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Generative AI

125 posts

figma3 min readCurated summary

The VS Code Method: Tightening a developer’s inner loop | Figma Blog

The post argues that developers are most productive when they can sustain the “inner loop”: writing, compiling, debugging, and iterating without disruptive context switching. VS Code’s approach is to bring more outer-loop activities—collaboration, project management, design inspection, and AI assistance—into the editor. The result is not only faster development, but better code quality, greater energy, and improved developer satisfaction. ## The Inner Loop and Flow - The inner loop is the repeated cycle of writing code, compiling, debugging, and continuing in the code editor. - The outer loop includes activities outside the editor, such as: - Checking bug trackers - Updating tickets - Responding in Slack or Teams - Reviewing documentation - Switching between projects and terminals - Staying in the inner loop builds “inertia,” increasing speed and productivity over time. - Interruptions also cause developers to lose mentally loaded context, including edge cases and future plans for the code. ## Reducing Distractions in VS Code - VS Code uses features and extensions to help developers remain focused inside the editor. - Zen Mode hides interface elements and creates a distraction-free workspace. - Even small UI changes, such as collapsing a sidebar, can interrupt concentration as the brain recalibrates. - Developers can customize VS Code with extensions that match their preferred workflows. ## Bringing Outer-Loop Work into the Editor - The broader goal is to move as many tasks as possible into the developer’s existing workflow. - Integrations can reduce switching between: - VS Code and GitHub - Code and project-management tools - Development tools and design platforms - The Figma for VS Code extension lets developers access and inspect designs without leaving the editor. - AI tools such as GitHub Copilot provide proactive, non-intrusive code suggestions. - GitHub reports that Copilot increases coding speed by 55%, while 75% of AI-using developers report greater fulfillment. ## Collaboration Without Breaking Focus - Collaboration is essential but often disruptive when it requires meetings or prolonged chat exchanges. - VS Code integrates GitHub features so developers can manage issues, review code, and submit pull requests from the editor. - The ideal collaboration model lets multiple people remain in their own inner loops rather than requiring constant synchronous interaction. - Direct comments and embedded collaboration can preserve focus while keeping teams connected. ## A More Connected Developer Workflow The VS Code team envisions an inner loop that eventually includes all the tools developers need. Until then, teams should prioritize integrations that eliminate unnecessary switching and tedious manual work. Keeping developers in flow improves productivity, code quality, energy, and overall happiness.

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Generative AIFigmaGithubGithub Copilot+3
figma2 min readCurated summary

Breaking In: A Guide To Landing Your First Product Design Role | Figma Blog

Figma’s guide explains how aspiring product designers can land their first role by presenting strong work, communicating their thinking, and showing how they learn from challenges. It emphasizes that a portfolio should be personal but focused on clear storytelling, accessible navigation, and evidence of design impact. The strongest projects are those where candidates can explain their decisions, trade-offs, constraints, and outcomes. ## Crafting a Portfolio - Portfolios do not need to follow one prescribed format or use a specific website builder. - The priority is to: - Showcase your best work. - Tell compelling project stories. - Make the portfolio easy to navigate. - Represent your individual perspective and skills. ### Lead with Your Best Work - Your first project creates the initial impression, so lead with work that best demonstrates your: - Design skills - Creativity - Process - Product impact - If choosing between projects, feature the one you can discuss most naturally and explain most compellingly. - Strong case studies should include: - A clear problem statement - Important project milestones - An insightful conclusion - Relevant metrics and learnings - What you would change in retrospect - A project does not need to have been an overwhelming success. Honest reflection and lessons learned can create a stronger narrative. - Explain the reasoning behind design decisions, not just the final visuals. - Discuss the trade-offs, advantages, disadvantages, constraints, and implications of choices involving colors, components, styles, and copy. - Figma designer Tammy Taabassum argues that explaining “why” demonstrates product thinking more effectively than simply showing “what” was designed. ## Figma’s Early-Career Opportunities - Figma welcomes interns and new graduates onto its product design team. - Early-career designers are encouraged to contribute actively and bring new ideas rather than merely observe. - The guide is intended to explain what Figma looks for and how aspiring designers can prepare for its interview process. A practical approach is to build a portfolio around a few well-developed case studies, prioritize the work you can explain most clearly, and make your decision-making and learning as visible as the final design.

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Generative AIFigmaUser ExperienceProduct Design+3
figma2 min readCurated summary

The Long and Short of It: Issue no.6 | Figma Blog

Figma’s sixth issue of *The Prompt* examines how AI is changing design, engineering, product development, and human curiosity. Its central argument is that adopting AI requires more than learning new tools: it requires understanding which human skills—judgment, creativity, problem selection, and curiosity—remain essential. The issue presents AI as a collaborator that can expand creative and technical work without replacing the craft behind it. ## AI and the Future of Design - Figma’s design leaders ask what “good design” means as AI makes product development more accessible. - As execution becomes easier to automate, design judgment and strong craft principles become more important differentiators. - The focus is on building practical, thoughtful products with AI rather than pursuing novelty for its own sake. ## What Engineers Contribute Beyond Code - Figma CTO Kris Rasmussen argues that engineering is not merely the production of code. - Engineers provide value by identifying which problems matter and determining effective ways to solve them. - AI may commoditize portions of coding, but it also creates space for engineers to focus on architecture, judgment, problem framing, and higher-level innovation. ## Curiosity and Judgment-Free Questions - Perplexity CEO Aravind Srinivas describes AI-powered search as a continuation of encyclopedias and wikis. - The goal is to give people a source of answers without the social pressure or embarrassment that can inhibit curiosity. - AI can act as a “copilot” for exploration, though improving the reliability and usefulness of its answers remains an ongoing challenge. ## Humanoid Robots and Embodied AI - The issue considers whether humanoid robots are finally moving from science fiction into everyday reality. - Androids reflect humanity’s longstanding fascination with reproducing intelligence and ourselves in technological form. - Conversations with robotics builders explore both the promise and the risks of giving AI a physical body. ## The Role of Print and Material Experience - *The Prompt* is also an 80-page print magazine produced with Figma’s Brand Studio and designer Chloe Scheffe. - Vellum paper, illustration, color, layout, and physical materiality interpret the issue’s themes. - The print edition emphasizes experiences that digital tools and AI cannot fully reproduce, reinforcing the value of tangible, intentional creative work. AI’s greatest potential lies in extending human abilities rather than eliminating them. Designers and engineers should use it to increase experimentation and efficiency while preserving the judgment, creativity, and curiosity that give their work meaning.

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Generative AIAIProduct DesignSoftware Engineering+2
figma2 min readCurated summary

An Update on our Make Designs Feature | Figma Blog

Figma temporarily disabled its Make Designs AI feature after discovering that some generated mockups closely resembled real applications, including Apple’s weather app. The issue came not from model training, but from insufficiently reviewed components and example screens in Figma’s custom design systems. Figma removed the problematic assets and planned stronger quality assurance before relaunching the feature, later bringing it back under the name First Draft with updates. ## How Make Designs Works - Combines an AI model, contextual design-system data, and a user prompt. - Uses generally available models such as OpenAI’s GPT-4o and Amazon Titan, without additional fine-tuning. - Relies on separate mobile and desktop design systems containing hundreds of components and example compositions. - The language model selects, arranges, parameterizes, and themes components based on the prompt. - Amazon Titan generates the images used in the resulting designs. ## What Went Wrong - Figma reviewed the design systems during development and private beta testing. - Shortly before Config 2024, new components and example screens were added without sufficient vetting. - Some assets resembled patterns from real-world applications. - A prompt for a weather app produced results that appeared notably similar to Apple’s first-party design. - The incident was identified after designer Andy Allen raised the concern, prompting an immediate investigation. ## Figma’s Response - The team traced the similarities to assets in the underlying design systems. - Problematic components and examples were removed. - Make Designs was rolled back and disabled. - Figma postponed relaunching the feature while developing a more robust QA process. ## Future Direction - Make Designs was originally called “First Draft” to emphasize that AI output is only a starting point. - Figma wants users eventually to connect the feature to their own company design systems. - This could reduce the time spent locating, assembling, and configuring components. - Figma maintains that designers remain essential for refining drafts into meaningful user experiences. - The feature was later re-enabled with updates and renamed First Draft. Figma’s experience highlights the need to carefully audit not only AI models but also the data, components, and examples supplied to them. AI-generated designs should be treated as starting points that require professional review and creative refinement.

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Generative AIAIDesign SystemsUser Interface+3
figma2 min readCurated summary

What is Good Design in the Age of AI? | Figma Blog

AI is making product creation faster and more accessible, increasing the importance of design as a differentiator. Figma argues that designers should not chase automation or trends, but apply enduring principles such as empathy, creativity, and solving real user needs. The future of good design will depend on experimentation, stronger design-to-code connections, pragmatism, and new forms of human–AI collaboration. ## AI Creates a New Design Inflection Point - Like the iPhone’s launch in 2007, AI is introducing a new medium that requires experimentation and the development of new interaction patterns. - Early mobile products often forced desktop experiences onto smaller screens; today, many AI products similarly rely on basic chatbots and templates. - Designers can unlock AI’s potential through: - Richer interactions - Intuitive gestures - Patterns designed specifically for AI - AI can generate code, designs, and complete applications from prompts, allowing teams to move rapidly from concept to creation. - As more people participate in product development, thoughtful design becomes increasingly important for products to stand out. ## Codifying the Fundamentals of Good Design - Figma’s AI feature for generating initial UI drafts needed to understand the mechanics of good design. - Because large language models are text-oriented, generating high-quality visual interfaces is more difficult than generating text or code. - A complete rulebook for design is impractical: - Good design contains too many contextual details to define exhaustively. - Extremely large prompts exceed technical token limits. - Figma instead focused on reducing design expertise to a small set of broadly applicable principles. - Teaching AI requires designers to make their intuitive knowledge explicit by creating rules that are: - Clear - Concrete - Practical - General enough to apply across many interfaces - The process resembles teaching design: instructors must break complex judgment into principles that others can understand and use. ## Design’s Continuing Role - AI should elevate design rather than simply automate it. - The most valuable design foundations remain relatively constant despite technological change. - Designers’ roles may evolve, but their understanding of users, creativity, and ability to solve meaningful problems remain essential. ## Practical Direction Teams should treat AI as a new design medium, not merely an automation tool. They should experiment with AI-native interaction patterns while grounding products in concise, teachable design principles and genuine user needs.

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Generative AIFigmaAIDesign Systems+2
figma3 min readCurated summary

Why Are We So Afraid Of Code As A Commodity? | Figma Blog

AI may commoditize code production, including language translation and design-to-code workflows, but that does not eliminate the need for engineers. The article argues that engineering’s lasting value lies in identifying the right problems, understanding users and constraints, and designing elegant, maintainable systems. AI should therefore be viewed less as a threat and more as a tool that expands creativity and shifts engineers toward higher-level decision-making. ## Code Generation Is Not the Same as Engineering - AI can increasingly: - Translate between programming languages, such as Python and C++. - Generate code more efficiently. - Convert designs into implementations using frameworks such as React, TypeScript, Kotlin, and Jetpack. - Design-to-code is comparable to translating between programming languages because modern design tools already represent designs in structured, code-like forms. - Producing code is only one part of engineering. Engineers must also: - Decide which problems are worth solving. - Choose appropriate solutions. - Create abstractions for reasoning about complex systems. - Balance correctness, simplicity, context, and constraints. - Framework-specific expertise becomes less valuable over time than first-principles reasoning about the common ideas underlying different platforms. ## The Art and Creativity of Engineering - AI is expected to automate rote work, potentially freeing engineers to focus on more creative activities. - There are often many viable ways to build a system; AI may expose additional approaches that engineers would not have considered. - Engineers remain responsible for evaluating tradeoffs among those options. - Technical implementation is presented as a creative discipline in which constraints can inspire better solutions and product decisions. ## Embracing Shifts in Engineering Roles - Engineering work begins before coding: - Teams discuss user needs. - They triage problems. - They align on what to build and how to approach it. - As AI handles more low-level implementation, coding will represent a smaller portion of an engineer’s responsibilities. - Engineers will spend more time prioritizing, aligning teams, interpreting context, and making product and system-level decisions. - The abstraction level of software development is rising as AI takes responsibility for increasingly lower-level parts of the technology stack. ## What AI Will Not Commoditize - AI still struggles to fully understand: - What users actually need. - The context surrounding a problem. - Conflicting constraints and product priorities. - How to compose intuitive, maintainable systems. - Engineers will continue to add value by reasoning from first principles and solving technical challenges from the ground up. - The central question is not whether AI automates design-to-code, but how engineers use that automation to work faster and explore better solutions. The practical recommendation is to embrace AI for repetitive implementation work while developing the higher-level skills that remain difficult to automate: problem selection, user understanding, system design, tradeoff analysis, and creative technical reasoning.

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Generative AIReactTypeScriptDesign To Code+2
figma2 min readCurated summary

Inside the Redesigned Figma, Where Your Work Takes Center Stage | Figma Blog

UI3 is Figma’s third major redesign, created to manage the complexity accumulated over nearly a decade of new features. Its central goal is to put users’ work—not the interface—at the center while preserving professional workflows and making Figma more approachable. The redesign also establishes a flexible foundation for products such as Dev Mode, Figma AI, and Figma Slides. ## Why Figma redesigned the interface - **Center users’ ideas:** New capabilities such as interactive components and AI risked making the interface feel crowded or distracting. - **Serve both beginners and experts:** Figma aimed to become more intuitive for newcomers without removing the power and familiar ergonomics professional designers rely on. - **Adapt to modern design practices:** Reusable components, generative AI, and higher-level abstractions require tools that provide building blocks rather than focusing only on individual pixels. - **Prepare for the future:** UI3 supports Figma’s broader ecosystem, including closer connections between design and code, Dev Mode, Figma AI, and the newly introduced Figma Slides. ## Clearing the stage for the canvas - The redesign began by maximizing canvas space and reducing visual distractions. - Figma experimented with extreme concepts, including interfaces that appeared only on hover and layouts where panels constantly appeared and disappeared. - These approaches made the workspace feel unstable, so the team moved toward a more consistent solution: - Resizable and collapsible panels - A slim toolbar positioned at the bottom of the canvas - The ability to hide the interface completely and reveal panels when needed - The new structure creates a roomier workspace and provides a consistent framework for switching between Figma products. ## Iterative design process - Figma explored hundreds of interface variations. - Designers built and used many prototypes internally to evaluate how each approach affected real workflows. - Features and controls were deliberately removed until the team identified what users genuinely missed, then selectively restored them. UI3’s practical recommendation is to give designers more control over the interface: keep tools accessible, but allow the canvas and the work itself to take priority.

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Generative AIFigmaDesign SystemsUser Experience+2
figma3 min readCurated summary

Should robots be building our homes? | Figma Blog

Icon CEO Jason Ballard argues that robotics and AI could make housing faster, cheaper, more durable, and more sustainable. Icon’s robots 3D-print cement-based walls, while its Vitruvius AI system is intended to generate designs, budgets, schedules, and eventually robotic construction instructions. Ballard sees the same technologies eventually supporting construction beyond Earth, including on the Moon. ## From Housing Mission to Robotics - Ballard’s interest in construction grew from work with homeless shelters and sustainable building in Colorado. - Although he once planned to become an Episcopalian priest, he chose to pursue housing affordability, dignity, beauty, and comfort as his life’s mission. - A master’s degree in space resources also shaped his interest in using robots for construction in extreme environments. ## Why Icon Focused on 3D-Printed Walls - Icon believed advanced software and robotics could improve construction. - The company targeted walls because they are among the slowest, most complex, and labor- and material-intensive parts of building. - Its system extrudes layers of cement-based material reinforced with steel rods. - The printed wall replaces much of conventional construction, including framing, insulation, drywall, sheathing, finishes, and siding. ## Claimed Benefits of 3D-Printed Homes - Faster and potentially more affordable construction. - Walls rated to withstand fires for two hours and 57 minutes and winds up to 250 miles per hour. - Some residents in Icon’s 3D-printed neighborhood reportedly pay as little as $17 per month in energy costs. - Icon says its homes have performed strongly in testing for compressive strength, bending, energy efficiency, fire, flooding, hurricanes, and termites. - Ballard also emphasizes that the homes can be aesthetically appealing, not merely functional. - He predicts that conventional stick-frame construction could eventually become obsolete or even prohibited. ## Vitruvius and AI-Driven Architecture - Icon had long worked on automating architectural tasks but struggled with the complexity and computational demands involved. - Building projects must account for budgets, schedules, designs, and highly localized permitting requirements. - Building codes vary across thousands of jurisdictions, making them difficult to interpret and apply. - Icon began pursuing generative AI training roughly two years before the interview. - The company collected floor plans, building designs, permits, and related documents to create what Ballard describes as the world’s largest architectural dataset. - Vitruvius is intended to produce architectural designs and construction plans, then translate them into instructions for Icon’s robots. ## Construction on Earth and Beyond - Icon’s broader ambition is to use autonomous construction systems in challenging environments. - The company is collaborating with NASA on potential infrastructure for the Moon. - Ballard presents lunar construction as an extension of the same incremental process used to improve housing on Earth. Icon’s approach combines automated design with robotic construction rather than treating AI as a standalone design tool. If its performance and cost claims continue to hold up in real-world projects, the technology could offer a promising alternative to traditional construction, particularly where labor, materials, or access are limited.

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Generative AIAIAutomationRobotics+4
figma3 min readCurated summary

Ovetta Sampson on Inputs and Outputs | Figma Blog

Minimum viable data is the idea that AI projects should begin with representative, high-quality data—not with the most powerful model or feature. Ovetta Sampson argues that model outputs are overwhelmingly determined by their inputs, which reflect human choices and historical biases. Product builders should therefore question whether AI is necessary, who it serves, and whether the data is equitable enough to avoid harming overlooked groups. ## Data Quality Shapes AI Outcomes - The quality of an AI system depends primarily on the data used to train and operate it. - Decisions about what data to collect, exclude, label, and measure determine who the system recognizes and how it behaves. - Data is never purely objective: it is generated, engineered, and transformed by people. - Treating data as disconnected from human lives can produce “traumatized data sets,” embedding social, cultural, and economic harms into models. ## The Consequences of Omission - Historical datasets often exclude entire groups: - U.S. credit and mortgage models were developed before women could independently obtain mortgages or credit cards. - The U.S. Census did not recognize LGBTQ individuals until 2021, despite those people existing in earlier populations. - When people are absent from the data, models may fail to serve them or may expose them to harmful decisions. - The central question is not simply whether data exists, but whether it represents the people affected by the system. ## Define the Problem Before Choosing AI - Teams should first identify the problem they are trying to solve and determine whether ML or AI is appropriate. - The fact that a problem can be addressed with AI does not mean it should be. - Builders should ask: - Who is the product for? - Is the data equitable and sufficiently high quality? - What is the minimum data and technology needed? - Could the proposed solution increase human risks or reduce people to data points? - Minimum viable data means collecting what is necessary for a useful, responsible solution rather than indiscriminately gathering more data. ## Putting People Back in Control - Product builders and the public need to participate in decisions about how AI systems are designed and governed. - Important questions include who defines “good” data, who decides what enters a training set, and how much data is truly required. - Sampson recommends learning from work such as *Weapons of Math Destruction*, *Ghost Work*, and research on the lack of attention given to data work in AI development. AI development should start with the people affected by a system and the data needed to represent them fairly. Choosing the smallest appropriate dataset and validating its quality can be more responsible—and more effective—than pursuing larger models or unnecessary AI features.

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Generative AIAIMachine LearningData Quality+2
figma2 min readCurated summary

Meet Figma AI: Empowering Designers with Intelligent Tools | Figma Blog

Figma AI is a suite of tools designed to help designers overcome creative blocks, work faster, and explore ideas more easily. Rather than treating AI as hype, Figma presents it as a practical way to solve common workflow problems, including finding existing designs and components. The features are initially available in a limited beta and are free during 2024, though usage limits and future pricing may change. ## Figma’s Practical Approach to AI - Figma AI builds on the company’s earlier AI features for FigJam. - The tools target several stages of design work: - Finding inspiration and existing assets - Exploring different design directions - Automating repetitive tasks - Generating interfaces from text prompts - Figma emphasizes helping designers remain efficient and creative rather than replacing their judgment. ## Visual Search - Visual Search allows users to find similar designs by: - Uploading an image - Selecting part of a canvas - Entering a text query - Results are drawn from team files the user can access. - Relevant frames can be inserted directly into the current working file. - Figma plans to expand search to Community files, with attribution, links to source files, and access to creators’ other work. ## AI-Enhanced Asset Search - Asset Search now uses semantic understanding rather than relying only on exact keyword matches. - A search such as “primary button” can find a component named `btn_large`. - The system considers the meaning and typical use of design elements, making components in large or complex design systems easier to discover. - The goal is to make finding assets feel more natural and reduce time spent searching through files and libraries. ## Beta Availability and Pricing - Figma AI and UI3 are being rolled out through a limited beta. - Users can join through Figma’s help menu by selecting **“Join UI3 + AI waitlist.”** - Features are free during the beta period, which runs through 2024. - Figma may introduce beta usage limits as it evaluates demand and infrastructure costs. - Pricing for general availability will be announced later. Figma AI is positioned as an assistive layer within the existing design workflow: it helps users locate useful starting points and reduce friction while leaving creative decisions with the designer.

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Generative AIAIDesign SystemsUser Experience+2
figma3 min readCurated summary

Navigating the Promise and Pitfalls of AI | Figma blog | Figma Blog

AI’s promise is real, but useful AI products will emerge through experimentation rather than a race to ship features. Figma’s research suggests AI is already transforming individual workflows—especially for developers—while having a smaller effect on collaboration and foundational design work. To deliver lasting value, teams must focus on how AI reshapes products, industries, and group work, not just on model capabilities. ## Research and Methodology - Figma surveyed more than 1,800 designers, executives, and developers. - Participants came from the US, Canada, Australia, the UK, Japan, France, and Germany. - The survey ran from February 26 to March 3, 2024. - The report combines survey findings with discussions involving AI and design experts. - Its central premise is that AI’s impact depends heavily on product design and user experience—not only on the power of large language models. ## AI’s Uneven Transformation of Workflows - Developers were 60% more likely than designers to say AI had transformed the products they work on. - Developers use AI for daily tasks such as generating starting points and translating between programming languages. - AI-generated output is currently perceived as more reliable for developer workflows. - Designers may use AI to turn mockups into code, but much of design’s foundational work still involves: - Understanding user needs - Exploring problems nonlinearly - Learning about the broader problem space - AI initiatives increasingly originate outside design, with programmers, subject-matter experts, and stakeholders contributing ideas. ## AI Must Improve Collaboration, Not Just Individual Productivity - Eighty-five percent of respondents said AI had affected their personal productivity or workflows. - Common uses include text and image generation, brainstorming, and using AI as a sounding board or thought partner. - Respondents were three times more likely to report significant changes to individual workflows than to collaborative ones. - AI has not substantially changed group activities such as alignment or meeting facilitation. - Truly transformational AI products will need to support how teams work together, rather than focusing only on isolated tasks performed by individuals. ## Long-Term Effects Across Industries - Respondents in technology, professional and business services, and retail most often expected significant AI-driven changes to their products and services: - Technology: 41% - Professional and business services: 40% - Retail: 39% - Healthcare, energy and utilities, and telecommunications respondents expected the least impact over the following 12 months. - The report argues that realizing AI’s full potential requires considering how major institutions and essential services—not just software products—will evolve. ## Experimentation Before the Product Race - AI development is still characterized by experimentation, play, and research. - Teams face pressure to release new AI features quickly as new products, applications, and research appear constantly. - The recommended approach is to embrace uncertainty, iterate thoughtfully, and determine which ideas genuinely create value. - As the technology matures, the most successful products will likely come from careful exploration rather than simply adding AI features because of market hype.

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Generative AIAILarge Language ModelsMachine Learning+2
figma3 min readCurated summary

AI + Design: Figma Users Tell Us What’s Coming Next | Figma Blog

Generative AI’s impact will depend not only on technical capability but also on how effectively it is designed into products and everyday workflows. A Figma survey of more than 1,800 designers, developers, and executives shows high expectations for AI, but limited evidence that current implementations are delivering meaningful value. The findings point to a risk of “AI feature fatigue” and suggest that thoughtful, user-centered design will determine whether AI becomes genuinely useful. ## Survey Scope and Methodology - Figma surveyed more than 1,800 users between February 26 and March 3, 2024. - Participants included designers, developers, and executives across the US, Canada, Australia, the UK, Japan, France, and Germany. - The research examines how organizations view AI’s near-term impact and how teams are incorporating it into products. ## High Expectations, Limited Results - 89% of respondents expect AI to affect their company’s products or services within 12 months. - 37% anticipate a “significant or transformative” impact. - Executives are especially likely to view AI as important to company goals. - Despite this optimism, 72% of people whose products include AI say it has only a minor or non-essential role. - Only about one-third report improvements in business metrics such as revenue, costs, or market share. - Fewer than one-third say they are proud of what they have shipped. ## The Risk of AI Feature Fatigue - Figma researchers observed growing indifference toward adding “yet another AI feature.” - More than 20% of teams building AI products identify failure to solve a real user need as a major challenge. - This concern is particularly strong among designers. - Fewer than half of respondents working on AI products or features have shipped anything, indicating that a larger wave of AI products may still be coming. - Organizations risk flooding the market with novelty features that users do not find useful. ## Designing AI Into Existing Products - One-third of respondents rank integrating AI coherently into existing products as a top challenge. - Simply adding AI without improving the overall user experience is unlikely to drive adoption. - Teams need to help users understand: - What AI tools are available - When those tools are useful - How AI improves existing workflows - The article uses ChatGPT as an example: its rapid adoption was driven partly by a simple, accessible conversational interface, even though the underlying model capabilities already existed. - Good design can make powerful technology more approachable, aligned with user expectations, and easier to use. ## Practical Implication AI products are more likely to succeed when they address concrete user problems rather than adding AI for its own sake. Organizations should prioritize coherent product integration, clear user experiences, and measurable improvements over ambitious but disconnected features.

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Generative AIFigmaAIUser Experience+2
figma2 min readCurated summary

The Long and Short of It: Issue no.2 | Figma Blog

Figma’s “Rework your work” issue presents small improvements, AI, prototyping, and community creativity as ways to challenge outdated workflows. Its central argument is that meaningful progress often comes from improving everyday experiences while adopting tools and practices that expand what teams can accomplish. ## Small Improvements, Significant Impact - Product quality-of-life changes can have more impact than flashy features users rarely use. - Sho Kuwamoto argues that frequent, practical improvements—such as saving time or anticipating user needs—are foundational to a strong product experience. - Figma’s “Little Big Updates” emphasizes that subtle changes can create broad effects. ## Breaking Barriers with AI - FigJam’s generative AI features are designed to lower the barrier to visual collaboration while expanding creative possibilities. - AI can: - Generate custom templates and diagrams - Summarize board content - Sort sticky notes - These capabilities help product teams plan, synchronize, and brainstorm more efficiently. - The goal is not only automation, but making collaborative tools accessible to more people. ## Prototyping as a Core Practice - Prototyping is presented as an essential part of product development rather than a final presentation step. - Prototypes make design ideas tangible so collaborators can explore, critique, and refine them. - A strong prototyping culture gives designers more agency and helps the broader team gain insight earlier in the process. - Integrating prototypes throughout development can streamline workflows and improve decision-making. ## Supporting Community Creators - Figma’s Creator Fund provides grants for free widgets, plugins, and files created for the Figma Community. - Featured projects include: - Tools for building immersive environments - A design-to-code plugin - A text animation tool - These community-made resources have helped nearly a million people create in Figma. ## Reframing Design Careers - The “Rabbit hole” section encourages designers to focus less on proving their worth and more on letting the quality of their work speak for itself. - Figma cites its State of the Designer report, in which 69% of designers said their job prospects had improved. - Together, these ideas frame design as a field shaped by practical craft, evolving tools, and continued professional momentum. The issue recommends reworking established habits through incremental product improvements, thoughtful AI adoption, continuous prototyping, and support for creative communities.

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Generative AIFigmaPrototypingFigjam+2
figma2 min readCurated summary

Shipping Hype: PMs on What it Takes to Bring AI Features to Market | Figma Blog

AI’s rapid rise has pressured companies to launch features quickly, but hype alone does not produce useful products. Product leaders at Figma, Asana, Duolingo, and LinkedIn argue that successful AI development starts with real user problems, clear definitions, and realistic expectations about current models. AI should be treated as a tool for improving valuable workflows—not as a solution looking for a problem. ## Start with User Problems - Teams should identify user needs before deciding whether AI belongs in the feature. - Figma PM Conor Woods recommends asking: - Can the problem benefit from a large existing data set? - Is some margin of error acceptable? - Is AI genuinely improving the experience, or merely hiding poor UX? - LLMs are well suited to tasks such as organizing information and generating summaries, but they are unreliable when perfect accuracy is required or when they must invent entirely new experiences. - AI-generated inaccuracies and hallucinations are unavoidable with current models, making AI inappropriate for high-stakes, precision-critical tasks. - Asana uses a simple test: does the feature save users meaningful time? - Its Smart Status feature drafts project updates, reducing a task from roughly 20 minutes per week to two minutes and making the return on investment immediately clear. ## Specify the Problem Precisely - Generative AI can serve many different underlying needs, which makes vague feature descriptions dangerous. - Saying “we’ll summarize text” leaves open important questions about the user’s actual goal. - A user might want a summary to: - Understand a document’s subject - Identify action items - Extract decisions or other specific information - Product teams need to define the desired outcome and detailed use case rather than relying on broad descriptions of AI capabilities. - Greater specificity helps designers, engineers, and stakeholders develop a shared understanding of what the feature should do. AI features are most effective when they address a concrete, measurable user problem and acknowledge the limits of current models. Teams should define the user outcome first, then determine whether AI is the appropriate and trustworthy way to achieve it.

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Generative AIAILarge Language ModelsUser Experience+2
figma2 min readCurated summary

Introducing AI to FigJam | Figma Blog

FigJam’s new AI features are designed to solve practical collaboration problems rather than serve as a novelty. Users can generate meeting templates and diagrams from plain-language prompts, summarize brainstorms, and automatically organize sticky notes. Figma argues that this lowers the barrier to visual collaboration while helping experienced users move more quickly from ideas to action. ## AI-Powered FigJam Features - Generate templates for weekly syncs, brainstorms, reviews, and other meetings. - Create visual timelines and organizational charts from a simple prompt. - Summarize the contents of a brainstorm or meeting. - Sort and group sticky notes by theme automatically. - Customize generated outputs based on common workflows and best practices. ## Lowering the Barrier to Visual Collaboration - FigJam AI lets users describe their goals in everyday language instead of learning specialized design software. - A prompt such as “I need a meeting with four people” can produce an initial meeting template. - This approach makes visual collaboration more accessible to people without design backgrounds. - It supports Figma’s goal of “lowering the floor and raising the ceiling”: making the product easier to use while expanding what users can accomplish. ## Solving the Blank Canvas Problem - Starting with an empty FigJam file can make users unsure how to begin. - AI acts as an initial brainstorming partner, helping users move toward actionable next steps. - Tasks such as summarizing complex discussions or synthesizing ideas into categories can take significant manual effort. - Automating this work allows teams to focus on discussion, decision-making, and higher-level collaboration. ## Building AI Around Real User Problems - Figma says its product team drew on its own experience using FigJam to identify useful applications. - The features focus on everyday collaboration needs rather than adding AI for its own sake. - Templates and prompts are based on established practices and common use cases. - The article presents generative AI as a way to make visual tools more useful and approachable across disciplines. FigJam AI is positioned as a practical assistant for getting started, organizing information, and reducing repetitive work. Its main value is helping more people participate in visual collaboration without requiring them to master design tools first.

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