Design

The first strategic decision for any startup isn't pricing or positioning. It's whether you're chasing rabbits (thousands of small customers) or hunting whales (few large enterprises). Most founders get this wrong because they copy what worked for someone else. But it's your product's inherent characteristics that determine the right path, not your peers’. Here's how to decide what path is best for your company: 1. Start with how value gets created If users can experience meaningful value alone in under 10 minutes, you're built for rabbits. Think Figma, Notion, or Gamma - single-player mode works before anyone else joins. But if value only emerges after integration across an organization, you need whales. Workday and Palantir require company-wide commitment to deliver any ROI. 2. Let physics drive your tactics Choose rabbits and you need transparent pricing, growth engineers, and universal messaging. Your north stars are activation rate (percentage who reach their first success) and K-factor (how many new users each user brings). Choose whales and you need enterprise sales, custom pricing, and ROI calculators. Your north stars are pipeline coverage and contract values. The tactics aren't interchangeable. 3. Know when to expand According to a16z research, successful rabbit companies typically add enterprise sales around $20-30M ARR. That's when organic pull from multiple Fortune 500 domains justifies the investment. Whale companies rarely add successful self-serve unless they discover a true single-player use case. Timing matters more than most realize. 4. Understand the hidden risks Rabbits can destroy unit economics if support scales linearly with users. We've seen companies lose money on every customer while growing rapidly. From a user growth perspective, they're succeeding - but each milestone only tightens the noose. Whales create concentration risk - when one customer is 30% of revenue, they effectively own your roadmap. Both paths have failure modes many founders don't see coming. 5. Commit fully or fail Companies that try to serve both segments from day one almost always fail. You can't optimize for velocity and enterprise procurement simultaneously. Pick your path based on your product's nature, then build everything - team, metrics, culture - around that choice. The irony is that total commitment to one path is what eventually lets you transcend it. Slack went all-in on rabbits first, then eventually served everyone. Atlassian built through self-service before adding sales. Figma reached $34B through rabbits. Salesforce built an empire on whales. The path mattered immensely for each of them. Startups fail far more often from trying to serve everyone than from picking the wrong path.

Turning Heat into Comfort: Smart Innovation in Saudi Arabia ☀️➡️❄️ In Saudi Arabia’s scorching 50°C heat, a mosque has been transformed into a cool oasis with 250 smart umbrellas—a fusion of technology, design, and climate adaptation. 🔹 Heat-Resistant Materials – Fireproof, wind-flexible fabric reduces glare and keeps the space cool. 🔹 Built-in Cooling System – Each umbrella features 16 mist fans, creating a refreshing microclimate for visitors. 🔹 Seamless Aesthetics – Designed to fold away elegantly, enhancing both functionality and beauty. More than just shade, this project showcases how smart design can reshape environments and improve daily life. 🌍 💡 What other technologies can be adapted to combat extreme climates? Let’s discuss! 👇 #Innovation #SmartDesign #Technology #ClimateSolutions #SustainableFuture

🌎 Designing Cross-Cultural And Multi-Lingual UX. Guidelines on how to stress test our designs, how to define a localization strategy and how to deal with currencies, dates, word order, pluralization, colors and gender pronouns. ⦿ Translation: “We adapt our message to resonate in other markets”. ⦿ Localization: “We adapt user experience to local expectations”. ⦿ Internationalization: “We adapt our codebase to work in other markets”. ✅ English-language users make up about 26% of users. ✅ Top written languages: Chinese, Spanish, Arabic, Portuguese. ✅ Most users prefer content in their native language(s). ✅ French texts are on average 20% longer than English ones. ✅ Japanese texts are on average 30–60% shorter. 🚫 Flags aren’t languages: avoid them for language selection. 🚫 Language direction ≠ design direction (“F” vs. Zig-Zag pattern). 🚫 Not everybody has first/middle names: “Full name” is better. ✅ Always reserve at least 30% room for longer translations. ✅ Stress test your UI for translation with pseudolocalization. ✅ Plan for line wrap, truncation, very short and very long labels. ✅ Adjust numbers, dates, times, formats, units, addresses. ✅ Adjust currency, spelling, input masks, placeholders. ✅ Always conduct UX research with local users. When localizing an interface, we need to work beyond translation. We need to be respectful of cultural differences. E.g. in Arabic we would often need to increase the spacing between lines. For Chinese market, we need to increase the density of information. German sites require a vast amount of detail to communicate that a topic is well-thought-out. Stress test your design. Avoid assumptions. Work with local content designers. Spend time in the country to better understand the market. Have local help on the ground. And test repeatedly with local users as an ongoing part of the design process. You’ll be surprised by some findings, but you’ll also learn to adapt and scale to be effective — whatever market is going to come up next. Useful resources: UX Design Across Different Cultures, by Jenny Shen https://lnkd.in/eNiyVqiH UX Localization Handbook, by Phrase https://lnkd.in/eKN7usSA A Complete Guide To UX Localization, by Michal Kessel Shitrit 🎗️ https://lnkd.in/eaQJt-bU Designing Multi-Lingual UX, by yours truly https://lnkd.in/eR3GnwXQ Flags Are Not Languages, by James Offer https://lnkd.in/eaySNFGa IBM Globalization Checklists https://lnkd.in/ewNzysqv Books: ⦿ Cross-Cultural Design (https://lnkd.in/e8KswErf) by Senongo Akpem ⦿ The Culture Map (https://lnkd.in/edfyMqhN) by Erin Meyer ⦿ UX Writing & Microcopy (https://lnkd.in/e_ZFu374) by Kinneret Yifrah

I have spent hundreds of hours analyzing design systems. One of the things that confused me for many years is how to structure color scales and tokens. I have experimented with multiple structures at different sizes of design systems, and at a high-level recommend the following approach: 1. Primitive Colors Your design system foundations should always start with a full color scale that is based on your brand identity. We call these colors Primitives, and your variable/token collection should look like this: - purple-600 - purple-500 - purple-400 - And so on.. To create a Primitives palette you will want to start from your main brand colors and use a tool like UIColors, Supapalette, Colorbox to expand to the full scale. (links in comments) This is a great foundation to have, as it gives you a set of shades that can be used in different ways, and ensures all of them have consistent hues, saturation and brightness. However, Primitive colors are simply not effective when used directly in your designs: - They create ambiguity - Their names have no contextual meaning - They are often misused due to similarity If you have had the “why are there 20 different shades of gray?” conversation with an engineer, you know what I mean. So let’s see how we can improve that. 2. Semantic Colors This is my default recommendation to all product design teams that don’t have a highly complex design system. What you will want to do here is create a new variable collection named Semantic, which is what’s visible in your design files, and comprises of: - Brand / Action - Text - Link - Border - Icon - Surface / Background - Bias - Data / Charts Each color should point to a primitive value, e.g. - text-primary → gray-800 - text-secondary → gray-600 - text-tertiary → gray-400 This takes a bit of setting up, but creates immense long-term value. A great example of a simple, theme-level Semantic structure is Shopify’s Polaris (link in comments) 3. Component-level Semantic Lastly, if you are working on a design system with a lot of complexity and, ideally, a dedicated design systems team, you might want to add another level of hierarchy and specify colors at a component-level. In this structure, you would want to create color tokens based on how they are used in each component. - input-text-filled → text-primary - input-text-placeholder → text-secondary - input-text-disabled → text-tertiary This eliminates all guesswork, but also increases the complexity exponentially. It does serve a purpose though. As design systems scale, you may find that: - A theme-level semantic structure is too restrictive - There is still some guesswork - Decisions need to be documented. An example of this is Uber’s Base and Adobe’s Spectrum design system, linked in the comments. I’m curious to know, what structure are you using for your design system and what has worked well for you? — If you found this useful, consider reposting ♻️ #uidesign #designsystems #productdesign

AI will drive 2x growth in data centre power capacity in 5 years. This means data centres must evolve. This week, I visited Barcelona to see how Schneider Electric assembles its prefabricated and containerised data centres at its factory. Also had the chance to attend briefings by senior members of its data centre division. Here are my thoughts. 𝟭/ 𝗔𝗜 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 AI is here to stay. There are no pathways that don't include AI in some shape or form. But compute systems with lower power demands won't disappear either. This means our current way of designing data centres - often carving out a separate section for AI while non-AI workloads reside elsewhere, won't work. We need a new approach to cooling for maximum flexibility and sustainability for supporting both AI and non-AI workloads. - A new end-to-end design approach. - Partnerships for an AI-inclusive ecosystem. - New systems suited for this new paradigm. 𝟮/ 𝗗𝗶𝗱 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘀𝗮𝘆 𝗹𝗶𝗾𝘂𝗶𝗱 𝗰𝗼𝗼𝗹𝗶𝗻𝗴 Unsurprisingly, the new AI-centric data centres of the future must support liquid cooling. < 𝟰𝟬/𝗸𝗪 𝗿𝗮𝗰𝗸𝘀 - Liquid cooling offers better efficiency. ~ 𝟱𝟬/𝗸𝗪 𝗿𝗮𝗰𝗸𝘀 - Air cooling still possible, but barely. > 𝟱𝟬/𝗸𝗪 𝗿𝗮𝗰𝗸𝘀 - Liquid cooling is a must-have. I just wrote a post about why liquid cooling is the future of data centres yesterday. (Read: https://lnkd.in/g5jhCNcX) 𝟯/ 𝗡𝗼 𝘄𝗮𝗹𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗽𝗮𝗿𝗸 But liquid cooling isn't trivial. - Local sustainability standards differ. - Not all liquid cooling solutions scale well. - Efficiency might come at the expense of other areas. Throw in the need for continued need for air-cooling, and it just gets... complicated. 𝟰/ 𝗠𝗮𝗸𝗶𝗻𝗴 𝗶𝘁 𝘄𝗼𝗿𝗸 What will the data centres of the future look like? The industry must come for a new generation of more sustainable, flexible, data centres. We need: - New data centre design and modeling software. - New high-capacity power trains, systems for AI. - Easy way to determine data centre efficiency. - Greater innovation across the ecosystem. And yes, Schneider Electric says it has developed a reference design for an AI-centric data centre with Nvidia - I'll share more about it in another post. 👉 Would love to hear your thoughts about how data centres must evolve. 𝗣𝗵𝗼𝘁𝗼: Schneider Electric's Sant Boi factory in Barcelona. --- My name is Paul Mah and I write about tech that matters in #EverydayTechStories 📆 Get weekly updates: www.techstories.co/updates 👀 See my other posts: www.techstories.co 🙋 Follow me on LinkedIn: https://lnkd.in/gu5EMKQg #datacentre

McKinsey & Company 𝗮𝗻𝗮𝗹𝘆𝘇𝗲𝗱 𝟭𝟱𝟬+ 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝗳𝗼𝘂𝗻𝗱 𝗼𝗻𝗲 𝗰𝗼𝗺𝗺𝗼𝗻 𝘁𝗵𝗿𝗲𝗮𝗱: ⬇️ One-off solutions don’t scale. The most successful projects take a different path: They use open, modular architectures that enable speed, reuse, and control. → Designed for reuse → Able to plug in best-in-class capabilities → Free from vendor lock-in This is the reference architecture McKinsey now recommends — optimized to scale what works while staying compliant. It consists of five core components: ⬇️ 𝟭. 𝗦𝗲𝗹𝗳-𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗽𝗼𝗿𝘁𝗮𝗹: → A secure, compliant “pane of glass” where teams can launch, monitor, and manage GenAI apps. → Preapproved patterns, validated capabilities, shared libraries. → Observability and cost controls built-in. 𝟮. 𝗢𝗽𝗲𝗻 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 → Services are modular, reusable, and provider-agnostic. → Core functions like RAG, chunking, or prompt routing are shared across apps. → Infra and policy as code, built to evolve fast. 𝟯. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 → Every prompt and response is logged, audited, and cost-attributed. → Hallucination detection, PII filters, bias audits — enforced by default. → LLMs accessed only through a centralized AI gateway. 4. 𝗙𝘂𝗹𝗹-𝘀𝘁𝗮𝗰𝗸 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → Centralized logging, analytics, and monitoring across all solutions → Built-in lifecycle governance, FinOps, and Responsible AI enforcement → Secure onboarding of use cases and private data controls → Enables policy adherence across infrastructure, models, and apps 5. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 → Modular setup for user interface, business logic, and orchestration → Integrated agents, prompt engineering, and model APIs → Guardrails, feedback systems, and observability built into the solution → Delivered through the AI Gateway for consistent compliance and scale The message is clear: If your GenAI program is stuck, don’t look at the LLM. Look at your platform. 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E

Invisible UX is coming 🔥 And it’s going to change how we design products, forever. For decades, UX design has been about guiding users through an experience. We’ve done that with visible interfaces: Menus. Buttons. Cards. Sliders. We’ve obsessed over layouts, states, and transitions. But with AI, a new kind of interface is emerging: One that’s invisible. One that’s driven by intent, not interaction. Think about it: You used to: → Open Spotify → Scroll through genres → Click into “Focus” → Pick a playlist Now you just say: “Play deep focus music.” No menus. No tapping. No UI. Just intent → output. You used to: → Search on Airbnb → Pick dates, guests, filters → Scroll through 50+ listings Now we’re entering a world where you guide with words: “Find me a cabin near Oslo with a sauna, available next weekend.” So the best UX becomes barely visible. Why does this matter? Because traditional UX gives users options. AI-native UX gives users outcomes. Old UX: “Here are 12 ways to get what you want.” New UX: “Just tell me what you want & we’ll handle the rest.” And this goes way beyond voice or chat. It’s about reducing friction. Designing systems that understand intent. Respond instantly. And get out of the way. The UI isn’t disappearing. It’s mainly dissolving into the background. So what should designers do? Rethink your role. Going forward you’ll not just lay out screens. You’ll design interactions without interfaces. That means: → Understanding how people express goals → Guiding model behavior through prompt architecture → Creating invisible guardrails for trust, speed, and clarity You are basically designing for understanding. The future of UX won’t be seen. It will be felt. Welcome to the age of invisible UX. Ready for it?

The Financial Times!!! So exciting to see something I wrote appear on pink paper! My op-ed on AI and copyright ownership has just been published in today’s Financial Times. It’s such a testament to the profile of William Fry in this area and the work of my colleagues that our thought leadership on AI is being published internationally in such a prestigious medium. The piece is titled Who owns the copyright for AI work? and it addresses one of the most pressing and under-examined questions in intellectual property today: what happens to copyright when creative works are generated without a human author? In the piece, I set out how different jurisdictions are taking sharply divergent approaches. The US has drawn a firm line, insisting that copyright requires human authorship. China has taken the opposite approach, recognising AI outputs as protectable works where human input shapes prompts and refinement. Meanwhile, Ireland and the UK sit in a middle ground, with provisions for computer-generated works that may prove unstable as courts and governments revisit their relevance. I argue that this global divergence creates real-world problems for businesses, from software and media to corporate transactions, because the same AI-generated output might be protected in Beijing but freely usable in Boston. I also examine what this means in practice. Companies cannot simply assume that copyright will protect AI-generated material. Contracts and IP strategies will need to change. For example, if AI-generated code is not protected by copyright, firms may need to rely more on trade secrets and confidentiality agreements. This is especially critical as disputes over ownership begin to move from theoretical debate into litigation. The Financial Times is a paper I have long admired for its ability to capture these global debates with clarity and authority, so I am delighted that my analysis on AI and copyright is featured there. It is an issue that will only become more urgent as generative AI systems reshape how we create, compose, and code, and I am thrilled William Fry is contributing to the conversation at this level. Big bucket list tick for me personally! With thanks to Elaine Moore. Go out and read it/buy it/subscribe to it today! :)

Inaccessibility is all around us - but sometimes we’re doing it without even realising. I’ve made every one of these mistakes in the past. It wasn’t until someone took the time to point them out that I learned how inaccessible I was being - despite having good intentions. Here are 5 ways you might be being inaccessible, without even knowing: 1. Long LinkedIn headlines or overuse of emojis. Screen reader users hear your full headline every single time you post or comment. Every. Single. Time. Even when it’s truncated visually. That can mean hearing your full job title, emojis, and taglines multiple times before even reaching your post content. Try to keep your headline under 100 characters or two lines max - it makes a huge difference. 2. Long email signatures, HTTP links, and unlabelled images. Screen readers will read out every line - including things like “H-T-T-P-colon-slash-slash…” for full URLs. Images without alt text are completely invisible to screen reader users. Keep it short and simple, and use alt text wherever you can. Put only essential info in your email signature and put two dashes at the top to signal your signature is starting. And remember, it’s not your marketing tool. When was the last time you actually bought something from an email signature?! 3. Not running documents through the accessibility checker. You run a spell check, so why not an acceeeibility check? It’s a quick step, but it can flag things like heading structures, contrast issues, and missing image descriptions. It takes seconds and makes a big impact. 4. Using colour alone to convey meaning. For example, “I’ve marked the important cells in green” doesn’t help if someone can’t perceive colour easily. Neither does “I’ve shaded the cells for our RAG status”. Always add a label, icon, or another indicator. 5. Using all lowercase hashtags. #thisisnotaccessible - screen readers can’t parse where one word ends and another begins. Use camel case instead - #ThisIsAccessible - so screen readers pronounce the words correctly. Small changes, big impact. If you’ve made some of these mistakes before - welcome to the club. We learn, we improve, we do better. #DisabilityInclusion #Disability #DisabilityEmployment #Adjustments #DiversityAndInclusion #Content #A11y

Housing has become a national issue on the campaign trail, but at the end of the day, housing is a local issue. Mistakes -- and solutions -- predominantly come at the city level. I wanted to better understand how cities and developers can work together for win/win solutions, so I invited the Mayor of Bellevue, WA, Dr. Lynne Robinson onto the podcast. Apologies for shameless plug, but I think anyone with serious interest in solutions should hear what she has to say. Few takeaways: 1) Bellevue -- the economic anchor to Seattle metro's east side -- is a great example of a city that takes a pro-development approach. They've built a ton of Class A apartments, but also a ton of much-needed affordable housing in prime locations in partnership with the housing charity arms of Amazon and Microsoft. As a result of pro-supply housing policy, Mayor Robinson notes that Bellevue rents have fallen even as demand remains strong. 2) What can the federal government do to stimulate housing supply if this is mostly a city issue? Mayor Robinson makes the case we need to invest in housing like we invest in transportation-- and specifically to build live/work/play communities, not just homes alone. 3) What can developers do to improve their odds of getting projects approved? Mayor Robinson advises developers to take a collaborative approach with cities to understand their goals and priorities, and pro-supply cities like Bellevue will give more leeway/exemptions to developments that can incorporate those things so that it's a win/win. That's what I love about Mayor Robinson's approach, too. She understands that to build more housing, cities must partner with developers for win/win solutions. If you just try to bully them around and make their jobs more difficult or less profitable, you will lose out on new housing to other cities -- and ultimately your city (and its residents) will suffer from that. Links in comments for those interested in hearing more. #housing