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 Design 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 biggest mistake people make after building something like what we’ve covered over the last several weeks is assuming the work is done. They write the Career Vision Narrative, define the mission, set the filters, inventory their skills, map their network, drop it all into a tidy PDF, feel good about themselves for a few days, and then the file goes into a folder where it collects dust because life happens.

The system only works if you use it, constantly and actively, as a living thing rather than an artifact you finished in one weekend.

Make it visible

Rule one for a system that actually functions is that it has to be somewhere you can see it. Not a Notion workspace you’ll never open again, not a PDF buried four folders deep on your hard drive, but somewhere visible and persistent enough that it’s harder to ignore than to engage.

A few things that work for me. Print your Career Vision Narrative and tape it above your desk, because there’s something about seeing your own words in physical space that does work the digital version doesn’t quite manage. Keep your mission and filters on a single sheet that lives in your notes app or your calendar, somewhere you’d actually reference more than once a quarter. Pull your Career Hypotheses into your weekly planning doc so you’re checking which ones are proving true and which ones are quietly falling apart. And build yourself a digital home for the whole system in Notion, Airtable, Miro, or whatever tool you actually open, so the OS lives in one place and you have somewhere to land when you go looking for it.

The tool isn’t the point. The point is that the system has a footprint in your life that isn’t easy to forget about.

Use it to make decisions

This is where the system becomes useful instead of just nice to look at.

When a new project lands in your inbox, run it through your filters before excitement or fear or external pressure has a chance to take over. Get the cold facts on the table first, then let the feelings into the conversation, and decide once you’ve actually looked at both.

An example. A job offer comes in with a great title, a significant raise, and stock options, the kind of package that should trigger an immediate yes. But two of your filters are “allows for 3+ hours of uninterrupted deep work daily” and “serves a mission I care about.” You do the research and find that the role is light on deep work and heavy on stakeholder management, and the company’s mission is making a social media platform better at engagement and ad targeting, which is something you’ve literally said out loud you don’t care about. The role fails both filters, and actually fails most of your filters once you keep going down the list. So you don’t take it.

That sounds simple in writing, but in the moment, with the title and the money sitting on the table, it isn’t simple at all. The only thing that makes it simple is having written down what matters to you in advance, so you’re not deciding in the heat of the moment based on what feels good right now.

Share it, strategically

You don’t have to keep all of this to yourself, and there’s real value in letting other people in.

Share your mission with your manager so they understand the throughline of what you’re trying to build. That conversation might steer them toward giving you projects that actually matter to you, or it might surface that your current role isn’t aligned with where you’re heading. Either piece of information is useful.

Share your Career Hypotheses with a mentor and ask them to push back. Telling someone you trust “I believe learning AI-assisted design will make me more valuable in civic tech roles” gets you a real reaction from someone with perspective, instead of you marinating in your own thinking.

Share your skill goals with peers for accountability, because once your peer group knows you’re trying to get strong at design systems this year, they can point you at resources, opportunities, and people you wouldn’t have found on your own.

Sharing this stuff isn’t the same as broadcasting it. It’s selective transparency with the handful of people who actually care and can help.

Recognize when to pivot

Your Career OS will change. Your vision will evolve, your mission will shift, and that doesn’t mean you got it wrong the first time, it means you’re paying attention. There are a few specific triggers worth watching for.

Your Career Vision Narrative is starting to feel impossible in your current environment. You wanted deep work, your organization is meetings-by-default, and nothing about that is going to shift in the next year. That’s a signal.

Your mission has changed and your current role is irrelevant to it. You used to care about corporate e-commerce and now you care about civic technology, and your director role at a retailer doesn’t serve the new direction. That isn’t a problem to solve, it’s clarity to act on.

You’ve hit a learning plateau. You’ve been in this role for three years, you’ve learned what there is to learn, and you’re not growing anymore. That doesn’t mean panic, it means it’s probably time to move.

Market shifts are reducing the value of your current skill set. AI is reshaping design work right now, and you can either pretend you don’t need to adapt or you can put learning AI-assisted design into your hypothesis stack. The market is telling you something, and it’s worth listening.

Build a review rhythm

This isn’t a set-it-and-forget-it artifact, so build the check-ins into your calendar.

Quarterly, look at your skills inventory and ask whether you’ve gotten stronger in the areas you said mattered, or whether new skills are showing up that you didn’t expect. Update your hypotheses, see which ones are still true and which are being disproven, and decide what new experiments you want to run. Look at your network gaps and identify the people you should be connecting with.

Annually, do the bigger refresh. Revisit your Career Vision Narrative and ask whether it still resonates or whether what you actually want has shifted. Adjust your filters and check whether they still reflect your values, and whether there are new criteria that matter now that didn’t matter a year ago. Reassess your mission and see if it still energizes you.

When something significant happens, like a big move, a burnout moment, a market crash, or a breakthrough in your thinking, revisit the whole thing. Sometimes a crisis is exactly what forces you to get clear about what actually matters.

Tools to keep it alive

You need something to prevent it from going into a drawer.

Notion if you like building dashboards. Coda if you want something that feels more like a document. Miro or FigJam if you’re visual. Trello if you want dead simple. Google Calendar if you mostly need a recurring event that drags you back to the OS once a quarter.

The tool isn’t the thing. The system is the thing. The tool is just what makes the system visible and actionable.

Full circle

This whole series started with a question: what if you designed your career with the same rigor and intentionality you bring to designing a product?

A product OS is a system that helps you make consistent decisions, with constraints and criteria and feedback loops that evolve as real-world input comes in. Your Career OS is the same idea applied to your own life. It isn’t a one-time plan so much as a design practice, and the more you use it the more it adapts with you, gets more accurate, and actually starts guiding your decisions instead of just looking nice on a page.

I spent most of my early career making moves based on what sounded good in the moment, the promotions that looked perfect on paper and the companies with great brands and the roles with the right title. Some of those moves worked out and some didn’t, but I wasn’t making them systematically. I was making them based on noise.

The Career OS gave me a way to think systematically about something that’s usually chaotic and emotional. It gave me a decision engine, and that engine has been worth more to me than any title or raise.

Your turn

Do an OS review. Have you lived a week that actually resembles your Career Vision Narrative, and how did it feel? Does your mission still light you up, or has it shifted? Which of your Career Hypotheses have you proven or disproven, and what did you learn from the ones that didn’t work? Do your filters still make sense, or have your values changed? What new experiments do you want to run in the next quarter?

Write it down, not to make a perfect document but to know where you actually are and where you’re trying to go.

That’s your system. Use it, let it be messy, let it be wrong sometimes, because that’s how it gets better.

Thanks for reading Systems & Signals through this entire series. If you’ve built any part of your Career OS, I’d love to hear about it. The system only becomes real when you use it, and it only stays alive when other people use theirs too.

We’re back to standard posting later this week. See you then!

— Justin

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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 Design 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

Last time, I walked you through writing your Career Vision Narrative — describing the day you actually want, not the one you think you’re supposed to want, and building a mission statement that gives you a throughline when everything else is chaotic.

Here’s what happens next: something exciting finally lands in your inbox.

Interesting opportunities are going to come across your desk. Your boss is going to ask you to take on a new project, a startup founder you respect is going to pitch you on a fractional role, maybe a recruiter calls with a role that sounds perfect on paper.

In the moment, all of it will feel urgent: the FOMO, the flattery of being asked, the fear that saying no means missing something you can’t get back. That’s exactly when you need your filters.

A yes/no filter is what lets you make consistent decisions without getting swallowed by emotion in the moment. It’s a set of criteria you write down when you’re clear and calm, so that when chaos arrives, you’re not deciding based on anxiety or excitement. You’re deciding based on what actually matters.

How to build your filters

The filters come directly from your vision and mission. If your mission is civic technology, then “Will this expand my work in civic tech?” is a filter. If your vision includes three or more hours of uninterrupted deep work daily, then “Does this role protect that time?” is a filter.

Some examples of strong filters:

• Does this opportunity align with my stated mission?

• Does it move me toward my Career Vision Narrative, or further away?

• Will it expand my skills in a direction I actually want to go?

• Will it bring me into rooms with people I respect and want to learn from?

• Can I maintain my values while doing this work?

• Is the compensation aligned with the value I’m bringing?

• Does it offer enough autonomy, or does it require too much politics to move forward?

Will this energize me or drain me?

Write five to seven filters that matter most to you, then actually use them. A job offer comes in — before you feel excited or scared, run it through the filters. Does it hit most of them? If it hits three out of seven, that’s data. Three out of seven isn’t a yes. It’s a maybe that needs a real conversation.

I learned this the hard way. Early in my career, I was offered a director role with a serious title bump and a raise. It should have been an obvious yes. But when I ran it through my filters — back then, more informal, but filters nonetheless — I realized the role would have required me to be in a physical office five days a week, attend four to six meetings a day, and spend most of my time managing personalities instead of building. It failed three of my core filters immediately.

Saying no to that role felt terrifying at the time. In retrospect, it was the easiest decision I never made easily. Because I had the filters to lean on, I could say: this is a really good opportunity, and it’s not for me.

Skills inventory

Most of us have a resume, and a resume is a marketing document. It tells a lie of omission — it includes only the stuff that sounds impressive and leaves out everything that doesn’t fit the narrative you’re trying to sell.

A skills inventory is honest about what you actually know how to do, at what level, and what you want to get better at. Think about it across a few areas: your core craft (UI, UX research, design systems, prototyping, accessibility, whatever the work actually is), your strategic layer (product strategy, facilitation, analytics, storytelling, business acumen), your interpersonal range — especially the ability to make things happen through people who don’t report to you — and your technical fluency at the edges, whatever sits at the border of “I can do some of this myself.”

For each skill, rate it honestly: Expert (you could teach it), Proficient (you do it regularly and do it well), Beginner (you can do it but you’re still learning), or Curious (you want to get good at this).

The goal isn’t to make everything Expert. The goal is to understand what you have, what’s central to your mission, and what’s worth investing in next. Highlight the skills most central to your mission first. Then identify two or three areas you want to deepen in the next year or two — not five, not ten. Where are the gaps that actually matter?

Map your learning and influence network

You have a network. Everyone does. But most people treat it like a contact list, a bunch of names for when they need something. Think about it differently: your network is where you learn and where you have influence, and both sides of that are worth mapping.

Your mentors are the people who’ve taught you something significant — not necessarily anyone with a formal title. Former managers, senior colleagues, people you admired from afar and reached out to. People who’ve shaped how you think. Your peers are the people doing similar work at similar levels, people you learn from through osmosis, watching how they handle problems, what they prioritize, how they talk about their work.

Then there are people you learn from at a distance — writers, speakers, designers whose thinking shapes yours, whether you know them or just follow them from a distance. And then there’s the other direction: if you write, teach, speak, or put your thinking out into the world in any form, who’s listening? Teaching someone else to think about design differently is a form of influence and growth, and it matters more than most people give it credit for.

Look at the map and find the gaps. Do you have mentors in the areas where you want to grow? Do you have peers who actually challenge you? Are you getting input from people who think differently than you do, or is it mostly an echo chamber? Are you influencing anyone, or is everything coming in?

You might need to invest in one layer specifically — a mentor in AI-assisted design, peer relationships in the civic tech world, finding your way outside your usual circles, or starting to write and teach in order to actually solidify your thinking. These are the moves that compound over a decade. They’re unglamorous and they don’t look like climbing a ladder, but they’re how you actually get good.

Putting it together

None of these — the filters, the skills inventory, the network map — are documents you write once and file away. They’re tools. They work because they’re yours, built from your mission and vision, and they evolve as those things evolve.

Your turn:

Write five to seven yes/no filters that reflect your mission, vision, and values. Make them clear enough that you could hand them to a friend and they’d understand how you make decisions.

Then think about a recent opportunity you said yes or no to and run it through them.

Would you have made the same choice?

If the answer is no, that’s worth sitting with — either the filters need adjusting, or you made the call based on fear or excitement instead of what you said mattered to you. Either one is useful.

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 Design 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

Every year, during performance cycles, I’d sit down with people on my team to talk about their professional development goals. Some came in knowing exactly what they wanted. Most didn’t. And the ones who struggled most weren’t the ones who lacked ambition; they were the ones who’d gotten so fluent in the language of performance reviews that they’d stopped thinking about what they actually wanted and started thinking about what they were supposed to want.

When someone was really stuck, I’d stop the conversation and ask them to do something that felt almost sideways: don’t write a goal yet, write a story. Describe a perfect day at work. What are you doing? Who are you working with? How does it feel when you close your laptop?

Then we’d build backwards from there.

That exercise unlocked something the goal-setting frameworks never could, and it’s the foundation of what I’m going to walk you through here: the first real piece of your Career OS, which starts with something that sounds almost too simple: writing a vision instead of a plan.

The difference matters

A plan is prescriptive. It says: “Do A, then B, then C, and you’ll end up at D.” It’s concrete, measurable, and usually totally wrong because the world doesn’t cooperate with your timeline.

A vision is directional. It says: “This is what a good day looks like. This is what I’m trying to build toward.” It’s flexible, and survives contact with reality better.

Step one: Your Career Vision Narrative

Before you think about titles, companies, or salaries, I want you to do something that feels almost embarrassingly simple. Strip everything away and describe the experience of the workday you want.

Remove the noise of what you think you should want. Remove the voice that says “that’s not realistic” or “nobody does that.” Just reveal what actually energizes you.

Here’s the prompt: Describe a day at work you’d be excited to wake up for. What are you doing? Who are you working with? Where are you? How does the work feel? How does it end?

Let me give you an example, because this feels too open-ended otherwise:

I wake up without an alarm, make coffee, and walk into my home office. By mid-morning, I’m on a video call with two collaborators (one in London, one in Toronto) discussing early design concepts for a civic technology project. The conversation is sharp, a little bit funny, and we’re actually moving the work forward instead of trying to convince someone that we should move forward. The afternoon is quiet, focused time for prototyping. I’m listening to music, thinking in pixels, making something real. No Slack chaos. No meetings for the sake of meetings. I wrap up at 5 p.m., satisfied that we made progress toward something meaningful, and close my laptop before heading out for a walk.

It’s specific and sensory, and it tells you something real about what matters: asynchronous time across time zones rather than a 9-to-5 office, collaboration that actually moves the work rather than just gathering feedback, autonomy with nobody waiting for permission, and a workday that ends at a sane hour.

A vision. Infinitely more useful than a five-year plan that will be obsolete in 18 months.

Step two: Define your mission

A vision describes the day. A mission describes the why.

Over a long career, you get to choose what problem you’re solving, what kind of impact you’re chasing, what kind of change you’re building. That’s your throughline, the thing that ties your decisions together when everything else is chaotic.

The format:

I am committed to [impact] for [audience] through [skill or approach].

Let me give you an example from the civic tech space, because I think about that a lot:

I am committed to making digital public services accessible to every resident, regardless of age, income, or ability, through human-centered design and inclusive technology.

That’s specific enough to guide decisions. It eliminates a bunch of things right away — you’re not going to jump at a fintech startup if your mission is civic services, and it opens things up in other directions. You might teach, speak, consult, all in service of that mission.

Your mission won’t stay static forever. But right now, in this season of your career, what’s the impact you want to dedicate yourself to? What problem keeps you up at night? What kind of change do you want to be part of?

Write that down.

Step three: Career hypotheses

Plans assume you know what’s true. But you don’t. Nobody does.

So instead of writing a plan, treat your career moves as experiments. State them as hypotheses.

The format:

I believe that [career action] will lead to [desired outcome], and I’ll know it’s true if [success indicator].

Here are a few real examples:

I believe that taking a fractional design lead role at two early-stage startups will teach me how to operate with less organizational structure and more autonomy, and I’ll know it’s true if I can run a full design process with minimal meetings and still ship quality work.

I believe that launching a course on design strategy will expand my reach and build a secondary income stream, and I’ll know it’s true if I get 50 paying students in the first cohort and at least 3 consulting leads from it.

I believe that moving to a non-profit will let me work on problems I actually care about, and I’ll know it’s true if I’m energized by my work three months in and my impact aligns with the org’s real needs.

The beauty of the hypothesis frame is that it’s OK to be wrong. “I thought this would teach me X, but it taught me Y” is valuable information.

When you get enough of these hypotheses running, your career stops being something you’re trying to execute and becomes something you’re genuinely learning from.

Why this matters more than a five-year plan

The world doesn’t slow down for your plans. Markets shift. Technologies change. You change. A detailed five-year plan becomes a monument to what you thought was true, not a guide for what’s actually happening.

But a career vision — a clear sense of what a good day looks like, what impact you’re chasing, what bets you’re willing to make and why — stays useful. It’s a foundation that everything else builds upon.

I often joke that PowerPoint is my main design tool, but it’s not so much a joke as an actuality. And I’ve found that the single most useful “document” I’ve ever created wasn’t a plan but a page that said: Here’s what I care about. Here’s what a good day looks like. Here are the experiments I’m running. Here’s how I’ll know if they’re working.

That page has guided about a thousand decisions. The five-year plans I wrote? I don’t even know where they are.

Your turn:

Find 10 to 15 quiet minutes. No interruptions. No research, no planning, no editing. Write continuously, describing your ideal work day. What are you doing? Who’s in the room? How does it feel?

Don’t overthink it. Don’t make it pretty. Just write.

When you’re done, read it back and highlight the patterns. What keeps showing up? Autonomy? Connection? Meaning? Impact? Travel? Stability?

Those patterns are telling you something true about yourself. Write that down too.

That’s your vision. Everything else builds from there.

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 Design 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

For most of us, the career ladder is so obvious it’s almost invisible. Junior designer, mid-level designer, senior designer, maybe staff, maybe director, maybe VP. One direction. Keep climbing.

I spent most of my twenties and thirties not questioning it. At Ahold Delhaize, there was a clear rung structure, and climbing it felt like winning. But somewhere in my early forties, I started noticing something: the people I most admired weren’t necessarily the ones furthest up the ladder. Some had stepped off it entirely. Some had built something sideways. Some had refused to let the ladder be the only measure of success.

That realization — that there might be more than one shape a design career could take — changed how I thought about the next decade.

The ladder assumes one thing: more authority, more pay, more visibility equals more fulfillment. But what if that math doesn’t work for you? What if climbing toward management feels like moving away from the work you actually want to do? What if you don’t want one job so much as you want to stitch together a bunch of things that matter to you?

That’s where the lattice comes in.

A lattice isn’t a ladder. It’s a network of paths, some of them horizontal, some vertical, some diagonal. Some paths overlap. Some cross. You can move through it according to what you actually want — not according to a predetermined sequence. The lattice is about career optionality: having more than one way to design your work life.

Why alternative models matter right now

Three reasons, all of them practical.

First: resilience. If your salary depends on one employer, one role, one market, you’re fragile. When the market shifts — and it does, constantly — you’re exposed. But if your income comes from teaching a course, a fractional role, client work, and selling a product you built, losing one stream doesn’t crater you. You adapt.

Second: autonomy. This one’s personal for me. I’ve been in roles where every decision had to go through five layers of approval. Where a simple idea took six months to ship. Where I spent more time in meetings explaining why I wanted to do something than I spent actually doing it. That’s not resilience. That’s slow-motion frustration.

A lattice career lets you own more of that. You pick your clients. You choose your collaborators. You set your hours. You decide what you’re building next.

Third: alignment. Most designers got into this work because we care — about people, about systems, about making things better. But the longer you stay in this field, the harder it becomes to ignore the contradiction at its core: you care about impact, but you’re building features for a product that nobody needed. Or solving problems for people you’ll never meet, in contexts that don’t matter to you.

A lattice career lets you say no to that. You can chase missions that resonate. You can choose clients and causes you actually believe in. You can stop waiting for alignment and build it into your structure.

The four paths

Not all of these will appeal to you. That’s the point.

1. Co-ops & Collectives

This is the worker-owned model. A group of designers forms a cooperative, shares ownership, makes decisions democratically, and splits the profit. The risk is shared. The reward is shared. The example I think about most is Design Action Collective — designers pooling their skills to serve nonprofits and social enterprises that otherwise couldn’t afford design work.

The upside: you control your work, your culture, your values. You’re not answerable to investors or shareholders.

The downside: you have to be good at the parts of business that aren’t design — sales, accounting, conflict resolution. Cooperatives are harder to scale. And if a partner leaves, everything gets complicated fast.

2. Fractional & Portfolio Careers

You’re not an employee anywhere. You’re a designer-entrepreneur who stitches together multiple revenue streams.

You might work as fractional head of design for two or three startups, each getting 10-15 hours a week. You teach a course on design strategy. You build and sell digital products. You write. You consult on the side.

The example I know best: a former design director I worked with who decided that climbing the VP ladder wasn’t her thing. She now serves as fractional design lead for two early-stage companies, teaches a course at a local university, and is building a SaaS product for design teams. She makes more money than she did as a director, has way more autonomy, and hasn’t been in a useless all-hands meeting in three years.

The upside: you design your own schedule, your income is diversified, you’re not bored.

The downside: you’re juggling. Marketing yourself takes energy. There’s no safety net — no health insurance from an employer, no 401k match, no built-in community. You have to be disciplined about money.

3. Tool-Building

You identify something designers need and you build it. A Figma plugin. A template system. A process framework. A course.

Look at Steve Ruiz. He started building tools for design. Now Tldraw is a real company with customers and revenue. Or take any designer who’s built a successful course and realized they can make more teaching than designing.

The upside: if it works, it scales. You’re not trading hours for dollars the same way. You can build once and sell many times. You own the asset.

The downside: it takes serious energy to get it off the ground, and there’s no guarantee it will. You’ll spend a lot of time on stuff that doesn’t ship.

4. Education & Public Writing

You become the expert voice. You teach, you write, you speak at conferences, you build a platform.

Jared Spool did this. So did so many designers who realized their real value wasn’t in one product — it was in helping other designers get better.

The upside: you scale your impact. Your ideas reach people you’ll never meet. You build a following that becomes its own kind of leverage and opportunity.

The downside: you have to be comfortable with visibility, with being critiqued, with the grind of consistent output. Not everyone wants their career to be public.

The lattice is yours to shape

Here’s what matters: these paths aren’t mutually exclusive. You might blend them. You might spend two years doing fractional work while building a tool on the side, then shift to teaching. You might start in a co-op and eventually go solo. The lattice moves with you.

Exploring alternatives isn’t about rejecting traditional employment. It’s about having more options. It’s about not defaulting into the ladder just because it’s there.

The question I’ve been sitting with for the last few years: What would I choose if I weren’t climbing anything?

And that’s the question worth asking yourself too.

Your turn:

Pick one of these four paths that feels even slightly interesting to you. What attracts you about it? What concerns you? What’s one small step you could take in the next 30 days to explore it — not commit, just explore? Maybe it’s a conversation with someone doing it. Maybe it’s an hour of research. Maybe it’s just writing down what’s appealing about it.

The lattice only exists if you start to see it.

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 Design 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 higher up you go in an organization, the more your actual value shifts toward work that nobody measures — and if you can’t articulate that work, you will lose out on promotions, roles, and compensation to people who can. Not because they’re better than you, but because they’ve figured out how to make the invisible visible, and you haven’t.

This is the central tension of career growth for designers and design leaders: the work that matters most is often the work that’s hardest to point to. By the time you’re competing for a senior role or making the case for a raise, a portfolio of polished screens won’t capture the thing that actually made you indispensable. What will capture it is your ability to name, frame, and communicate the work that shaped how things got made — even when that work never had a deliverable attached to it.

I published a piece a while back called “The Invisible Work of Design Leadership,” and it clearly struck a chord, not because it was saying anything new but because a lot of people read it and realized they’d been doing invisible work for years without ever naming it, documenting it, or giving themselves credit for it.

So what is invisible work, exactly? It’s everything your manager doesn’t see in a status update, everything that won’t show up in your portfolio, everything that doesn’t have a commit message or a line item on a roadmap. It’s the kind of work that shapes outcomes so fundamentally that people forget it was ever work at all — they just think that’s how things are supposed to be.

Some examples:

Stakeholder navigation. You’re in a meeting where the CMO is pushing for a feature that directly contradicts what research told you six months ago. You could shut it down. Instead, you ask questions. You reframe the underlying need. You find a way to get to the same place the CMO wants to go, but through a path that doesn’t break the system you’ve designed. Later, everyone thinks it was a great idea. Nobody remembers that you basically negotiated two different worldviews into alignment.

Team protection. You’re getting constant escalations from leadership about firefighting instead of building the roadmap. Your team is demoralized because they can’t finish anything. So you start acting as the filter. You take the escalations. You absorb some of them. You reframe others. You push back when you need to. You’re creating what people call “air cover” — a buffer so your team can actually do their best work instead of living in constant crisis mode.

Problem reframing. Someone brings you a solution in disguise. They say “We need a design for a new flow.” You could just design the flow. Instead, you ask “What problem are we trying to solve?” Turns out they’re solving the wrong problem. You help them see it. You propose testing a different hypothesis first. The redesign would have cost weeks and confused users. The reframed approach solves it in two days.

Strategic influence. You notice that three different product initiatives are all asking for overlapping design work because nobody’s coordinating. So you set up a meeting. You create a simple roadmap that shows how they could sequence differently. You propose a shared component strategy that would speed everyone up. Suddenly the three initiatives that were competing for the same limited design capacity can move in parallel. You didn’t do the work. But the work got better because you influenced how it was sequenced.

Here’s the thing: if you don’t document and communicate this work, nobody else will.

Your manager might notice it eventually. Your peers might appreciate it in the moment. But unless you actually name it, frame it, and show it, a lot of people will just assume it was easy or inevitable or that somebody else did it. This isn’t self-promotion — it’s clarity. It’s the difference between being the person who “makes things look good” and being the person who “shapes how good things get made.”

I developed a simple framework for capturing this work, and I’ve shared it with a lot of people who’ve found it useful. It goes like this:

When [system problem] was creating [friction], I [invisible contribution]. As a result, [outcome].

It’s simple but it forces you to be specific:

• System problem: What was actually broken or at risk? Not “the team was scattered” but “we had three competing design initiatives with no shared roadmap, and people were asking me daily to prioritize between them.”

• Friction: What was that creating? “People were working in silos. Decisions were inconsistent. Engineering was waiting for clarity.”

• Your invisible contribution: What did you actually do? “I created a sequencing roadmap and proposed a shared component library.”

• Outcome: What changed? “All three initiatives could move in parallel. We shipped 40% faster than if they’d been sequential.”

That’s it. That’s the template. And it works because it forces you to think in terms of systems and outcomes instead of just describing tasks.

When [our stakeholders were locked in disagreement about user research findings], I [framed the research in business terms and showed how both perspectives could be partially right]. As a result, [we moved from debate to a shared hypothesis we could test].

When [my team was in constant triage mode], I [started acting as a filter for escalations and pushing back on scope]. As a result, [we protected enough capacity to ship the roadmap instead of just firefighting].

When [design decisions were being made without understanding how they’d affect the broader system], I [started asking questions in design reviews that connected individual decisions to larger strategy]. As a result, [we caught inconsistencies before they became costly rework].

This framework also solves the problem I opened with — proving your value when the main things you do aren’t measurable or visible. In a world where management increasingly wants to see clear ROI and impact, invisible work can make you feel like you’re constantly defending your existence. But the moment you start naming it, framing it, and showing how it connects to outcomes, it’s not invisible anymore — and the case for your next role starts writing itself.

The tricky part is actually doing this without sounding like you’re taking credit for other people’s work or being obnoxious about it. The key is context and honesty. You’re not saying “I made this happen.” You’re saying “I contributed this to a system where a lot of people were doing important work.”

I usually document this stuff in a few places:

In my own notes. I keep a running list of things that happened that quarter that wouldn’t show up on anyone’s metrics but that mattered. When it comes time to have a conversation about growth or compensation or a new role, I have actual examples instead of a vague sense that I’ve been valuable.

In conversations with my manager. Not as a demand or a negotiation, but just as context. “I want to flag something that doesn’t show up in the standard deliverables. This quarter I spent a lot of time helping three initiatives get aligned on sequencing. It freed up about 40% of design capacity that would have been lost to context switching.” My manager’s response is usually “Oh yeah, I noticed that happened.” The benefit is it’s no longer invisible to them.

In presentations or updates to leadership. When I’m showing work to stakeholders, I sometimes include a slide about how the work got unblocked. “We could ship this fast because we solved this coordination problem three months ago.” It connects the dots and shows that the work that made the delivery possible is worth understanding.

In my own reflection. I try to do this monthly: What invisible work happened this month? What problems did I help solve that nobody’s going to measure? What air cover did I create? What conversations did I enable? If I can’t articulate it, then I’m probably not being intentional about it.

The reason I’m emphasizing this now is that invisible work is the realm where conductors live. It’s where your real strategic value lives. It’s the work that AI won’t automate and that your next company will actually care about — even if your current company takes it for granted.

When you make invisible work visible, two things happen: First, you actually start getting credit for what you’re doing. Second, you start being more intentional about doing it. Because the work that’s easy to forget is the work that’s easy to deprioritize or to let slip away.

Activity: Document Three Examples

Pick the last three months. Identify three pieces of invisible work you did. Not three projects. Three examples of work that shaped how projects happened or what got made or how decisions got made.

Use the template:

When [system problem] was creating [friction], I [invisible contribution]. As a result, [outcome].

Write each one. Be specific. Don’t oversell it. Just be clear.

Then do one more thing: share at least one of them with your manager or a peer you trust. Not as a negotiation. Not as a demand for recognition. Just as context. “I want you to know about something that happened this quarter that might not be obvious.”

The first time you do it, it’ll feel weird. You’ll worry about sounding like you’re bragging. You won’t. You’ll just sound like someone who understands their own work.

And that’s the person who gets to design their career instead of just watching it happen.

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 Design 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

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Last week I talked about the shift from craftsperson to conductor, which is the idea that your value as a designer is moving from the artifacts you produce to the outcomes you shape. A few people pushed back on that, and I get it. It can sound like I’m saying craft doesn’t matter anymore, which I’m not. Craft still matters. But the definition of craft is changing underneath us, and if you’re not paying attention to what’s replacing the old version, you’re going to wake up one morning wondering why your skillset feels stale.

So let’s get specific. What are the hard skills that are actually shifting? And which soft skills — the ones designers have always been great at — are about to matter more than ever?

The hard skills that are changing

I want to talk about two things that aren’t on most designers’ radar yet but will reshape the work within the next few years: Generative UI and designing for non-deterministic systems.

Generative UI is coming, and it changes everything about how we think about interfaces.

Right now, most of what we design is deterministic — you design a screen, a user taps a button, and something predictable happens. The interface is a fixed thing that you’ve specified in advance, and the engineering team builds exactly what you’ve defined.

Generative UI flips that. Instead of designing every possible screen state, you’re designing systems that generate interface on the fly based on context, user behavior, data, and AI inference. The interface isn’t a fixed artifact anymore — it’s an output of a system that’s making real-time decisions about what to show, how to show it, and when.

Think about what that means for your day-to-day work. You’re not pushing pixels on a screen that ships exactly as you drew it. You’re defining rules, constraints, and guardrails for a system that will compose the interface itself. You’re designing the logic of the experience, not just the layout. You’re thinking about edge cases that don’t have a single right answer because the system might generate something different every time.

That’s a fundamentally different kind of design work. It’s closer to systems design than visual design. It requires you to think in terms of ranges, tolerances, and acceptable outputs rather than fixed specifications. If you’ve ever worked on a design system, you already have some of the mental models — you’ve thought about how components behave in different contexts. Generative UI takes that several levels further.

Non-deterministic systems are the bigger shift, and most designers aren’t ready for it.

Here’s what I mean. A traditional product is deterministic: the same input produces the same output, every time. You design for that predictability. You can map every user flow because the system behaves consistently.

AI-powered products aren’t like that. The same prompt can produce different responses. The same user action might trigger different system behaviors depending on context the user can’t see. The experience is probabilistic, not fixed. And that breaks most of the frameworks designers have been trained on.

How do you design for trust when the system might give a different answer to the same question? How do you handle error states when the system doesn’t even know it’s wrong? How do you set user expectations when the experience is different every time? How do you create coherence across an interface that’s being partially generated in real time?

These aren’t hypothetical questions. If you’re working on anything that touches LLMs, recommendation engines, personalization systems, or AI-assisted workflows, you’re already designing for non-determinism. You just might not have a vocabulary for it yet.

The designers who figure this out first — who develop intuitions for designing confidence levels, graceful degradation in AI outputs, user controls for probabilistic systems, and feedback mechanisms that help both the user and the model get better — those designers are going to be extraordinarily valuable. Because right now, almost nobody is good at this. The field is wide open.

What else is shifting:

Prompt design is becoming a real skill, not a novelty. Understanding how to craft prompts that produce reliable outputs — and how to design interfaces that help users craft better prompts — is going to be table stakes for product designers within a couple of years. If you’re not experimenting with this now, start.

Data literacy is no longer optional. You don’t need to be a data scientist, but you need to be comfortable reading dashboards, understanding what metrics actually mean, and using data to inform design decisions rather than just gut feel. AI products generate enormous amounts of behavioral data. Designers who can interpret that data and translate it into design decisions will have a significant edge.

Prototyping with code is more valuable than ever. Not because designers need to ship production code, but because the fastest way to test ideas in AI-powered products is often to build a working prototype rather than a static mockup. If you can spin up a quick prototype that actually calls an API, you can test real interactions with real data instead of imagining them.

The soft skills that matter more now, not less

The part that doesn’t get talked about enough is this: as the hard skills shift toward systems thinking and technical fluency, the soft skills that designers have always been quietly great at become more valuable, not less.

Empathy as the real differentiator

AI can generate an interface. It cannot sit with a user and understand the anxiety they feel when a system makes a decision they don’t understand. It cannot read a room full of stakeholders and figure out which unstated concern is actually blocking the project. It cannot build the kind of trust with a product team that comes from years of showing up, listening carefully, and consistently advocating for the people who use the thing.

The more automated the production side of design gets, the more the human judgment side matters. The ability to synthesize messy qualitative data into a clear insight. The ability to hold complexity without rushing to a solution. The ability to say “I think we’re solving the wrong problem” in a room full of people who’ve already committed to a direction.

Storytelling is how you create organizational will

I’ve said this before, but it bears repeating: the designers who move the needle aren’t the ones with the best mockups. They’re the ones who can tell a story that makes a VP of Engineering care about a user experience problem. That skill — translating design thinking into language that resonates with people who don’t think in interfaces — is not automatable. It’s deeply human. And it’s the thing that turns a design recommendation into an actual shipped change.

Facilitation is the invisible superpower

The ability to run a room — to take six people with competing priorities and help them find alignment without anyone feeling steamrolled, to ask the right question at the right time, to create space for the quietest person to say the thing everyone needs to hear. AI is nowhere near being able to do any of that.

If you’re a designer who can facilitate well, you’re already operating in conductor mode whether you realize it or not. That skill becomes more valuable as organizations get more complex, as cross-functional teams become the default, and as the pace of decision-making accelerates.

Cross-functional fluency is your bridge

Designers have always lived between worlds — between engineering and business, between user needs and organizational constraints, between what’s ideal and what’s shippable. That boundary-spanning ability? It’s exactly what’s needed as AI transforms product development. Someone has to translate between what the model can do, what the user needs, what the business wants, and what’s ethically responsible. Designers are naturally positioned to be that translator.

The actual point of all this is that the skill stack isn’t replacing soft skills with hard skills — it’s adding a new layer of technical fluency on top of the human skills that were always the foundation. The designers who thrive in the next five years will be the ones who can speak both languages — who understand Gen UI and non-deterministic systems and who can facilitate a workshop, tell a story, read a room, and build trust.

That’s a higher bar than “make beautiful interfaces.” It’s also a more interesting career.

Activity: Audit Your Skill Stack

Draw two columns. On the left, list the hard skills you use most often in your current role. On the right, list the soft skills you rely on — even the ones that don’t feel like “real” skills.

Now circle anything in the left column that AI can currently do at 70% or better. Be honest. That’s the work that’s going to be commoditized first.

Then look at the right column. Which of those skills do you use constantly but never talk about? Which ones have you been treating as “just part of the job” instead of recognizing them as core to your value?

Finally, look at the gaps. Where are Gen UI, non-deterministic design, prompt design, or data literacy in your stack? You don’t need to be an expert tomorrow. But if they’re nowhere on your radar, that’s a signal worth paying attention to.

Before we get into the heavy stuff, I want to start somewhere better. On Tuesday, four astronauts launched aboard Orion and left Earth orbit for the first time since Apollo 17 in 1972. As I’m writing this, they’re cruising toward the moon, about to break the distance record set by Apollo 13. Fifty-four years between crewed flights beyond low Earth orbit, and we’re finally doing it again. Reid Wiseman, Victor Glover, Christina Koch, and Jeremy Hansen are farther from the planet right now than any human being has been in half a century, and there’s something genuinely exciting about that, even when the rest of the week’s news makes you want to close your laptop.

Also, I love this: during a livestream at 2 a.m. on Thursday, Wiseman radioed Houston because he had two Microsoft Outlooks open on his onboard computer and neither one worked. NASA had to remote into his machine from 90,000 miles away. We can send humans around the moon but can’t make Outlook behave. Some things are truly universal.

Last thing on Artemis: this official NASA poster is sick.

You can grab the full, high-resolution version from Wikimedia for background wallpaper purposes.

One more thing before we get into the week's news: a review paper dropped this week in Brain Sciences that I think deserves a minute, especially if you’ve ever fallen down the “is consciousness quantum?” rabbit hole like me (I mean, who hasn’t…?) Oscar Arias-Carrión, Emmanuel Ortega-Robles, and Elías Manjarrez surveyed the full landscape of quantum-inspired theories of consciousness — microtubule vibrations (shoutout to an OG, Roger Penrose), nuclear spin networks, zero-point field resonance, entanglement-structured learning, all of it — and tried to assess where the actual evidence stands as of right now.

What I find interesting about this topic is how consciousness continues to be so resistant to explanation that serious researchers keep reaching for the most exotic physics available, and even then the best honest answer is “we don’t know yet.” Not every hard problem has a breakthrough around the corner, even one as fundamental as “what is consciousness even?”

Okay. Now the heavy stuff. Oracle cut up to 30,000 people this week via a 6 a.m. email so it could take on $58 billion in debt to build AI data centers. A Los Angeles jury found Meta and YouTube liable for designing platforms that are deliberately addictive, applying the same legal framework used for tobacco to interface design decisions. The job market data keeps showing design roles flat while PM and engineering surge. And AI enforcement is intensifying across a dozen states even without a federal law.

A lot happened. Here’s what I’ve been tracking this week.

Oracle’s $58 billion answer to “what are people worth?”

The numbers are stark enough on their own. Oracle cut between 20,000 and 30,000 employees this week, roughly 18% of its global workforce. The hardest-hit divisions were Revenue and Health Sciences SaaS, each losing about 30% of staff. The method was a mass email at 6 a.m. EST on Tuesday, signed only “Oracle Leadership,” with system access revoked within hours and severance details promised via DocuSign.

But the number that turns this from a layoffs story into a systems story is $58 billion. That’s how much new debt Oracle has taken on in two months to fund AI infrastructure. TD Cowen analysts estimated the headcount reduction could generate $8 to $10 billion in incremental free cash flow, which means the math here is explicit: the company calculated exactly how many human salaries it takes to finance an AI buildout, and then it cut them.

The mechanism is the same one buried in every “AI transformation” pitch deck that lands on a leadership team’s desk, even if nobody says it out loud. Oracle just said it out loud.

• Oracle cutting thousands in latest layoff round as company continues to ramp AI spending — CNBC

• Oracle layoffs hit — via a 6 a.m. email — HR Executive

• Oracle Layoffs To Affect 20K to 30K Jobs — Dataconomy

A jury just called social media a defective product

On March 25, a Los Angeles jury found Meta and YouTube liable for designing platforms that are deliberately addictive, and ruled that both companies knew their products harmed minors and failed to warn users. The plaintiff, identified as KGM, began using YouTube at age 6 and Instagram at age 9, posting 284 videos before finishing elementary school. She developed body dysmorphia, anxiety, and depression by age 10, with a therapist testifying that her social media use and self-image were inseparable.

The damages were $6 million total, split 70/30 between Meta and YouTube. The dollar amount is almost irrelevant compared to the legal logic underneath it: the jury treated infinite scroll, autoplay, algorithmic recommendations, and persistent notifications as design defects, the same framework that held tobacco companies liable for engineering addiction, now applied to interface design decisions.

Internal Meta documents entered into evidence showed executives discussing efforts to bring in users as young as possible, with one memo noting that 11-year-olds were four times as likely to keep returning to Instagram as users of competing apps. Another referenced the company’s strategy to “win big with teens” by “bringing them in as tweens.”

There are roughly 2,000 similar lawsuits pending. Amnesty International called for the verdict to lead to structural design changes. Both companies have said they’ll appeal, but the precedent is set: a jury of twelve people looked at how these products were designed and concluded the design itself was the problem. If you’ve ever worked on engagement metrics, growth loops, or retention features, that framing should land differently now.

• Jury finds Meta, YouTube liable for social media addiction — Al Jazeera

• Jury finds Meta and Google negligent in landmark trial — NPR

• Landmark verdict must lead to design changes — Amnesty International

• What the verdict means for social media design — Scientific American

The standoff nobody wants to name

Fast Company ran a piece this week asking why designers, engineers, and PMs are in a “three-way standoff,” and the framing is off but the observation is right. The actual dynamic looks a lot more like a merger that nobody agreed to and nobody’s leading.

The data from Lenny’s report that I flagged last week keeps connecting to new things. PM openings are at a three-year high (7,300+), engineering is at 67,000+ globally, and design has been flat since 2023. But the really interesting data point from the Fast Company piece is about team ratios: some companies have moved from 1 PM per 4 engineers to 2 PMs per 1 engineer, which isn’t a tweak to the existing model so much as a completely different organizational theory about what product work actually is.

Meanwhile, 64% of product builders now identify with two or more roles, according to Figma’s annual report. PMs are expected to understand design and data. Designers are expected to understand constraints and metrics. Engineers are closer to discovery and customer conversations than they’ve ever been. The boundaries that used to define these jobs are dissolving, and the teams that are winning are the ones that stopped trying to police the overlap.

The part that sticks with me: a lot of design’s organizational leverage came from being the only function that could do a certain kind of work. But when everyone can produce a version of that work with AI-assisted tools, the leverage has to come from somewhere else. Strategy, systems thinking, the judgment calls that AI can’t make and that don’t fit neatly in a portfolio but are impossible to ship without. I wrote about this shift in detail in From Craftsperson to Conductor, and the data from this week keeps reinforcing the same thesis: the role is changing shape whether we’re ready or not, and the people who adapt fastest are the ones treating the overlap as the job rather than a threat to it.

• Why are designers, engineers, and product managers in a ‘three-way standoff’? — Fast Company

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

• From Craftsperson to Conductor — Systems & Signals

AI enforcement is here, with or without a federal law

Morgan Lewis published a thorough landscape analysis this week on AI enforcement, and the headline finding runs against most people’s assumptions: there’s no comprehensive federal AI law, but enforcement is intensifying anyway. The FTC is going after companies making misleading AI claims under Section 5. The SEC is targeting “AI washing” in investor disclosures. State attorneys general are using existing consumer protection statutes to prosecute AI-related harms. And seven distinct categories of AI litigation are emerging, from algorithmic pricing antitrust cases to copyright claims over training data to biometric privacy violations.

The pattern is familiar to anyone who’s watched regulatory cycles: when the legislature stalls, enforcement fills the gap through creative interpretation of existing authority. For product teams, the question has moved well past “is there a law?” and into “which of the fourteen overlapping enforcement frameworks applies to what we’re building?” The Colorado AI Act takes effect June 30, Indiana’s privacy enforcement grace period just started April 1, and universal opt-out mechanisms are now required in 12 states. None of it is theoretical.

• AI Enforcement Accelerates as Federal Policy Stalls and States Step In — Morgan Lewis

• Colorado’s Landmark AI Law Coming Online — Brownstein

Agentic AI meets the DMV

Deloitte’s Government Trends 2026 report landed this week, and the most interesting section is on how agentic AI is starting to reshape government service delivery. Not in a theoretical way. Estonia’s Bürokratt system is coordinating services across agencies in real time, and Abu Dhabi’s TAMM platform now connects over 1,000 services from more than 90 providers into a single experience that adapts to individual needs.

The shift here is from siloed, form-driven government websites to systems that figure out what you actually need and coordinate the response across departments you’d never know existed. If you’ve ever spent an afternoon bouncing between three different government websites trying to figure out which agency handles your specific problem, you understand why this matters. And if you work in service design or civic tech, the pattern is one you’ve been advocating for: stop designing around the org chart and start designing around the person.

The catch, and Deloitte is clear about this, is that agentic systems handling sensitive government data need governance frameworks that don’t exist yet in most jurisdictions. The technology is ahead of the policy, which should sound familiar by now.

• Agentic AI: Government Customized Service Delivery — Deloitte Insights

Experiential retail becomes the baseline

A NetChoice report this week confirmed something that’s been building for a while: 46% of retail leaders now rank omnichannel experience as their top growth priority, and the stores getting the most investment aren’t the ones optimizing square footage for product density. They’re the ones turning floor space into experience.

The most visible example right now is Wake The Tiger, which is opening an 80,000-square-foot immersive art and retail space at Westfield London. But the trend goes deeper than flagship spectacles. Stores are being redesigned as fulfillment hubs, community spaces, and experience centers where the primary function of the physical space is to do something a screen can’t. The retail teams that are winning this aren’t thinking about “in-store vs. online” anymore, they’re thinking about what the physical environment can offer that justifies someone getting off the couch, and then connecting that moment to everything digital.

If you’re in spatial design, experience design, or working on the physical/digital boundary, the job is changing fast. The stores being built right now look more like exhibits than shops, and the design challenge is closer to environmental storytelling than traditional merchandising.

• Why experiential retail is the new standard for 2026 — NetChoice

One more signal

Salone del Mobile 2026 runs April 21-26 in Milan, and the theme shift is worth noting. Last year was human-centric. This year is “A Matter of Salone,” which moves the conversation from the person to the raw material itself, with circularity, design-for-disassembly, and material passports that trace the origin and lifecycle of every component taking center stage. Exhibitors are expected to bring biomaterials like mycelium and algae-based plastics alongside a revival of low-impact craft techniques, and SaloneSatellite’s theme is “New Crafts for New Worlds.” OMA is designing the master plan.

1,900 exhibitors from 32 countries. If you’re in industrial or physical product design, this is the event that sets the vocabulary for the next two years, and this year it’s saying we’ve moved past debating whether sustainability matters and into how it gets built into the material itself.

• Salone del Mobile 2026 — Official

• OMA to Design Salone Contract Master Plan — ArchDaily

Coming this week: The next three issues of the Design Your Next Move series are in the pipeline, and they pick up right where From Craftsperson to Conductor left off. First up is The New Skill Stack, which gets specific about which hard skills are actually shifting and which soft skills are about to matter more than ever — generative UI, non-deterministic systems, and why the designers getting hired are the ones working at the intersection of judgment and capability. After that, Make the Invisible Visible digs into the work nobody sees but everyone depends on: stakeholder navigation, team protection, problem reframing, and a simple framework for documenting the contributions that never make it into a portfolio. And then The Career Lattice lays out four paths beyond the default ladder, because the assumption that more authority equals more fulfillment stopped being true for a lot of us a while ago.

See you tomorrow — Justin

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 Design 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 Design 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 Design 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.

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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.