Fast Fashion Trends Applied to the Tech Industry

Software is having its fast fashion moment. Hear me out I’ve been struggling to find the right analogy for the disruption happening in software development. I’ve heard people mention things like the innovation at Toyota leading to the overhaul of manufacturing processes. But that still operates in a world where production is scarce and expensive, where you need to spend a lot of your time deciding what’s worth building. With agentic coding, we’re seeing a shift to a world where production becomes abundant. You can just build things when you need them, and not everything needs to be production-grade or last for years like a Toyota. This might be a stretch for this audience, but I think fast fashion is actually the better analogy. There are a lot of parallels. In fashion, they went from set cycles controlled by tastemakers and big brands. There were fixed releases tied to events like New York or Paris Fashion Week. That maps pretty closely to how software used to work. Long cycles, big launches, centralized control. Then there’s the production process itself. It moved from high-quality, highly influential designers setting trends to a much more democratized ability to produce. That leads to more availability and more trends overall. There’s also the idea of “artisanal” coding. We’re already starting to hear people talk about code written by hand as artisanal software. That sounds a lot like expensive, small-batch, and selective high fashion versus abundant variety and mass-produced clothing. With agentic coding, we can now simply produce any software we dream up. Another implication is consumption. The amount of software people create and use is going to explode. When supply was limited and controlled by large companies, consumption was naturally constrained. Now people can just build what they want, when they want. I’m even starting to hear about “throwaway software.” You spin something up for a demo, a prototype, or even a one-off dashboard for a single meeting. That’s very similar to fast fashion where people started buying something for a single use rather than for durability or longevity. So that’s the analogy. From there, the question becomes: what needs to change in software development to adapt to this shift? What can we learn from the shift in manufacturing that led to fast fashion? Fast fashion moved from something scarce and taste-driven to something abundant and accessible. Every part of the system had to be reinvented to keep up. Software is heading in the same direction. With code production mostly automated, the pressure shifts to everything around it: planning, review, deployment. That’s where the resistance is now. Requirements for security, compliance, testing still matter. But they also don’t get to define the pace. They’re just the next problem to solve. Because the market won’t care that your code is higher quality if you’re too slow.

Quick commerce might create new rails for fashion in India. But AI is about to rewrite the stack. It won’t just improve margins or automate workflows. It will reshape how demand is created, what gets made, and how we buy. Here’s my prediction: 1. Search becomes intent-led Nobody wants to scroll through 400 SKUs. AI will learn your taste, body, budget, event, and mood, and surface five things that just work. Think: Spotify-style discovery, but for clothes. Discovery becomes contextual, not chaotic. We’re already seeing this in early interfaces like Perplexity’s shopping copilots. 2. Assortments get micro-targeted Massive catalogs are a liability. AI lets brands adapt SKUs dynamically, by user, region, season, even returns history. Shein scaled fast fashion through supply speed, but never cracked fit. Newme is flipping the model by doing weekly drops of 10–15 SKUs based on real-time feedback As merchandising behaves like content, inventory becomes a live system. 3. Returns are engineered out Returns were the biggest margin killer. Now they’re a solvable product problem through predictive sizing + fit-tech + try-at-home delivery. Zalando and H&M are already running fit-tech integrations + virtual try-ons at scale. Fit-tech will become table stakes. 4. Supply chains go real-time From design to drop to replenish to clear. AI enables live demand forecasting, smarter markdowns and faster reaction cycles. Urbanic, Zara, and Myntra are tightening feedback loops using browsing + returns + trend signals Fashion will respond to signals, not seasons and less dead stock will lead to better margins. 5. Shopping shifts from search to recommendation Shopping will shift from browsing to context-driven nudges. AI copilots will shop with you, not for you. Voice-first agents are already live. AI doesn’t just improve conversion: it changes the loop. The next generation of fashion brands will scale through personalization, fit precision, intelligent curation, and habit-forming UX Fashion will live at the intersection of fast-moving infrastructure and intelligent systems. This wont change how we buy. It will change what gets made.

In SaaS, product-market fit has always been a 'moving target', but at least it would 'last' for a few years. Now, it's quickly becoming a treadmill, and you'll only have a few months until you have to reinvent and progress. As Sam Altman suggests, SaaS may start to look a lot like fast fashion: a) Rapid to design b) Cheap to produce c) Trendy, but often disposable Anyone can now spin up a product in days, not months. Markets get flooded, switching costs fall, distribution gets noisy. 1) For founders, this changes the game: Product-market fit isn't a finish line. You can’t find it once, then sit back and monetise like its 2020. You’ll be chasing it again and again as customer needs and trends and competitive products shift faster than ever. Elena Verna says, "you can be at $100M revenue and still never graduate from 'product–market fit finding' stage." 🤯 Churn benchmarks will get much higher for all but the winners. It's already normal to see 50% annualised churn - and to treat this as a user acquisition process. You'll have to build with churn in mind. And aim for the "happy smile graph" (thanks Kyle Poyar), where you see lots of user cancellation initially, but then an uptick in usage and revenue from those that remain over time 😊 And your edge won’t come from speed alone, but from trust, brand, ecosystems, and community — assets that compound when features don’t. 2) For VCs and analysts, the signal shifts too: The winners won’t be the ones who launch fastest, but the ones who can repeatedly adapt, sustain distribution, and keep customers through multiple hype cycles. They want to back founders who have the resilience to rebuild for PMF every quarter! Are we ready for a time where chasing PMF is a constant sprint, not a tangible milestone? And will we see what has happened with fast fashion, where there is a noticeable kick-back against the trend? To break the cycle of purchasing cheap Shein and Primark garments, some are reverting to buying quality products - often in thrift stores. These products don't change - but they don't break either, and carry a more permanent quality. 🫣

“We are entering the fast fashion era of SaaS very soon.” - OpenAI CEO Sam Altman. What does this mean for knowledge work? 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗶𝘀:  • Fast fashion took designs from the runway to the store in days.   • AI is now doing the same for software.   • New features, once a multi-quarter build, can be replicated and shipped in a weekend. When models, code scaffolding, and hosting are commodities, replication becomes effortless. An AI feature released on Monday can have ten clones by Friday. This applies beyond SaaS. Knowledge work is subject to the same pressures. 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗺𝗲𝗮𝗻𝘀: The edge shifts from inventing features to owning the full experience. Execution becomes an orchestration problem:  • Select the best models at the moment of use  • Route intelligently for speed and cost  • Build trust with consistent results  • Lock in through deep workflow integration As HubSpot CEO Dharmesh Shah says, “Experience is your moat.” 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: This dynamic is already reshaping the AI landscape:  • Platform players like Microsoft, Google, and Meta fold new AI features into their ecosystems faster than startups can defend them  • Model routing and orchestration layers are emerging as critical infrastructure  • Distribution power now decides survival, not code originality For knowledge workers, this means tools that can automate tasks need to be paired with workflows that enhance human creativity, judgment, and decision-making. The value shifts from creating the output to embedding those outputs where they matter most. 𝗧𝗵𝗲 𝗿𝗼𝗮𝗱 𝗮𝗵𝗲𝗮𝗱: By 2030, the SaaS winners will move like Shein, think like AWS, and run like utilities. They will own:  • Distribution rails: instant reach to millions  • Workflow lock-in: being where the work happens  • Switchable models: blending performance and cost in real time The same will be true for knowledge work leaders. The winners will:  • Integrate deeply into creative and decision-making workflows  • Enable seamless collaboration between humans and AI systems  • Prioritize trust and transparency in every interaction As Perplexity CEO Aravind Srinivas puts it, "we’ve got to sleep with that fear that your competitor will take your idea and do it better, then use that fear to out-innovate them." In the AI "fast fashion era", that means doing it every single week, not just for software, but for ideas, creativity, and expertise.

"Fast fashion" threat comes for software, wrong-footing many investors! 👕🤖 This week, software investors have been rattled. ▶️ monday.com plunged 30% in a single day. ▶️ SAP lost nearly €22 billion in market value. ▶️ Salesforce, Adobe and others are deep in the red this year. The reason? A growing fear that AI will rewrite the rules for software — making applications faster, cheaper, and easier to build than ever before. And at the center of the conversation is a warning from OpenAI CEO Sam Altman: ➡️“The sector could enter a fast fashion era very soon.”⬅️ What he means is the following: In “fast fashion,” products go from concept to customer in weeks, but at the cost of durability and defensibility. Apply that to software, and the old moat of long development cycles and high switching costs could shrink dramatically. 📉 The market reaction Investors are questioning whether the traditional software model (long timelines, premium pricing, and sticky customers) can survive in an AI-powered world. Some analysts call it "the death of software" narrative. Software is among the weakest performers within tech this year, with a basket of software stocks trading near the lowest levels since January versus a group of semiconductor shares (see chart). 📈 The contrarian view Others see opportunity. Morgan Stanley just upgraded Monday.com, arguing the stock’s selloff already "more than incorporates" the AI risk. ❓The bigger question for investors: If AI makes building software as quick and commoditized as designing a t-shirt, how do companies keep their edge? The answer may lie in: ▶️ Brand and trust ▶️ Deep integration with customer workflows ▶️ Proprietary data that AI alone can’t replicate We’re watching an inflection point, one that could punish the complacent but reward those who adapt faster than their competitors. That actually applies to both — software companies and investors! 😌 Post based on Bloomberg reporting by Henry Ren #AI #Software #Investing #Technology #SamAltman #Innovation

Fashion scales through code, not clothes. The modern brand builds digital infrastructure first, collections second. Digital leaders are pulling 30-40% of their sales online, while those falling behind struggle at under 20% with clunky online-offline experiences. This gap is widening with 94% of Millennials and Gen Z researching online before visiting physical stores. So, what tech powers today's scaling fashion brands? Shopify or Adobe Commerce handle storefront and checkout experiences. Lectra manages product lifecycles while Akeneo: The Product Experience Company organizes product information. Customer experience gets boosted with True Fit's virtual try-ons, Algolia's lightning-fast search, and Akamai Technologies keeping everything running smoothly. Social advertising, CRM systems, and marketing automation bring customers in. Robust ERPs like SAP keep operations flowing. Tying it all together? Smart middleware ensuring seamless data integration. It's not the trendsetters but the tech-integrators that will write fashion's next chapter. Retailers and brands, what tech investments have delivered the biggest ROI for your brand? Source: Digital Fashion Academy and Heuritech 👠 P.S. Love tracking fashion's evolution? Follow me for regular insights on where style is headed next. 

𝗔𝗜 𝘅 𝗙𝗮𝘀𝗵𝗶𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗻𝗶𝗰𝗵𝗲 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 𝗮𝘁𝘁𝗿𝗮𝗰𝘁𝗶𝗻𝗴 𝘀𝗲𝗿𝗶𝗼𝘂𝘀 𝗩𝗖 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻… It’s not the loudest AI sector, but it’s one of the most quietly consistent. Funding into AI fashion startups has stayed steady, even as overall VC spend has dipped. Why? Because the global fashion industry is worth $𝟮.𝟯𝗧 𝗯𝘆 𝟮𝟬𝟯𝟬, and AI is solving real problems across it. 𝗪𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗺𝗼𝗻𝗲𝘆’𝘀 𝗴𝗼𝗶𝗻𝗴: 𝟭. 𝗧𝗿𝗲𝗻𝗱 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 Startups like @𝗭𝗵𝗶𝘆𝗶 𝗧𝗲𝗰𝗵 and FINESSE are spotting viral styles before they go mainstream, helping brands move faster and waste less. 𝟮. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 Raspberry AI and BLNG AI are using AI to turn sketches into 3D mockups, streamlining how collections are made. 𝟯. 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 Daydream and Lily AI improve how shoppers find what fits them, and how retailers sell smarter. 𝟰. 𝗙𝗶𝘁 & 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝘁𝗿𝘆-𝗼𝗻𝘀 𝗧𝗲𝗰𝗵 𝗳𝗿𝗼𝗺 Doji, Veesual and IAMBIC are reducing returns with avatars, diverse models and custom-fit footwear. 𝟱. 𝗦𝗺𝗮𝗿𝘁 𝗺𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 From real-time defect detection (Smartex.ai) to AI-powered textile recycling (Refiberd, and Solena Materials Limited), the backend is getting an upgrade too. Fashion is fast, wasteful and competitive. AI offers a way to stay ahead. 𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗼𝗿 𝗵𝗶𝗿𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗶𝘀 𝘀𝗽𝗮𝗰𝗲, 𝗹𝗲𝘁’𝘀 𝗰𝗼𝗻𝗻𝗲𝗰𝘁. 𝗧𝗵𝗲 𝘁𝗮𝗹𝗲𝗻𝘁 𝗿𝗮𝗰𝗲 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝘀𝘁𝗮𝗿𝘁𝗲𝗱. #AI #AIstartups #SanFranscisco #NYC

Just dropped a new episode of LOOPED IN 🎙️ Dr. Kitty Y. M. Yeung is building at the intersection of AI, fashion commerce, and next-gen supply chains, bouncing between Heidelberg, San Francisco, and Hangzhou to make it happen. We unpack: - How AI-generated #UGC can replace shipping samples and make marketing scalable - Why the next wave is “everyone becomes an #influencer” (and what that changes for brands) - The bigger ambition: an AI-driven fashion supply chain moving toward MOQ = 1, meaning zero inventory - What a “Starbucks for fashion” could look like — consumer-facing, customizable, and fast - Why microfactories + automation could make production more local, flexible, and resilient Next, Vincent Djen, Kitty and Trine Y. should sit together and combine resources on building the future fashion tech infrastructure. If you’re working on on-demand production, creator commerce, AI in retail, or circular fashion, this one is for you. 🎧 Spotify https://lnkd.in/gDzYmmXM 🍏 Apple https://lnkd.in/gw_jj4Z3 #FashionTech #AI #CreatorEconomy #OnDemandManufacturing #SupplyChain #CircularEconomy #RetailInnovation #ClimateTech #D2C #onshore #overproduction #MOQ1 #ondemand #rodinia

Sam Altman recently said we’re entering the fast fashion era of SaaS. Here’s what that means for you as a founder: 1. Software is getting cheaper to build. Tools like Lovable, Bubble, Claude, and ChatGPT are already making it possible to launch full apps in days, not months. Think Zara for code: 👗 Looks high-end 💸 Built dirt cheap ⚡️ Made lightning fast 2. Distribution > everything else. Zara wins because it moves fast and saturates the market. Same goes for SaaS now. Your product isn’t enough. It’s your content, your strategy, your distribution that makes the difference. 3. The customer is the roadmap. Got a feature request on Thursday? Ship it by Sunday. That’s where this is going: Hyper-personalized software built to meet hyper-specific demand, instantly. Just like fashion reacts to trends, founders now must react to users. Those who move slow? They won’t be around long… And, unlike real fast fashion, more people building more apps with AI might actually turn out to be a net positive for society.

Zara pioneered fast fashion. Shein deleted the middlemen. Guangzhou, 2015. Chris Xu saw the bottleneck: Chinese factories could make anything but had no idea what Western teens wanted. The solution wasn't faster factories. It was removing the humans between signal and production. By 2016, Xu connected 5,000 suppliers into algorithms tracking TikTok trends. Those signals triggered micro-batch test orders directly. No merchandisers, no buyers, no creative directors. Zara: 2-3 weeks. Shein: 3-7 days. The real disruption was clockspeed, generating 20x more SKUs than Zara or H&M. Result: $38B revenue vs Zara's $6B. While competitors digitized human workflows, Shein deleted them. The Moat Isn't Tech. It's Negative Space Shein's advantage isn't what they built. It's what they removed: → No seasonal collections → No designer ego → No SKU minimums → No fixed inventory bets Plus: Shein pays suppliers weekly, not 90 days. Fast payment funds fast production and locks in 6,000 exclusive factories. Competitors can't copy this without destroying their identity. Switching costs aren't technical. They're existential. Why Now? The Algorithmic Shift When AI and manufacturing converge, digital systems coordinate physical production in real-time. The old playbook required human interpretation at every step. When algorithms read demand signals and trigger production directly, companies don't optimize workflows. They delete them. Traditional fast fashion: Trendspotters → Designers → Merchandisers → Buyers → Factories Each handoff adds days. Each loses signal. Shein's LATR model creates a closed loop: → Algorithm detects signals → Micro-batch produced → Sell-through determines reorder → Winners scale, flops vanish 5,000 suppliers within 5 kilometers, all linked digitally. Physical proximity enables digital velocity. The Universal Pattern Every industry has coordination taxes waiting to be deleted: → Law firms bill endless hours reviewing contracts AI can analyze in seconds → Insurance pre-authorizations drag on decisions algorithms make instantly The constraint isn't talent or capital. It's coordination latency. It's about removing humans from the wrong parts of the system. Coordination is friction. Judgment is leverage. Keep humans where they add value. Eliminate them where they don't. Final Principle: Don't Optimize the Tax. Remove It. You should be designing around its absence. The winners won't be those who automate everything.  They'll be those who know what not to automate. Recommendation: Sangeet Paul Choudary new book Reshuffle goes much deeper on this topic of algorithmic coordination and Shein. (Full case study sent to newsletter subscribers)