselle is a repeat founder who sold his first company, the Y Combinator-based mobile email startup reMail, to Google. His second company, the native advertising startup Namo Media, he sold to Twitter before Elon Musk purchased the social network and rebranded it to X.

Nearly a decade ago, Cselle worked at Twitter as a group product manager, focusing on the home timeline, user onboarding, and logged-out experiences. Cselle left Twitter in 2016 for Google, where he was director at the tech giant’s Area 120 incubator for spin-offs.

Cselle began working on Pebble, originally called T2, in 2022 with Michael Greer, Discord’s ex-engineering head. Pebble, whose microblogging service emphasized safety and moderation, grew to a small but engaged community and raised funding from angles including Android co-founder Rich Miner.

Ultimately, though, Pebble struggled to maintain meaningful growth. The company shut down last October, reemerging as a Mastodon instance in November.

In May, Cselle joined the accelerator South Park Commons, where he worked on a range of generative AI prototypes including an homage to the viral HQ Trivia.

Csell’s hiring reveal comes the same weekend as OpenAI rival Anthropic gains its own high-profile recruit: Embark founder Alex Rodrigues. Rodrigues, who led autonomous trucking firm Embark through a SPAC merger in 2021 (and subsequent fire sale to Applied Intuition in 2023), said on Friday that he’d be joining Anthropic as an AI safety researcher.



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Women in AI: Sophia Velastegui believes AI is moving too fast

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As a part of TechCrunch’s ongoing Women in AI series, which seeks to give AI-focused women academics and others their well-deserved (and overdue) time in the spotlight, TechCrunch interviewed Sophia Velastegui. Velastegui is a member of the National Science Foundation’s (NSF) national AI advisory committee and the former chief AI officer at Microsoft’s business software division.

Velastegui didn’t plan on having a career in AI. She studied mechanical engineering as a Georgia Tech undergrad. But after a job at Apple in 2009, she became fascinated by apps — especially AI-powered ones.

“I started to recognize that AI-infused products resonated with customers, thanks to the feeling of personalization,” Velastegui told TechCrunch. “The possibilities seemed endless for developing AI that could make our lives better at small and large scale, and I wanted to be a part of that revolution. So I started seeking out AI-focused projects and took every opportunity to expand from there.”

AI-forward career

Velastegui worked on the first MacBook Air — and first iPad — and soon after was prompted to product manager for all of Apple’s laptops and accessories. A few years later, Velastegui moved into Apple’s special projects group, where she helped to develop CarPlay, iCloud, Apple Maps, and Apple’s data pipeline and AI systems.

In 2015, Velastegui joined Google as head of silicon architecture and director of the company’s Nest-branded product line. After a brief stint at audio tech company Doppler Labs, she accepted a job offer at Microsoft as general manager of AI products and search.

At Microsoft, where Velastegui eventually came to lead all business app-related AI initiatives, Velastegui guided teams to infuse products such as LinkedIn, Bing, PowerPoint, Outlook, and Azure with AI. She also spearheaded internal explorations and projects built with GPT-3, OpenAI’s text-generating model, to which Microsoft had recently acquired the exclusive license.

“My time at Microsoft truly stands out,” Velastegui said. “I joined the company when it was in the midst of huge changes under CEO Satya Nadella’s leadership. Mentors and peers advised me against making that jump in 2017 because they viewed Microsoft as lagging in the industry. But in a short window, Microsoft had started making real headway in AI, and I wanted in.”

Velastegui left Microsoft in 2022 to start a consulting firm and head product development at Aptiv, the automotive tech company. She joined the NSF’s AI committee, which collaborates with industry, academia, and government to support basic AI research, in 2023.

Navigating the industry

Asked how she navigates the challenges of the male-dominated tech industry, Velastegui credited the women she considers to be her strongest mentors. It’s important that women support each other, Velastegui says — and, perhaps more importantly, that men stand up for their female co-workers.

“For women in tech, if you’ve ever been part of a transformation, adoption, or change management, you have a right to be at the table, so don’t be afraid to take your seat there,” Velastegui said. “Raise your hand to take on more AI responsibilities, whether it’s part of your current job or a stretch project. The best managers will support you and encourage you to keep pushing ahead. But if that’s not feasible in your 9-5, seek out communities or university programs where you can be part of the AI team.”

A lack of diverse viewpoints in the workplace (i.e. AI teams made up mostly of men) can lead to groupthink, Velastegui notes, which is why she advocates that women share feedback as often as they can.

“I strongly encourage more women to get involved in AI so our voices, experiences, and points of view are included at this critical inception point where foundational AI technologies are being defined for now and the future,” she said. “It’s critical that women in every industry really lean into AI. When we join the conversation, we can help shape the industry and change that power imbalance.”

Velastegui says that her work now, with the NSF, focuses on tackling outstanding fundamental issues in AI, like a lack of what she calls “digital representation.” Biases and prejudices pervade today’s AI, she avers, in part due to the homogenous makeup of the companies developing it.

“AI is being trained on data from developers, but developers are mostly men with specific perspectives, and represent a very small subset of the 8 billion people in the world,” she said. “If we’re not including women as developers and if women aren’t providing feedback as users, then AI will not represent them at all.”

Balancing innovation and safety

Velastegui sees the AI industry’s breakneck pace as a “huge issue” — absent a common ethical safety framework, that is. Such a framework, were it ever to be widely embraced, could allow developers to build systems with speed without stifling innovation, she believes.

But she’s not counting on it.

“We’ve never seen technology this transformative evolve at such a relentless pace,” Velastegui said. “People, regulation, legacy systems … nothing has ever had to keep up at the current speed of AI. The challenge becomes how to stay informed, up-to-date, and forward-thinking, while also aware of the dangers if we move too fast.”

How can a company — or developer — create AI products responsibly today? Velastegui champions a “human-centered” approach with learning from past mistakes and prioritizing the well-being of users at its core.

“Companies should empower a diverse, cross-functional AI council that reviews issues and provides recommendations that reflect the current environment,” Velastegui said, “and create channels for regular feedback and oversight that will adapt as the AI system evolves. And there should be channels for regular feedback and oversight that will adapt as AI systems evolves.”



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