Proprioception is the body’s sense of where its parts are in space. Marcin Wichary borrows the term for software that knows where its hardware lives: where the buttons are, where the ports are, where the camera is. His proposed design principle:

The rule here would be, perhaps, a version of “show, don’t tell.” We could call it “point to, don’t describe.” (Describing what to do means cognitive effort to read the words and understand them. An arrow pointing to something should be easier to process.)

Wichary walks through a series of examples, mostly from Apple: the Apple Pay animation that points at the side button, the iPad camera prompt that points to the physical lens, Dynamic Island camouflaging missing pixels as a functional UI element. The one that caught my eye is the device Simulator matching the physical dimensions of your actual phone on-screen and staying accurate even when you change the display density. Reminds me of one of the earliest selling points of the Mac’s 72dpi—it matches the real world: 72 points to an inch.

The MacBook Neo is where Wichary applies the principle and finds Apple falling short. The new model has two USB-C ports with different speeds, and macOS notifies you with text:

I think this is nice! But it’s also just words. It feels a bit cheap. macOS knows exactly where the ports are, and could have thrown a little warning in the lower left corner of the screen, complete with an onscreen animation of swapping the plug to the other port – similar to what “double clicking to pay” does, so you wouldn’t have to look to the side to locate the socket first.

Thu Do set up Figma MCP + Claude Code and audited her entire design system in 10 minutes. The setup took 4 hours. But the reframe she arrives at matters more than the tooling:

Design tokens used to be “nice to have” for consistency. Now they’re infrastructure for AI-to-code-to-design workflows. AI agents read tokens to understand design intent. Proper tokenization = accurate code generation. Inconsistent systems = AI making wrong assumptions.

The bar for design systems just shifted from visual consistency to machine readability.

Buzz Usborne on what happens when AI takes on more responsibility in a product:

AI doesn’t simply make products smarter — it redistributes thinking and decision-making between humans and machines. When AI absorbs cognition, it also inherits responsibility. And when it inherits responsibility, the cost of its mistakes rises.

Usborne frames this through three forces that determine whether AI features survive or fail: trust, value perception, and cognitive effort. They amplify each other. Low trust increases perceived effort. High effort reduces perceived value. Low value further undermines trust.

His answer is to earn autonomy through interaction, not demand trust upfront:

Trust does not always need to precede adoption, it can emerge through usage. Salesforce’s findings show that “Human validation of outputs is the biggest driver in trusting the outcome, over consistently accurate outputs.” In other words, users trust systems they can interrogate, shape, and verify. And instead of designing AI products that are perfect, we can earn trust by designing experiences that are controllable.

Controllable over perfect.

Most product teams adding AI start by building a new surface for it. A custom panel. A chat sidebar. A dedicated AI workspace. Alexandra Vasquez, writing for Bootcamp, describes her team making exactly that mistake:

We built a custom AI panel with its own navigation, input styles, and button treatments. It looked “futuristic” in the prototype. In user testing, people kept asking where things were and how to get back to their actual work. We had created a separate product inside our product.

The fix was simple: they deleted the panel and put agent actions in the same menus, modals, and toolbars people already used. Slack does this with its /command structure. Notion uses the same slash menu for manual and AI actions. The pattern is existing UI that happens to be smarter.

Vasquez argues most “AI failures” are actually system failures that agents expose at scale:

Designing for agents means treating information architecture and workflows as foundational. Before building an agent, audit your system’s foundations: Are labels consistent? Do hierarchies make sense? Can a new team member navigate workflows without constant help? If humans struggle, agents will fail faster and at scale. Fix the system first.

She’s right. And there’s a more radical version of this: agents don’t need human UI at all. As long as the APIs are available, an agent can complete tasks without ever touching a button or reading a screen. The interface is for the human, not the machine.

But that’s exactly the problem. If the agent bypasses the interface, the human’s ability to express intent and verify output becomes the whole game. Intent has to be crystal clear. Feedback has to be immediate and legible. And there’s a huge amount of trust to earn before anyone is comfortable letting an agent operate in the background on their behalf. Vasquez lands here too:

The AI model is the last thing we discuss, not the first. These are product decisions, and designers have outsized influence here.

The model is the least interesting part. The interesting part is designing the trust.

Jason Lemkin, writing for SaaStr, identifies a structural problem with niche SaaS vendors: the TAM is too small to fund the engineering team that would make the product great. His argument is about what happens when customers can finally do something about it:

Before vibe coding, building a custom app almost never made sense. Custom development cost $50K-$100K minimum, took months, and you owned a buggy codebase forever with no support. The math didn’t work. Vibe coding changes the math. When you can build a working application in hours instead of months, the question stops being “can we afford to build this?” and becomes “can we afford to keep using a product that doesn’t do what we need?”

Lemkin’s SaaStr team replaced a $10K/year sponsor portal in days. Then they built “10K,” an AI marketing agent that ingests four years of their data to run Monday meetings and generate a daily executable marketing plan. No vendor built it because the TAM for “exactly Jason Lemkin’s Monday meeting” is one.

The threat gradient for vendors:

Small niche tools with $5K-$50K contracts — thin markets, thin engineering teams, products that evolve slowly. Your customers now have a real alternative to waiting for your roadmap. They’ll build around you.

But Lemkin is honest about the other side:

We now manage 10+ vibe coded apps and 20+ AI agents. That’s real overhead. It’s manageable because the apps pull their weight. But be honest about what you’re taking on.

Three humans and 20+ agents is an impressive ratio and a fragile one. Maintenance is yours permanently. No support ticket. Complexity compounds. The vendors most at risk are the $10K-$50K niche tools whose moat was the cost of custom development. That moat is gone. The ones that survive will be the ones whose value lives in accumulated domain data, not in features a customer can rebuild over a weekend.

The question for vertical SaaS used to be: how do I make a better tool for this professional? Julien Bek, writing for Sequoia Capital, argues the question has changed:

If you sell the tool, you’re in a race against the model. But if you sell the work, every improvement in the model makes your service faster, cheaper, and harder to compete with. A company might spend $10K a year for QuickBooks and $120K on an accountant to close the books. The next legendary company will just close the books.

Bek draws a clean line between intelligence work (rule-based execution AI can already handle) and judgment work (experience, taste, strategic calls):

Writing code is mostly intelligence. Knowing what to build next is judgement. […] Deciding which feature to build next, whether to take on tech debt, when to ship before it’s ready.

That split tells product builders where to start: outsourced, intelligence-heavy tasks where a budget line already exists and the buyer is already purchasing an outcome. Replacing an outsourcing contract is a vendor swap. Replacing headcount is a reorg. Start with the swap.

But the part that should reshape how designers think about product strategy is the convergence thesis:

Today’s judgement will become tomorrow’s intelligence. As AI systems accumulate proprietary data about what good judgement looks like in their domain, the frontier will shift. Copilots and autopilots will converge.

This is data recipes given a business model. The moat for the next generation of vertical products won’t be the interface or even the model underneath it. It’ll be the compounding dataset of domain-specific decisions—what “good” looks like in insurance brokerage or medical coding or contract law. Every task the autopilot completes teaches it something the copilot never learns, because the copilot hands that knowledge back to the human.

Bek maps this across a dozen verticals with TAM estimates. Worth reading the full piece if you’re thinking about how to build the next generation of AI tools.

In high school and through college, I worked at a desktop publishing service bureau in San Francisco. We had Macintosh computers and Linotronic imagesetters (super hi-res laser printers), not Linotype machines. Down the street, those traditional type shops still existed, but their business was already thinning out. Occasionally a graphic designer would send us type to set, and we’d do it in QuarkXPress. The fact that the job landed on our desk at all told you everything about where the industry was headed. The shop’s real business was pre-press and color separations, and eventually direct-to-plate eliminated even that.

Erika Flowers has been building out her Zero-Vector Design framework, and two of her pieces read as a pair. “Zero Stage to Orbit” on UX Magazine uses the rocket equation as a structural lens for the design-to-development pipeline. “The Last Typesetter” on her Substack uses the death of the typesetting profession to make the same argument from a different direction. Together they make the case that the design role, not the skill, is dissolving.

In “The Last Typesetter,” Flowers draws on Sennett:

When suddenly everyone could set type, the difference between good typography and bad typography went from an industry concern to a public epidemic. Bad kerning everywhere. Rivers running through justified text. Orphaned words dangling at the tops of columns like socks left on a clothesline. The people who understood typography were needed more than ever.

But not as typesetters.

Richard Sennett wrote about this in The Craftsman: the difference between a skill and the institutional container built around that skill. Containers look permanent until they are not. The skill outlives every container it has ever occupied.

That’s what happened at the service bureau. The skill—color, typography, print production—survived. The container—the shop, the role, the apprenticeship—did not.

In “Zero Stage to Orbit,” Flowers maps the pipeline onto rocket science:

Each stage in the traditional pipeline is designed to compensate for the limitations of the previous one. Research to inform design. Design to spec for developers. Specs to survive handoff. QA to catch what handoff broke. Retros to discuss why QA caught so much. Process to manage process.

Fuel to carry fuel. The modern development pipeline is not a solution. It is a multi-stage rocket. And most of the energy is going to overhead.

The overhead diagnosis is sharp, and the launch pad economy—consultancies, workflow tools, Agile coaching certifications—has a financial interest in keeping the rocket grounded.

Flowers addresses why the “unicorn” solution failed:

The design technologist did not fail because no one person can possess all the skills. The design technologist failed because no one can hold all the skills while still fighting gravity. They were still launching from the ground, still hauling the translation overhead, just with one person doing all the hauling instead of a team.

The problem was never the number of stages. It was the gravity well.

A product manager I work with recently told me he could think of a solution to a user need, but not a creative solution the way the designer on his team could. Specialization produces real expertise. The design technologist wasn’t wrong about the vision. They were wrong about the physics. AI changes the gravity, not the skills.

What separates both pieces from the standard “AI changes everything” take:

I am also uncertain here, also mid-journey, also discovering orbit’s real constraints in real time. My career, work, and livelihood are just as much at risk as everyone else’s. But that doesn’t discount the facts about the transition to new capabilities.

She’s out on a limb, reflecting a shift the entire industry can feel, without pretending she has the map. In “The Last Typesetter,” she puts it more bluntly: “Defend the role, or follow the skill.”

The skill will survive. It always has. But the transition is real, and not everyone can afford to be mid-journey. Truthfully, I am uncertain either. The thing I’ve loved to do since the 7th grade, the thing that has been my identity for most of my life is changing, possibly dissolving into something else.

If you’re a designer who feels the ground shifting but doesn’t know where to step, Erika Flowers built a free, structured curriculum for exactly that moment. Zero-Vector Design is her framework for collapsing the handoff between design and engineering, using AI agents as crew rather than replacements. The distinction she draws between this and vibe coding is worth internalizing:

You bring the systems thinking, the architecture, the years of knowing what good looks like. The AI extends your reach, not your judgment. Speed without intention is just faster failure. Speed with intention is leverage.

Six levels, 60+ lessons, all free. Worth bookmarking.

Weber Wong’s “artifact thinking” names the problem: creative work that produces one-off outputs, each beginning from scratch. Prompts are artifacts. Skills are not.

Nick Babich, following up his earlier roundup of Claude skills, looks at Anthropic’s skill-creator, a meta-skill that generates and evaluates new skills. His framing of what a skill actually is:

Many people explain the role of a skill as a set of instructions that Claude automatically activates for a particular task. While this is a correct way to describe its behavior, it’s better to think of a skill as a recipe. Just like when we cook something, we rely on a recipe to do the job correctly, Claude will rely on a dedicated skill.

Recipes compound. You refine them, share them, adapt them for new contexts. Prompts are disposable. Skills persist.

And now skills can write other skills. Babich walks through the full skill-creator setup, and the most interesting detail is the self-evaluation loop:

The great thing about Skill Creator is that it triggers a process that evaluates the quality of output a newly created skill will produce. This evaluation is exactly what helps you achieve better results with your skill.

Worth following along if you’re building your own. (And you should be!)

Designers have been saying this for years. Cameras don’t take pictures, photographers do. Tools don’t make you a better designer. Now the PM world is arriving at the same conclusion.

Shreyas Doshi argues that AI tools will commoditize across companies—any effective tool becomes common knowledge—and the only durable career moat is the human judgment applied on top of AI outputs. He calls it “Product Sense.”

Tools have never been a significant source of alpha in product success and that is not changing with AI tools. What this means for you personally is that - while you can and should use all the AI tools you can - you cannot bank on your use of those AI tools today to provide you a long-term advantage in your product career.

Replace “product people” with “designers” and this could be a post on my blog. The five skills Shreyas decomposes Product Sense into—empathy, simulation, strategic thinking, taste, creative execution—are skills good designers have cultivated under different names for decades.

The piece includes an appended Claude conversation that stress-tests the argument. The sharpest exchange challenges the Silicon Valley orthodoxy that fast B+ beats slow A+:

In practice, the B+ decision made quickly tends to create a cascade of follow-on decisions, each of which is also slightly off, and you end up significantly off-course in ways that are expensive to correct. Whereas the A+ decision, even if it takes longer, tends to set you on a trajectory where subsequent decisions are easier and more obvious. The compounding effect favors quality of judgment, not speed of judgment.

Good judgment compounds. Bad judgment compounds too, just in the wrong direction.

Eugene O’Neill had a line: “Critics? I love every bone in their heads.” I think about it whenever someone proposes that what design really needs is more people who understand it without doing it.

Jon Kolko, writing for Interactions Magazine, argues that design is experiencing a disciplinary “turn”—away from making and toward literacy. Drawing on Richard Buchanan’s 1992 framework of design as a “liberal art of technological culture,” he proposes a future with fewer practitioners and more people who can read, critique, and discuss designed artifacts without designing them.

Rather than viewing design as an applied craft, a liberal art of technological culture recasts design as a way of understanding our role in the designed world around us. It’s difficult for many practitioners to imagine this, because making things is so integral to the idea of design, and embedding design in the humanities is very different from viewing it as an organizing principle like the humanities. But if design is not about making things, but instead about understanding the things that are made, vocation is no longer a goal of design education.

Kolko’s diagnosis is sharp—the layoffs, the AI anxiety, the assembly-line feeling of modern product design. And he sits with the discomfort rather than cheerleading:

As a craftsperson and a maker, I don’t like the way this turn feels, because it appears threatening to the fundamentals of the profession. Understanding design without making things seems impossible. I don’t like this development as an educator either, because it means my students, trained to be practitioners, may find no design jobs, despite getting a very expensive education. But as someone observing the various trends chipping away at what is actually meaningful about being a designer—our ability to humanize the dysfunction of technological change—I am drawn to this turn.

I respect the honesty. But I have a love/hate relationship with critics. It’s easy to throw stones from a perch. When you’re in it—fighting organizational politics, staring at data, listening to customers, compromising with engineering—the outcomes are never as clean as you’d hoped. Design literacy matters. But literacy divorced from practice produces critics, not designers. The world doesn’t need more critics. It needs more people who understand why the compromises were made via lived experience.

Three people at three different companies, same conclusion. Former Apple designer Jason Yuan calls intelligence “the new materiality” in the previously linked Fast Company piece. Brian Lovin says Notion’s design team can’t design AI products in Figma because the material doesn’t live there. Jenny Blackburn, Google’s VP of UX for Gemini, puts it most directly.

Eli Woolery and Aarron Walter, writing for Design Better, synthesized interviews they’ve done with Google design leaders across YouTube, Search, and Gemini. Blackburn’s framing:

The model is the material that we are designing with, and the more you understand the material, the more you can innovate with it.

You can only direct as well as you understand. But this material behaves unlike anything designers have worked with before. Blackburn on the risk of over-constraining it:

One of the challenges is that these models are so capable. In many ways, they’re actually more capable than you even expect as a designer, and so the risk is that you actually add too much UI that limits the value that the model can provide that would come if you just facilitated a direct conversation between the user and the model.

The Gemini team’s response is smart. When users wrote too-short prompts for custom Gems, they didn’t add a tutorial. They added a “magic wand” that expands the prompt but doesn’t submit it. The user reviews, edits, learns. Teaching without lecturing.

Every previous design material—pixels, paper, aluminum—is deterministic. You shape it, it stays shaped. AI models are probabilistic. Same prompt, different results. Understanding this material isn’t like understanding clay. It’s like understanding weather.

The piece also covers YouTube’s disciplined “bundles” strategy and Search’s AI reimagining. Worth the full read.

I believe in the shokunin mentality. Obsessive iteration, pursuing mastery across decades. But the designers building at the frontier right now are telling a different story.

Mark Wilson, writing for Fast Company, visited Cursor, Anthropic, OpenAI, and Krea in San Francisco. Former Apple designer Jason Yuan, now building his own AI startup:

“You can’t do the old school Apple thing of like, create lickable craft and interface. You can’t because, by the time you’ve done the best interface for ChatGPT 3, you’re on GPT 6.”

That stings a little. The Apple tradition assumes the target holds still long enough to polish. When the platform shifts every few months, polish is a liability.

Anthropic’s head of design Joel Lewenstein is making the same bet:

“Things are moving so fast that we just have to experiment faster. Convergence is hard. Because you have to figure out what’s shared. You have to build that shared path. You have all of the fringe things that people loved on these other systems. And there’s too much changing too quickly.”

Anthropic built Cowork in five or 10 days (depending on who you ask). Ship first, converge later.

What’s telling is who’s embracing this. Yuan and Abs Chowdhury—both former Apple designers, trained in the tradition of lickable craft—have each gone all-in on vibecoding at their startups. Chowdhury transferred rough designs from Photoshop(!) straight into AI code tools. Yuan built his first product mostly alongside AI:

“There’s a new reason to raise lots of money, which is compute. If you have lots of conviction, and you know exactly what you want, like, why would you hire another 20 other people right now to tell you what you’re doing? It’s a coordination cost.”

Yuan calls this the best time to be an “auteur.” The designer who doesn’t wait for engineering to realize the vision, who directs AI the way a film director directs a crew. It’s the orchestrator gap playing out in real time.

I’m not ready to abandon the shokunin mentality. But I’m starting to think the object of obsession needs to shift, from polishing pixels to refining judgment. The craft isn’t in the surface anymore. It’s in knowing what to build.

Wilson’s full piece covers a dozen people across the industry and is worth reading end to end.

Notion built a prototype playground for their designers. It’s a single Next.js repo with shared styles and slash commands for deployment. The infrastructure is solid. The adoption question is harder.

Brian Lovin, talking to Claire Vo on How I AI:

It’s still a Next.js app. It’s still React and TypeScript and Git and branches and it’s just a lot of concepts to throw at someone who maybe is used to only prototyping in Figma or they’re intimidated by a terminal or code. And so I’m trying to figure out like how do we make this thing more approachable? How do we make it easier to onboard but also not dumbed down, right? I want people to learn how to use computers. I want people to even subconsciously absorb the ideas of git and branching and pull requests and merging.

“Make it easier but not dumbed down” is the tension every team building AI design tooling is going to hit. Lovin wants designers to actually learn Git, not just have it abstracted away. That’s a bet on long-term capability over short-term adoption. If Notion, with its engineering culture and resources, is still working through this, the rest of the industry has a longer road than the demos suggest.

But Lovin makes a sharp case for why the effort is worth it, especially for AI product design:

I don’t think you can design a good chat experience in Figma. You can design what the chat input looks like. You could design a little chat bubble and a send button and a dropdown for model picker. I think all that’s fine in Figma, but what you can’t design in Figma is what it actually will feel like to use that thing. I probably should have said this at the very beginning, but the reason Prototype Playground existed is because when I started working on Notion AI, I was literally designing conversations in Figma — the user’s going to say this, and then the AI is going to say this, and then it’s going to work perfectly and create a page or a database. You mock these golden paths in Figma and then the engineers go and they build it. And it just doesn’t work that way, right? You send a message, the AI gets stuck, or asks a follow-up question, or does the wrong thing and you need to correct it.

This is the strongest argument I’ve heard for code-first prototyping of AI features. Static mocks enforce golden-path thinking. Real models surface the messy middle: the weird follow-ups, the latency that changes how an interaction feels, the error states you’d never think to mock up.

And yet:

I still use Figma. I probably still spend 60 to 70% of my time in Figma. There’s just certain things that you’re making that don’t need to be in the browser. They don’t need to be coded up. You can just look at it and be like, “Yeah, that’s roughly right. We should just ship that.”

So even the person who built the Prototype Playground still spends most of his time in Figma. Figma isn’t dying just yet, but its scope is narrowing. But for AI features specifically, Lovin’s case is hard to argue with: you need the real model running to know if the design works.

The interview gets most interesting when Lovin describes his operating philosophy for AI agents and how to get them to run longer:

My philosophy on this has been anytime the AI asks you to do something, you should, before responding, try your best to see if you could teach the AI to answer that question for itself. […] So, for example, I’ve taught Claude, “Hey, check your work. One, you can run commands like eslint, right? And like look for actual TypeScript errors.” The second is you can give it access to MCP tools. […] Before installing this, Claude would say to you, “Hey, I’ve implemented your feature. Go take a look at it and let me know what you think.” And remember, our rule is anytime Claude tells you to do something? Ask if you can teach it to do that thing for itself. So, I don’t want to have to look at the browser every time to see if I did it correctly. So, instead, I teach Claude, “Actually, you should be the one to go and open the browser.”

Every interruption from the AI is interrupting your flow state. That’s orchestration in practice: building infrastructure that lets the AI handle its own quality checks so you the designer stays in the flow of deciding what to build and whether it’s right.

Lovin again:

You want your designs to encounter reality as early as possible. And if you imagine this gradient of like I’m scribbling on a napkin on one side to I’m shipping to production and showing customers on the other side, our goal as designers is to move up that gradient towards prod as quickly as possible. […] I just find that when you’re designing something in Figma and then you actually try it in the browser, in the browser you notice a ton of problems. All of a sudden you’re clicking things, you notice loading states, you notice “ah, that didn’t quite work on this screen size.”

Encounter reality as early as possible. That’s the whole argument in six words. There’s a lot more in this conversation, and it’s worth the full watch.

On Jayneil Dalal’s Sneak Peek, Domingo Widen, a staff designer at Intercom, walks through their version of an AI-native design org: Figma MCP plus Claude Code plus Code Connect, producing prototypes that deploy as PRs to GitHub. Designers never check the code. Engineers get real components, not AI hallucinations.

The trick is in the plumbing:

This is something that designers don’t understand, that sometimes they use the MCP without an actual proper code connection, which is good, right? Like the link that you’re sending to AI, it’s going to include a lot of information around the spacing, the token, the color. But it’s not real code connection. The real power that you find is that when you actually connect these components. […] You’re actually giving Claude the actual path to that component in the codebase. so that when you send the link, the button already exists under this path. You don’t need to create it again. You can just import it.

Without Code Connect mapping every component to its import path, AI gets visual information but reinvents components from scratch. The judgment is encoded in the infrastructure, not the model.

Widen again:

In the background, every single pattern that we add to the system, every single component that we add to the system, it becomes a new piece of code that designers can use to prototype, that PMs can use to prototype, that engineers can use to prototype and build. And it’s kind of like a compounding effect essentially. So the more things we add to our design system in terms of components and patterns, the better cleanups that we do and the more tunings that we do, everybody kind of can benefit from them.

The compounding is real, but so is the upfront cost. Intercom needed a dedicated team, a prototyping hub, documentation, tutorials, and months of skills engineering to get here. A 20-person startup isn’t replicating this workflow anytime soon.

I wrote about this gap after getting pushback on my own AI-in-design arguments. The tooling works if you already have the infrastructure and the experience. For most designers, that’s not where they are yet.

AI tools made designers faster. The question nobody’s answering is whether their organizations can keep up.

Cameron Worboys, head of product design at Cash App, talking to Michael Riddering on Dive Club:

I think the biggest blockers across all of the tech industry in the next 2 years will not be the speed of building. It’s going to be the operational side and being able to move something from like we have built this thing. How does it move through the operational cogs of product development in order to like get it live to customers? So my view is like how do we set ourselves up for the new world? You have to make sure that your organization is capable at running at the same speed as the AI tools. And these AI tools move fucking fast.

The bottleneck migrated. Building isn’t the constraint anymore. Getting what you’ve built through approvals, reviews, compliance, and deployment is. Cash App’s response has been radical: they’ve flattened to three management layers (they call it “core plus three”), deleted design crits, and are pushing every designer to ship production code.

Worboys on what quality actually looks like at this speed:

The quality piece, there’s a misconception that it comes from a designer sitting in some cave for 3 months and pontificating about the future of software. It literally doesn’t. It comes from reps and the speed which you can be wrong and the speed that you can go again and experiment and experiment and experiment. And I think that’s what we’ve seen change, is the amount designers can produce has exponentially increased and the amount of like bureaucracy and layers you need to run an organization has changed a lot as well.

Quality through iteration, not pontification. That’s always been true, but when each iteration takes minutes instead of days, the gap between teams that ship and teams that sit in review becomes enormous.

Worboys on where this leads:

I believe one of the primary ways which you will create lock-in in the new world is creating apps that feel completely one of one. […] When you think about the future of software development and where it’s going with generative UI, there is nothing in the future that’s going to prevent us from creating these completely one of one experiences. So that’s what is top of mind for me at the moment. And I do think we will get there relatively quickly, that every Cash App does feel unique and completely designed around the person. And then from a business perspective, it creates this deeper, harder to quantify emotional connection with a product that is the same as like your wardrobe. Clothes are by and large like an expression of personal identity.

This is the most concrete product bet I’ve seen on generative UI. Not widgets inside a chat window. Entire apps shaped around the individual. I still think core app chrome should stay stable. But Worboys is betting that consumer fintech is where that line starts to blur.

I’ve been playing around with Pencil along with Paper, both newer agentic design tools. The multi-agent demo is genuinely impressive—six AI agents designing an app simultaneously, each with its own cursor, name, and chat on the canvas.

Tom Krcha, Pencil’s CEO, speaking on Peter Yang’s channel, on the format bet at the center of the product:

It’s generating basically a descriptor for the design. And then what you can do, you can essentially ask it what kind of code you want to convert it into. Because we wanted to make sure that it’s sort of platform agnostic. […] So we have this platform agnostic file format. We call it .pen. It’s essentially just JSON-based format. We wanted to really build this format to be agentic from the ground up.

Krcha frames it as “agentic PDF.” I could get behind platform agnosticism as a philosophy, but I need more convincing. The .pen format is still a translation layer between the design and the code. That means migration from Figma, especially for teams with established design systems. And I’m skeptical that a button in Pencil’s built-in design system will correctly map to the right reusable code component when the agent translates .pen to production code. I need to test it out more for myself.

We have enterprises using that for this specific purpose, to convert their design systems into pen format and make sure that it lives in the Git. This is the source of truth for everybody now.

“Source of truth” is doing heavy lifting in that sentence. For teams with mature design systems, the source of truth is the code component, not a JSON representation of it.

This is a pretty impressive demo nonetheless, and it’s a moment of delight to give agents a name and a “face” if you will. Krcha:

Those cursors, it seems like a small touch, but it’s the first time I have seen AI humanized. It feels like there’s someone there. It’s crazy, it’s just a cursor.

Designers aren’t leaving Figma. They’re outgrowing what Figma was built to do.

Punit Chawla, writing for Bootcamp:

Designers are slowly shifting to a building first mindset. Which means that a good chunk of UI designers are moving quickly to AI coding platforms to bring their ideas to life. The “Vibe Coding” trend wasn’t just another tech bubble, but a wake up call for designers to create life like prototypes and MVPs from day zero. In fact, PMs and designers at Meta have publicly stated how they are showing working products instead of UI prototypes.

The shift is real, but “leaving” is the wrong word. Designers aren’t abandoning Figma. They’re adding tools that do things Figma was never designed to do. Figma’s role is narrowing from everything-tool to exploration-and-iteration tool. That’s not the same as dying.

Chawla’s strongest point is structural:

Some companies are built different with a completely separate infrastructure. For Figma to change their infrastructure from the bottom-up will be very difficult. Let’s not forget they are a publicly traded company. Risking major changes can mean risking billions in stakeholder investments. Companies like Cursor on the other hand are built to be building first/coding first products, hence a major advantage.

This is right. Figma’s architecture was purpose-built for collaborative vector editing, not code generation. Bolting on AI code output is a fundamentally different engineering problem. When Figma Make launched, I scored it at 58 out of 100, and it’s getting better, but it’s competing against tools that were born for this.

Where I’d push back is on the builder framing. Designers aren’t becoming coders. They’re becoming directors. A designer who orchestrates AI agents against a design system solves the handoff problem more fundamentally than one who vibe-codes an MVP. One eliminates the bottleneck. The other just moves which side of it you’re standing on.

Chawla hedges his own headline:

Don’t get me wrong, Figma is still the best tool for a majority of creatives and has a strong hold on our day-to-day workflow. Making any strong predictions at this point will be very ill-informed and it’s best to avoid making any conclusions as of now.

Fair enough. But the question worth tracking is whether Figma can expand fast enough to remain relevant as the deliverable shifts from mockups to working software.

Designers are builders by nature. We break problems apart, iterate through uncertainty, and treat process itself as something to be shaped. That instinct is exactly what Pete Pachal, writing for Fast Company, identifies as the dividing line in the age of agents:

We’ve trained a generation of office workers to work within software with clear boundaries and reusable templates. If there’s an issue, they call IT. Any feature request gets filtered and, if you’re lucky, put on a roadmap that pushes it out 6-12 months.

In short, most people don’t have a builder mentality to begin with, and expecting them to suddenly be comfortable working and creating with agents is unrealistic.

Pachal draws the line at mindset, not coding ability:

Builders don’t need to be coders, but they do have characteristics that most workers don’t: They seek to understand the process beneath their tasks, and treat that process as modifiable and programmable. More importantly, they see failure and iteration as tolerable, even fun. They thrive in uncertainty.

That’s the design process. What Pachal frames as rare in the broader workforce is default operating mode for most designers. We want to make things. We fiddle with tools and rebuild workflows for fun. The builder mentality isn’t something designers need to acquire; it’s the reason most of us got into this field.

Pachal again:

You don’t have to build agents to matter in an agent-driven workplace. But you do have to understand the systems being built around you, because soon enough, your job will be defined by defaults someone else designed. Most professionals will not build agents. But everyone will work inside systems builders create.

Pachal is describing the orchestrator gap at scale, not just in design but across all knowledge work. And it suggests designers are uniquely positioned to be on the right side of it. Shaping how people interact with systems has always been the job description.

Every design system is an exercise in compression. You take contextual reasoning—why this spacing, why this type scale—and flatten it into tokens and components that can ship without the backstory.

Mark Anthony Cianfrani:

the reason that your line height is set to 1.1 is because your application is, or was at one point, very data-intensive and thus you needed to optimize for information density. Because one time someone complained about not being able to see a very important row in a table and that mistake cost so much money that you were hired to redesign the whole system. But that’s a mouthful. You can’t throw that over the wall. An engineer can’t implement that. So we make little boxes with all batteries included.

All of that reasoning gets flattened into line-height: 1.1. The token ships. The reasoning doesn’t. Every design system makes this trade-off: you lose the why to gain portability.

Cianfrani argues we don’t have to accept that trade-off anymore:

LLMs give us the ability to ship our exact train of thought, uncompressed, a little bit lossy but still significantly useful. Full context that is instantly digestable. Instead of shipping <Boxes>, ship a factory.

Design systems were never the end goal. They were the best compression format we had. Components and tokens became the shipping containers because the full reasoning was too unwieldy to hand off. That constraint is loosening. In spec-driven development, that factory looks like a structured document: design intent expressed in plain language that AI agents build against directly. The spec is the reasoning, uncompressed.

Even if the AI bet doesn’t pay off:

And if this whole AI thing turns out to burst, at least you’ve improved the one skill that some of the best designers I’ve ever worked with had in common—the ability to communicate their design decisions into words.

The compression problem was always worth solving, with or without LLMs.

The transparency question in autonomous interfaces—what to surface, what to simplify, what to explain—needs a concrete framework. Daniel Ruston offers one.

Ruston names the next layer: the Orchestrated User Interface, where the user states intent and the system generates the right interface and executes across multiple agents. The label is less interesting than what it demands from designers:

We can no longer design rigid for “Happy Paths.” We must design for Probabilistic UX. The designer’s job is no longer drawing the buttons; the designer’s job is defining the thresholds for when the button “presses itself” or when the system needs user to clarify, correct or control.

Ruston makes this concrete with a confidence-threshold pattern:

Low Confidence (<60%): The system asks the user for clarification or provides a vague response requiring follow-up (“Which Jane do you want me to schedule with?”). Medium Confidence (60–90%): The system makes a tentative suggestion (“Shall I draft a reply based on your last meeting?”). High Confidence (>90%): The system acts and informs (“I’ve blocked this time on your calendar to prevent conflicts”).

That’s the design lever most AI products skip. They either act without explaining or ask permission for everything. The threshold gives designers something to actually spec: not “should the system do this?” but “how sure does it need to be before it does this without asking?”

Ruston borrows a metaphor from aviation to describe what this visibility should look like:

Analogue cockpits require pilots to look at individual gauges and mentally build a picture of the aircraft’s “system” state. The glass cockpit philosophy shifts the focus to a human-centered design that processes and integrates this data into an intuitive, graphical “picture” of flight.

Same problem, different domain. Most AI products today are analogue cockpits: individual agent outputs, raw status messages, no integrated picture. The confidence thresholds tell the system when to act. The glass cockpit tells the user what’s happening while it acts.

The shift from mockups to code is one thing. The shift from designing tools to designing autonomous behavior is another. Sergio Ortega proposes expanding Human-Computer Interaction into Human-Machine Interaction. The label is less interesting than what it points at.

The part that matters for working designers is the transparency problem:

This is where design must decide what to show, what to simplify, and what to explain. Absolute transparency is unfeasible, total opacity should be unacceptable. In short, designing for autonomous systems means finding a balance between technological complexity and human trust.

When a system makes decisions the user didn’t ask for, someone has to decide what gets surfaced. Ortega:

The focus does not abandon user experience, but expands toward system behavior and its influence on human and organizational decisions. Design is no longer only about defining how technology is used, but about establishing the limits of its behavior.

And the implication for design teams:

When the machine acts, design becomes a mechanism of continuous balance.

The design industry spent a decade burying skeuomorphism. Flat won. And now that AI can generate any flat interface in seconds, physicality is interesting again.

Daniel Rodrigues and Lucas Fischer, writing for Every, describe designing the iOS app for Monologue, a smart dictation tool. Rodrigues studied Braun radios and Teenage Engineering synthesizers, and at one point found himself crouched beside his apartment light switch watching how the shadow moved. His defense of skeuomorphism:

Skeuomorphism has been accused of being overdone, and fairly so, but I think of it as borrowing the credibility that physical things naturally have, like weight, shadow, and texture. Something similar to the way a real button communicates—without explicit explanation—that it can be pressed.

This isn’t a texture pack in Photoshop. Rodrigues studied how light behaves on a physical button and rebuilt that behavior in SwiftUI. The texture is functional, not decorative: it tells you the thing is pressable. Rodrigues and Fischer:

Not every AI product needs skeuomorphic buttons and custom sound effects, but the bar for what “functional” means is shifting. AI is making it faster and cheaper to build “functional” products, so the ones that endure are those where someone thought about what it feels like to use them. For us, that meant studying physical objects, exploring 20 wrong directions to find one right one, and hiring a musician to build sounds we could have pulled from a stock library.

The design process isn’t dead. It’s changing. My belief is that the high-level steps are exactly the same, but where designers spend their time is being redistributed.

Jenny Wen, head of design for Claude at Anthropic (formerly at Figma), on Lenny’s Podcast:

This design process that designers have been taught, we sort of treat it as gospel. That’s basically dead. I think it was sort of dying before the age of AI, but given now that engineers can go off and spin off their seven Claudes, I think as designers, we really have to let go of that process.

It’s a strong headline. But Wen then describes her actual day-to-day, and it sounds familiar:

We are still prototyping stuff. I’m still mocking stuff up. I think it’s just I have a wider set of tools now, and I think the proportion of time I spend doing each thing just has changed.

So the process isn’t dead. The proportions shifted. Wen breaks it down:

A few years ago, 60 to 70% of it was mocking and prototyping, but now I feel the mocking up part of it is 30 to 40%. And then there’s that other 30 to 40% there that is now jamming and pairing directly with engineers. And then there’s a slice of it that is now implementation as well.

What’s missing from that breakdown is user research and discovery. Wen mentions having a researcher on the team, mentions reading studies and feedback, but those activities don’t factor into the breakdown at all. For a team building products where, by Wen’s own admission, “you can’t mock up all the states” and “you actually discover use cases as you see people using them,” you’d think research would be eating a larger share of the pie, not disappearing from the conversation entirely. In my day-to-day, the designers on my team spend 30–40% on discovery and flows. Maybe 40–50% on mockups and prototypes. We’re basically already at her breakdown.

There’s also a context problem. Wen’s “ship fast, iterate publicly, build trust through speed” approach makes sense for Anthropic. They’re building greenfield AI products where nobody knows the right interaction patterns yet. The models are non-deterministic. Labeling something a “research preview” and iterating in public is the right call when the design space is that undefined.

That approach gets harder with a product that has an established install base. When you’re updating features that millions of people depend on, “ship it and iterate” has real costs. Sonos learned this. Or if your product is mission-critical as Figma learned when it shipped its UI3 and designers revolted. Or worse, an essential service like a CRM or operational software. The slow, unglamorous work of discovery and user testing exists because breaking what already works is expensive. Wen has the advantage of building greenfield — there’s no install base to protect. Not every team has that luxury.

The interview gets more interesting when Wen turns to hiring. She describes three archetypes: the “block-shaped” strong generalist who’s 80th percentile across multiple skills, the deep T-shaped specialist who’s in the top 10% of their area, and then a third she says the industry is overlooking:

My last one is probably the one that I think we’re all overlooking, which is what I call the crack new grad. It’s just somebody who’s early career and feels, like, wise and experienced beyond their years, but is also just very humble and very eager to learn. I think this person is really interesting right now because I think most companies are just hiring senior talent, folks that have done things before, are super experienced, but given how much the roles are changing and what we’re expected to do is changing, I think having somebody who almost has a blank slate, and is just a really quick learner and is really eager to learn new tactics and stuff like that, and doesn’t have all these baked in processes and rituals in their mind, that’s super valuable.

Wen’s “crack new grad” maps closely to the strategies I wrote for entry-level designers: build things, get comfortable with AI tools, be what Josh Silverman calls the “dangerous generalist.” Someone without baked-in rituals who learns fast and ships. That a design leader at a frontier lab is actively looking for this profile matters, because most of the industry is still filtering for ten years of experience.