The Design Your Next Move Series:

• Part 1: Your Design Career Won’t Be Killed by AI; It’ll Be Killed by Inertia

• Part 2: Think Like a Designer, Act Like a Strategist

• Part 3: From Craftsperson to Conductor

• Part 4: The New Skill Stack

• Part 5: Make the Invisible Visible

• Part 6: The Career Lattice

• Part 7: Write Your Career Vision

• Part 8: Filters, Skills, and Your Influence Network

• Part 9: Your Career OS Is a Living System

The craft that built us

When I started in design, I was obsessed with craft — beautiful typefaces, pixel-perfect spacing, learning every tool inside out. I’d spend hours on a single interface, tweaking contrast ratios, testing different interaction patterns, building components that could scale. That obsession was essential, because it’s how you learn the discipline, how you build confidence, and how you develop the judgment that lets you know when something is actually good instead of just finished.

Twenty years in, I still care about craft, but if I spent most of my time optimizing pixels and perfecting layouts, I’d be irrelevant in about eighteen months.

The automation problem

Here’s why that matters: the craftsperson skills that got most of us into design are being automated. I don’t mean designers are obsolete — I mean the work is changing. AI can turn a wireframe into a polished mockup in seconds, component libraries have eliminated the need to design every screen from scratch, and template systems churn out reasonable layouts faster than any human designer ever could. The tasks that used to require deep design thinking — the ones that felt like magic when you were early in your career — are increasingly being handled by tools or standardized processes.

If your value is “I make things look good,” that value is eroding, not because you’re not good at it, but because the market no longer needs you to be the person who makes things look good when it can hire a tool for that.

What a conductor actually does

So the designers who are going to stay valuable, stay interesting, stay employed are the ones who’ve made a transition — not away from design, but beyond it, from craftsperson to conductor.

Think of an orchestra conductor. The conductor doesn’t play every instrument, and in fact the best conductors aren’t necessarily the best instrumentalists. What they do is set the tempo, cue the entries, shape the dynamics, and keep the whole group aligned on what they’re trying to create together. They understand each instrument’s role in relation to everything else, and they know which parts need to be louder, which need to recede, and where the real emotional force lives. That’s the shift that matters now.

In craftsperson mode, you’re executing design specs, refining interfaces, and optimizing visual polish — your value is in the quality of the work you produce. In conductor mode, you’re shaping outcomes, enabling others, translating between worlds, and ensuring coherence across a system — your value is in the decisions that get made, the alignment you create, and the problems you help the organization see more clearly.

When you’re operating as a conductor, you’re spending as much time in alignment conversations as you are in Figma, and your peers come to you for clarity on the bigger picture rather than for design feedback. You’re asking “What’s the real problem we’re trying to solve?” before anyone agrees on a solution, and you’re influencing the sequence and scope of work rather than just executing what’s handed to you. You’re the person removing roadblocks so other people can do their best work.

Why this can’t be automated

And here’s what makes this transition critical right now: conductor skills are nearly impossible to automate. You can generate a layout, but you can’t generate strategic clarity. You can template an interface, but you can’t template the conversation that helps a VP of Product understand why a user research finding actually matters to their roadmap. You can build a component library, but you can’t automate the invisible work of keeping a demoralized team convinced that their work matters.

When you shift to conductor mode, you become harder to replace — you’re influencing decisions rather than just executing deliverables, you’re seen as a strategic partner rather than a cost center that happens to use Figma, and you’re future-proofing yourself against automation because your value isn’t in the thing you make but in the direction you help the organization move.

I often joke that PowerPoint is my main design tool, but it’s not so much a joke as an actuality. I spend way more time in slides than I spend in design tools these days, not because I’ve abandoned design, but because the work that matters most isn’t the interface anymore — it’s the story, the alignment, and the decision-making framework that helps the right people understand why we’re making the choices we’re making.

How to know if you’re already there

How do you know if you’re already operating in conductor mode? You spend as much time in alignment conversations as in your actual design tool, you often ask “What’s the real problem we’re trying to solve?” before anyone has decided on a solution, and peers come to you for clarity on the bigger picture rather than just design feedback. You influence the sequence and scope of work instead of just executing what’s handed to you, you spend energy creating air cover so your team can do its best work, and you think in outcomes instead of outputs. You say no sometimes, because you understand that capacity is a design constraint, not a moral failing.

The good news is that this transition isn’t about abandoning what you’re good at — it’s about adding a new lens on top of it. You still make things look good, you still care about interfaces and user experience, but you’ve added a layer that asks “What does this interface need to accomplish for the business? How does it fit in the larger system? What conversation does it enable? What’s the one thing that has to be true for this to matter?”

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Give yourself permission

The hard part isn’t learning to think this way, because most designers who’ve been in the field long enough already have some of these instincts. The hard part is giving yourself permission to do it. There’s still a lot of cultural baggage in design that says “real design is visual design” or “real designers are makers” or that the work that matters most is the output you can put in your portfolio, and none of that is true anymore, if it ever was.

The designers I admire most — the ones who have the most influence, the most agency, the most job security in an uncertain market — are the ones who’ve figured out how to be both craftspeople and conductors. They still care about the interface, but they care more about why that interface is being built, what conversation it enables, how it fits into the larger system, and what it’s supposed to accomplish. That’s not a diminishment of craft — it’s the maturation of it.

A different kind of rigor

The work you do as a conductor is just as rigorous as the work you do as a craftsperson, it’s just rigorous about different things. Instead of pixel-perfect layouts you’re optimizing for clarity and alignment, instead of visual perfection you’re optimizing for outcomes, and instead of individual interfaces you’re thinking about the system they’re all part of.

And here’s the thing that makes this shift feel urgent: the market is moving faster than a lot of design teams realize. Companies are restructuring, tools are automating, and AI is accelerating the commoditization of the visual work. The craftspeople who built their entire identity around excellence in execution are going to have a much harder time in the next five years than the people who’ve already started thinking like conductors.

Activity: Where are you already a conductor?

Don’t start from zero — you’re probably already doing some of this work, even if you don’t think of it that way.

Think back to the last month. When have you brought people together to solve a problem, even informally? When have you advocated for a different direction, even when it was risky? When have you asked “Why are we doing this?” and actually changed the conversation? When have you protected your team from chaos, or helped someone see something they weren’t seeing before

Write down three examples, and be specific about what happened, what you did, and what changed as a result.

That’s conductor work, and you’ve already started doing it. The question is whether you’re going to lean into it, own it, and build your identity around it — or whether you’re going to keep apologizing for the time you spend away from your design tool and acting like it’s not real work. Because it’s the most real work you do.

The Design Your Next Move Series:

• Part 1: Your Design Career Won’t Be Killed by AI; It’ll Be Killed by Inertia

• Part 2: Think Like a Designer, Act Like a Strategist

• Part 3: From Craftsperson to Conductor

• Part 4: The New Skill Stack

• Part 5: Make the Invisible Visible

• Part 6: The Career Lattice

• Part 7: Write Your Career Vision

• Part 8: Filters, Skills, and Your Influence Network

• Part 9: Your Career OS Is a Living System

Here’s something I’ve noticed over twenty years of designing products: the best designers aren’t the ones obsessed with pixels — they’re the ones who can zoom out and see the system. They understand that a button doesn’t exist in isolation, that it’s part of a flow, which is part of a feature, which is part of a product, which lives in a market with competitors and economic constraints and user behaviors and organizational incentives. They understand the leverage points, they know which decisions actually move the needle and which are just rearranging deck chairs, and they see patterns repeating and ask why instead of just accepting them.

And yet most of us never apply that same thinking to our own careers.

We design systems for other people all day — we map user journeys, identify friction, trace cause and effect, spot feedback loops. We ask questions like “What’s really driving this behavior?” and “What would happen if we changed that variable?” and “Are we solving the symptom or the problem?” And then we go home and treat our own careers like they’re things that just happen to us.

You wake up one day and realize you’re burned out, so you look for a new job. You get that job, and after six months, you’re burned out again. You tell yourself the company was just broken, and you move to the next one — same story with a different cover. Or you notice you keep getting stuck at a certain level, so you keep grinding: you work harder, stay later, take on more projects, and your skills get better and your portfolio gets stronger, but you still can’t seem to break through. So you assume the problem is you: you’re not good enough, or the company doesn’t value design, or the market is just unfair.

What if the problem isn’t you? What if it’s the system you’re operating in?

Systems thinking changes that equation, because it means looking at your career the way you'd look at any product you're trying to improve:

Inputs: What are you actually feeding into this system? Time, obviously, but also energy, relationships, specific skills, your values, your willingness to advocate for yourself, your availability for unpaid emotional labor, how much you’re willing to compromise.

Outputs: What are you actually getting back? Not what you wish you were getting — what you’re actually getting. Income, types of work, learning opportunities, visibility, influence, reputation, burnout, resentment, boredom, growth.

Feedback loops: What are the signals that tell you whether this system is working? Recognition from people who matter to you, exhaustion, interesting problems showing up in your inbox, stagnation, opportunities — or those voices in your head during your commute that are either excited or dreading what’s coming.

Most people never map this stuff out because it feels weird or self-serving to treat your career as a system to optimize, but it’s really just clear-eyed thinking — the same thinking that already makes you a good designer.

Here’s a real example from my own life. A few years back, I realized I was perpetually exhausted — not the good kind of busy, but the kind where you finish a project and can’t celebrate because the next crisis is already heating up. I’d move to a new company thinking a fresh start would help, and within a few months, I’d notice the same pattern emerging.

So I asked myself what was actually in this system.

Inputs: I was putting in lots of time, lots of energy, a high degree of excellence-obsession, a willingness to say yes to basically anything, and very little pushback when scope exploded. I was also implicitly trading other things — time with family, sleep, mental space — to fund the work.

Outputs: I was getting income, which was good, and I was getting interesting problems and talented colleagues. But the outputs I wasn’t getting were the ones that actually mattered for sustainability: energy, rest, a sense that the work I was doing was staying done, and any proof that my burnout was actually producing proportional impact.

Feedback loops: The signals were impossible to ignore — I felt exhausted all the time, every project spawned three more projects, people kept adding things to my plate because I’d never said no, and my own creativity was getting crushed under the weight of other people’s urgencies.

I could have blamed the company, or blamed myself for not being resilient enough, or just accepted that this was how design leadership works. Instead, I asked a different question: what levers can I actually pull here?

I couldn’t change the market or the fact that companies are unpredictable, but I could change what I was feeding into the system. I could get more intentional about which projects I said yes to, protect my team from some of the volatility by making different decisions about scope and escalation, change how I communicated my availability, and reduce the one input that was genuinely discretionary: my willingness to absorb crisis on crisis without any boundaries.

That’s what it looks like to think like a designer about your own career.

The craftspeople among us — and most of us are craftspeople — tend to assume that excellence is the answer: work harder, get better, and the system will reward you. And sure, excellence matters, but excellence alone doesn’t fix a broken system. A broken system will just use your excellence to make the burnout more efficient.

Systems thinking means asking different questions: What patterns keep repeating? What invisible forces — organizational politics, cultural expectations, my own assumptions — are shaping my outcomes? Which decisions actually move the needle, and which are just consuming my attention? What feedback loops are reinforcing the outcomes I don’t want, and what would change if I shifted just one variable?

The reason this matters now is that your career no longer comes with a built-in safety net. Companies won’t manage your growth for you, titles won’t mean the same thing in your next role, and the skills that protected you five years ago might not protect you in five more. You can’t rely on the system being stable, so you have to become the person who’s actively designing your system instead of just adapting to it.

And here’s the good news: that’s actually easier than it sounds, because you already do this work. You already know how to spot patterns, identify friction, trace cause and effect, and find leverage points — you’ve just never turned that lens on yourself.

Activity: Audit Your Career System

Same exercise as last week, but deeper. Take that circle you drew with inputs and outputs, and now trace some actual feedback loops.

Pick one output you want more of — learning, influence, impact, whatever — and trace it backward to the inputs that are supposed to generate it. Are those inputs actually happening? How much energy are you really spending on them, and are they connected to the right activities?

Now pick one output you’re getting too much of, whether that’s burnout, busywork, or resentment, and trace it backward. What inputs are feeding that? What decisions are you making that sustain it? Be honest about which of those inputs are actually your choice and which ones you’re telling yourself are fixed.

You’re not trying to solve it this week — you’re trying to see it clearly, and that’s where change actually starts.

The Design Your Next Move Series:

• Part 1: Your Design Career Won’t Be Killed by AI; It’ll Be Killed by Inertia

• Part 2: Think Like a Designer, Act Like a Strategist

• Part 3: From Craftsperson to Conductor

• Part 4: The New Skill Stack

• Part 5: Make the Invisible Visible

• Part 6: The Career Lattice

• Part 7: Write Your Career Vision

• Part 8: Filters, Skills, and Your Influence Network

• Part 9: Your Career OS Is a Living System

Lenny Rachitsky published his latest job market analysis last week. If you’re a designer, the numbers should make you uncomfortable.

There are roughly 5,700 open design roles globally. That number has been flat since early 2023. Not declining, but not growing either. Just flat. Meanwhile, PM openings have climbed to 7,300 and engineering is north of 67,000. In mid-2023, there were actually more open designer roles than PM roles. That ratio has flipped. PM demand is now 1.27x designer demand and pulling away.

Lenny’s theory on why design hasn’t bounced back the way PM and engineering have: AI is letting engineers move so fast that there’s less opportunity and less desire to involve the traditional design process.

Less desire to involve the traditional design process.

I’ve been turning that sentence over in my head since I read it. Not because it’s wrong, but because it confirms something I’ve been watching play out for a few years now, and that a lot of designers I know have been quietly feeling but not saying out loud.

The ground has shifted and most of us are still walking like it hasn’t.

I spent the first twelve years of my design career following a pretty straightforward formula. Junior designer, mid-level, senior, maybe eventually management. The ladder had rungs. Titles meant something consistent across companies. If you were a “Senior Product Designer,” you had a rough idea of what that entailed.

That predictability is gone, and Lenny’s data is just the macro view. Zoom in and the picture gets messier.

Role Instability. A “Product Designer” at one company could be doing completely different work at another. At one place, you’re deep in research and strategy. At another, you’re executing design specs fed to you by product managers. Same title. Utterly different roles. There’s no standard definition anymore, which means you can’t rely on titles to tell you what you’re actually getting into — or what your value really is.

The Track Blurring. For years, people talked about the “Individual Contributor track vs. management track” like they were two clean paths diverging in a yellow wood. Now they’re tangled. Senior ICs report to junior managers. IC roles demand more people-leadership skills. Management tracks require hands-on craft. Nobody agrees on which one leads where.

Organizational Volatility. Reorgs aren’t rare anymore. They’re the baseline. I’ve watched designers thrive in one structure only to have their entire trajectory upended when the org chart gets redrawn. Three levels disappear. New reporting relationships appear overnight. What you built political capital for yesterday becomes irrelevant today.

Automation of Core Skills. This one connects directly to Lenny’s data. The craft skills that got most of us into this field are being automated. AI can turn a wireframe into a polished mockup in seconds now. Prebuilt component libraries have reduced the “craft every screen from scratch” work that used to be the bulk of the job. Template systems churn out reasonable layouts. When engineers can move that fast without waiting for a designer to hand off specs, the question stops being “how do we make this look good?” and starts being “do we even need someone dedicated to making this look good?”

That’s not a comfortable question. But it’s the one the market is asking.

None of this means design is going away. It means the game is changing, and the rules that kept us safe five years ago won’t protect us anymore.

The old model assumed stability. It assumed that if you gained expertise in your current role, that expertise would stay valuable. It assumed the ladder would still be there next year, with the same rungs in the same places. It assumed that climbing it was the primary goal.

What we’re actually living in is more like a jungle gym. Non-linear. Messy. No clear “top.” And a lot of people are still trying to climb it like it’s a ladder.

That’s where a Career OS comes in.

Not a five-year plan. Not a motivational framework. Not another system promising you the secret to executive presence or the one weird trick to get promoted. I’m talking about something more fundamental: a living, adaptable system for making intentional decisions about your work, your growth, and your trajectory — knowing that the ground you’re standing on might shift.

A Career OS is how you maintain agency when the organization can’t promise stability. How you stay true to what matters to you when your role description changes every eighteen months. How you build resilience into your career instead of hoping the company will provide it.

Think of it the way you’d design any system: Inputs (what you’re putting in — time, energy, relationships, skills, values), Outputs (what you’re getting back — income, impact, learning, reputation), and Feedback loops (the signals that tell you whether the system is working — recognition, burnout, growth, stagnation).

Most people don’t think about their career this way. They react to what happens. They accept the role they’re offered, adapt to the org chart they’re given, move on when they can’t take it anymore. They treat career growth like it’s something the company does to them instead of something they design.

I’ve done that. A lot of people in my position have. And at a certain point, you realize you’ve been optimizing for inputs and outputs that don’t actually matter — climbing a ladder that’s leaning against the wrong wall.

A Career OS flips that. It starts with clarity about what you actually value, what you actually need, and what you’re actually good at. Then it uses systems thinking to understand how those three things can stay aligned even when everything around you shifts. It gives you levers to pull. It helps you spot patterns before they become crises. It turns your career from something that happens to you into something you’re actively designing.

This is the first of a series on building that system. Over the next several weeks, we’ll walk through what it looks like to think like a designer about your own career, why the craftsperson mindset that got most of us here needs to evolve, how to make the invisible parts of your value visible, and concrete ways to build intentionality into the decisions you make every day.

You don’t have to wait for stability to come back. You don’t have to hope the ladder gets reassembled. You can build something more resilient than that.

Activity: Map Your Current System

Take twenty minutes and do this without overthinking it. Draw a simple circle. On the left side, list three inputs you’re regularly feeding into your work — time, energy, specific skills, relationships, values, whatever feels true. On the right, list the outputs you’re actually getting back — income, types of work, learning opportunities, reputation, whatever’s real.

Then the harder part: trace some lines between them. Which inputs are actually driving the outputs you want? Which are just noise? Are there inputs you’re spending energy on that aren’t producing anything meaningful? Are there outputs you want that aren’t connected to any real inputs?

You don’t need to solve it or fix it this week. Just see it. Most people never do.

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I spent most of the week sitting with a data point I couldn’t shake, and by Friday it had connected to about four other things I’d been tracking. The design job market, an accessibility deadline almost nobody’s prepared for, Figma slowly becoming a different company, and a zoning decision in Minneapolis that says more about design than most design discourse does. There’s a thread running through all of it if you squint: who gets to decide what gets built, and what happens when the answer changes faster than the people doing the building can adapt.

Here’s what’s been open in my browser:

The ratio flipped

This is the one I kept coming back to. Lenny Rachitsky published his latest State of the Product Job Market report, and the numbers confirm something a lot of us have been feeling but couldn’t put data to. Design roles have been essentially flat since early 2023, about 5,700 open globally. That number hasn’t moved. Meanwhile, PM openings surged past 7,300 and engineering is at 67,000-plus. The demand ratio between PMs and designers has flipped for the first time: in mid-2023, there were more open designer roles than PM roles. Now PMs lead at 1.27x.

Lenny’s hypothesis for why is worth sitting with: AI-enabled engineering velocity is reducing the perceived need for traditional design process. Engineers are shipping functional UI faster than designers can mock it, and PMs are filling the concept gap with AI-assisted first drafts. The design bottleneck that justified headcount for a decade is getting routed around.

NN/g’s State of UX 2026 report landed around the same time with a complementary read. Their framing: “design deeper to differentiate.” UI is commodified. Design systems and AI tools have made competent interfaces tablestakes. The competitive advantage has moved upstream to strategy and judgment. The practitioners who thrive will be the ones treating UX as problem solving, not deliverable production.

If those two reports aren’t the same argument from different angles, I don’t know what is.

• State of the Product Job Market in Early 2026 — Lenny’s Newsletter

• State of UX 2026: Design Deeper to Differentiate — NN/g

• UX Designer Job Market Reality: What Changed in 2026 — UX Playbook

Four weeks until the biggest accessibility deadline in U.S. history

April 24, 2026. That’s when every state and local government serving 50,000 or more people must comply with WCAG 2.1 Level AA for their websites and mobile apps. Not guidelines, but requirements with DOJ enforcement behind them.

The scope is wider than most people realize. It covers public websites, mobile apps, third-party portals (the ones you use to pay a water bill or file a permit), and document libraries including PDFs and Word docs that are still publicly available. Automated remediation tools aren’t enough; the DOJ explicitly says manual testing is required.

I’d bet real money that the majority of affected jurisdictions aren’t ready. And for anyone in accessibility consulting, gov-tech, or civic design, the demand spike from this deadline is going to be significant.

• April Deadline Approaching for ADA Standards — MRSC

• First Steps Toward Complying — ADA.gov

• New Digital Accessibility Requirements in 2026 — BBK Law

Figma bought its way out of being a design tool

Figma’s acquisition of Weavy (reportedly north of $200 million, their largest ever) is less about what Weavy does and more about what Figma wants to become. Weavy brings a node-based AI canvas where you can feed prompts to multiple models simultaneously (Sora, Flux, Ideogram, Seedance), compare outputs, and edit results with professional image and video tools. As Figma Weave, it adds media generation and VFX capability to the Figma platform.

Pair that with Config 2025’s announcements (Sites, Draw, Buzz, Make) and Figma has gone from four products to eight in under a year. They’re not competing with Sketch anymore. They’re competing with Adobe, Canva, and Framer at the same time. Whether that ambition survives contact with Figma’s current stock price is another question.

• Introducing Figma Weave — Figma Blog

• Figma Acquires Weavy — TechCrunch

• Why Figma’s Acquisition of Weavy Matters — The Motley Fool

Colorado’s AI law arrives June 30, and it puts designers in the frame

After a year of delays and political back-and-forth, Colorado’s AI Act takes effect June 30. It’s the first enforceable U.S. state law that goes after algorithmic discrimination directly, not just data privacy. If you deploy a high-risk AI system, you now need to document the risks, disclose how it works, and run impact assessments. “Reasonable care” is the legal standard, which means someone will eventually have to define what that looks like in practice.

If you’re designing anything that uses AI to make decisions about people, whether that’s a hiring tool or a lending interface, this is your homework. The EU AI Act’s high-risk rules follow in August. The compliance era for AI isn’t coming; it’s here.

• Colorado’s Landmark AI Law Coming Online — Brownstein

• Colorado SB24-205 — Colorado General Assembly

• 2026 AI Legal Forecast — Baker Donelson

“Phygital” retail grew up

EuroShop 2026 happened last month, and for the first time the “phygital” retail stuff felt like infrastructure instead of a trade show gimmick. AI-powered smart mirrors recommending products. Virtual “endless aisles” letting customers configure the full catalog from the store floor. Adaptive lighting systems that shift atmosphere based on time of day and customer traffic. Modular visual merchandising that lets you swap a campaign in hours, not weeks.

The sustainability angle was just as notable. Recycled waste panels, certified wood, reusable modular structures. It’s not optional anymore — it’s how the stores are being built. For anyone in retail design, the job description quietly shifted from “store design” to “system design” and EuroShop made that visible.

• EuroShop 2026: 5 Retail Design Trends — CAAD Design

• EuroShop 2026: Lighting as Experiential Dimension — VMSD

One more tab…

Minneapolis eliminated single-family zoning citywide a few years ago, and the data is now in: land values in upzoning corridors outperformed the citywide average by 28 to 44 percent over three-year windows. The “post-zoning urbanism” movement is gaining traction in other cities adopting similar mixed-use overlays. If you think zoning isn’t design, you haven’t looked closely enough at what it determines about how people actually experience their city.

• Designing for What Comes Next — Urban Land Institute

• Smart Cities Outlook for 2026 — Smart Cities Dive

Coming this week: The demand gap data from Lenny’s report landed the same week Cameron Moll posted something I haven’t been able to stop thinking about. His argument: while design has been debating craft and taste, everyone else started shipping. The numbers back him up. Monday’s issue gets into what the ratio flip actually means, how orgs are restructuring around AI velocity, and whether the version of design most of us trained for still has a market. It’s the sharpest piece I’ve written in a while and I’m not sure everyone’s going to like it.

See you tomorrow — Justin

I’ve been noticing something about myself that I’m not sure I like.

The loop goes like this: I think of an interaction concept, feed it to an AI tool, and watch it take shape in minutes. The concept doesn’t even have to be good. It could be half-formed and ugly, but the speed of seeing an idea become something real, right now, in front of me, is intoxicating. One idea leads to three. Three leads to a rabbit hole that eats the rest of my evening. I look up and it’s 1 AM and I’ve built something I didn’t plan to build, forgotten to eat dinner, and I’m somehow both wired and completely drained. This happened to me the other night and then I couldn’t sleep; I went almost 24 hours straight without rest.

If that sounds familiar, there’s a decent chance you’re neurodivergent and you’re not alone. I have ADHD.

There’s a growing body of conversation, research, and personal testimony pointing to a pattern: neurodivergent people, particularly those with ADHD, are finding AI tools unusually magnetic — not just useful, but the kind of thing you have trouble putting down. The novelty-reward loop that AI enables maps almost perfectly onto how ADHD brains are wired to seek stimulation, and the result is a tool that feels like it was designed for you, right up until it starts wearing you out.

The interest-based nervous system meets infinite novelty

ADHD brains don’t run on importance, they run on interest. Motivation ignites through novelty, challenge, urgency, or personal fascination, not because something shows up on a to-do list marked “high priority.” This is well-documented in clinical research and anyone who’s ever spent four hours reorganizing their desk instead of answering one email already knows it intuitively.

AI tools slot directly into that wiring. Every prompt is a fresh stimulus. Every response is a small surprise. The iterative nature of working with an LLM, where you refine, redirect, and regenerate, creates exactly the kind of variable reward pattern that ADHD brains find almost impossible to disengage from. It’s the same mechanism that makes slot machines work, except this time the output is a functioning prototype or a first draft of something real, which makes it feel productive rather than wasteful, which makes it even harder to stop.

A UK Department for Business and Trade study found that neurodiverse workers were 25% more satisfied with AI assistants than their neurotypical colleagues and were more likely to recommend the tools to others. That’s a real gap, and it makes sense when you think about what these tools actually do. They reduce the friction that neurodivergent people have fought against their entire careers — the blank page, the executive function overhead of just getting started, the energy it takes to organize your thoughts before you can act on them. AI handles that scaffolding so the interesting part can start immediately.

Jeff Putz, a developer who was diagnosed with ADHD and autism spectrum disorder a few years ago, wrote about his experience coding with AI tools and landed on something that resonated: the tools help, but they also create new problems. During the 30-plus seconds it takes for code generation, he described the urge to do something else as “overwhelming,” estimating his attention shifted “at least a hundred times” within five minutes. The irony of needing coping strategies for a tool designed to help you cope wasn’t lost on him.

The productivity trap

Here’s where it gets uncomfortable. Adults with ADHD already lose an estimated 22 days of workplace productivity per year due to symptoms like disorganization, forgetfulness, and impulsivity. That’s essentially trying to squeeze a full year’s work into eleven months, which creates a baseline of chronic overextension that most neurodivergent professionals just live with. AI tools offer a way to close that gap, and they do, but they also introduce a new failure mode: the inability to stop when the gap is closed.

Because ADHD hyperfocus doesn’t come with a built-in off switch. When the task is interesting enough, you don’t stop at “done.” You keep going until the interest burns out or your body gives up first. And AI tools, by continuously generating new threads to pull on, keep the interest alive far longer than it would survive on its own. The technology that helps you function during the day can quietly become the thing that keeps you going at 2 AM when you should have been asleep three hours ago.

Some people have started building explicit boundaries. One person I came across in my research described a personal rule: no AI after 11 PM, because the tools would stimulate new ideas precisely when they needed to be winding down. That’s a coping strategy for a coping strategy, which tells you something about the dynamic at play.

The scaffold can become the cage

An EY study of over 300 neurodivergent employees found that 85% believe AI workplace tools can create more inclusive environments, with 91% viewing them as valuable assistive technology. Those numbers are encouraging and, I think, accurate. For people who’ve spent their careers fighting their own executive function just to do the baseline work, having a tool that handles the organizational overhead changes what’s possible.

But there’s a less-discussed failure mode that a piece on Medevel captured well: ADHD brains operate with lower baseline dopamine, which makes them more vulnerable to the variable reward patterns that modern tools are built around. The same mechanism that makes AI useful to you also makes you more susceptible to it. Extended use of highly stimulating tools creates dopamine downregulation, where normal, non-AI-assisted work starts to feel impossibly dull by comparison. That’s not laziness, that’s a neurological withdrawal state, and it’s something worth being honest about.

The design of AI tools doesn’t help. Most of them are built for engagement, not for sustainable use. There’s no “you’ve been at this for four hours, maybe take a walk” prompt. There’s no friction designed into the experience to help you disengage. These tools are built to keep you in the loop, because that’s what makes them useful, but for people whose brains already struggle with disengagement, “useful” and “compulsive” live uncomfortably close together.

What I’m still working out

I don’t have a clean resolution for this. I know that AI tools have made me measurably more productive. I know they’ve helped me build things I couldn’t have built alone, or at least not at the speed I’ve been building them. I also know I’m tired in a way that feels different from normal work fatigue, the kind of tired that comes from running your brain at redline for hours because the tool keeps feeding you reasons not to stop.

What I keep coming back to is that the conversation around neurodivergence and AI has been dominated by the “superpower” framing. And there’s truth in it. Neurodivergent brains are wired for the kind of associative, nonlinear thinking that gets the most out of these tools. But “superpower” is doing a lot of work in that framing, and it’s glossing over the part where the power source isn’t unlimited. Hyperfocus is a resource that depletes, and AI tools are unusually good at draining it.

The more useful framing might be this: AI tools are the best assistive technology many neurodivergent people have ever had, and they’re also the most seductive burnout accelerant. Both things are true, and pretending the first cancels out the second isn’t helping anyone.

If you’re in this boat, you probably already know. The question isn’t whether these tools work for you. It’s whether you’re working with them, or whether they’re working you.

If this resonated, I’d like to hear how you’re managing it. I’m still figuring it out myself.

Sources and further reading:

• People with ADHD, autism, dyslexia say AI agents are helping them succeed at work — CNBC

• Coding with AI and ADHD — Jeff Putz

• The Hidden Trap: Why Tech Workers with ADHD Are Falling for Apps That Steal Their Focus — Medevel

• AI as a Neurodivergent Scaffold — Jelena Stricak (Medium)

• Outsourcing Executive Function with AI — Hacking Your ADHD

• Toward Neurodivergent-Aware Productivity (academic paper) — arXiv

• How AI Is Redefining Accessibility — MadeCurious

From component library to AI reasoning layer

The framing that keeps coming up in conversations with design systems practitioners, and that showed up quietly but unmistakably in the agenda at Into Design Systems this year, is that a design system is no longer just a shared language for designers and engineers. It’s the actual specification that generative UI tools reason from when they produce output. A spec, in the same way that an API contract or a data schema is a spec: the thing that defines what valid output looks like, and against which everything generated gets implicitly or explicitly evaluated.

And if the conference framing felt abstract, IBM and SAP just made it concrete, arriving at the same conclusion through entirely different architectures. When that happens independently, it stops being a trend and starts being a fact.

Carbon MCP, now in public preview, is an MCP server that connects AI assistants directly to the Carbon Design System knowledge base, letting AI tools query components, tokens, icons, usage guidelines, and code examples in real time instead of relying on whatever the model absorbed during training. It works across Cursor, Claude Code, Claude Desktop, VS Code with Copilot Chat, and IBM’s own tooling. The design system is no longer a reference that a developer might check; it’s the live context that the model reasons from while it generates code.

SAP has taken the same core idea and applied it differently, shipping four MCP servers (CAP, Fiori Elements, SAPUI5, and MDK) that together expose the entire Fiori design system to AI coding assistants. The Fiori MCP server lets an AI model generate and modify full Fiori-compliant applications from natural language prompts, scaffolding list reports, object pages, and flexible column layouts that adhere to SAP’s design patterns by construction rather than by hope. The SAPUI5 server exposes a dedicated tool for retrieving coding standards and guidelines, and the Fiori server’s documentation search queries across Fiori elements, annotations, UI5 resources, and tooling docs. All of SAP’s design guidance, available as queryable context at generation time.

Most teams haven’t made that mental shift yet, and it shows in how their systems are structured. Built for human legibility, not for machine consumption, which is a meaningful distinction when the consumer is increasingly a code generation model instead of an engineer reading documentation. What IBM and SAP have both done, through different architectures and for different ecosystems, is treat that distinction as a product problem worth solving at the infrastructure level. The result in both cases is a design system where constraints aren’t just documented; they’re enforced at the point of generation.

How tokens, APIs, and encoded constraints actually bound AI output

The mechanism is more direct than most people realize, and it runs through every layer of the design system.

Tokens are the most immediate constraint. When a generative UI tool produces a component, it gets bounded by the token system, the variables defining color, spacing, and typography. Every naming convention decision, every token hierarchy choice, every ambiguity in how a semantic token maps to a primitive is now also a decision about what range of outputs the AI is capable of producing. A poorly structured token system doesn’t just make things harder for engineers; it produces a wider, less predictable distribution of AI-generated UI, because the model ends up resolving ambiguities on its own instead of being bounded by clear, well-scoped values.

Component APIs are the next layer, and the logic is the same: a well-specified button component with explicit variant definitions and clear prop contracts produces predictable AI output, while an underspecified one gives the model enough room to pattern-match toward whatever it’s seen most often in training data. The output is usually technically functional and wrong in ways that are hard to articulate in a code review, because the wrongness lives in the details of variant selection and interaction pattern, not in the syntax.

Documentation, crucially, is not part of this equation in the way most teams assume. Or at least, it wasn’t until MCP changed what documentation can do. A model generating UI couldn’t read a Notion page and decide to follow it, so any constraint that lived only in written guidance rather than encoded in the component specification itself was a constraint the model didn’t have. What both Carbon MCP and SAP’s Fiori MCP do is collapse that gap from different directions: Carbon makes usage guidelines, accessibility rules, and interaction patterns queryable context that the model retrieves before it writes a line of code, while SAP’s servers embed design guidance directly into the generation workflow. The model doesn’t look up what a Fiori list report should contain; it scaffolds one that already conforms to SAP’s patterns, with the design rules enforced by the tooling rather than left to the model’s judgment. In both cases, the design system’s documentation becomes part of the spec, not a companion to it.

How the teams getting ahead of this are making their systems machine-readable

The teams getting ahead of this are treating their design systems as source material for model context, restructuring not just what the system contains but how it’s formatted, so that generative tools can actually consume it. The practical version of this looks like exporting token dictionaries as structured JSON rather than PDF documentation, writing component API specs in formats that map to the inputs a model needs to make a bounded decision, and encoding usage constraints directly into component definitions instead of maintaining them as a separate layer of guidance that the model will never see.

Carbon MCP and SAP’s Fiori MCP servers are the two furthest-along versions of this approach that are publicly available, and the contrast between them is worth understanding. Carbon exposes the design system as a queryable knowledge base where an AI tool can explore components, retrieve token values, pull code examples, and get answers to documentation questions through a standardized protocol instead of scraping or training. IBM has turned Carbon into a queryable API for design decisions. SAP has taken a more opinionated path: the Fiori MCP server doesn’t just expose information for the model to reason from, it executes structured operations (generating applications, adding pages, modifying controller extensions) where the design system’s constraints are baked into the execution logic itself, so the model never gets the chance to deviate from Fiori patterns in the first place. Both approaches work. And the fact that two of the largest enterprise design systems in the world arrived at MCP as the integration layer, independently and through different architectures, tells you this isn’t a niche bet.

Some organizations are going further still and using their design systems as the retrieval layer in a RAG workflow, a technique that grounds a model’s output in specific, real-world source material instead of relying solely on what it learned during training. A model queried to build or modify UI first retrieves the actual token values and component specifications from the system before generating output, so the result reflects what the product specifically requires, not what the model has seen most often at training time. The teams doing this well aren’t waiting for tool vendors to figure out how to consume design systems; they’re making their systems legible on their own terms. Carbon MCP and SAP’s Fiori servers are the proof that this has moved past the experimental phase and into shipped infrastructure.

Why the format of your design system now determines AI output quality

The implication that doesn’t get discussed clearly enough is that the format and structure of a design system now has direct downstream consequences for AI output quality. A token system documented in Confluence and a token system published as a well-structured JSON schema are not two ways of capturing the same information; they’re two different levels of AI-readiness, and the difference shows up immediately in the range and consistency of what generative tools produce. The teams with the most leverage over generative UI output aren’t necessarily the ones with the most mature or comprehensive design systems; they’re the ones whose systems are structured in ways that AI tools can actually reason from.

That’s a new design criterion, and for most teams, it’s one the current system wasn’t built to satisfy. Carbon MCP and SAP’s Fiori servers are worth paying attention to not because every team should replicate IBM’s or SAP’s exact approach, but because when two of the largest enterprise design systems in the world independently ship MCP servers as part of their developer tooling within the same quarter, it gets hard to call that a coincidence. The question isn’t whether your system needs to be machine-readable. It’s how far behind you are on making it so.

Further Reading

• Carbon MCP Overview

• SAP Fiori MCP Server (GitHub)

• SAP’s Official MCP Servers

• SAP Fiori MCP Server First Release

• SAP UX Q1 2026 Update

If you have thoughts on this, I’d genuinely like to hear them. I’ve been trying to track which teams are ahead of this and which are discovering it the hard way, so reply and tell me where your system stands.

— Justin

Starting today, I’m adding Open Tabs to the mix — a subscribers-only Sunday brief featuring a curated look at what’s worth paying attention to from the past week. Think of it as a Sunday morning read with your coffee: shorter takes, pointed observations, relevant links. The regular weekly issues aren’t going anywhere — they’re back starting this week, in fact, after a longer-than-intended hiatus. This is additive.

Here’s what caught my attention this week.

Figma’s falling and can’t get up

Figma IPO’d last July at $33/share, peaked at $142 within 48 hours, and is now trading around $21. I’m not great at math, but I’m pretty certain that’s down 84% from its high. I’ve written previously about Figma’s strategic drift in the lead up to the IPO, which has resulted in some unfortunate blind spots for CEO Dylan Field.

One of those blindspots — somehow — was Google. This week Figma’s stock dropped another 12% in two days after Google launched Stitch, a “vibe design” tool that “turns natural language prompts into high-fidelity UI and interactive prototypes” for free. Ah, the honeymoon phase before the inevitable platform enshitification commences.

The market’s read is clear: the core value proposition of a design tool is increasingly something AI gives away for free, for now.

Here’s a small sampling of the Figma doom-and-gloom that’s been circulating this week:

• Figma’s Stock Drops 12% in Two Days After Google Releases ‘Vibe Design’ Product — CNBC

• AI Is Killing Figma: A Capital Structure Story — Dave Friedman

• Figma IPO Hotshot Joins Our Imploded Stocks — Wolf Street

The design job market isn’t bouncing back

Speaking of doom-and-gloom: The share of designers finding a new role within three months dropped from 68% in 2019 to 49.5% in 2024 — and analysts aren’t modeling a bounce back in 2026. This isn’t post-layoff turbulence as the roles being eliminated aren’t coming back in their previous forms.

• UX Job Market Reality: What Changed in 2026 — UX Playbook

• Why the UX Job Market Is Changing in 2026 — UX Planet

• Why Is the UX Job Market Such a Mess Right Now? — Jared Spool

The UX middle is hollowing out

NNGroup’s 2026 UX Benchmark confirms a split that’s been forming for a while: senior practitioners are gaining more influence at product tables while mid-level roles face sustained pressure from AI tooling that now does execution work well enough. The middle isn’t disappearing slowly, it’s moving faster than most have planned for.

NNGroup: The State of UX in 2026 →

Colorado’s AI liability law lands on designers, not just engineers

Colorado’s AI Act puts UX designers directly in the accountability chain for consequential algorithmic decisions that touch on employment, lending, and healthcare. Most design teams don’t know it’s coming. The compliance frameworks it creates will fundamentally reshape how algorithmic systems get designed and built.

Colorado SB 205 — Artificial Intelligence →

Policy tends to arrive faster than practices do, so if you work on any product that makes automated decisions affecting real people, this is your homework.

Most government digital services aren’t accessible, and the government knows it

A March 2026 GSA report found that only about one-third of the federal government’s most-viewed websites meet legal accessibility requirements, with agencies scoring an average of 1.96 out of 5 across all technology. Half of agencies don’t regularly test for accessibility at all. The kicker: remediation teams are shrinking as AI gets layered on top of these already-broken foundations.

• Much of the Government’s Technology Isn’t Accessible, Internal Report Finds — Nextgov/FCW

• Government Websites Are Littered With Accessibility Issues — Route Fifty

Support for accessibility is often the difference between a service that works and one that doesn’t for a significant portion of the population. A government self-assessment scoring 1.96 out of 5 is not exactly a rounding error (although with DOGE puppeteering the corpse of the former US Digital Service, who knows really… maybe things are actually worse).

Coming this week: Design systems are becoming a spec that LLMs can leverage to drive consistent, high quality UI. Issue #039 gets into what that actually means in practice: how tokens and component APIs bound AI output and what the teams getting ahead of this are doing differently to make their systems machine-readable.

Until then — Justin

“If designers don’t engage deeply with these [AI] systems, others will define their behavior, defaults, and values for us. And history suggests those defaults won’t prioritize clarity, agency, restraint, or human dignity unless someone insists on them.”

The anxiety about AI and what it might mean for the future of work, let alone design, is loud. Before we can talk about experience design or interaction patterns, the conversation almost always collapses into a single, unavoidable question: Is this going to take my job?

You can feel it everywhere: in team meetings, office hallways, Slack threads, and late-night messages from colleagues who haven’t slept particularly well since they last had to answer an executive’s question about how AI is being used to “accelerate outcomes.” Even when it’s never said outright, the anxiety dominates the emotional register of AI-related conversations. Curiosity gives way to self-preservation and exploration gets superseded by threat detection.

That reaction isn’t irrational, especially as we watch software generate layouts, write copy, summarize research, and produce artifacts that used to require real time, skill, and effort. When output starts to look indistinguishable from competence to the untrained eye, it’s not paranoia to wonder whether the ground is shifting beneath you because it is.

When Anxiety Blocks the Real Conversation

The problem is that AI anxiety has started to consume all available oxygen. When that happens, it blocks a more important conversation that will actually determine whether designers lose relevance or become more essential in the years ahead.

“AI” has collapsed into a single word that absorbs far too much meaning. It gets treated like a force of nature, something like the weather: either it washes over us and we adapt, or it wipes us out entirely. We’re told there’s no agency here, only inevitability. When fear gets aimed at something this vague, there’s no productive response available, leaving only denial or resignation.

The Cost of Treating AI as a Monolith

What’s missing is specificity and proactivity.

AI doesn’t show up as one thing. It shows up as distinct experiences, like any other digital software, and those experiences differ in meaningful, consequential ways, especially in how they distribute authority, agency, and responsibility between humans and machines. Some systems speak, some act on our behalf, and others shape what we see, trust, and decide.

Each of these carries distinct risks that are magnified when human-centered design and research methods aren’t applied. Each demands different kinds of design judgment and fail in different ways when things go wrong.

The Three Assumptions Fueling the Anxiety

Based on my conversations with other designers, a lot of the anxiety seems to trace back to three assumptions — ones designers sometimes internalize themselves, and that leaders and stakeholders often take as gospel:

• That because AI can generate artifacts, it must also be capable of judgment.

• That because it can speak fluently, it must understand.

• That because it can act autonomously, it must be trustworthy.

None of those assumptions actually hold.

What’s Actually Changing

Once you start pulling on that thread, the picture changes. What’s really happening isn’t that design work is disappearing, but the nature of the systems we’re designing is fundamentally shifting.

We’re moving away from largely deterministic software (systems that behave the same way every time) and toward probabilistic systems that operate on likelihoods, inference, and approximation. This shift doesn’t eliminate design work or the core of what makes quality experiences possible, but it should change how we think about applying design expertise.

Where Design Judgment Now Carries the Most Weight

The hardest part of design is no longer producing outputs. It’s deciding how much authority a system should project, where humans must remain in control, and how uncertainty is communicated instead of hidden. Those decisions don’t scale automatically, and they absolutely should not be delegated to a model.

Seen through that lens, the anxiety designers are feeling isn’t a sign that the profession is ending, but that it’s concentrating into fewer, higher-stakes decisions. We’re no longer just designing interfaces. We’re designing how knowledge is framed, how action is taken, and how much control people should retain over otherwise automated systems.

And this is where we have a choice.

Hoping It Goes Away Isn’t a Strategy

It’s tempting to disengage by letting fear harden into skepticism, or to hope that AI will follow the arc of NFTs or the metaverse hype cycle and suddenly disappear. But sitting back and hoping this all blows over is abdication and not strategy. The more we lean away from AI and its prevailing narrative, the less likely we’ll be able to clearly define and showcase the criticality of design in this new era.

That doesn’t mean we ignore the ethical issues. There are real, unresolved concerns around how large language models have been trained through very direct intellectual property theft. There are clearly unethical and extractive uses of this technology, and we must be vocal about them and advocate for ethnical controls in our work. But it’s a mistake to collapse all uses of AI into that critique. When applied within organizations on wholly owned or consented data, many of those ethical concerns shift meaningfully and the design responsibility only increases.

If designers don’t engage deeply with these systems, others will define their behavior, defaults, and values for us. And history suggests those defaults won’t prioritize clarity, agency, restraint, or human dignity unless someone insists on them.

What This Series Is About

Before we talk about tools, prompts, or workflows, we need a clearer mental model of the kinds of AI experiences actually showing up in products today, as well as what each one asks of designers.

That’s what this new AI series is about.

In the next issue, I’ll start with the most visible category: direct AI interaction, where the interface speaks, confidence is easy to project, and epistemic mistakes get amplified at terrifying scale. From there, we’ll move on to agentic systems that act on our behalf, and finally to AI-enriched experiences that reward restraint and judgment more than novelty.

The anxiety is real, but it isn’t the conclusion. It’s the signal pointing toward where the most critical work of design now lives, and an invitation to engage with it head-on.

Over the last five years, my title has stayed rooted in design while my work hasn’t. My days are spent obsessing over sequencing, dependency management, and investment justifications — fewer critiques, more roadmaps. I find myself in more conversations about cost of delay than whether or not a button should be squared or rounded.

That shift wasn’t accidental, or even unusual. It’s a byproduct of how modern organizations concentrate influence, and where designers increasingly choose to operate within that reality.

In flatter organizations accelerated by AI, automation, and constant pressure to “do more with less,” influence no longer follows title or headcount. It follows proximity to decisions. And the closer designers get to those decisions, the more their roles begin to resemble product management.

For many designers, that resemblance eventually becomes an official transition.

Flattening Strategy and Execution

Fewer layers mean fewer buffers, so strategy and execution collapse closer together. AI accelerates delivery timelines, shrinking the distance between a decision and its consequences. In this environment, the most valuable people aren’t those who execute the fastest, but the ones who can anticipate what’s about to break — and who it will break for — before it does.

That skill set should already feel familiar.

Designers are trained to:

• See systems, not just their visible artifacts

• Understand end users not as abstract personas, but as people navigating real constraints and tradeoffs

• Identify gaps between intent and lived experience

• Close those gaps through deliberate, often preventative choices

• Use craft to make direction tangible, legible, and trustworthy

In business terms, these are risk-management skills grounded in empathy for how decisions land in the real world. When designers step into product management, many are surprised by how much of this foundation carries over.

But just as many underestimate what doesn’t.

What Will Feel Familiar in a Product Management Role

Designers moving into product management often experience an early sense of fluency.

You already:

• Write user stories or acceptance criteria (even if informally)

• Translate abstract goals into concrete outcomes

• Balance competing stakeholder inputs

• Advocate for the user while navigating constraints

• Think in terms of journeys, not isolated features

Much of modern product management — especially in mature digital organizations —relies heavily on skills designers already practice daily. This is why designers often ramp quickly and appear “naturally good” at the role.

That early comfort, however, can mask a deeper shift.

What Will Be Fundamentally Different

The biggest change isn’t what you think about, it’s what you’re accountable for.

1. Decision Ownership Replaces Advising

As a PM, you don’t just shape decisions — you own them. Ambiguity no longer resolves through critique or iteration alone; it resolves through prioritization and commitment. Tradeoffs stop being theoretical and become binding.

You’re no longer one of several voices pushing toward a better answer. You’re the person expected to choose the best one and then be held accountable to it.

2. Technical Depth Becomes Non-Optional

While designers often collaborate closely with engineering, product management demands a different level of technical fluency:

• Understanding system architecture and constraints

• Speaking credibly about feasibility and sequencing

• Anticipating downstream technical costs

• Communicating in the language of engineering leadership

At higher levels, this expectation only increases. If your technical partners don’t trust your decision-making — because you use the wrong language, gloss over complexity, or appear disinterested in how systems actually work — your effectiveness will erode quickly.

This is one of the sharpest learning curves for designers stepping into product roles, and a critical point of potential failure.

3. Outcomes Eclipse Experience Quality

Experience quality still matters, but it’s no longer the primary measure of success. Delivery, impact, and timing take precedence. A “better” solution that ships too late can be worse than an imperfect one that ships on time.

For designers whose identity is tightly coupled to craft and pixel-level excellence, this can be disorienting. I’ve seen designers make the transition to PM successfully in every other way — strategy, roadmapping, communication, stakeholder management— only to struggle here.

Letting go of perfection without letting go of standards is harder than it looks.

Working With Designers When You Used to Be One

This is where the transition often gets unexpectedly tricky.

When I first moved into a product management role earlier in my career, I had a hard time separating my designer instincts from my PM responsibilities. I knew how I would have solved the problem as a designer, and I carried that knowledge into every conversation with my assigned UX partner.

At first, that felt like an advantage, but in practice it created tension.

I either over-influenced the work — implicitly steering design decisions — or over-corrected by pulling back too far, hesitant to challenge design directions even when they conflicted with broader product constraints. In trying not to be “that PM,” I sometimes failed to be the PM my designer needed.

The shift required learning something counterintuitive:

As a PM, your job is not to be the best designer in the room. It’s to create the conditions where good design decisions can be made, and to intervene only when tradeoffs demand it.

That means:

• Being explicit about constraints instead of smuggling them into feedback

• Trusting your designer’s craft while still holding the line on outcomes

• Letting go of how something is designed, while staying accountable for why

Former designers who struggle in PM roles often aren’t too opinionated — they’re insufficiently clear about where their authority now begins and ends. If you’ve ever been a design leader, this all should sound familiar and will certainly help if you choose to make the transition to product management.

Why Designers Choose This Path

I’ve seen senior designers, and even design leaders, move laterally into product management roles. This is often framed as a loss for design, but I don’t necessarily see it that way.

Designers who make this move aren’t fleeing design — they’re responding rationally to where authority and accountability now sit, and choosing to practice their craft more broadly.

Designers understand this intuitively. Many move into product management not because they want different work, but because they want clearer leverage and agency.

A Caution and an Opportunity

The real risk isn’t designers becoming product managers, but designers assuming the transition is mostly semantic. Product management will reward your systems thinking, your ability to close gaps, and your comfort with ambiguity. But it will also test:

• Your tolerance for irreversible decisions

• Your willingness to trade craft for momentum

• Your technical acumen and credibility

• Your comfort being accountable for outcomes you don’t fully control

For some designers this is all energizing, but for others it can be a deal breaker.

Choosing Deliberately

Designers don’t need to become product managers to be influential. But if you do make the transition, it helps to do so with clear eyes.

You’re not just changing roles. You’re changing where and how you exercise influence.

Whether you stay in design or step into product, the work is the same at its core:

• Make complexity legible

• Make tradeoffs explicit

• Deliver positive outcomes for customers and the business

The rest is just a title.

It’s Sunday afternoon, football on, and I’m about to have lunch delivered by a robot. Somewhere in a nearby parking lot, a little boxy vehicle with two glowing ring lights — “eyes,” I guess — is navigating crosswalks to bring me a chicken wrap I could’ve made in ten minutes. It’s cute, because it was designed to be. A friendly, anime-eyed courier rolling toward me on four wheels, making it easy to forget that it took a job from an actual human being.

I’m saving five dollars — not because of a promo code or a loyalty perk, but because there’s no one left to tip in this absurd chain of convenience.

This is the modern miracle: a $10 lunch for $20, mechanically delivered and morally dissonant.

I tell myself it’s fine. It’s been a long week, I don’t feel like cooking, and besides, this is the future, right? But as the little robot rolls up to my door, I can’t shake the feeling that what we’ve really built isn’t the future at all. It’s an intricate system designed to protect us from ever feeling inconvenience.

The problem is that comfort scales. What starts as a small luxury — skipping the line, splitting a payment, tapping instead of typing — becomes a default expectation. We build technologies to erase friction, and in doing so, we erase the very effort that used to connect us to consequence. The economy adjusts, labor shifts, and before long, we’re paying real people less so we can feel slightly less bothered.

We used to design for delight. Now we design for ease. And somewhere along the way, ease became the enemy of meaning.

The Comfort Economy

Comfort has become the organizing principle of modern life. We’ve built an economy where the highest good is the absence of friction — the shortest line, the fastest route, the fewest taps. The underlying assumption is that ease equals progress, and every moment saved is value created.

Amazon taught us this lesson better than anyone. Next-day delivery started as a marvel of logistics, an engineering triumph. Today, it’s a moral gray zone wrapped in a cardboard box. Every package represents an invisible supply chain of human exhaustion and environmental cost — sprawling fulfillment centers, overnight shifts, and carbon-choked “last mile” delivery routes that now account for nearly a third of all urban traffic emissions. We don’t see the trucks idling at 3 a.m. or the drivers racing algorithms to hit quotas. We see a smiling arrow and a tracking number that reassures us our needs are being met — instantly.

The same logic governs our finances. Buy Now, Pay Later promised flexibility and democratized access, but it quietly re-normalized consumer debt as a lifestyle. It converts our future discomfort into present convenience, monetizing our inability to wait. The system works because it feels painless — until it doesn’t.

Every comfort is borrowed from somewhere: a driver’s time, a warehouse worker’s body, a planet’s capacity to absorb another round of expedited consumption. The costs are simply hidden far enough away that we can mistake them for efficiency.

We call it innovation, but it’s really anesthesia — a collective numbing of what it takes to make life feel effortless.

Discomfort as a Feature, Not a Bug

We’ve spent two decades engineering away discomfort — the wait, the walk, the planning, the patience — all the minor frictions that once tethered effort to outcome. But the absence of friction doesn’t make life better; it just redistributes it. Someone, somewhere, is still carrying the weight you put down.

The economy of comfort is built on asymmetry. Those with means buy time; those without sell it. Every tap-to-order convenience depends on an invisible workforce navigating low wages, unstable hours, and algorithmic oversight to make the miracle happen. We’ve created a hierarchy of effort: the affluent get frictionless living, and everyone else becomes the friction.

What used to be shared labor — cooking, cleaning, driving, waiting — has been offloaded to the gig layer of the economy. The apps that promise empowerment have instead produced a subclass of flexible workers optimized for other people’s (read: my) laziness. The more convenient the service, the less stability behind it. Comfort compounds for those who can afford it and corrodes opportunity for those who can’t.

Even our attempts at “ethical consumption” are steeped in irony. You can offset your carbon at checkout while still expecting free next-day delivery. You can tip the delivery driver generously, but it won’t change that their pay structure assumes you will. We’ve built a system where empathy is optional, but immediacy is not.

For all our talk about “human-centered design,” the truth is we’ve centered the human who pays — not the human who labors. Discomfort used to remind us of connection, of interdependence. Now, it’s a signal that something went wrong in the customer journey.

What Comfort Costs Design

Let’s imagine the other side of my robot-delivered lunch.

Somewhere across town, a gig worker named Luis starts his day at 6:00 a.m. The app tells him where to go, what to carry, and how fast to get there. He’ll earn less than minimum wage after expenses — gas, maintenance, the service fee that somehow doesn’t serve him. He’s working this weekend because his daughter’s antibiotics cost more than the copay he can afford. He doesn’t have benefits or predictable hours, but he does have ratings. And he knows that one delayed delivery, one wrong turn, one moment of human error could mean fewer jobs tomorrow.

I never see Luis. I just see a glowing progress bar that tells me lunch is on its way.

This is how the system keeps itself tidy: every bit of effort we remove from the customer’s experience has to land somewhere else. We don’t erase friction; we outsource it. And in that transaction, design becomes a weapon of abstraction — the art of making hard things invisible.

When we celebrate frictionless design, we rarely ask whose friction we’re erasing. We optimize flows, streamline journeys, automate empathy. We hide human strain behind loading animations and clever copy that says, Your order is being prepared.

Our tools have made it easy to treat ethics as a configuration option — something to toggle on after MVP, or after funding, or after scale. But the truth is: design doesn’t just shape experiences; it shapes economies. Every tap that saves me a step costs someone else a stride.

At some point, we need to decide whether the role of design is to remove effort or to redistribute it more justly.

Choosing Discomfort

The robot leaves, and for a moment I just stand there, watching its little ring-light eyes blink before it turns and rolls back down the street. Somewhere out there, it’s already on its way to the next stop, performing its task with perfect composure — polite, tireless, and empty. It doesn’t need to rest, or eat, or be tipped. It just moves.

I unwrap the sandwich and feel the hum of absurdity in my apartment. An entire ecosystem just mobilized so I wouldn’t have to. The software, the warehouses, the investors, the grid — all calibrated so I could sit in comfort, alone, watching a game while a machine did the labor once done by a person. It’s efficiency at a planetary scale, and yet it feels strangely hollow.

I’m not exempt from this system; I’m embedded in it. I’ve spent a career building technology meant to make life easier — smoother checkouts, faster pages, fewer steps. All good intentions. But good intentions don’t balance the ledger when the result is a world designed to shield us from even mild inconvenience, all while externalizing its discomfort onto those least equipped to bear it.

The truth is, progress has always required discomfort. Every meaningful experience — learning, creating, connecting, growing — involves friction. When we erase that, we erase part of what makes us human.

Maybe the antidote isn’t to reject convenience outright but to reintroduce choice — to treat discomfort as a signal, not a defect. To occasionally walk instead of scroll. To wait instead of click. To remember that friction can be a form of participation, a reminder that effort still matters.

Next time, maybe I’ll walk down the block and order my food from a real person. Maybe I’ll hand over a few extra dollars for the privilege of looking someone in the eye and saying thank you. A small act, but in a world that treats effort as failure, it might just be a start.

Recent headlines have brought the Abundance movement back to the forefront of my mind — both its promise and its failings.

First, in Australia, the government announced that millions of citizens will begin receiving free electricity credits in 2026, the result of a solar boom so large it’s producing more energy than the grid can store. The sun had given more than enough, and the government decided enough was worth sharing.

Meanwhile in the United States, the Trump administration canceled what would have been the nation’s largest solar project — a Nevada initiative capable of powering hundreds of thousands of homes — while withholding billions in SNAP food aid from starving families.

Australia looked at surplus light and asked, “How do we share it?” America looked at the same sun and asked, “Who deserves it?” Then it turned off the lights, locked the pantry, and sent more untrained ICE agents to terrorize the streets.

It’s tempting to think of these stories — about energy, food, and state power — as separate. But they share a thread: how abundance is managed, and who gets to experience it.

Australia’s decision to redistribute its solar surplus is a design of reciprocity — an acknowledgment that shared light sustains shared life. The Trump administration’s acts of cancellation and withholding are designs of control; of scarcity used as leverage and fear as public policy. Both are expressions of belief: one believes abundance grows when shared while the other believes abundance must be rationed to preserve power and drive profits.

Over a decade ago, Peter Diamandis and Ray Kurzweil turned Abundance into a creed — a techno-spiritual promise that exponential technologies would deliver humanity from scarcity. At Singularity University, they preach that innovation can fix what politics can not. In their telling, the future isn’t a democratic project; it’s an engineering problem awaiting the right optimists with more access to capital than common sense.

Over the years, that vision found eager apostles among Silicon Valley’s most powerful.
Peter Thiel framed technological acceleration as a way to bypass what he sees as the “failures of democracy,” openly arguing that freedom and equality are incompatible. Marc Andreessen, in his Techno-Optimist Manifesto, elevated abundance to a civilizational mandate — a moral justification for unrestrained growth and elite stewardship of the future.

In their hands, “abundance” became less a promise for everyone and more a permission structure for the few. They believe that if technology is destiny, then those who control it are destiny’s rightful authors.

That worldview has always aligned comfortably with anti-democratic power. Thiel’s early backing of Trump — and his reemergence as a key donor (including his sponsorship of JD Vance) — fused techno-optimism with authoritarian politics. Andreessen’s venture networks remain deeply intertwined with the administration’s AI ambitions and surveillance-first policies.

Together, they helped turn abundance from a hopeful metaphor into an ideological shield; a way to claim moral high ground while building systems that concentrate power in their own hands.

That ethos is on full display each year at Abundance360, Diamandis’s summit where entrepreneurs and executives pay upwards of $55,000 to hear about asteroid mining, longevity startups, and AI salvation narratives. The aesthetic is one of limitless possibility — as long as you can afford the ticket.

Outside those rooms, the contrast is painful. While attendees imagine uploading consciousness and extending life spans, millions of people are simply trying to extend the groceries in their fridge. While the techno-elite dream about post-scarcity, families in the U.S. wait for food aid deliberately withheld by their own government.

This is the tragedy of the Abundance ideology as it exists today: It frames abundance as something that trickles down from innovators, not something that arises through the participation of ordinary people. It sells optimism as a product, while treating structural inequality as a rounding error.

Abundance, in this worldview, is something the powerful dispense rather than something human beings deserve.

Italian design theorist Ezio Manzini offers a different lens. In Design, When Everybody Designs, he describes design as a shared social act; the everyday ways people organize to improve collective life. For Manzini, abundance doesn’t emerge from technological acceleration, but from human cooperation. It’s not the result of innovation alone, but of participation. He calls this social innovation: design as the distributed intelligence of communities — citizens, neighbors, workers — shaping systems around mutual care rather than control.

In Australia, the Solar Sharer Offer isn’t fully co-designed in the formal policy sense — but the abundance it redistributes was co-designed in practice. The country’s solar boom exists because millions of households installed rooftop panels over the past two decades, creating the world’s most decentralized renewable grid. Ordinary citizens collectively built the surplus the government is now preparing to share. In that sense, the program is less a top-down gift and more a recognition of a participatory design process already underway: a distributed public effort, accumulated over years, that turned individual choices into national abundance.

The Trump administration and its techno-authoritarian support network, by contrast, represents the death of co-design — a collapse of participatory capacity. Its withholding of food aid and sabotage of renewable energy are deliberate choices that strip people of authorship over their own conditions. It’s a design of domination.

Manzini warns that societies dominated by centralized systems lose their social imagination. When people stop believing they can influence the systems around them, design stops happening and fear replaces creativity.

Under those conditions, “Abundance” becomes impossible — not because resources are lacking, but because agency is. A society without co-design cannot sustain abundance, it can only consume what someone else controls.

Thanks for reading issue 35 of Systems & Signals. If you want more essays on design, power, and the systems shaping our world — plus occasional tools, templates, and bonus issues — you can subscribe here:

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When design performs for itself, the user becomes an audience instead of a participant.

For the better part of the last decade, digital design has lived in an age of restraint. Flat color palettes, soft neutrals, geometric sans-serifs, and interfaces so rational they nearly disappeared into the background. It was the aesthetic of logic and efficiency; the visual language of the machine age translated for the screen.

But every age of restraint eventually triggers its opposite. In the late 19th century, the mechanical efficiency of the Industrial Revolution gave rise to Art Nouveau, a movement that rebelled against mass-produced sameness through hand-crafted ornamentation, natural forms, and flowing lines. Its beauty was a protest — a reminder that even in an age of machines, a human’s touch still mattered.

Over a century later, digital design is having its own Art Nouveau moment. After years of minimalism and systems thinking, we’re seeing a similar backlash against uniformity — interfaces that shimmer, animate, and emote. Apple’s “Liquid Glass” gleams like jewelry. Airbnb’s redesign moves with cinematic rhythm, every transition a performance. Many modern car’s interiors seem designed more to be photographed than driven. Even Tesla’s Cybertruck — whose brutalist geometry I find visually exhausting — is a product of this same impulse: design as statement, spectacle for its own sake at the expense of function.

The parallels are hard to miss. Art Nouveau tried to restore the soul of the handmade to a world that had become too mechanical; today’s “extra” era is trying to re-infuse digital products with the emotional richness we stripped away in the name of scalability and systemization. Both were born out of a need to restore feeling to a world that had grown too mechanical, and both tend to confuse expression with empathy. Some of those exquisitely curved Art Nouveau chairs weren’t exactly comfortable to sit in, but they looked alive — vibrant, organic, almost breathing.

Today’s trendy interfaces share that same energy: dazzling, expressive, and sometimes just as uncomfortable to live with. They remind us how easily the pursuit of emotional richness can slip into theatricality, how a design that aims to feel human can still forget the human using it.

Across industries, design is rediscovering ornament, movement, and emotional weight after a decade of deliberate minimalism. But somewhere in this rush to feel again, we’ve lost sight of why design exists in the first place.

When “Extra” Had a Purpose

This isn’t the first time digital design has gone over the top. Long before flat design, there was skeuomorphism — the era of stitched leather, wood grain, and metal textures that defined early iOS. The Game Center looked like a casino table lined in green felt, Notes mimicked yellow legal paper, and the calculator was modeled after a Braun original, glossy buttons and all.

It’s easy to laugh at now, but those design choices weren’t arbitrary. They served a very real purpose beyond being fun for designers to create.

We were moving from interacting with computers through peripherals — mice, keyboards, dials — to touching pure glass. Skeuomorphism made that leap feel natural. When a button looked like a button, you knew you could press it. It gave users the visual confidence to trust touch. It wasn’t solely decoration; it was instruction.

Skeuomorphism was scaffolding that helped us adapt to a new medium, and once we learned, Apple took it away.

The Age of Restraint

In 2013, Jony Ive — already legendary for his mastery of Apple’s hardware design — took over the company’s software design direction. With iOS 7, he led the charge into what became known as the Flat UI revolution, stripping away textures, gradients, and shadows in favor of a cleaner, more “honest” aesthetic. Ive saw the digital interface and the physical device as parts of the same unified product, and his goal was to design in a way that respected both. If skeuomorphism was about translating the physical world into digital form, Ive’s philosophy was about embracing the digital medium for what it was.

Google followed soon after with Material Design, a framework that found its own way to balance realism and restraint. Buttons subtly lifted upward to meet your finger on the glass, adding just enough depth to preserve affordance without clutter. It was thoughtful, measured, and deeply rational — design as geometry, movement, and light.

These two philosophies — Apple’s austere honesty and Google’s mathematically structured tactility — defined the visual language of the 2010s. They made design scalable, consistent, and accessible. They also made it safe and boring.

So it’s no surprise that designers have grown restless. The pendulum, as it always does, has swung the other way.

Design’s “Extra” Era

Today’s “extra” moment is the backlash. Designers are reaching again for texture, motion, and depth in an effort to reclaim feeling. But the outcomes vary wildly in purpose and restraint.

Apple’s Liquid Glass feels luxurious but verges on spectacle. Airbnb’s redesign is beautiful yet sometimes distractingly choreographed. Lexus’s concept interiors drip with futurist fantasy, while Tesla’s Cybertruck embodies the opposite kind of maximalism — cold, aggressive, and performative in its defiance of beauty. I may not like it, but it’s a perfect example of the same underlying phenomenon: design for design’s sake.

All of these examples share one thing in common — they want to be noticed. They remind us they were designed. And that’s precisely the problem.

When design performs for itself, the user becomes an audience instead of a participant. The more we draw attention to the chrome of an experience, the less we focus on why it exists at all. It’s like admiring a statue in the lobby without being able to find the elevator. Beautiful, maybe — but you’re still stuck. When ornamentation becomes the point, usability and design for quick and seamless comprehension takes a back seat.

A Personal Reminder of Why This Matters

I was reminded of this tension recently after switching phones. I’d been using an iPhone 15 Pro Max, but the arrival of iOS 26 pushed me over the edge. The update bogged down performance and introduced Apple’s new Liquid Glass aesthetic, which added visual noise where there used to be calm. Buttons shimmered, text blurred through transparency, and the entire system seemed intent on making me notice it. The polish was undeniable, save for the apps where it was simply broken, but so was the fatigue.

This week I switched to a Pixel 10. It’s Google’s own hardware, which means it runs the purest — and frankly blandest — version of Android. And that’s exactly what I love about it. The interface feels simple, fast, and clear. It has just enough animation to reinforce understanding, but it never competes with the content. The overly “Expressive” qualities of Material 3 Expressive — dynamic themes, wallpaper-driven palettes — are optional flourishes. They make the device feel more personal, not more performative. It’s calm, measured design that supports what I’m doing, not how the software wants to be perceived.

After just a few days, it’s striking how refreshing boring design can feel after a short experience fighting with Apple’s new over-the-top design language.

A Better Kind of “Extra”

That’s the balance we’ve been missing — emotion without ego. Design that feels alive but doesn’t ask to be admired.

Google’s Material Expressive points in that direction — a framework that reintroduces shape, color, and motion with clear purpose. Its “extra” isn’t for attention; it’s for comprehension. It’s the kind of design that reminds you what emotion can do when it’s in service of understanding.

The Pendulum and the Lesson

Design’s “extra” era was inevitable. After years of reductionism, we were bound to crave personality again. But we shouldn’t mistake this swing toward embellishment for progress. Skeuomorphism was extra for a reason — it helped people learn to use a new medium. Ive’s flat design shift was honest for a reason — it helped digital mature into something independent and confident, re-contextualizing hardware and software as parts of the same whole. Today’s excess, by contrast, often feels like decoration in search of justification, an aesthetic trying to fill the emotional void left by our obsession with efficiency.

The best design has never been defined by whether it’s flat, glossy, or animated. It’s defined by whether it helps people accomplish what they came to do — without friction, confusion, or spectacle. Unfortunately, it seems the popular trend today is for design to do the opposite in service of its creator’s ego.

Want to go deeper on building a sustainable, future-proof design career?
Download my free guidebook, Designing a Resilient Design Career — a practical framework for navigating change, finding your leverage, and staying adaptable as the industry and our craft evolve in this era of AI and uncertainty.

You’ve been doing everything right. You deliver great work, your peers trust you, and leadership leans on you for the hardest problems. You’re the designer others come to when things get stuck, but when the next promotion cycle rolls around, your name isn’t on the list.

You look around and think: What else could I have done?

Most designers who hit this moment assume the path forward is about perfecting what they already do — getting faster, more polished, more consistent — but the truth is far less linear. Getting promoted in a large company isn’t about mastering your craft; it’s about demonstrating that you can handle more complexity and that the business is ready for you to do so.

The illusion of linear growth

In early design careers, growth feels predictable as you learn, improve, and get rewarded for doing so. The lines between levels are clear, and the expectations are usually measurable. But as you move into senior roles, that clarity fades.

At this stage, promotions aren’t about how good you are at design. They’re about how ready you are to operate in systems that are more complex than the ones you’ve already mastered.

In every large company I’ve worked in, there was an unspoken rule: to move up at senior levels, your scope of influence — or the complexity of your work — had to roughly double.

That might mean leading a broader portfolio of experiences instead of a single product, shifting from designing features to designing systems, or shaping cross-functional strategy instead of just executing it.

The higher you go, the more you’re expected to think in systems rather than artifacts.

Promotions depend on context, not just competence

This is the part that stings: you can’t get promoted into a vacuum.

Even if you’re performing well above your current level, there needs to be space in the organization for your next one. Sometimes, that space doesn’t exist yet. Sometimes, it exists in theory but not in budget. And sometimes, no one has yet articulated the business problem that your next level of skill could solve.

That’s why so many designers plateau. They’re waiting for an opening instead of helping create one.

I’ve learned this lesson the hard way. Every time I’ve helped start a new design ops or design system function at a Fortune 500 company, it wasn’t because someone handed me a charter or headcount. It was because I could see a pattern: design inefficiency, inconsistency, or bottlenecks slowing teams down. I named the problem, made the case for change, and built a small experiment to prove the value.

When people saw the impact, the role emerged around the work.

Promotions often work the same way. The organization has to see and feel the value of what your next level looks like before it formalizes the title.

Stretching into greater complexity

So what does it actually mean to “stretch into complexity”? It’s not about working longer hours or juggling more projects — it’s about shifting how you think and where you focus.

At some point, mastery of craft becomes table stakes. What matters next is your ability to scale your impact. That can mean turning design patterns into reusable playbooks, improving how teams collaborate, or bridging gaps between functions like design, product, and engineering.

You’re no longer being measured on what you can produce, but on what others can do because of the systems you’ve built.

Think of it as designing at the meta level — the systems that enable great design to happen repeatedly and predictably.

Show the value before the title

Here’s the unfair part: you usually have to prove the value of that new level before anyone recognizes it.

That’s frustrating, but it’s also how trust is earned in large systems.

The key is to identify a problem the business already cares about — something tied to cost, velocity, consistency, or customer impact — and show how your approach to design can improve it. Then make the outcomes visible. Translate your design improvements into language the business understands: faster releases, fewer dependencies, higher conversion, lower support costs.

And this isn’t something you have to navigate alone. A good manager should help you find these opportunities, align them with strategic priorities, and make sure the right people see the results.

If your manager isn’t helping you do that, have the conversation. Leaders worthy of the title should want to help their people grow into greater complexity.

Trajectory over titles

The best designers I’ve worked with eventually stop thinking about promotions as checkpoints. They start thinking about trajectory.

They ask:

• How can I expand my influence without losing touch with the craft?

• How can I create systems that outlive my direct involvement?

• How can I shape the environment so others can grow too?

That shift — from chasing recognition to designing systems of impact — is often what triggers the next opportunity.

Promotions follow momentum, momentum follows visibility, and visibility comes from solving real problems at a level of abstraction that no one else is thinking about yet.

Make the next level inevitable

Growth in design leadership rarely arrives on schedule or in the form you expect. It happens when your impact starts to ripple beyond your immediate work — when you stop optimizing for personal advancement and start shaping the systems that let others thrive. The next step up isn’t about proving you deserve a bigger title; it’s about making yourself impossible to ignore by solving problems the organization hasn’t yet learned how to articulate. Titles will follow the trajectory you create — not the other way around.

It started as a feeling, a kind of static in the air around me. Nothing was broken, exactly, but nothing was working quite right either. Well, some things were actually broken, namely my sanity.

Every interaction with a device — my phone, my headphones, my monitor — carried the same faint sense of friction: a click that didn’t register, a prompt that shouldn’t exist, and a feature that solved a problem I didn’t have.

By the end of the week, it felt like I was living inside an omnipresent cloud of broken technology and digital experiences.

My Week with Modern Technology

It began innocently, albeit frustratingly, enough. My AirPods Max, the $550 noise-canceling marvels, refused to pair with my iPhone at the gym for what felt like the 550th time. How I made it through my hour-long lifting session without the Challengers soundtrack is a question to be studied by academics.

Then came the iOS 26 “upgrade,” which left my iPhone 15 Pro Max feeling sluggish and half-responsive. Entire regions of the screen ignored taps and text blurred illegibly into mush under Apple’s new “Liquid Glass” UI. The fact this happened days before my last phone payment was not lost on me.

My overpriced OLED monitor joined the rebellion next, asking if I wanted to “switch inputs” every time I accidentally nudged my desk. The pop-up included an “Always Yes” option, but of course, no “Always No.”

At work, we’ve disabled Figma’s AI features — on purpose — yet I was greeted by a cheery “Try AI” prompt on the dashboard anyway. No, I’d prefer to not train the model on my team’s work to the benefit of our competition, thank you very much.

When I open Office 365 to search for a document, I am always greeted by a blank chat window asking if I’d like to “Ask Copilot.” No, I’d like to find my file, thanks. What frustrates me even more is that Copilot is the second navigation option below Search, because Microsoft knows all I want to do is find that file, but they’ve clearly chosen to push their pet AI anyway.

My Smart TV decided I needed to re-pair its own remote, which failed and left the remote effectively bricked.

And to cap it off, the Chicago Sports Network (CHSN) app — which I subscribed to for Bulls and Blackhawks games — played me a silent music note icon instead of the game.

All of this happened in a single week, and it’s not coincidence.

The Death of Baseline Usability

These aren’t edge cases; they’re what happens when companies quietly decide that baseline usability and quality no longer matter.

For years, “usability” meant something simple but profound: it just works. The unsung labor of UX designers, QA testers, and accessibility specialists made sure interactions were reliable, consistent, and human-scaled. That kind of work doesn’t make headlines or shareholder decks, and it doesn’t feed engagement graphs or drive glowing AI demos. So, it’s slowly being erased.

Today, the gravitational pull of modern tech has shifted toward novelty and automation. Every design decision is filtered through engagement metrics, growth OKRs, and “AI integration opportunities.” The result is a digital ecosystem obsessed with appearing intelligent rather than being usable — everything looks smarter, but feels and functions infinitely dumber.

How We Got Here

To understand how we arrived in this mess, you have to rewind a few decades — back to when usability was a science and not an afterthought.

In the mid-20th century, Human Factors Engineering emerged from aerospace and military design, where poorly labeled switches or confusing interfaces could literally cost lives. That discipline — built on psychology, ergonomics, and systems thinking — laid the groundwork for what we now call user experience design.

By the 1990s and early 2000s, those ideas had migrated into the software world. Companies like IBM, Apple, and Microsoft hired human-computer interaction (HCI) specialists, usability engineers, and cognitive scientists to test and refine interfaces before release. “User testing” was standard practice, and “the user” was treated as a real person, not an abstraction.

These roles didn’t exist to make things pretty or drive engagement — they existed to protect usability as a measurable standard of quality.

When something was confusing, it was a defect. When something was slow, it was a bug. And when something failed usability testing, it didn’t ship.

That ecosystem has quietly collapsed.

Today, those once-specialized roles have been absorbed into generalized digital product design tracks, where the incentives are entirely different. Designers are now expected to be “full-stack” — part researcher, part strategist, part conversion optimizer. The metric isn’t usability anymore; it’s performance against growth KPIs.

The subtle shift is that design’s value is increasingly measured by its ability to influence user behavior toward profit-generating outcomes — clicks, conversions, engagement — rather than its ability to make those behaviors intuitive, humane, or equitable. The result is a workforce of talented generalists who are stretched too thin to defend the craft of usability itself.

Meanwhile, others who used to safeguard baseline quality — QA testers, accessibility specialists, and others — have been thinned to the bone or automated out entirely. Their work is invisible by design: when they succeed, nothing happens. No headlines. No engagement spike. Just a product that quietly works. That invisibility made them the first casualties of efficiency mandates and AI optimism, but they were the final line of defense between a functioning system and the chaos we’re starting to live through — the invisible labor of baseline experience acceptability.

Without these disciplines, our digital world has no governor. Bugs become features, annoyances become expectations, and accessibility becomes perpetually “post-MVP”, if considered at all. And somewhere in that fog, the very idea of usability — once the proud, empirical heart of good design — simply drifted away.

The irony is that we didn’t lose usability because it stopped mattering. We lost it because it stopped being visible to those in power. It doesn’t move dashboards or earn headlines, and you can’t A/B test your way into empathy. And so, the very people who made technology livable — the testers, the accessibility advocates, the human-factors engineers — were written out of the story. What’s left are systems that appear more intelligent than ever, yet understand us less with each release.

Re-Centering Usability

This isn’t nostalgia for a simpler time — it’s a plea for accountability. If AI is the next industrial revolution, usability needs to be part of its social contract. Without it, we’re left in a permanent beta test, debugging our own lives.

The truth is that usability was never glamorous. It was the invisible work that made technology humane — the discipline that said “just because it’s possible doesn’t mean it’s good.” Somewhere along the way, that principle stopped fitting neatly into business models.

But it’s not too late to bring it back.

Re-centering usability means putting people — not engagement metrics — at the heart of design again. It means hiring specialists who care about the feel of interaction as much as the flow of revenue. It means treating accessibility, performance, and polish not as polish, but as ethics.

Signal to Watch

Companies that still employ — or promote — dedicated usability specialists will soon stand out not for having the smartest tech, but for having the only tech that reliably works. And that will be a critical business advantage in a world that’s otherwise gone mad with ads that paper over broken experiences.

Hi there, and happy Saturday!

As a little make-good for not publishing a standard issue earlier this week, I’m sharing one from the archives — a piece that still feels as relevant today as when I first wrote it.

My regular publishing schedule resumes on Tuesday with a new essay: The Great Usability Recession.

And a fun update: I can finally share the project referenced below — the Fifth Third Bank mobile app, which went on to become JD Power’s #1 rated regional banking app. We developed it alongside the bank’s next-generation branch design, which gave us the opportunity to retroactively apply a unified omnichannel design language. This was only possible thanks to a robust design token architecture that emerged from our decision to “build the design system backwards.”

I’m still deeply grateful for having had this experience and for the incredible cross-functional team of geniuses I learned so much from along the way.

Starting at the End

A handful of years ago, I joined a Fortune 500 as a design leader, excited to work within an organization that had invested in a new design system. My first meeting with my design system designers was a review of YAML and JSON files. There was no style guide, no audit of existing components with inconsistencies called out, no mock-ups of any kind yet. Just… code. I put on my best stone face and thought: “Something is seriously off here; why are we talking development before aligning on common patterns and visual styling?” We had started at the end of the process, not at the beginning. In hindsight, starting with the end in mind was the best possible decision the team could’ve made despite my initial dismay.

What is a Design System, Really?

First, let me clarify what I mean by “design system” to set the stage. Here’s the Nielsen Norman Group’s definition, with my extension in italics:

A design system is a set of standards intended to manage design at scale, using reusable components and patterns while bridging designer and developer workflows to deliver rapid experience improvements.

Without tight integration into developer workflows, even the best design system is little more than a detailed digital style guide. While valuable for aligning designers and providing individual developers guidelines (think on the level of mock-up inspection tools in Figma), it does little to eliminate redundancy and inconsistencies in the built experience. Each designer is responsible for adhering to the guidelines, as is each developer — assuming clear front-end guidelines exist and are tied back to the system guidelines. There are several built-in points of failure. An ideal design system is architected across both design and development workflows to truly realize the value in the definition above: managing design at scale, from the initial pixel placed in design software to the delivered experience.

Introducing design tokens, and just a little existential dread

Design tokens are the great enablers of flexible, scalable design systems. Like a physical token, a design token is a stand-in representing a value. The ribbon of tickets won playing skeeball are similar; they represent a value set by the arcade and are meaningless outside that context. Suppose you want to have an existential crisis. In that case, money’s physical and mental constructs are also tokens — the value they represent fluctuates constantly based on external factors, but a $1 bill is always a $1 bill. Or, if you prefer a more designer-y simile: tokens are the sub-atomic particles in the Atomic Design model; the protons, electrons, and neutrons that build atoms or components. A button’s shape, color, and drop shadow can change, but it’s always a button.

Examples of the “sub-atomic particles” that design tokens can represent:

• Color

• Spacing

• Stack order

• Fonts — color, size, decoration

• Shadows

• Shape

• …really, anything that makes up a designed element.

For your consideration: an absolute nightmare

Let’s say your company’s brand team one day decides to change the company’s primary colors to magenta and olive green. First off, I’m sorry. Second, ask yourself: how much work will it be to implement this change across every digital touchpoint in the customer’s journey? If lucky, the development teams thought ahead and architected web, mobile, and other (potentially in-store) applications to absorb this change seamlessly. In reality? There’s probably a lot of hard-coded styling in the ecosystem that requires a ton of manual effort. And that’s just color. The re-brand also requires a re-think of the entire visual layer over top of the core experience.

Sound like a nightmare? I lived it. Our brand color changed at the company mentioned above, which led to two different primary brand colors throughout our experiences. We also learned we had more than 50 shades of gray in one of our mobile applications alone through the audit spurred by the brand updates. There were dozens of other examples. We had inadvertently built a patchwork quilt of an experience far below the industry standard. The solution? Update the most critical screens and punt on the rest; we were re-platforming anyway.

Develop before design, but in a good way

It was too late for the existing codebase to make meaningful changes to eliminate all the inconsistencies built up over six years. Luckily, executive leadership had recently won buy-in from the Board of Directors to completely re-platform all consumer-facing experiences to more modern technologies. A window of opportunity opened: we integrated design tokens from the start of the re-build, affording maximum design flexibility and control in the built experience. Remember those YAML and JSON files I reviewed in my first weeks at the company? Those were the humble beginnings of the design token approach that powers the company’s new experiences today. Consider my initial perspective re-calibrated.

Enough tech talk, you say — what about the design in the design system? Here’s the other benefit of taking a tokenized approach: final decisions on design and related guidelines can be deferred until a new design language and associated guidelines are ready. Given the multitudes of stakeholders at the Fortune 500, approval of a new digital design language was a long, drawn-out process. We didn’t have all design decisions codified down to the token level when the re-build began, but that didn’t matter. When the new design language was finalized, a single developer, working alongside a designer, would be able to update design tokens and, like magic, change the look and feel of the total app experience in a matter of minutes, not weeks or months.

If you are trapped in a cycle of mock-ups not matching reality, I strongly suggest talking to your development partners about the power of design tokens. While my example above is extreme — not everyone will get the opportunity to re-architect an entire front-end ecosystem — there are many options to improve design-to-development workflows that can take advantage of tokenization. And if you’re starting fresh with a new product, there’s no reason not to build in tokens from the start and make them a core part of design and front-end development practices.

I’ve spent dozens of hours in Manor Lords. It’s one of the most quietly ambitious games I’ve ever played as it’s a rare blend of patience, realism, and beauty that rewards attentiveness over efficiency. Every hamlet I’ve built feels alive: villagers hauling logs across muddy roads, livestock crowding the gates at dusk, the gentle rhythm of seasons shaping survival. It’s the sort of world that reminds you what design can do when it’s driven by care instead of scale.

That’s why Greg Styczeń’s recent post hit me. He’s the solo developer behind Manor Lords, and his message to the community wasn’t the usual patch note update. It was a confession about burnout, refactoring, and the painful process of turning a one-person vision into something a team could carry forward.

He talked about aligning visions with new designers, helping programmers untangle “a giant solo dev spaghetti” of code, and discovering how even “innocent tasks” became deep rabbit holes. What was once an elegant, intuitive project had become a living, breathing system that no longer responded to the same instincts that created it.

And that’s what fascinates me. Because Manor Lords isn’t just a game about managing a medieval settlement — it’s also a mirror of what happens when any creative system begins to scale.

The Leadership Paradox of Scale

Vision vs. Systemization

Greg’s update reads like a study in transition from maker to manager and from craft to coordination. The friction he describes is inevitable for anyone who’s ever built something and then tried to grow it. Systems eventually need governance. Design eventually needs process. And the hardest part of that shift isn’t the work itself but the feeling that structure might dilute the spark that made the work special in the first place.

The Loss of Momentum

His apology — “Sorry for taking so long” — also says something about our expectations. Players, investors, and audiences have been trained to equate progress with velocity. But the kind of progress that keeps systems alive — refactoring, reorganizing, laying better foundations — often looks like stasis from the outside. In reality, it’s the slow, invisible work of ensuring the world doesn’t collapse under its own weight.

The New Craft: Alignment as Design

Greg’s line about needing to “align visions” with his new team is quietly profound. Alignment is the new craft. Once systems reach a certain complexity, design becomes less about pixels or polygons and more about coherence — making sure everyone shares a mental model of what good looks like. That’s leadership as design practice.

The Myth of the Auteur

The romantic image of the lone visionary — the auteur who bends complexity to will — rarely survives contact with growth. Systems resist control. They evolve. What once felt like an expression of pure creative identity becomes a negotiation between people, processes, and priorities. The goal stops being total control and shifts toward coherence at scale.

Reflections from the Field

As someone who’s built and led design systems, I recognize the tension Greg describes. That “spaghetti codebase” has its equivalents in sprawling design libraries, legacy CMS schemas, or ad hoc workflows that worked when the team was small but buckle under scale. The instinct to start over — to “refactor the world” — is both necessary and dangerous.

What Manor Lords captures so beautifully, even in its own development, is that governance is not bureaucracy — it’s stewardship. The same patience it demands from players — planning ahead, maintaining balance, adapting to resource scarcity — is now what its creator must practice to sustain the game itself.

When I moved for my new job, I thought the hardest part would be adjusting to my new city, or maybe the loneliness of starting over. What I didn’t anticipate was how much I’d lose my grip on routine things, including my mental health care.

Between moving boxes and onboarding, I never made time to transfer my prescriptions to a local doctor. That meant, unintentionally, I cycled off both an SSRI for depression and anxiety, as well as a stimulant I’d been prescribed for my late ADHD diagnosis.

The last couple weeks have been a strange, dizzying ride with random crying fits, days where focus felt impossible, and work that demanded more energy than I could give. Over that time I’ve been a poor leader, colleague, and spouse. But now that I’m coming out the other side, I’ve realized something unexpected: this version of me — flawed, raw, unoptimized — is the one I grew up with.

This is my default operating system. And honestly? It’s both terrifying and freeing.

I’m not ruling out going back on medication. I know how much it helped me stabilize and function at my best. But for this brief moment, I’m sitting with what it means to simply be myself and to feel the unfiltered rhythms of my own mind, however uneven they might be.

Maybe this is what authenticity really means: not some aspirational version of balance or productivity, but the willingness to exist within the full range of our wiring.

In design and in life, we often talk about optimization — for efficiency, for engagement, for conversion. But sometimes, maybe the hardest and most honest work is learning to live with the system as it is.

Buy Now, Pay Later (BNPL) was once the sleek new way to make big purchases feel lighter — a clever alternative to credit cards promising four simple payments, zero interest, and none of the guilt. It started with indulgences: shoes, furniture, and frivolous tech. But in 2025, that same model has crept into far more ordinary places. You can now split the cost of your groceries, your Uber Eats order, even your gas. Klarna, Affirm, and Afterpay aren’t just embedded at checkout anymore — they’re quietly threading themselves through the fabric of everyday life.

How design makes debt feel different

BNPL isn’t simply a payment option; it’s a carefully constructed psychological experience. Every detail, from the language to the interface, draws from the same playbook behavioral economists have used to explain why people act against their own long-term interests.

We overweight the present and undervalue the future — what researchers call hyperbolic discounting. The promise of “four easy payments” reframes a $100 item as something you can rationalize in small, bite-sized pieces. By decoupling the cost from the moment of consumption, the design softens the emotional sting of spending — the pain of paying that MIT researchers have shown is central to our financial decision-making.

The interface helps it along. Installments are often pre-selected and “Pay in full” is the opt-out. The design language — soft gradients, friendly typography, soothing copy — signals safety and self-care. The result is that a financial product feels almost therapeutic. You aren’t taking on debt, you’re practicing mindfulness through flexibility.

It’s often clever, frictionless, and it’s teaching millions of people to borrow as a default behavior tied to survival itself.

From splurges to survival

In just a few years, BNPL has expanded from luxury spending into the realm of daily necessity. A LendingTree survey found that one in four users has used BNPL for groceries, up sharply from just 14% the year before. The same pattern shows up in Supermarket News, which reported a 40% year-over-year surge in BNPL grocery purchases. Klarna even partnered with Uber Eats and DoorDash to let people split dinner into installments (CBS News).

For middle- and lower-income households, that shift is enormous. BNPL can feel like a lifeline when wages fall short of needs — a way to bridge the gap between paychecks, smooth cash flow, and avoid overdraft fees. It can help families keep food on the table without resorting to payday loans or high-interest credit cards.

But that same accessibility is what makes it dangerous. Once you normalize installment payments for perishables, debt stops feeling exceptional. You’re not financing a big-ticket item anymore; you’re financing survival.

A Kansas City Federal Reserve study found that BNPL users tend to be more financially constrained, with less savings and lower credit scores than the general population. Many carry multiple loans across different providers, each unaware of the others. The Consumer Financial Protection Bureau found that 63% of BNPL users open several loans per year, and 41% admit to at least one missed payment.

The defaults may be small, but the pattern is large. When enough people rely on short-term debt to eat, it says something about the economy and about what we’re designing people to believe is normal.

Designing debt as comfort

What’s striking about BNPL isn’t its complexity — it’s how simple it feels. The apps are elegant and calm, the flows are stripped of friction, and even payment reminders sound friendly: “You’re doing great — one more payment to go!” It’s not the voice of a creditor; it’s the voice of a coach.

The UX principles behind these systems are the same ones used in fitness apps and habit trackers: make progress visible, reduce barriers to action, keep the feedback loop positive. BJ Fogg’s Behavior Model describes it neatly: when motivation, ability, and a trigger converge, a behavior happens. BNPL maximizes all three. You want the item, you can afford the installment, and the “Pay in 4” button is right there, glowing in your cart.

Even features that appear responsible like limit alerts, repayment trackers, and reminders reinforce the system’s rhythm. You’re not encouraged to stop using BNPL; you’re encouraged to use it better and repeatedly.

The behavioral mechanics are brilliant, but the ethics are messy.

Invisible debt, visible design

Most BNPL loans don’t appear on credit reports, which means they’re invisible to traditional credit scoring systems. A CFPB report noted that this invisibility creates a blind spot — both for consumers, who underestimate how much they owe, and for lenders, who can’t see the full risk.

This opacity keeps the illusion alive: if no one sees the debt, it doesn’t really exist. But of course it does — just not in the places our systems are built to measure.

That may change soon. Regulators in the U.S. and Australia are exploring mandatory credit-reporting for BNPL accounts. When that happens, the hidden debt load will surface. Defaults may spike before stabilizing. The system will appear riskier, but it will also become real.

When design outruns policy

Governments are trying to keep pace. The CFPB recently classified BNPL providers under the Truth in Lending Act, granting consumers clearer refund and dispute rights. The U.K.’s Financial Conduct Authority will enforce affordability checks by 2026, while the E.U.’s Consumer Credit Directive 2 and Australia’s new rules take effect even sooner.

Yet legislation moves on quarterly timelines, while design iterates weekly. The law can make BNPL more transparent, but it can’t make it less seductive. No disclosure statement competes with a pastel-colored button and the promise of instant gratification.

As policymakers chase compliance, the real work of shaping behavior happens at the interface level. The moral frontier is not the loan agreement — it’s the checkout screen.

The system beneath the smoothness

We’ve spent a decade celebrating frictionless design as progress. But friction, used wisely, is what helps people make thoughtful choices. BNPL stripped that friction away and replaced it with comfort by not just removing obstacles, but by removing pause.

For the affluent, that’s convenience, but for the vulnerable, it’s exposure. When the ability to buy food now and pay later becomes a normalized part of the user experience, we’re no longer optimizing transactions — we’re shaping survival within the constraints of a broader, more broken system.

The next crisis won’t look like 2008. It’ll look quieter: a gradual erosion of self-awareness, where millions live one installment ahead, never quite realizing they’re still in debt from last week’s dinner.

Design did that — not maliciously, but meticulously. And if design can teach people to borrow by default, it can also teach them to stop and reflect. The question is whether we have the courage to design for that.

If you build for fintech, retail, or experience design — ask yourself:
Are you smoothing the path, or sanding away people’s sense of consequence?
Because the difference between those two might be the distance between convenience and crisis.

As I’ve covered before, design careers are in flux. For years, the career ladder climb was straightforward: from individual contributor to manager to director+, with each rung representing less craft and more coordination. Today, that ladder is starting to collapse inward. What’s emerging instead is more of a player–coach model where leaders split their time between hands-on design and directing the work of others.

Perhaps unsurprising given the state of the economy, this isn’t just a design story. Across industries, companies are deliberately eliminating middle-management layers to cut costs and increase speed. A recent Wall Street Journal analysis (its headline — Your Boss Doesn’t Have Time to Talk to You — spiked my own guilt) noted that firms like Meta, Lyft, and Disney are shrinking the “managerial middle” to “move faster and reduce overhead.” Design orgs, often pressured to demonstrate ROI, are being forced to follow the same playbook.

The result: smaller teams, flatter structures, and greater expectations placed on each leader, and all of this is being accelerated by the rise of AI.

Why Now?

1. Tighter purse strings in the post-COVID era.
During the pandemic many tech companies went on unprecedented hiring sprees. Demand for digital products skyrocketed in e-commerce, streaming, delivery, and remote collaboration. Design and product teams grew rapidly to keep pace, sometimes doubling or tripling in size between 2020 and 2022.

But as growth slowed and economic conditions tightened, companies have started to course correct. Rising interest rates, reduced VC funding, and a sharper focus on profitability have pushed firms to cut back. Meta, Amazon, Google, and others all acknowledged in 2023–24 that their headcounts had outpaced sustainable growth. The result is what Business Insider recently called a shift to “fewer managers, flatter teams, and faster decisions.”

Design orgs mirror this broader correction. The surface area of work — multiple platforms, omnichannel journeys, personalization, AI integration — hasn’t shrunk, but the headcount supporting it has. That gap is being bridged by player–coach roles, which allow companies to run leaner while still maintaining standards of quality and pace.

2. The AI shift in craft.
AI has become the elephant in the room for every design team. Tools like Figma Make, MidJourney, or ChatGPT can generate layouts, copy, and even interface components in seconds. But that doesn’t mean the craft of design is being automated away. If anything, it creates new problems: low-quality outputs, biased recommendations, hallucinations, and designs that look good on the surface but collapse under real-world use.

Rather than celebrating AI as a replacement for designers, it’s more accurate to view it as a force that changes where the real work happens. Generation is the easy part; it’s editing, constraining, and evaluating that separate a good experience from a brittle one. Leaders must be prepared to direct their teams toward higher-order skills:

• Editing — deciding what should be kept, discarded, or reworked from AI-generated output.

• Constraints — embedding brand, accessibility, and ethical guardrails so systems don’t veer into harmful or incoherent directions.

• Evaluation — building methods to test usefulness, accuracy, and impact, since AI-generated experiences can “feel right” but fail in practice.

In other words, the AI wave doesn’t remove the need for design craft; it makes the craft more complex. Instead of pixel-perfect screens, designers are now asked to shape prompt systems, evaluators, and content models that scale across contexts. Leaders who approach AI with uncritical optimism risk hollowing out their teams’ value. Leaders who meet it with a clear-eyed skepticism — and prepare their people for these new forms of craft — will be the ones who steer design into a sustainable role alongside AI.

3. The collapse of middle management.
Across industries, middle managers are being squeezed out as organizations pursue flatter structures. A recent Forbes article reported that middle management jobs accounted for a disproportionate share of recent white-collar layoffs, with many companies viewing these roles as costly layers that slow decision-making. Automation and AI have also played a role, taking on some of the coordination and reporting tasks that once justified a manager’s position.

For design orgs, this same logic applies. Roles once focused on approvals or coordination are being trimmed back in favor of leaders who are still in the work. Instead of a chain of managers relaying updates, organizations increasingly rely on “doer-leaders” who can span strategy, execution, and mentorship in a single role.

Enter the Evaluators

To understand why the player–coach role matters so much in an AI-driven workflow, you need to understand evaluators.

Evaluators are automated tests or frameworks that measure whether AI outputs meet the bar for usefulness, tone, safety, and brand consistency. They can range from lightweight rules (checking if an answer includes a disclaimer) to complex multi-metric models (scoring accuracy, bias, or customer sentiment).

For design teams, evaluators are quickly becoming the equivalent of QA for AI-driven experiences:

• Consistency: Do generated UI elements or copy align with brand voice and accessibility standards?

• Safety: Are recommendations free from bias or harmful suggestions?

• Utility: Does the feature actually help the user complete their task?

Building and curating these evaluators is itself a design activity — one that demands taste, systems thinking, and ethical literacy. And because player–coaches are still hands-on, they’re often the ones writing prompt patterns, defining evaluator criteria, and teaching teams how to use them in practice. I plan to cover evaluators in more detail in future issues given their criticality and potential for mis-use.

What This Looks Like in Practice

In many orgs, design ladders are reshaping into a three-track model:

• System-ICs (Staff/Principal). Hands-on contributors focused on patterns, tokens, and coherence across experiences.

• Player–Coaches (Leads, Manager). Splitting time between directing pods and shipping critical work.

• Org Leaders (Director+). Fewer in number, focused on portfolio alignment, cross-functional capital, and talent health.

Instead of a designer moving away from craft as they climb, the expectation is now: stay close to the work, but expand your leverage with AI as one tool in your toolbox.

A typical player–coach week might include:

• Framing product bets with PM/Eng on Monday.

• Pairing with designers or engineers midweek to unblock a flow or prototype.

• Reviewing evidence and deciding “ship, cut, or iterate” on Thursday.

• Logging outcomes and updating patterns, prompts, or evaluators on Friday.

The artifacts that matter are no longer just Figma comps — they can include design tokens, evaluator dashboards, experiment readouts, and prompt libraries.

The Risks of Flattening

This shift isn’t without trade-offs. Eliminating layers can leave juniors stranded without mentorship. Calling someone a “player–coach” without giving them time, authority, or scope is a recipe for burnout. And if player–coaches end up doing all prioritization, they drift into shadow PM territory — papering over gaps in the product triad rather than strengthening it.

To make the model work, orgs need intentional systems: structured crits, pairing calendars, guilds, and mentorship frameworks. Otherwise, flattening creates fragility.

Where This Could Go

• 12–18 months: Player–coach becomes the default stepping stone to leadership. Staff and Principal ICs are expected to have fluency in AI evaluation systems.

• 3 years: Design engineering and AI design QA (red-teaming, evaluator coverage, outcome scorecards) are mainstream. Career ladders explicitly reward editing, orchestration, and decision quality, not just delivery volume.

As Julie Zhuo argues in The Making of a Manager, leadership isn’t a promotion — it’s a different craft. In today’s world, that craft looks more like orchestration than oversight.

Closing Thoughts

The evolution of design careers mirrors the pressures shaping companies at large: leaner teams, fewer layers, and more demand for leaders who can do and direct in the same breath. The player–coach isn’t a temporary stopgap—it’s the new normal. If the past era of design leadership was about scaling headcount and managing processes, the next one will be about scaling leverage: taste applied through systems, decisions grounded in evidence, and craft reframed for an AI-infused world.

For individual designers, this is both challenge and opportunity. The challenge is that expectations are higher and more complex than ever. The opportunity is that design has never been closer to the core levers of business — data, technology, and decision quality. The leaders who thrive won’t just manage work; they’ll shape the systems and narratives that determine how entire organizations design.

If you’re early in your career, start practicing now for the roles you’ll grow into. Learn how to frame decisions with evidence, not just pixels. Get comfortable reading data dashboards as easily as crit boards. Treat design systems, prompts, and evaluators as creative materials — not constraints. Most of all, practice editing and orchestration, because in the next era of design, those will be the highest forms of craft — whether we like it or not.

I’ve long been fascinated by the evolution of design education. I’ve advised programs like Mount Mary University on how to better prepare students, and earlier in my career I worked full-time in higher education at both the University of Cincinnati and UCF. Those experiences taught me a lot about what schools do well, and what they often miss.

Here’s the uncomfortable truth: even the best programs still leave students unprepared for the work that matters most.

Yes, you’ll learn the tools and techniques. You’ll graduate with a portfolio of screens, flows, and case studies. But the skills that actually determine success in UX — the ones that shape careers and outcomes — rarely show up on a syllabus.

So, consider this a letter to you — the next generation of designers — about what it really takes.

1. Relationships are the real leverage

Doing excellent solo work will carry you early in your career. But the farther you go, the more your effectiveness depends on how you work with others.

• Build relationships across team boundaries — engineers, PMs, marketers, operators. Some of your best design work will come not from pixels but from reorganizing how work flows between people.

• Treat relationships like design problems: how can I reduce friction, create alignment, and generate clarity?

• Influence comes through trust. Trust comes from consistency, empathy, and respect.

The most powerful design work often looks like relationship design: creating new ways of organizing the work itself.

2. Humility is a superpower

Designers don’t have all the answers. You’ll be wrong more often than you’d like to admit. That’s not weakness — that’s the job.

• Ask questions, even when you feel you should already know.

• Seek critiques early and often.

• Learn from everyone—engineers, data analysts, customer service reps, executives.

Arrogance slams doors. Humility opens them.

3. Tie design to business outcomes

Good design doesn’t “speak for itself.” You need to connect your work to what the business values.

• Don’t just say, “This makes it easier for users.” Say, “This reduces checkout time, which increases conversion.”

• Show how design reduces costs, saves time, or builds loyalty.

• Speak the language of outcomes, not outputs.

If you can’t tie your work to results, you’ll be seen as cosmetic. If you can, you’ll be trusted as strategic.

4. Systems thinking is non-optional

Design isn’t just about screens — it’s about the systems that shape them. That includes the systems inside your organization.

• Look at how teams are structured, how information flows, and where bottlenecks slow decisions.

• See the connections between design choices, operational realities, and customer impact.

• Recognize that sometimes the best design intervention is not a new interface, but a new workflow.

Systems thinking makes you indispensable because it connects the micro (a single interaction) to the macro (how the organization functions).

5. Design is the great aligner

One of design’s greatest powers isn’t in the polished deliverables for production — it’s in its ability to align people.

• Use your skills to paint a picture of the future that sparks excitement and conversation.

• Don’t aim for perfection; aim for clarity. A rough concept that gets people nodding and contributing is worth more than a pixel-perfect file no one reacts to.

• Invite others to build on your ideas. When people see their fingerprints in the vision, they become invested in making it real.

The real magic of design often happens in the moments where a sketch, a prototype, or a storyboard gets people moving in the same direction.

6. Heuristics still matter

Design education often skips over the basics, but they’re timeless.

• Learn usability heuristics and keep them at the front of your process.

• Master the art and science of information architecture. Clear navigation solves more problems than flashy visuals ever will.

• Use principles as your compass when tools and trends shift.

Fads will come and go, but heuristics are your grounding in reality.

7. Politics is just human behavior

“Office politics” has a bad reputation, but it’s unavoidable. Politics is simply how people pursue their goals in groups. Ignore it, and you’ll be blindsided. Understand it, and you’ll thrive.

• Learn what motivates your colleagues—career goals, incentives, fears.

• Don’t manipulate; empathize. Seeing what drives behavior helps you anticipate it.

• Building genuine relationships gives you an advantage, because you’ll see the person behind the role.

Politics isn’t dirty—it’s just context. Navigating it with integrity is part of leadership.

8. The craft is changing, but the fundamentals aren’t

AI can generate interfaces, write copy, and even predict behaviors. But it can’t decide what’s worth building—or whether it’s ethical. That’s your job.

• Use AI to accelerate exploration, not replace thinking.

• Stay rooted in empathy and judgment — qualities machines can’t replicate.

• Remember: tools change, but the designer’s responsibility to shape outcomes remains the same.

If you remember nothing else:

• Relationships > solo brilliance.

• Outcomes > outputs.

• Principles > trends.

• Systems thinking > surface fixes.

• Design aligns > divides.

• Politics > ignorance.

Your tools will change and your org will reorganize repeatedly. But if you master these fundamentals, you’ll always have a place and a—purpose in de—ign.

Welcome to the work. We need you.

—A Designer in 2025