Design in the age of vibes
What the new wave of new AI design and dev tools, — Bolt, V0, Lovable, and Figma Make — mean for the future of software design.Prompt by the author, image generated by Sora.This article builds on reflections I shared last July in The expanded scope and blurring boundaries of AI-powered design, outlining what’s changed in a short time, and what it means for those designing software and leading design teams.Like many others, I’ve been exploring tools like Bolt, Lovable, V0, and most recently Figma Make, looking at how they are changing the way we build software today, and what that means for the future. For those who may not know, these tools are part of a new wave of AI-powered design and development platforms that aim to speed up how we go from prompt to prototype, automating front-end code, generating UI from prompts, and bridging the gap between design and engineering. Bolt is now the second fastest-growing product in history, just behind ChatGPT.While the AI hype hasn’t slowed since ChatGPT’s launch, it’s quickly becoming apparent that these tools represent a step change, one that is rapidly reshaping how we work, and how software gets built.A example of the Bolt.new UI interfaceThis shift didn’t start with AIEven before the recent explosion of AI tooling, design teams have been evolving their approach and expanding their scope of impact. Products like Figma enabled more fluid communication and cross-disciplinary collaboration, while design systems and front-end frameworks like Material, Tailwind, Radix and other libraries helped codify and systematise best practices for visual design, interaction an accessibility.This enabled designers to spend more time thinking about the broader systems, increasing iteration cycles — and less time debating padding. While such tools and frameworks helped to elevate the baseline user experience for many products, in enterprise SaaS in particular, they have had their share of criticism from the resulting sea of sameness that they generated. AI tools are now accelerating and amplifying some of the consequences, both positive and negative. These products represent not just a tooling upgrade, but a shift in what design is, who does it, and how teams are built.Design has evolved from the design of objects, both physical and immaterial, to the design of systems, to the design of complex adaptive systems. The evolution is shifting the role of designers; they are no longer the central planner but rather participants within the systems they exist in. This is a fundamental shift — one that requires a new set of values— Joi Ito, MIT Media LabWhat AI tools are making possibleThis new wave of AI tools can generate high-quality UIs from a prompt, screenshot, or Figma frame. Work that once required a multidisciplinary team and weeks of effort — from concept to coded prototype — can now happen in a matter of hours. Best practices are baked in. Layouts are responsive by default. Interaction logic is defined in a sentence. Even connecting to real data is no longer a blocker, it’s part of the flow.Lovable, one of the many new AI design and full-stack development tools launched recentlyThese tools differ from popular IDE-based assistants like Cursor, Copilot and Windsurf in both purpose and level of abstraction. UI-based tools like Bolt automate many of the more complex and often intimidating parts of the developer workflows; spinning up environments, scaffolding projects, managing dependencies, and deploying apps. That makes sense, given that many of them were built by hosting platforms such as Vercel and Replit.With this new speed and ease of use, designers don’t need to wait on engineers to see how something feels in practice. They can test ideas with higher fidelity faster, explore more variations, and evolve the experience in tight feedback loops.Figma Make: Start with a design and prompt your way to a functional prototype, fast — all in Figma.This shift has also given rise to what some are calling ‘Vibe coding’, a term coined by Andrej Karpathy, that captures this expressive, real-time way of building software. Instead of following a strict spec or writing code line by line, you start with a vibe or loose concept, and use these tools to sculpt the idea into something functional. You prompt, tweak, adjust components, and refine until it feels right. It’s intuitive, fast, and fluid.There’s a new kind of coding I call “vibe coding”, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It’s possible because the LLMsare getting too good.— Andrej KarpathyIn this new paradigm, the output isn’t just faster, it’s driven by rapid judgement and intuition, not necessarily depth of technical experience. In addition, the barrier to entry for non-designers to explore ideas has lowered too. Now that anyone can create compelling, usable apps with front-end and back-end logic, what does that mean for design?I would love to say that this means more time spent on outcomes and higher-impact work for designers, but it’s more likely to disrupt the foundations of what it means to be a designer. The boundaries between the classic product triad of design, engineering and product management were already blurring but this looks like it will accelerate even more.We are in the middle of a significant industry shift, we’re heading into a period of rapid, unpredictable change.. While testing some of these new AI tools, I have had several ‘oh shit’ moments where I get a sense of how things might evolve…. this is what copywriters and others in similar writing roles must have felt when ChatGPT first came out.The author, while vibe codingWhat this might mean for designAs UI generation becomes commoditized, the value of design shifts upstream. With that, the scope of what is expected from design will shift. Future designs team are likely to be smaller, and more embedded in product strategy. As companies grow, design functions won’t necessarily need bigger design teams, they will need higher-leverage ones.Meanwhile, designers and engineers will work more closely together — not through handoff, but through shared tools and live collaboration. In enterprise environments in particular, much of the engineering work is not so much about zero-to-one implementation but about working within and around established technical constraints. As front-end becomes commoditized, engineers will shift their focus further upstream to establishing strong technical foundations and systems for teams to build from.From years of experience to mindsetSome worry this shift will reduce opportunities for junior designers. It’s true there may be fewer entry-level roles focused on production work. But AI-native designers entering the field now may have an edge over seasoned professionals who are tied to traditional methods.In an AI-driven world, knowing the “right” design process won’t matter as much. Technical skills, domain expertise and a strong craft will still help, but what really counts is getting results — regardless of how you get there.The greatest danger in times of turbulence is not the turbulence, it is to act with yesterday’s logic— Peter DruckerMindset will matter more than experience. Those who adapt fast and use AI to learn new domains quickly will stand out. We are already starting to see this unfold. Tobi Lutke, CEO of Shopify recently stated that AI usage is now a baseline expectation at Shopfiy. He went even further, starting that “Before asking for more headcount and resources, teams must demonstrate why they cannot get what they want done using AI”.This demonstrates that adaptability and AI fluency are becoming core expectations. In this new landscape, titles and years of experience will matter less. Designers who can leverage AI as a force multiplier will outpace and outshine those relying on traditional workflows or rigid processes.Speed isn’t everythingNote that I didn’t use the word taste, which many now describe as critical in the AI era. I cringe a little when I hear it — taste feels vague and subjective, often tied to the ‘I’ll know it when I see it’ mindset the design industry has been trying to shake off for years. While I get the intent, I prefer to describe this as judgment: the ability to make calls informed by experience and grounded in clear intent, shared principles, and a solid grasp of user and technical context — not personal preference or aesthetic instinct. When you can create infinite variations almost instantly, judgment is what helps you identify what’s truly distinct, useful and worth refining.What does this mean for designing within enterprise environmentsI lead the design team at DataRobot, a platform that helps AI builders create and manage agentic, generative and predictive workflows within large enterprises. We’ve been exploring how AI tools can augment design and development across the org.Screens from the DataRobot AI platformWhile these tools are great for initial ideation, this is often only a small part of the work in enterprise environments. Here, the reality is more complex: teams work within deeply established workflows, technical frameworks, and products with large surface areas.This differs from consumer design, where teams often have more freedom to invent patterns and push visual boundaries. Enterprise design is about reliability, scalability, and trust. It means navigating legacy systems, aligning with highly technical stakeholders, and ensuring consistency across a broad suite of tools.For us, one of the clearest use cases for AI tooling has been accelerating early-stage concepting and customer validation. While most of our focus is on providing infrastructure to build and manage AI models, we’ve recently expanded into custom AI apps, tailored for specialized workflows across a broad range of industries and verticals. The number of UI variants we would need to support is simply too vast for traditional design processes to cover.Some examples of DataRobot applications — both production and concept.In the past, this would have meant manually designing multiple static iterations and getting feedback based on static mocks. Now, AI tools let us spin up tailored interfaces, with dynamic UI elements tailored for different industries and customer contexts, while adhering to our design system and following best practices for accessibility. Customers get to try something close to the real output and we get better signal earlier in the cycle, reducing wasted effort and resources.In this context, the strict frameworks used by tools like V0are an advantage. They provide guardrails, meaning you need to go out of your way to create a bad experience. It’s early days, but this is helping non-designers in particular to get early-stage validation with customers and prospects.This means the role of the design team is to provide the framework for others to execute, creating prompt guides that codify our design system and visual language, so that outputs remain on brand. Then we step in deeper after direction is validated. In effect, we’re shifting from execution to enablement. Design is being democratized. That’s a good thing, as long as we set the frame.Beyond the baselineAI has raised the baseline. That helps with speed and early validation, but it won’t help you break new ground. Generative tools are by their nature derivative.When everything trends toward average, we need new ways to raise the ceiling. Leadership in this context means knowing when to push beyond the baseline, shaping a distinct point of view grounded in reality and underpinned by strong principles. That point of view should be shaped through deep cross-functional collaboration, with a clear understanding of strategy, user needs, and the broader market.In a world where AI makes it easier than ever to build software, design is becoming more essential and more powerful. It’s craft, quality, and point of view that makes a product stand out and be loved.— Dylan FieldWhat to focus on nowFor individual contributors or those just starting out, it can feel daunting and difficult to know where to start:Start experimenting: Don’t wait for the perfect course, permission or excuse. Just jump in and run small tests. See how you can replicate previous briefsin order to get a feel for where they excel and where they break.Look for leverage: Don’t just use these tools to move faster — use them to think differently. How might you explore more directions, test ideas earlier, or involve others upstream?Contribute to the system: Consider how you might codify what works to improve patterns, prompts, or workflows. This is increasingly where high-impact work will live.If you’re leading a design team:Design the system, not just the UI: Build the tools, patterns, and prompts that others can use to move fast.Codify best practices: Think how you might translate tribal knowledge into actionable context and principles, for both internal teams and AI systems.Exercisejudgement: Train your team to recognize good from average in the context of your product. Establish a shared language for what good means in your context, and how you might elevate your baseline.Final thoughtsThe UI layer is becoming automated. That doesn’t make design less important — it makes it more critical. Now everyone can ship something decent, but only a great team can ship something exceptional.AI might handle the pixels, but it’s just a tool. Design’s purpose is clearer than ever: understanding users, shaping systems, and delivering better outcomes. AI tools should amplify our capabilities, not make us complacent. This means that while we integrate them into our workflows, we must continue to sharpen our core skills. What Paul Graham said about writing applies equally to design.When you lose the ability to write, you also lose some of your ability to think— Paul GrahamThis article was written with the assistance of ChatGPT 4o.John Moriarty leads the design team at DataRobot, an enterprise AI platform that helps AI practitioners to build, govern and operate predictive and generative AI models. Before this, he worked in Accenture, HMH and Design Partners.Design in the age of vibes was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.
#design #age #vibes
Design in the age of vibes
What the new wave of new AI design and dev tools, — Bolt, V0, Lovable, and Figma Make — mean for the future of software design.Prompt by the author, image generated by Sora.This article builds on reflections I shared last July in The expanded scope and blurring boundaries of AI-powered design, outlining what’s changed in a short time, and what it means for those designing software and leading design teams.Like many others, I’ve been exploring tools like Bolt, Lovable, V0, and most recently Figma Make, looking at how they are changing the way we build software today, and what that means for the future. For those who may not know, these tools are part of a new wave of AI-powered design and development platforms that aim to speed up how we go from prompt to prototype, automating front-end code, generating UI from prompts, and bridging the gap between design and engineering. Bolt is now the second fastest-growing product in history, just behind ChatGPT.While the AI hype hasn’t slowed since ChatGPT’s launch, it’s quickly becoming apparent that these tools represent a step change, one that is rapidly reshaping how we work, and how software gets built.A example of the Bolt.new UI interfaceThis shift didn’t start with AIEven before the recent explosion of AI tooling, design teams have been evolving their approach and expanding their scope of impact. Products like Figma enabled more fluid communication and cross-disciplinary collaboration, while design systems and front-end frameworks like Material, Tailwind, Radix and other libraries helped codify and systematise best practices for visual design, interaction an accessibility.This enabled designers to spend more time thinking about the broader systems, increasing iteration cycles — and less time debating padding. While such tools and frameworks helped to elevate the baseline user experience for many products, in enterprise SaaS in particular, they have had their share of criticism from the resulting sea of sameness that they generated. AI tools are now accelerating and amplifying some of the consequences, both positive and negative. These products represent not just a tooling upgrade, but a shift in what design is, who does it, and how teams are built.Design has evolved from the design of objects, both physical and immaterial, to the design of systems, to the design of complex adaptive systems. The evolution is shifting the role of designers; they are no longer the central planner but rather participants within the systems they exist in. This is a fundamental shift — one that requires a new set of values— Joi Ito, MIT Media LabWhat AI tools are making possibleThis new wave of AI tools can generate high-quality UIs from a prompt, screenshot, or Figma frame. Work that once required a multidisciplinary team and weeks of effort — from concept to coded prototype — can now happen in a matter of hours. Best practices are baked in. Layouts are responsive by default. Interaction logic is defined in a sentence. Even connecting to real data is no longer a blocker, it’s part of the flow.Lovable, one of the many new AI design and full-stack development tools launched recentlyThese tools differ from popular IDE-based assistants like Cursor, Copilot and Windsurf in both purpose and level of abstraction. UI-based tools like Bolt automate many of the more complex and often intimidating parts of the developer workflows; spinning up environments, scaffolding projects, managing dependencies, and deploying apps. That makes sense, given that many of them were built by hosting platforms such as Vercel and Replit.With this new speed and ease of use, designers don’t need to wait on engineers to see how something feels in practice. They can test ideas with higher fidelity faster, explore more variations, and evolve the experience in tight feedback loops.Figma Make: Start with a design and prompt your way to a functional prototype, fast — all in Figma.This shift has also given rise to what some are calling ‘Vibe coding’, a term coined by Andrej Karpathy, that captures this expressive, real-time way of building software. Instead of following a strict spec or writing code line by line, you start with a vibe or loose concept, and use these tools to sculpt the idea into something functional. You prompt, tweak, adjust components, and refine until it feels right. It’s intuitive, fast, and fluid.There’s a new kind of coding I call “vibe coding”, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It’s possible because the LLMsare getting too good.— Andrej KarpathyIn this new paradigm, the output isn’t just faster, it’s driven by rapid judgement and intuition, not necessarily depth of technical experience. In addition, the barrier to entry for non-designers to explore ideas has lowered too. Now that anyone can create compelling, usable apps with front-end and back-end logic, what does that mean for design?I would love to say that this means more time spent on outcomes and higher-impact work for designers, but it’s more likely to disrupt the foundations of what it means to be a designer. The boundaries between the classic product triad of design, engineering and product management were already blurring but this looks like it will accelerate even more.We are in the middle of a significant industry shift, we’re heading into a period of rapid, unpredictable change.. While testing some of these new AI tools, I have had several ‘oh shit’ moments where I get a sense of how things might evolve…. this is what copywriters and others in similar writing roles must have felt when ChatGPT first came out.The author, while vibe codingWhat this might mean for designAs UI generation becomes commoditized, the value of design shifts upstream. With that, the scope of what is expected from design will shift. Future designs team are likely to be smaller, and more embedded in product strategy. As companies grow, design functions won’t necessarily need bigger design teams, they will need higher-leverage ones.Meanwhile, designers and engineers will work more closely together — not through handoff, but through shared tools and live collaboration. In enterprise environments in particular, much of the engineering work is not so much about zero-to-one implementation but about working within and around established technical constraints. As front-end becomes commoditized, engineers will shift their focus further upstream to establishing strong technical foundations and systems for teams to build from.From years of experience to mindsetSome worry this shift will reduce opportunities for junior designers. It’s true there may be fewer entry-level roles focused on production work. But AI-native designers entering the field now may have an edge over seasoned professionals who are tied to traditional methods.In an AI-driven world, knowing the “right” design process won’t matter as much. Technical skills, domain expertise and a strong craft will still help, but what really counts is getting results — regardless of how you get there.The greatest danger in times of turbulence is not the turbulence, it is to act with yesterday’s logic— Peter DruckerMindset will matter more than experience. Those who adapt fast and use AI to learn new domains quickly will stand out. We are already starting to see this unfold. Tobi Lutke, CEO of Shopify recently stated that AI usage is now a baseline expectation at Shopfiy. He went even further, starting that “Before asking for more headcount and resources, teams must demonstrate why they cannot get what they want done using AI”.This demonstrates that adaptability and AI fluency are becoming core expectations. In this new landscape, titles and years of experience will matter less. Designers who can leverage AI as a force multiplier will outpace and outshine those relying on traditional workflows or rigid processes.Speed isn’t everythingNote that I didn’t use the word taste, which many now describe as critical in the AI era. I cringe a little when I hear it — taste feels vague and subjective, often tied to the ‘I’ll know it when I see it’ mindset the design industry has been trying to shake off for years. While I get the intent, I prefer to describe this as judgment: the ability to make calls informed by experience and grounded in clear intent, shared principles, and a solid grasp of user and technical context — not personal preference or aesthetic instinct. When you can create infinite variations almost instantly, judgment is what helps you identify what’s truly distinct, useful and worth refining.What does this mean for designing within enterprise environmentsI lead the design team at DataRobot, a platform that helps AI builders create and manage agentic, generative and predictive workflows within large enterprises. We’ve been exploring how AI tools can augment design and development across the org.Screens from the DataRobot AI platformWhile these tools are great for initial ideation, this is often only a small part of the work in enterprise environments. Here, the reality is more complex: teams work within deeply established workflows, technical frameworks, and products with large surface areas.This differs from consumer design, where teams often have more freedom to invent patterns and push visual boundaries. Enterprise design is about reliability, scalability, and trust. It means navigating legacy systems, aligning with highly technical stakeholders, and ensuring consistency across a broad suite of tools.For us, one of the clearest use cases for AI tooling has been accelerating early-stage concepting and customer validation. While most of our focus is on providing infrastructure to build and manage AI models, we’ve recently expanded into custom AI apps, tailored for specialized workflows across a broad range of industries and verticals. The number of UI variants we would need to support is simply too vast for traditional design processes to cover.Some examples of DataRobot applications — both production and concept.In the past, this would have meant manually designing multiple static iterations and getting feedback based on static mocks. Now, AI tools let us spin up tailored interfaces, with dynamic UI elements tailored for different industries and customer contexts, while adhering to our design system and following best practices for accessibility. Customers get to try something close to the real output and we get better signal earlier in the cycle, reducing wasted effort and resources.In this context, the strict frameworks used by tools like V0are an advantage. They provide guardrails, meaning you need to go out of your way to create a bad experience. It’s early days, but this is helping non-designers in particular to get early-stage validation with customers and prospects.This means the role of the design team is to provide the framework for others to execute, creating prompt guides that codify our design system and visual language, so that outputs remain on brand. Then we step in deeper after direction is validated. In effect, we’re shifting from execution to enablement. Design is being democratized. That’s a good thing, as long as we set the frame.Beyond the baselineAI has raised the baseline. That helps with speed and early validation, but it won’t help you break new ground. Generative tools are by their nature derivative.When everything trends toward average, we need new ways to raise the ceiling. Leadership in this context means knowing when to push beyond the baseline, shaping a distinct point of view grounded in reality and underpinned by strong principles. That point of view should be shaped through deep cross-functional collaboration, with a clear understanding of strategy, user needs, and the broader market.In a world where AI makes it easier than ever to build software, design is becoming more essential and more powerful. It’s craft, quality, and point of view that makes a product stand out and be loved.— Dylan FieldWhat to focus on nowFor individual contributors or those just starting out, it can feel daunting and difficult to know where to start:Start experimenting: Don’t wait for the perfect course, permission or excuse. Just jump in and run small tests. See how you can replicate previous briefsin order to get a feel for where they excel and where they break.Look for leverage: Don’t just use these tools to move faster — use them to think differently. How might you explore more directions, test ideas earlier, or involve others upstream?Contribute to the system: Consider how you might codify what works to improve patterns, prompts, or workflows. This is increasingly where high-impact work will live.If you’re leading a design team:Design the system, not just the UI: Build the tools, patterns, and prompts that others can use to move fast.Codify best practices: Think how you might translate tribal knowledge into actionable context and principles, for both internal teams and AI systems.Exercisejudgement: Train your team to recognize good from average in the context of your product. Establish a shared language for what good means in your context, and how you might elevate your baseline.Final thoughtsThe UI layer is becoming automated. That doesn’t make design less important — it makes it more critical. Now everyone can ship something decent, but only a great team can ship something exceptional.AI might handle the pixels, but it’s just a tool. Design’s purpose is clearer than ever: understanding users, shaping systems, and delivering better outcomes. AI tools should amplify our capabilities, not make us complacent. This means that while we integrate them into our workflows, we must continue to sharpen our core skills. What Paul Graham said about writing applies equally to design.When you lose the ability to write, you also lose some of your ability to think— Paul GrahamThis article was written with the assistance of ChatGPT 4o.John Moriarty leads the design team at DataRobot, an enterprise AI platform that helps AI practitioners to build, govern and operate predictive and generative AI models. Before this, he worked in Accenture, HMH and Design Partners.Design in the age of vibes was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.
#design #age #vibes
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