Serkan Ayan, PhD

Figma is skipping the traditional IPO roadshow and going public via direct listing. And it's 40x oversubscribed. Most companies avoid direct listings. In a traditional IPO, investment banks buy shares at a discount and then sell them to institutional investors. The banks act as underwriters, which means they guarantee a minimum price and provide stability. Direct listings are different. No underwriters. No discount. No roadshow presentations to convince investors. Existing shares just start trading on the exchange at whatever price the market decides. This creates volatility risk as there's no price floor. But Figma doesn't need to worry about that. There's massive demand already baked in. When Spotify went direct in 2018, everyone predicted chaos. Instead, it traded relatively smoothly. Same with Slack in 2019. But why does this really matter for Figma? They capture the full market value instead of giving banks a discount. In traditional IPOs, banks often price shares 10-20% below market value to ensure a successful launch. That's money left on the table. Why give away value when you don't have to?

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“Everything is design and design is everything.” - Emily Campbell Today, Kim Lenox, Emily and I had a conversation with a thousand people to discuss Figma’s new study exploring how work is changing for 7 roles involved in product development. The quote above sums it up. Roles are blurring. And, more people than ever see design as an integral part of their work. The full story is longer and even more interesting: - 64% of respondents identified with two or more roles, and over a third said their responsibilities span three or more roles. - 56% of non-designers say design tasks like prototyping are a major part of their work, up 12 percentage points in a year - 19% of time is now spent with AI tools, expecting to increase to 27% in the next 12 months. This time is taken from solo work time rather than work with colleagues. And, 57% say they’re spending more time on work that’s higher value and strategic. - 53% say deep knowledge is still needed to do a task well, less than a quarter believe AI reduces the need for expertise. Matt Walker and team built the study of 1200 respondents and 50 interviews, covering 67 tasks. Alia Fite, Emma Webster, and Alex Praeger created an amazing report from the findings. Chad Bergman and Greg Huntoon answered so many questions in today's discussion. The key here is it’s not just design. It’s not just product development. And, I would guess this trend is actually is bigger than just this field. I think at the heart of these changes are a strong desire to communicate intent from one expertise to another. From marketing to design. From design to development. From PM to development. And, I think we’ll see even more of this blurring moving forward.

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Whether you’re trying to get hired or promoted, we’ve developed an obsession with “showing impact.” I’m guilty of it too. Many talented designers struggle with this. They work in organisations where tracking is incomplete, and they’re forced to choose between showing nothing or making up numbers. But here’s something to keep in mind: Awareness beats access. I’d rather work with someone who doesn’t have data but understands what their work affects. Than someone who has all the data and no idea what to do with it. If you don’t have perfect data, you can still show impact by thinking through these layers: 1. Behaviour --- What are the smallest user actions your design is meant to influence? For example: • Form submissions • Button clicks • Page visits These are leading indicators. You can directly influence them with design. 2. Product --- If hundreds or thousands of users perform that behaviour more often, what product metric would it influence? For example: • Activation rate • Engagement • Discoverability These are team-level outcomes. Your work should support one or more of them. 3. Business --- If that product metric improves, what business impact would it have? For example: • New MRR • Retention • Expansion These are your lagging indicators. They show up later, but they matter most to leadership. Even if you can’t measure these directly, talk about what you believe your work affected. Be honest about the gaps. You can also share what you would have tracked if the data had been available. If you can tell a plausible story using these three layers—behaviour, product, and business—you’re already showing strong strategic awareness. And you’re pointing to the organisational gaps that made measurement difficult. That mindset matters more than a dashboard.

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OpenAI's UX superpower decoded...  Their speed of predicting and shifting digital behaviours is insane.... My key takeaway: "It's about predicting problems before they explode" and OpenAI just master-classed this with GPT-5 👇 I've been following the GPT-5 news, excited about how it would revolutionise AI workflows. When the update dropped, my genuine reaction was... "It's good, but where's the flexibility I was used to?" I'd been using faster models for image generation and reasoning models for critical thinking. Suddenly, all my previous interactions defaulted to GPT-5. My immediate thought: "Great, now I need to dig through release notes and prompting guides again..." But to my surprise, the next day I found an option to select models for specific intents. My older chats still had the fast model for images. T This made me realize something bigger was happening. Turns out, thousands of other users had the same experience and OpenAI was ready for it. Here's the strategy I discovered 👇 This is the new era of UX design: ❌ Old way: Build → Launch → Fix problems ✅ New way: Build → Predict problems → Launch with solutions already ready I call this "Predictive Solutionising" - solving problems before users know they have them. What's your take on this approach? Rajeev, Saurabh, Simran, Subhasmita, Divyanshu, Prasad, Vaishali, Varun, Shivangi, Apoorva, Shivam, Tamil, Jaswanth, Bharaneesh, Jeenal, Ar. Kranthi, Krati, Sandeep, Vijay, Tanmayee, Praneetha, Susree, Pranaya, Sramana, Sujani, Krithika, Mariyam, Mehak, Abhijeet, Gunjan, Achyuth, pragya. #PredictiveDesign #AIUX #UXStrategy #AIStrategy #ChatGPT #OpenAI #DesignThinking #TechTrends #crmdes CareerReactor, O.P. Jindal Global University (JGU), Jindal School of Art & Architecture

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Great design delivers value to users faster. In my Glaringly Obvious conversation with Thomas Sutton, he made the case that product design teams must clearly define what design quality means, own it, and connect it directly to both user and business value. Without this clarity, design risks being misunderstood, undervalued, and ineffective. Love the conversation, check it out: https://lnkd.in/gVTU5jS5 Thomas’s product design quality framework maps a causal chain: → If design, technology, and support are good, people use the product. → If people use it and it works, you get product outcomes like user benefit, business value, and systemic impact. But here’s where it got me thinking: the speed of that chain matters. → Time to value for users How quickly someone feels the product is usable, useful, and enjoyable. → Time to create value for users   How fast the team can release improvements that increase that value. These two clocks run in parallel. When teams reduce the lag between user benefit and product iteration, design signals become clearer, trust builds faster, and business value compounds sooner. When they drift apart, users wait too long to see progress, and teams stall in endless refinement. In my conversations, here are some of the reasons decisions slow down: 1. Teams wait too long to test ideas, losing momentum. 2. They don’t have fast, clear ways to see what’s working. 3. Design work gets stuck in narrow solutions or tech limitations. 4. Research is incomplete, feedback overlooked, or siloed. 5. Too much refinement, not enough real signals. 6. Confirmation bias clouds decisions. 7.  Leaders struggle to align on user needs vs. business goals. 8. Limited resources stall iteration. That’s where collecting the right signals comes in. In our open framework Glare, we use UX metrics like usability, usefulness, and sentiment to quickly test whether design quality is landing. Helio helps us gather the metrics. They shorten both timelines: showing where users already get value, and where teams must act to deliver more. Building on Thomas’s framework: design quality goes beyond end outcomes. Quality also ensures the path to those outcomes is fast, clear, and measurable. #productdesign #uxmetrics #productdiscovery #uxresearch

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New Article: 20 Cognitive Biases in UX Research This week’s post is a practical guide to the biases that sneak into study design, synthesis, and decision-making. Each one comes with a plain-language definition, a real-world example, and an antidote you can use immediately. Enjoy! https://lnkd.in/eBMfrh9z

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Good UX earns trust. Dark UX breaks it, one misleading click at a time. Design should guide, not mislead. But some patterns are built to trick users into doing things they didn’t intend. They’re called dark patterns, and they’re more common than you think. In this post, I’ve shared some of them: 1. Hidden Costs – Charges that show up at the very end 2. Confirmshaming – Guilt-tripping users into clicking yes 3. Trick Questions – Wording that flips logic to confuse you 4. Roach Motel – Easy to join, painfully hard to leave 5. Disguised Ads – Ads made to look like regular content These tactics may work short-term, But they damage the user experience and break trust in the long run. As designers, we can do better. Design with clarity. Design with intent. Swipe through the carousel to learn how to spot (and avoid) dark patterns. #UXDesign #DarkPatterns #UIUX #DesignTips #EthicalDesign #ProductDesign #UXForGood

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