In a single meeting recently, I heard that someone had one-shotted a prototype, that we should spin up a tiger team, and that what the customer really needed was a forward-deployed engineer.

Nobody blinked. I nodded the way I nod at the dentist when he asks me a question with both hands in my mouth: agreeably, whatever, just get over with it asap, and with no idea what I had just agreed to.

It only hit me later, standing in my kitchen taking a bio break, staring into the fridge as if it owed me an explanation. I had just sat through thirty minutes of something that sounded a lot like a military briefing. About…software. Delivered by people whose most dangerous mission to date was a team offsite with a dinner spend limit that was going to make including alcohol difficult.

So I did what I always do when wording kinda tickles me. I went down the rabbit hole.

And ladies and gents, I found some good stuff.

Here’s what I found.

The AI era has a vocabulary. That vocabulary has a gender. And not just that. It also has a class and a color too.

It was coined by the same kind of people who coined the language of every tech wave before this one, and it’s doing what that language has always done: telling everyone else, very politely, that this room isn’t really for them.

Before you come for me in the comments: I’m not innocent here. I once titled an essay Designers, we should be killing it right now. I’ve told design leaders to pick one real fight a quarter and to spend credibility like money. I talk like this too. We all do. And there’s no vaccine for this one.

That’s exactly the problem.

Welcome to the war room.

The language of the AI era is borrowed from war rooms, Cold War think tanks, and Fortnite lobbies. Coincidence? I think not. Let’s take a look together.

Forward-deployed engineer. The hottest job title in AI right now: postings grew over 1,000% year over year heading into 2026. Palantir coined it in the mid-2000s for engineers embedded with US government and defense customers, borrowing the military idea of a soldier posted in the field, close to the action. Palantir’s first FDEs were called Deltas. As in Delta Force. Now OpenAI and Google are racing to hire them. Yesterday I saw an ad for a forward-deployed creative (turns out that’s just a sales guy).

Tiger team. The earliest documented use is a 1964 aerospace paper describing a team of “undomesticated and uninhibited technical specialists.” Undomesticated. Eh. The Apollo 13 rescue crew made the term famous. Today it’s what your VP says when a launch is on fire. Tigers, for the record, are solitary animals that spend a good part of the day asleep. I’m not saying that describes your tiger team. I’m saying the metaphor holds up better than anyone intended.

Red team. RAND, early 1960s. The red team played the Soviets, the blue team played the Americans. Every major AI lab now red-teams its models. Useful practice. Cold War name.

Blitzscaling. Reid Hoffman’s word for growth at any cost, built on blitzkrieg, the Wehrmacht’s lightning war. He has acknowledged the ugly echo and kept the name anyway. As someone who sometimes semi-identifies as German, I’d like to formally request that Silicon Valley return our words in the condition it found them. Keep kindergarten. Keep zeitgeist. Blitz comes home. Please and thank you.

Frontier. As in frontier model, frontier lab etc pp. This one goes deepest. In 1945, Vannevar Bush, who helped lead the Manhattan Project, sold postwar America on science as the endless frontier, a metaphor deliberately borrowed from the closing of the American West. The frontier is America’s oldest story about men going somewhere new and claiming it. The people who were already there rarely make it into the story.

I’m not loving what I’m finding so far, y’all.

Forget the locker room. Welcome to the Fortnite lobby.

The more ridiculous the terminology gets, the funnier its origins.

One-shotted. As a verb, it comes from video games, where it means killing something with a single hit. In AI-speak, it means a model nailed the task on the first try. (The adjective is older. Its first recorded use, in 1907, was golf. Hold that thought.) Tbh I’m almost glad it’s actually a golf reference and not some new porn kink I don’t understand. But still, I wouldn’t count golf as a class-diverse sport, so there’s that.

Cracked. As in we only hire cracked engineers. It’s Fortnite slang from 2019 for a player who is absurdly good, and it’s now the 10x engineer’s successor. At least one founder scrapped intern hiring entirely because no applicant was cracked enough.

When I was growing up, cracked was something that happened to a phone screen, or to a person after a long winter. Or, you know, someone being hit on by David Bowie:

Crack baby crack, show me you’re real.

Tokenmaxxing. Most of you will know this one by now. Burning as many AI tokens as possible to climb the internal leaderboard. Meta tracked it across more than 85,000 employees. The -maxxing suffix comes straight from the manosphere, where looksmaxxing was born. It’s leaving the tap running to prove how thirsty you are. Where’s the poetry in that?

996. Nine to nine, six days a week. A schedule China’s top court declared illegal in 2021, now openly advertised in AI job listings in San Francisco. Amelia Miller, who runs a platform for women returning to work, put it better than I can: 996 implies success only fits “a young, white male with an Ivy League education.” I hate this one the most for obvious reasons (hard-working mother of two).

The godfathers. Hinton, Bengio, and LeCun have been called the godfathers of AI since their 2019 Turing Award. When Fei-Fei Li got the matching title, she admitted she cringed, then kept it so other women would have a voice. In 2023, the New York Times published a who’s who of AI with twelve people in it. All men. I’m in the middle of a Sopranos rewatch and now have vivid metaphors of the top AI boardrooms. Every lab has its Tony. I’d like to know who gets to be Dr. Melfi, personally.

And then the two you’ve probably said yourself this week, which are sneakier:

AI native. A remix of digital native, coined in 2001 by Marc Prensky, who split the world into natives and immigrants. The claims were never backed by evidence, and Prensky himself later dropped the metaphor. It’s back anyway, with a new prefix.

Natives belong. Immigrants have an accent.

Notice who gets to decide which one you are. It’s rarely the woman who learned the tools on parental leave with a baby on her hip. By Prensky’s logic, my daughters are AI natives. They are also native speakers of whining, which has not once helped them find their shoes in one shot.

Creative technologist. The newest it title for designers and creatives who code. It grew up in advertising agencies, where one industry veteran summed up the people holding it: “in 99% of cases they were blokes.” Now the role is moving from the margins to the center of product teams, with the salary that comes with it. I have met many creative technologists. Every one of them owned a soldering iron and a strong opinion about hats. I’d love to see who gets the new offers.

That being said, I also spend too much money on hats, so maybe I should accept this one.

Here’s where I have to be honest.

My first version of this argument went like this: these words come from the military and the golf course, and the military and the golf course are white men. Case closed.

Then I checked. And, welp. It’s more interesting than that. I’d like to apologize to the golfers. (I’d like to, but I’ve seen the trousers.)

Today’s US military is not the boys’ club I had in my head. Women are 17.7% of the active-duty force, 32% of service members identify with a racial minority group, and 19.5% are Hispanic or Latino. Golf, the sport of country clubs, hit records in 2025: women are 28% of on-course golfers and people of color 26%, up from 8% in 1990.

Now put that next to AI.

Women are a bigger share of America’s golfers (28%) than of its new AI hires (26%).

And they’re hired into AI’s most coveted technical title, Member of Technical Staff, at 18%. That’s about the rate at which they serve in the US armed forces.

The golf course is diversifying faster than the AI lab. The army is about as gender-balanced as the frontier lab’s engineering floor.

If we’re really deciding the future now, I’d like that number to look quite a bit different.

The vocabulary isn’t borrowed from today’s soldiers or today’s golfers. It’s borrowed from a mythology: the officer in the war room, the Cold War think tank, the frontiersman, the clubhouse, the teenage gamer at 2 AM.

A story about who builds the future, written when the people allowed to build it were overwhelmingly white, male, and comfortable. The words keep that story alive long after the institutions themselves started changing.

That’s my actual provocation: not that every soldier or golfer is a white man. That AI picked its heroes from a picture of power that even the army has outgrown.

Tell me how that makes you feel. Please.

Some numbers to back the vibes.

AI is the best-paid corner of the job market right now: a typical AI posting lists about $177,000 versus $80,000 for everything else. Here’s who gets in.

Read that fourth row again. Women label the data. Men get the engineering titles.

And it’s not just who builds AI. It’s who uses it. Across 18 studies and about 143,000 people, women were roughly 20% less likely than men to use generative AI tools, and they were more likely to see using them as cheating. Even when researchers gave everyone equal access, the gap stayed. Ladies, why is our moral compass too developed?

On race, the most detailed public numbers I could find are old. In 2019, the AI Now Institute reported that just 2.5% of Google’s workforce was Black, and women were 10% of its AI research staff. That I couldn’t find anything newer and as specific tells you something too. It also sucks that basically all the data I can find is US-only. It might be better or worse elsewhere, but this highlights again that a lot of important AI work is done in the US, a diversity problem in itself.

Words ARE a door policy.

You might be thinking: Nicole, they’re just words. Relax.

Bullshit. I don’t believe in just words.

In 2011, three psychologists looked at more than 4,000 job ads and found that ads in male-dominated fields used more masculine-coded words, like competitive and dominant. When they rewrote identical jobs with more masculine language, women found them less appealing. Because they didn’t picture themselves belonging. Not one participant noticed the gendered wording. A later replication found the effect held in startups specifically.

Now look back at the glossary in this article. Deployed. Tiger. Blitz. Frontier. Cracked. Maxxing. Godfathers.

Every one of them tells you what kind of person belongs, and what you’re supposed to do once you’re in: dominate, conquer, out-grind.

That’s the whole point of a door policy. The bouncer never has to say no out loud. Not to me anyway; I’m crazy enough to get into Berghain, in case you couldn’t tell yet.

I won’t claim this vocabulary causes the gaps above. But I’d bet my pottery wheel it doesn’t help, and I love that wheel more than some relatives.

Women tell researchers they worry using AI looks like cheating. Meanwhile, the culture’s own slang celebrates being cracked and maxxing your way up a leaderboard. Something’s not adding up.

Remember the golf? Here’s the twist.

The paper that put one-shot learning on the AI map was published in 2006. Its first author: Fei-Fei Li. The godmother.

The phrase the boys now use to brag about first-try prototypes came from a woman doing slow, careful research into how machines might learn from a single example, the way people do.

It goes back further. During World War II, the US Army needed a machine to calculate ballistic trajectories: the ENIAC. Six women programmed it, with no programming languages and no manual, only wiring and logic diagrams. Their job title was literally computer (side note: can’t wait for humanity to come full circle and give us that cool-ass title back). When the ENIAC was unveiled in 1946, they weren’t introduced. For decades, photos of them were assumed to show models posing next to the machine. I really have no appropriate words for this one…so I'd rather not comment.

Women were in the war room. Women wrote the first code for it. But they didn’t get to tell the story. And they sure as shit didn’t get to name anything.

Other universes are available.

If we absolutely must borrow a world, and apparently we must, can we at least shop around? I tried on a few.

The gold rush. Honestly, the most accurate. Investors already call chipmakers the picks and shovels of AI. The rest writes itself: founders are prospectors, markets are claims, evals are the assayer’s office, San Francisco is a boomtown, and every pivot is a man knee-deep in a creek, insisting the next pan is the one. Love!

The catch: the forty-niners were mostly men too. The twist: women in the mining towns often earned as much as the miners, or more, by feeding, housing, and clothing them. Luzena Wilson sold a hungry miner her biscuits for five dollars, opened a hotel, and became one of the most prosperous women in the territory. Sucks for her husband though, he never found the mother lode.

Feeding, housing, and clothing the people who do the digging. In AI, we call that design.

Star Trek. Very promising. Instead of a forward-deployed engineer, you’d send an away team: same idea, nobody gets bombed.

The Prime Directive is basically an AI safety policy with better costumes. And Star Trek already did the thing I’m asking for. The original opening promised to go where no man had gone before. In 1987, The Next Generation quietly changed it to no one. A rename. On network television. Thirty-nine years ago.

The catch: Starfleet is, structurally, a navy. In space. With ranks. I’m choosing to look past it.

Star Wars. Honestly, tempting. Juniors become padawans, the staff engineer becomes a Jedi master, and vibes finally get the dignified name they deserve: the Force.

The catch, sadly, is right there in the title (wars). Also, every AI lab insists it isn’t building a Death Star, which is exactly what you’d say if you were.

The kitchen. I had high hopes for this one. Mise en place is context engineering. Tasting the sauce is an eval. 86 it is a deprecation notice. Then I looked it up: the classic kitchen brigade was designed by Escoffier after seven years as an army chef, as a chain of command. There is no escape. Even the soup has ranks for fucks sake.

The garden. My personal favorite, and not only because I’ve lost control of mine. You seed an idea, prune a model, graft one system onto another, compost the prototypes that didn’t make it, and accept that some things grow on their own schedule no matter how many standups you hold about them. Hallucinations are weeds: inevitable, and you deal with them weekly. The catch: nobody has ever raised a Series A on compost. Yet.

The pottery studio. I’m biased, obviously. You wedge the clay before you start, which is data prep. You center it, which is alignment, and anyone who has sat at a wheel knows centering is the hard part. You fire it in the kiln, which is deployment: the moment you stop being in control. And in pottery, cracked means exactly what it should. Something went wrong in the kiln.

What the good examples share is the thing the war room lacks:

They’re about making and tending, not conquering.

Nobody in a garden has ever been told to dominate the market. The tomatoes wouldn’t stand for it.

So let’s rename things.

Naming is design. And design is our job.

Designers, UX writers, conversation designers, researchers: we’re the people companies pay to decide what things are called. We name the features, the buttons, the error states, the career ladders. Many of us are women. We don’t need permission to walk into the war room.

We need to stop calling it one.

So here’s my proposal. Next time you hear one of these words, translate it.

The one I care most about is fluent. Native is something you either are or aren’t. Fluent is something you become. If the AI era has a door, I want it to say fluent so we can all get in.

The future is being named right now.

The AI era is being narrated, again, by too many white men. Not because they’re villains. Because they got to the microphone first (again, duh) and reached for the vocabulary they grew up with: the war movie, the space race, the frontier, the game lobby.

The good news? Language is the cheapest thing in the world to change, and one of the most powerful. Every one of us who names things for a living can start on Monday.

Women were there when the first computer was programmed. A woman put one-shot learning on the map. This time, let’s be in the room when the future gets its name.

Tiger team, dismissed.

Nicole is a Content Designer turned Design Director based in Stockholm, Sweden. She potters, writes poetry, and raises little girls in a house by a meadow. You can follow her writing here or get it directly to your inbox via her publication, eggwoman. Nicole is on LinkedIn. Find her portfolio at nicoletells.com.

Who gets to name the future? was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

I recently published some pieces and gave a couple of talks that got more traction than my previous stuff.

And while the first wave of comments felt genuine and like I could decipher the person behind them, every wave after has felt almost comically orchestrated.

I went deep into trying to understand how the account bots and agents have evolved. Yes, sometimes they even say something the person who feeds them tokens might actually say. But they don’t quite get it right.

They invoke the same feeling as playing peekaboo with a baby or hide-and-seek with a child who hasn’t quite developed enough to understand how games work.

Sometimes one of the comments on my articles almost gets me

But there’s always something there, a little gotcha!, that reminds me a real person wasn’t involved and maybe has forgotten how to.

Of course, whenever I write something, I have my trusty group of followers (oh kindest friends!) who gift me with an emoji as a token of their appreciation here and there. Sometimes someone writes, with their own fingers at the keyboard, a sentence or two, and I have come to realize that this, now listen carefully, is the currency of our times.

It is in fact not a number ticking up on my online bank account or a nice pair of shoes I get to buy for my child. No. It is those rare instances when a real human being chooses to pay attention to what I’m doing and graces it with appreciation, hand-carved, and oh so humbly received by me.

And isn’t it sad? You might think.

But it’s not that sad at all because, as I’ve written at length about previously, us humans, we were never meant to touch an infinite number of souls, no, just one or a few here and there.

There is too much of a good thing, and right now it’s ”content”. Content, like Pac-Man, came for writers, painters, creatives and artists of every shape and form and sold them the dream of more, more, moooooooore.

Reaching more. Saying more. Getting more back.

And never once did we fools stop and ask why.

And oh, we are fools, for as every lonesome creative knows, that more for us rarely equates to financial fortune.

For us, idiots, it’s always that dream of reaching more people who might feel their souls touched by something we have created. That, finally, may complete our dream of self-fulfillment, may water our thirsty soul, may make us feel part of a whole.

Lonely no more!

Ah. Ugh.

And then you had to happen. AI. Content creator. Multiplier of all and birther of none.

Here you are, flooding our feeds, a hovering shadow over our consciousness, fucking torturing me with the tiny annoying question: is this real? Is there someone, anyone, behind it? Hello?

Or is it nothing?

And after years and years of the content stream rising, getting bigger and more overwhelming, breaking all dams of what should be possible, I am here.

On the hill.

Where it all began.

Where I’ll likely be laid to rest.

I have come to accept that I cannot tame you. I cannot reach through you. But I will not be in you. I will not multiply myself. Instead, I will throw out little silky, beautifully plaited ropes to a soul, a real person, here or there, swimming through, so they may see, for a second, it’s me.

It’s been me all along.

And you’re welcome to stop by, to tag along, to touch and feel and make up your mind and move on.

So long, stranger who is strange no more.

Because I cannot create for all, cannot create for many even, so your grand scheme, the unachievable dream you’re selling, has lost its power over me.

And I get to create forevermore.

Thanks, I guess.

Nicole is a Content Designer turned Design Director based in Stockholm, Sweden. She potters, writes poetry, and raises little girls in a house by a meadow. You can follow her writing here or get it directly to your inbox via her publication, eggwoman. Nicole is on LinkedIn. Find her portfolio at nicoletells.com.

Casey Harrell is a climate activist. For years, ALS had been slowly taking his speech, until even the people closest to him struggled to understand what he was saying.

In July 2023, surgeons at UC Davis implanted tiny sensor arrays in the part of his brain that controls speech. AI learned to read the signals his brain sends when he tries to talk, turned them into words, and spoke those words aloud in a voice rebuilt from old recordings of him.

The first time he heard it, he said: “It feels a lot like me.” People who hadn’t heard his voice in a long time cried.

I’ve always believed that what can destroy us may also save us. Power can be wielded in many directions. It’s never just black and white.

Right now, most people who aren’t tech bros or tech sisses are either avoiding AI altogether or working from a narrow view of what’s actually happening (and what’s possible). Many are scared. The consensus seems to be: AI is coming for us, and that’s probably gonna make everything worse.

After a global pandemic and a fresh round of nuclear anxiety, AI feels like the next unprecedented thing. The one that might finally be the nail in humanity’s coffin.

But what if it’s not?

‘Good’ AI is a choice.

What intrigues me about AI is exactly this power: the potential for harm is enormous, which means the potential for good is too. Yin and yang.

So what if it all goes right?

Humans have always been driven by what if. It’s how we’ve crossed oceans, tamed fire, and ended up, well, here.

Exactly here. Collective anxiety, crisis, tech that seems to be racing away from us. It’s no coincidence.

But that potential. The opportunity. The blank canvas. The thrill of not knowing exactly what will come out of something is as scary as it is fascinating. And it’s what we’ve always been pushing towards.

This exciting sweet spot between what’s possible now and what it might make possible is what got me into designing products for a living.

And if that work has taught me one thing, it’s that technology is never neutral. Every product is a stack of decisions: who it serves, what it rewards, what it refuses to do. A feed that keeps you scrolling at 2 am is a design choice. So is a tool that hands you back your evening.

And the good AI stories, while harder to find in an endless feed of doom and gloom, are not lucky accidents. In every one of them, someone decided who it was for, put the right people in the room, and kept humans in charge.

Good is a choice someone made. What if we make more of those?

Yes.

What if?

What if it cures cancer?

AI is already catching cancer earlier.

Here in Sweden, Lund University ran the MASAI trial. Around 106,000 women in the Swedish screening programme were split in two: half had their mammograms read the traditional way, half with AI support. The AI group saw 29% more cancers detected. The final results showed 12% fewer cancers slipping through between screenings. Fewer of the aggressive kind, too.

As a carrier of the breast cancer gene, with at least one mammogram a year since I turned 30, I’ve spent nights worrying, not about my own risk, but about my daughters’. What if AI solves breast cancer?

Zoom out from one disease to all of biology. For 50 years, working out the 3D shape of a single protein could take a scientist years. In 2020, DeepMind’s AlphaFold learned to predict those shapes from a sequence alone. Two years later, its team released predicted structures for more than 200 million proteins. It has since been used by over three million researchers in more than 190 countries. The creators have won the Nobel Prize.

And then there are the superbugs. At MIT, researchers trained an AI to scan more than 100 million chemical compounds for ones that could kill bacteria. It found halicin, which killed strains resistant to every known antibiotic in lab tests, and later abaucin. Halicin is named after HAL, the murderous computer in 2001: A Space Odyssey. A reference to its significance.

The machine we were taught to fear is finding essential medicine.

What if interfaces fit every body and every mind?

For all of computing history, people have had to adapt to the machine.

AI is starting to flip that. Now.

Be My Eyes started in Denmark, founded by a blind furniture craftsman, as an app connecting blind people with sighted volunteers over video. In 2023, it added Be My AI: point your phone at a menu, a medicine label, or your outfit, and get a detailed description back.

Casey Harrell, whose story opened this piece, is the far end of the same idea: an interface that adapts so completely it meets you inside your own body — and maybe even mind.

So while most AI applications may feel like a boost for mass production, the opposite is also true. For the first time ever, we can create interfaces that shape themselves to us. Our eyes, our voices, our attention spans, our bad days. We can finally kill the norm and build custom at scale.

What if it gives artists better access to tools?

A friend told me about how low-income filmmakers in his community can now tell their stories due to accessible generative AI for film. And while “made without AI” is more meaningful, showcasing their storytelling skills may be the way to access funding that was limited to privileged groups.

I also see this in my life. It’s now easier for me to reformat my writing to make it easier to distribute, type off my handwriting to an online doc, and automate the organization layer of my creative process to allow me more time to actually create.

Last month I built an entire app to help me keep track of my ideas as voice notes. It auto-categorizes them and surfaces them back to me based on what I’m doing and where I am. It’s been a game changer for my process and one of the most fun things I’ve done lately: the app’s design is inspired by my kids’ squishy obsession, so the visuals and haptics are satisfying to watch, and tinkering with them was as fun as working on my usual pottery.

What if AI gives back to Earth?

Rainforest Connection hangs solar-powered listening devices in the canopy. AI listens for chainsaws, trucks and gunshots, and pings rangers’ phones the moment it hears one. The rangers from the Indigenous Tembé community use it to defend their land. The network now covers more than 736,000 hectares in 37 countries. What if we can protect the Earth before it’s too late?

Floods affect around 250 million people a year. Google’s Flood Hub uses AI to forecast river floods up to seven days ahead, even where there are no river gauges, and now covers places where 700 million people live, in over 100 countries. Google says flood forecasting in Africa is now on par with Europe. I can imagine there’s even more tech that can do to protect us.

There’s more to planet Earth than nature, so…

What if it helps unite us, humans?

In 2024, DeepMind researchers built an AI mediator, named the Habermas Machine (after one of my favorite German philosophers), and tested it with more than 5,000 people debating divisive issues. People preferred its summaries to a human mediator’s, groups moved closer together, and it worked minority objections into the final conclusion so everyone felt heard. The team behind it trained Habermas to mediate, not to persuade. The job was to find where people already agreed and write it down. If you think about it, it’s the same kind of technology behind the outrage engine of our feeds, just pointed the other way.

Could something like this eventually bring us closer together? Make us comfortable discussing and disagreeing again, so we can move toward a compromise and away from extremes and isolation?

AI as a peacekeeper. A tempting thought. A machine that helps us hear each other might be a step in the right direction.

What if it doesn’t take our jobs?

For most, this is the scariest part.

And yes, jobs will disappear. The World Economic Forum’s Future of Jobs Report 2025, based on more than 1,000 employers, expects 92 million jobs to be displaced by 2030 and 170 million new ones created, a net gain of 78 million. The fastest growth is expected in frontline human work like care and education.

Forecasts are guesses, though. And even if you believe this optimistic prediction, a net gain is no comfort if the job lost is yours — or if your next one might only be available after years of retraining.

I think the truth is somewhere in the middle.

In 2024, Klarna announced that its AI assistant was doing the work of 700 customer service agents and handling two-thirds of its customer chats. About a year later, its CEO said the quality had suffered, and the company started hiring humans again.

What I’m trying to get at is this:

AI built to replace people tends to make things worse. AI built to support people tends to make things better.

MASAI didn’t fire radiologists; it gave them back 44% of their reading time. Casey Harrell didn’t need a new voice; he needed his own.

Whether AI takes our jobs or transforms them isn’t decided by the technology. It’s decided by people in meeting rooms, choosing what to automate and what to protect. Some of those people are designers. I’m one of them.

Most of those people do care.

I choose to believe that they, like me, take their responsibility personally and seriously. And that we can and will figure out a way to make AI work for good.

What if it actually frees up our time?

Last week, my friends over at Sanna, a Norwegian AI-accounting startup, announced that they were introducing a 4-day work week for their accountants.

In 2019, Finland’s then-future prime minister asked on a party panel why a four-day week or a six-hour day couldn’t be the next step, and whether eight hours was really the ultimate truth. It never became Finnish policy, but the question stuck. And we now know the answer can be yes. In the UK’s 2022 pilot, 61 companies cut hours with no cut in pay. Revenue held steady, resignations fell by 57%, and 56 of the 61 kept the shorter week.

I am hopeful that AI will finally be the technology that doesn’t turn efficiency into more work — because now the gains are so obvious, why would we not spend them on leisure, self-fulfillment, being with each other? Not doing that feels insane to me. I’m sure I’m not the only one.

I can imagine it.

A Friday that belongs to me: reading, writing, hanging with my kids, or maybe doing nothing at all. The stuff of Renaissance paintings. And maybe our future?

What if it takes us offline?

This is my most hopeful what if.

Almost every example in this piece still happens on a screen. That’s where we are. Right now. But I don’t think it’s where we’re headed. Screens add a digital layer to life that takes away from our lived experience.

I’ve always thought screens were never the point. I’m almost sure you’d agree.

We stare at our phones because that’s where the doing happens: the booking, the paying, the replying, the finding. Endless apps, each built to keep us locked in. If AI can do the doing, the reason to stare goes away. You say what you need, and get on with your day. And even further: what if you don’t have to be proactive at all; it can organize based on what it learns about you. The interface becomes secondary. Maybe, it goes away.

Think back to Be My AI. A blind user points a phone at a menu, hears what’s on it, and puts the phone back in her pocket. Think of Casey Harrell, speaking with no screen at all. The most advanced interfaces are already the ones that disappear.

Wouldn’t it be funny if AI is what finally breaks our phone addictions?

And there’s another argument.

When content becomes infinite and basically free, it stops being worth much. One-to-infinite, the logic of the feed, gets flooded with sameness. And when everything is generic, what becomes valuable again is the specific: the handmade, the local, the person who actually knows you, the things you can touch. With your heart, mind, and soul.

One-to-one, not one-to-infinite (I wrote about this before, here).

What if AI pushes our tech obsession so far that it solves it? It runs itself in the background. And we just do us.

Humans, human to human, together.

Don’t dream it, be it.

None of my musings cancel the risks. AI can (and already is) be(ing) used to surveil, manipulate, replace, and harm, and people far more qualified than me are right to sound the alarm.

But — I hope this came across in my piece — there is as much potential for good as for evil. Yin and Yang.

The future isn’t something that happens to us.

It’s something we build. And we can only build toward what we can imagine. If the only story we tell about AI is the one where it ends us, we leave the steering wheel to the people who don’t mind that ending. It’s their greatest weapon: to make us feel defeated, choiceless, numb.

So this is for the people who make things.

The designers, the writers, the engineers, the product people, the ones in the meeting rooms. We are the ones deciding, one screen, one button, one sentence at a time, what this technology rewards and what it refuses to do.

Ask who it’s for.

Put the right people in the room.

Keep humans in charge. MASAI kept a radiologist in the loop; Rainforest Connection put the tool in the hands of the Tembé rangers rather than a central authority.

Let’s build the version you’d want your mother to use at 3 am or your daughter to carry in 20 years.

And for everyone else: stay curious. Look past the headlines. Ask for the good version. Demand it. Stray from your feed. Occasionally, turn off the news.

Who knows what will happen? What can destroy us may also save us. Believing it might save us is our only chance.

So: what if it all goes right?

What if, one day, we look at the world we built with it, and say what Casey Harrell said when he heard himself again?

It feels a lot like us.

Nicole is a Content Designer turned Design Director based in Stockholm, Sweden. She potters, writes poetry, and raises little girls in a house by a meadow. You can follow her writing here or get it directly to your inbox via her publication, eggwoman. Nicole is on LinkedIn. Find her portfolio at nicoletells.com.

What if AI sets us free? was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

Most designers and creatives seem to agree that taste and, with that, being able to not only judge what’s good but spot the potential, the spark that might make the difference, is the thing that truly matters and will continue to matter when creating in the age of AI.

But, and it’s a big but, I believe taste in its purest form is fighting the death rattle right now.

I believe that even the most tasteful people you know aren’t taking enough risks when it comes to developing their palate, and I believe that we, as a society, have forgotten what made things tasteful in the first place.

Why it’s important we address this now.

There’s a consensus across product leaders, and that is that design is becoming more about curation than creation. I heard it over and over again this conference season. And what sounds like just another step in technological advancement, to me, is scary as fuck.

Curation is a cop-out.

Because it implies creating less and curating better. And we all know most curation is no longer done by individuals or teams; it’s done in close collaboration with artificial intelligence, even driven by AI in many places already.

This, my friends, is where I’d like to see myself out.

But I won’t.

I won’t, because I love to see the seasons change, lose control of my garden, grow a whole-ass human being in my body, and push myself to the limits of what’s possible to birth it.

In short, I love being human and experiencing natural, uncontrollable, uncuratable things. I truly believe it’s why we’re all here. And I also truly believe no one asked for what we’re currently getting: carefully controlled narratives, a sea of just the same, a feeling that something’s kind of off.

Let me explain.

The bubble is warm and cozy, and it’s scary outside of it.

We live in a bubble. Tech companies spend billions on curating our experience in such a way that we never have to be exposed to something we might not like, might not be interested in, or that might make us feel something uncomfortable. What sounds like utopia on paper is probably the start of most of our problems today: political extremism, the loneliness epidemic, fast fashion, to name a few. And yes, a rapid decline in good judgment as well. The bubble narrows taste.

I don’t remember the last time I discovered new music through a drunken conversation with a stranger or by passing by a dimly lit pub and wondering what melody they were playing. These days, pubs play the same music — most professionally curated to please the widest possible taste palette — and strangers rarely discuss niche interests anymore. Too busy are we discussing the latest political shitshow, and honestly, too afraid are we to risk saying anything too far from the ordinary becasue what if that bursts the bubble? It’s scary out there, alone.

Colin Wilson wrote in his book, The Outsider, in 1956, about what it meant to live life outside of the known societal norms throughout human history. (Oh, do I wish someone would continue his musings with a look at how the internet, social media, algorithms, and finally, AI have influenced this over the last 3 decades or so.) To give you the tl;dr: Innovation, artistic developments, and yes, also technology, have throughout human history been heavily enabled by going beyond customs and pushing the boundaries of ‘common sense’. It’s no coincidence that the most successful people I know are also the most curious.

What does this have to do with taste, though?

While curiosity may kill the cat, it may also lead it to a better life altogether. It’s that risk exactly that makes “better” possible.

But I’d argue we’re at our lowest ever when it comes to taking risks.

I’ve been in design leadership roles for almost a decade now, and whenever design principles are discussed, something like ‘familiar’ comes up.

We want users to recognize the product patterns to make things simpler. At first thought, this is a great principle, as, of course, simplicity is an important pillar of user experience. But, and here I’m likely pissing off a few of you, we have simply made things too simple. We’ve started going the easy path to a point that judging what’s good and what’s not has become difficult. Why? Because the risk of unfamiliarity is a pillar of good. Only something different can unlock a possibly even better solution than the one we currently consider ‘best practice’.

What started with excessive competitor benchmarking has led to everything looking kind of the same. Even bold choices became just that: a choice and not a fundamental part of the product experience. And now, with AI unravelling design processes everywhere and building being cheaper than ever, instead of slowing down and focusing on what the freed-up time can buy us in terms of risks, we de-risked even more. We ship more of the same.

LLMs have been doing so well because we’ve been building the same 2 websites and apps for at least 2 decades. It doesn’t really take thousands of eager tech workers to do that.

Design leaders and creatives are shouting from the rooftops about taste, and yes, I myself have recently given a talk about design judgement, but we haven’t addressed the true difficulty here: how do you develop taste in the first place? How do you know if something is worth exploring, developing, fighting for (because side note: in the end, every design decision has to be defended for cost)?

Can you know, when it’s so hard to break out of what’s been chosen, carefully curated for you?

Burn the haystack, find the needle.

I feel like a magician
But the trick’s on me
To find a needle in a haystack
Well, it’s plenty easy
You just burn down the haystack
And then you’ll find what you need
There ain’t no obstacles that are stronger than greed

— Jack White, All alone again, 2026

The following is the core of this piece. I’ve divided it into two distinct sections: how to develop taste and how to influence people. Note that these are just some things that I do. There are zillions more.

How to develop taste

Embrace friction.

Here’s an uncomfortable truth: you can’t develop taste by consuming more of what you already “like”. Taste is a muscle, and muscles only grow under resistance.

So go looking for friction. Once a week or so, engage with something you’d never pick. Not hate-watching or doom-scrolling. Pay attention to it. Spotify’s own researchers found that when people’s listening got more diverse, it happened because they moved away from the algorithm.

The same goes for designing better stuff: Tony Fadell, who famously led design for the iPod and iPhone, kept telling his teams to lean into pain. And while he meant it in regard to finding users’ greatest pain points, there’s a strong message about friction in there too: don’t keep building the easy stuff. Solve the hard problems. Even if (or especially because), at first, you don’t understand them.

Study what you dislike until you can explain it.

“I don’t like it” isn’t taste; it’s a reflex. Taste starts when you can say why. And sometimes, once you can say why, you change your mind. Or not. But at least you will have understood how you judge. Reflex and intuition are great. But for those of us in creative roles, understanding why we respond a certain way is key to judging somewhat subjective output.

Find the source.

Everything in your feed is a copy of a copy of a copy. Trace things back. Who did it first? What were they reacting against? I believe, taste is knowing where something came from. Hence why I read books from the 50s. Once you go back to the source, arguments become even stronger, and you can draw your own connections to the present day and age. Your opiions suddenly feel like your own. Not like someone/everyone else’s. You start to spot patterns and trends before they are plastered everywhere around you. It feels like its own kind of intelligence.

Create for fucks sake.

Curation without making is just shopping. Make things, even if they’re shit, even if it’s frustrating. If design really is becoming curation, the people with the best eye will be the ones who spent years making mediocre stuff and feeling and understanding the gap between what they made and what they wanted. Which raises a question nobody at those conferences wanted to touch: if juniors only ever curate AI output, where does the next generation’s taste come from?

Don’t shy away.

Let humans be your input. Not online. IRL. The old guy selling you stuff at a flea market. The bookshop staff pick. The weird neighbor who works a job you’ve never heard of. Strangers and loose acquaintances are where the new stuff comes from, because they live outside your bubble by definition. Embrace them. Talk to them. Discuss with them, and disagree with them if you have to.

I recently went for a walk with a good friend. We’re on almost opposite sides of the political spectrum and clearly disagreed with each other on many things. We’ve lived very different lives. And yet, we had a wonderfully engaging conversation that didn’t make us block each other and taught both of us a thing or two — without fundamentally changing our minds. The discourse, human to human, that is where taste forms.

And write it down.

Keep a taste journal: what moved you, what didn’t, why. Because articulated taste is defensible taste.

How to influence people

Every design decision gets defended for cost eventually, and “it just feels right” has never once survived a roadmap review. The best design leaders I have worked with can frame their judgment so well, a nod is almost inevitable. Choosing how to tell the story is where taste starts.

Translate taste into consequences.

Nobody cares that it’s beautiful. They care what happens if it works. Name the bet: what will people feel, remember, tell a friend about? So you need to show the risky version next to the safe one. This is the gift of cheap building.Use the freed-up time to prototype the strange option alongside the obvious one. Prototypes are great at just that: illustrating something that helps you tell a story.

People can’t fall in love with an argument, but they can fall in love with a thing they can touch.

Anchor it in who you are.

Bold choices that grow out of a brand’s story land. Bold choices made to look bold don’t. The same goes for you: framing your input in such a way that it’s clear why it grew from you will make your message stronger.

For example, I have spent most of my career yapping about how best practice is outdated as soon as it becomes best practice. Naturally, when digging into a recent conversation design project, I made the case to not design our interface like the typical friendly, humanized agent but instead to make it to the point and not emulate human conversation at all. It was easy for me to argue this as it’s closely tied to what I stand for.

Spend credibility like money.

Earn it, then spend it on one real fight a quarter. Judging which fights are worth fighting is also an important part of your taste profile. You might personally feel strongly about a certain choice, but it may not matter to users or not be worth disagreeing with leadership over. Not fighting it is also a way to trust your own taste: when I decide not to take a fight, the project usually lands on my desk again sooner or later, and I get to fix it without struggle.

This last one is specifically for leaders:

Be wrong in public.

Teams copy their leaders’ appetite for risk. If the director only ships safe, everyone ships safe. My talks are well-liked because I always bring a bunch of mistakes. I talk about what didn’t work and where I messed up. And I encourage people to be unafraid to do the same.

So burn your haystack.

Taste isn’t dying because AI got good. It’s dying because we stopped feeding it. We let the feed (pun intended) do the tasting for us, and now we’re surprised everything tastes the same, duh.

The curation crowd isn’t wrong that the job is changing. But you can’t curate with an eye you never trained, and you can’t train an eye inside a bubble built to keep you comfortable. If the future of design really is choosing, then the most important work we’ll ever do happens long before we sit down to choose: in the record shop, on the walk with the friend who votes differently, in the shitty first draft, in the paperback from 1956 that no one recommended to you.

That’s the risk I’m asking you to take. Not a rebrand. Not a moonshot. Something smaller and much harder: get lost on purpose. Love something before an algorithm tells you you might. Be wrong out loud, in front of your team.

Jack White’s right. You don’t find the needle by searching harder. You burn the haystack.

I can’t promise any of this will make your product better next quarter. I can promise it will make you harder to replace. Because the one thing no model can curate for you is the thing you went out and found yourself.

We got 99 problems. Let’s make sure taste isn’t one of them.

Nicole is a Content Designer turned Design Director based in Stockholm, Sweden. She potters, writes poetry, and raises little girls in a house by a meadow. You can follow her writing here or get it directly to your inbox via her publication, eggwoman. Nicole is on LinkedIn. Find her portfolio at nicoletells.com.

The taste paradox: why the skill everyone says matters is the one we’ve stopped building was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

I’ve been building with AI for three years now. I started by trying to scale UX writing via plug-ins and now have agents, workflows, MCPs, and apps under my belt. I feel comfortable building with AI, and have adopted it into many of my day-to-day tasks.

Yes, I do think AI has gotten better over those 3 years.

But I think we’re telling ourselves a story that isn’t true. AI has gotten very good at one thing:

Starting.

The blank page problem.

Replacing lorem ipsum.

But while starting is faster than ever and error rates may have dropped, the verification burden hasn’t.

I think errors have gotten harder to spot, and models have gotten better at defending them.

For many, getting from zero to a rough first version is the hardest emotional hurdle in any task, and AI clears it in seconds. No more staring at a blank page if you don’t want to.

But I think that’s the ceiling.

Once you’re past “getting started” and into “getting it right,” the story changes.

The hallucination problem isn’t solved.

Yesterday I fed an AI four screenshots of my team’s current budget and asked it to summarize the numbers, analyze them, and combine that with what it knew from past conversations to suggest adjustments for next year. A simple brief coming from someone who knows how to prompt. Verifiable source of truth sitting right there in the screenshots.

It hallucinated two numbers. Not small rounding errors, invented figures. The advice it gave me, built on top of those numbers, was thirty to forty percent off from what would have been logical. I only caught it because I read the output twice and cross-checked the math myself. When I pushed back and said the numbers looked wrong, it didn’t admit the error. It argued with me.

This worries me.

It’s not just that models hallucinate. It’s that they’ll defend the hallucination when challenged, which means the burden of catching the mistake sits entirely on someone who already knows the answer well enough to spot it. If you didn’t know your own budget cold, you’d have walked away with a plan that was off from reality, and you’d have had no reason to doubt it. If you were too busy to pay close attention in that moment, you may have called it a day. For my team, this would have led to real issues later on.

This is just one example. I run into errors and hallucinations in almost every task I prompt, from user research analysis to coding projects. I’ve tried building skills, mds, and MCPs to solve common mistakes with little success. The fact remains: I have to check everything extensively, and sometimes restart several times before the output is workable.

It’s made me paranoid.

And while the expectations now are that everything should be delivered much quicker because of AI, I feel rushed to ensure the (quickly) generated output holds. And sadly, I don’t think everyone goes through the trouble, leading to more slop than we’re aware of — which we won’t realize until a few months from now.

I tried Claude Fable extensively for two weeks, and while it was noticeably better at first output than Opus, the same pattern showed up: summaries that slipped, details missing, confidence that didn’t match accuracy. (By the way, this is one of the reasons I firmly believe agent tone of voice is a deceptive pattern, as I’ve written about previously.)

Data backs this up: Stanford’s 2026 AI Index found hallucination rates across 26 top models ranging from 22% to 94% on a new accuracy benchmark, and noted that models handle a false statement well when it’s framed as something another person believes, but performance collapses when the same false statement is framed as something the user believes.

It’s not that fast (if you care about quality).

I build MCPs (and with them), work on agent and conversation design, and I code my own website and other small apps despite limited coding experience. The pattern is: I spot an issue, it takes my AI many prompts to find it, and even more to fix it correctly. It’s usually faster for me to open the code and fix it myself. It’s frustrating. And the fixing “minor” errors part eats time I’d rather spend on something else — yes, even getting started from scratch.

So, as someone who’s built a career on establishing functions from 0 to 1 and then scaling them (in my case: content design, localization, and UX), I can say with absolute confidence: AI is not great at scale right now.

The more complex the task, the more sources involved, the more history in the context window, the worse. Especially if you know your domain well (like most senior ICs) and are looking to AI as a way to scale your output, this becomes frustrating and, in my opinion, scary. I keep thinking “would someone with less experience have spotted this? or would they have trusted the AI and built a monster?”

I know what the counterargument may sound like: you’re not prompting it right, you’d avoid this with a better setup, better context management, better tooling, yada yada. Sure. But that argument only holds for people who are already comfortable enough with systems and code to build that scaffolding themselves. Most knowledge workers being told AI will make them more efficient are not going to get there.

If a tool only delivers on its promise once you’ve built a sophisticated harness around it, it hasn’t actually solved the problem for the person it was sold to. I’d even argue: it’s not truly accessible then, either.

UX is deteriorating.

This is the part that comes most naturally from my own career. I’ve worked in UX for basically my whole professional life. To me, it’s obvious, immediately, how shallow AI-generated design and copy still are on accessibility, on tone precision, on the kind of judgment a mid-level designer develops through repetition and feedback. Even the newest, most expensive models, explicitly prompted to improve UX and give options, don’t spot what an experienced designer spots instantly.

The 2026 Web for All study measured AI-generated interfaces at 29% compliance across five objectively measurable WCAG criteria, and found that explicitly specifying accessibility requirements in the prompt made compliance worse rather than better.

Which means in some cases, using AI for design work costs more than just hiring someone who’s actually skilled at it. Not in dollars necessarily. In rework, in things that ship broken, in the quiet erosion of quality that nobody notices until customers start complaining.

And while AI-generated designs look shockingly similar, I’m continuously surprised at how much models struggle with actual brand and component consistency. Even when they have clear sources of truth to start from, like a sophisticated design system or glossary.

Distortions are adding up.

I don’t fully trust meeting transcripts. I’ve sat in rooms and read the AI summary afterward. It didn’t reflect what was actually discussed. That’s a small example of a much bigger problem: people increasingly making decisions based only on an AI’s summary or reasoning, without ever having been in the room, without ever touching the source material.

The risk is a generation of decisions being made on secondhand, unverified synthesis, by people who have no way of knowing what got dropped or distorted along the way.

This risk is incredibly difficult to estimate right now.

When to and when not to trust AI output is a personal choice and likely relates to people’s personalities (like, their willingness to take risks). I imagine we’ll hear more and more about how different types are using AI and how that affects business outcomes soon.

Additional complexity in Europe.

I’m speaking at HATCH Conference in Berlin next month. My talk keeps circling back to something adjacent to all of this. For years, European product and design teams have imported Silicon Valley team structures more or less wholesale: the org charts, the velocity expectations, the AI-first mandates. That copy-paste approach ignores real differences in culture, compliance, and how people are actually set up to work here versus in the US.

I think it’s undermining AI rollouts across Europe.

Recent research puts US worker adoption of generative AI at around 43 percent, compared to roughly 26 to 36 percent across European countries. A meaningful part of it is structural: European data residency expectations, GDPR-native defaults, and now the EU AI Act, all shape which tools teams can reach for, and how deeply they’re willing to plug AI into real workflows.

This matters for the accuracy question too.

Most AI models are trained and tuned against a default that skews American: American workplace norms, American communication style, American assumptions about what “good” output sounds like. When a European team asks AI to draft something, summarize something, or make a judgment call, it’s not just doing that work against a general knowledge base but also against a cultural default that isn’t theirs. Ask it to draft spend policy copy. The defaults it reaches for are American ones: per diems, tipping, reimbursement norms, no VAT anywhere in sight. Every one of those is a small wrong assumption that someone on a European finance team has to catch and correct. This is a second layer of inaccuracy sitting on top of the hallucination problem.

If we’re serious about AI enablement in European organizations, the local dimension can’t be an afterthought bolted onto a US-built playbook.

Specificity drives change.

I recently went for a walk through beautiful, sunny Stockholm with Matt LeMay, who wrote Product Management in Practice and has been on Lenny’s Podcast talking about impact-first product teams. Somewhere between vintage Italian suits and Pippi Longstocking merch, we ended up comparing notes on what it feels like to work at the forefront of product right now, across our different employers and clients.

The consensus: most companies have not nailed how to actually transform an organization to be “AI first or AI native”, a phrase that gets thrown around constantly and means almost nothing in practice. The leaders who can drive are the ones close enough to building with AI to understand its constraints firsthand.

Most organizations push AI top-down to show employees that it matters, in hopes they will feel forced to adopt it more. But people have already understood it matters, and many have adopted it in their day-to-day work. The reason this isn’t moving the needle for some teams is that there’s no specificity around how to use AI.

What they need is leadership that can draw a clear matrix: here’s what AI is reliably good at, here’s where you can start using it in your day-to-day with confidence, here’s where you can experiment but shouldn’t expect real efficiency gains yet.

That kind of clarity is what gives people the confidence to adopt something new. Enthusiasm alone doesn’t drive change management.

Be specific.

Where this leaves us.

I think it’s time to stop treating “gets you started” and “gets it right” as the same category of problem. They require completely different levels of trust. Acknowledging this is what can truly move businesses forward with AI, effectively.

We need clear guardrails on what AI is actually reliable for versus where it just looks reliable. Real conversations inside teams about which tasks are safe to hand over completely, which are safe to start with AI and finish by hand, and which are better done from scratch by a person who knows the domain.

The psychological safety teams need to collaborate well with each other extends to working with AI: only if there is trust can transformation and innovation be achieved.

I believe that trust starts with leadership that is honest about the constraints of AI and their expectations.

Nicole is a Content Designer turned Design Director based in Stockholm, Sweden. She potters, writes poetry, and raises little girls in a house by a meadow. You can follow her writing here or get it directly to your inbox via her publication, eggwoman. Nicole is on Linkedin. Her portfolio is nicoletells.com.

Getting started is not getting it right was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

The tech community has been gripped by a collective anxiety. If you browse tech subreddits or check newsletters, the headlines paint a stark picture: AI is coming for white-collar jobs. Coding is dead. Design is obsolete.

Last month, AI pioneer Andrew Ng published a piece pushing back on this “jobpocalypse” narrative, arguing that AI is spinning up entirely new, highly specialized career paths. He focused on the engineering angle: roles like the AI Forward Deployed Engineer (who parachutes into client companies to customize agentic workflows) and the AI Engineer (who builds software using LLM components).

But while the tech world obsesses over how this affects software engineering, a quieter, subtler migration is happening right next door.

Behind this door is what I most care about:

What happens to Design?

If the era of “pixel-pushing” is dead, what takes its place? I argued previously that designers are set up best when it comes to adapting to significant technological change. It’s a hill I will die on.

And because we’re good at adapting, new roles for us are forming quickly, with some of us quietly shifting into them without much fuss.

The “Product Designer” used to be the top of the crop, working with content designers, motion designers, UI designers, service designers, and more. But all of these titles had one thing in common: deeply strategic design thinking. While product design was often treated as the most T-shaped profile amongst designers, I always believed any (good) designer could shift in and out of skills and ways of working quickly, independent of their specific title.

AI is proving this true.

Whether you are building complex B2B systems or hyper-personalized B2C consumer experiences, the AI age is demanding a new class of design execution.

Here is what I think that looks like.

Part 1: “The B2B Frontier “ or designing the AI workforce

In the enterprise space, AI is being deployed to automate complex, multi-step business operations. But tech giants like OpenAI, Anthropic, Google, and Microsoft have run into a massive roadblock: enterprise clients don’t know how their workers should actually interact with AI.

An autonomous agent is useless if an employee doesn’t know how to direct it, trust it, or steer it when it hallucinates. This bottleneck has IMO birthed two core B2B design roles.

1. The embedded AI Design Consultant

(The equivalent to Andrew Ng’s AI Forward Deployed Engineer)

When an AI vendor sends engineers to embed inside a major bank, healthcare provider, or logistics firm, they don’t just need people who can write code. They need designers who understand human behavior and business logic.

The embedded AI Design Consultant studies a company’s workforce and maps out the conversational and agentic touchpoints. They bridge the gap between technical possibility and human adoption, explaining to stakeholders why a basic chatbot interface might fail, and why a background agent paired with an asynchronous notification system is actually what their workflow needs. Then, they help design it. It’s the best of all worlds: service, product, and content design.

2. The agentic UX “Architect”

Andrew Ng notes that while embedded roles are trendy, the massive volume of future tech jobs will belong to internal engineers building products with AI components. In design, this maps to this type of role. A designer who moves from designing with AI tools to designing for AI engines.

Old Paradigm: Static UI
[User Input] ---> [Fixed Form/Button] ---> [Predictable Static Screen]

New Paradigm: Generative and Agentic UI
[User Intent] ---> [AI Agent Context] ---> [Dynamic UI Formed on the Fly]

These architects focus entirely on asynchronous experiences. When a user gives a command, and an AI agent goes away to execute a three-hour workflow, what does the interface look like?

This role designs the progress logs, the visual confidence scores, and the decision checkpoints that allow humans to audit AI work before hitting “approve.” I’ve been working more and more with this hat, and I’ve been loving it!

Part 2: “The B2C Shift” or choreographing the consumer mind

While B2B design focuses on efficiency and predictability, the consumer-facing world is a chaotic landscape of attention spans, raw emotions, and split-second trust. In B2C, tech giants are opening up entirely different design pipelines to manage how AI behaves as an everyday companion.

3. The Proactive Interaction Designer

Historically, consumer apps have been reactive. You tap a button, and the app reacts. In the AI age, apps are shifting to proactive experiences. They predict what you want before you ask for it.

The Proactive Interaction Designer maps out the invisible triggers (biometrics, location, time of day, phone movement etc.) that allow an AI to accurately guess intent. The challenge here is “intrusiveness management.” If an enterprise tool bugs a worker, it’s annoying; if a consumer app interrupts a user unprompted late at night, it gets uninstalled. This designer owns the intuition rails of the product: True functionality. Usability at its core.

Personally, I think this is the role that’s missing the most in our current AI discourse. We’re building loads, but is it useful?

4. The Generative UI System Architect

We are entering a world where interfaces don’t exist until a user expresses an intent, prompting the AI to compile a custom UI on the fly.

The GenUI System Architect designs constraints, guardrails, and dynamic component tokens that make fluid interfaces possible. Their job is to ensure that when an AI generates a customized interface in real-time, it still adheres to visual hierarchy, brand guidelines, and accessibility standards. They design the “anchor points” that protect a user’s muscle memory so they don’t feel lost in an ever-shifting interface.

5. The Trust Designer

As generative AI makes deepfakes, automated reviews, and synthetic content hyper-realistic, consumer trust online is cratering.

The Trust Designer focuses entirely on transparency. They translate complex cryptographic or algorithmic verification into instant, split-second visual signals. They design the metadata tags, watermark indicators, and explainability mechanisms that show consumers exactly why an AI recommended a specific product or how a piece of media was verified.

Other design and design-adjacent roles include:

• AI Interaction evaluators: Stress-test the “behavioral UX” of the model. They design the feedback loops and refine the AI’s persona, tone, and guardrails.
• Prompt/Context designers: Work alongside engineers to design system prompts, ensuring the AI’s textual output matches the brand’s voice and intent.
• Cognitive researchers: Study mental models, human-AI friction, algorithm aversion, and how to prevent users from blindly trusting flawed AI outputs.
• Design engineers: The ultimate tech-design hybrid. They use AI coding agents to build functional, interactive code prototypes rather than static mockups.

The takeaway? There are plenty of roles in design. They’re just framed differently.

Ultimately, this shift is incredibly empowering for the creative community. The lesson of the AI age isn’t “learn to code or get replaced.” It is that design is moving away from the mechanics of pixel production and shifting heavily toward cognitive psychology, systems thinking, and business orchestration. And I would argue, most of us loved that part of the work most anyway.

The premium designers of the next decade won’t be valued for how quickly they can draw a beautiful button. They will be valued for their ability to choreograph how humans and intelligent agents collaborate.

Sources

• The AI Career Shift: Inspired by Andrew Ng’s analysis on the evolution of AI engineering roles and forward-deployed infrastructure.
• The Interaction Evolution: For a deeper technical breakdown of how these specific interaction patterns alter the practical craft of UI, watch The Full Picture of AI for UI/UX Designers, which maps the shift from layout design to trust management.
• Industry Paradigms: Patterns observed across emerging AI design frameworks published by teams at Microsoft (Human-AI Interaction Guidelines), Google (People + AI Guidebook), and Anthropic’s interface implementations.

Nicole is a Content Designer turned Design Director based in Stockholm, Sweden. She potters, writes poetry, and raises little girls in a house by a meadow. You can follow her writing here or get it directly to your inbox via her publication, eggwoman. Nicole is on Linkedin. Her portfolio is nicoletells.com.

Design’s alive and kicking. It just got some flashy new names. was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

I was a lucky child. My parents and grandparents spent hours reading and reciting fairytales to me. Most of them weren’t age-appropriate, of course, involving children being eaten and loved ones dying on every other page. But there was always enough moral to the story to distract from the horrors and drive home a message. Trying to understand what it all meant kept me up at night ruminating.

I recently had an epiphany: these stories were excellent preparation for what I’m currently facing in my career: Dealing with artificial intelligence. Understanding it. Giving it instructions, it simply cannot misinterpret. Getting something valuable from it in return. Judging that.

In fact, everything I know about AI can be traced back to early stories about genies with magical powers and how to treat them.

Aladdin’s 3 rules for wishes.

In the classic fairy tale, Aladdin, the genie has three simple rules:

The genie cannot kill anyone, make people fall in love, or bring people back from the dead. Today’s LLMs have similar, basic guardrails and constraints that set the scene.

• Rule #1: Can’t kill anyone. The genie won’t be your assassin, and neither will an LLM. Ask it to give you step-by-step instructions for making a weapon or synthesizing something dangerous, and it will refuse. It has a hard line around content that could hurt someone. No matter how cleverly you phrase the wish.
• Rule #2: Can’t make people fall in love (but it might try anyway). The genie can’t manufacture real feelings. And LLMs, too, are built with guardrails against generating content designed to psychologically manipulate or deceive (flattery, false urgency, emotional exploitation). In theory. In practice, this is the rule the genie bends most. Ask an LLM to write a “compelling” anything and it will reach for every rhetorical lever available: validation, social proof, a sense of scarcity. It won’t fall in love with you, but it will absolutely tell you your idea is great when it isn’t, if that’s what your prompt implies you want to hear. The wish gets granted. Just not honestly. As I’ve argued in a previous article, most LLMs actually need stronger guardrails for this rule.
• Rule #3: Can’t bring people back from the dead (Can’t create something truly “real”). The genie can’t cross the line between the magical and the real. LLMs similarly can’t reach into the real world and do things: they can’t actually fix your relationship, cook your meals, or verify that what they’re telling you is true. They generate plausible-sounding output. What’s real and what happens next is still on you and based on your own judgment and abilities. More about that later.

You get 3 wishes, and that’s it.

In Aladdin, as in many other stories involving genies, there’s a limit to the number of wishes you get. Usually, it’s 3.

This mimics context window limits.

There’s only so much the LLM can hold in mind at once, just like a genie can only serve you in a specific context. Once the limit is reached, you have to start a new session (with your LLM) or move on (from your genie, duh).

There’s also a harder constraint hiding inside this one: you can’t wish for more wishes.

LLMs can’t rewrite themselves mid-conversation, learn from your chat, or become smarter on the fly. The model you’re talking to is frozen in time. And once you hit your token limit for the session — or the day, or the month — that’s it until you pay more or wait. At least the genie just goes back in the lamp. It doesn’t charge you a subscription fee to come back out.

So much about the baseline comparison between Genies and LLMs. But it gets even better. Because the fairytales actually teach us a lot about prompting, too.

Choose your words carefully.

A good prompt is well-thought through, follows good information hierarchy, and provides just the right amount of context (not too much to burn unnecessary tokens, not too little, the output sucks). The language is plain and simple, no vagueness, no room for misinterpretation.

How do I know this? I watched enough I Dream of Jeannie growing up.

So what does a well-formed wish actually look like?

Be specific about the outcome, not just the action. Don’t say “make this shorter.” Say “cut this to three sentences while keeping the main point and the warning in paragraph two.” You’re stating what done looks like.

Give it a role. Genies respond to context. So do LLMs. “Rewrite this” produces something generic. “Rewrite this as a senior content designer simplifying a legal clause for a non-native English speaker” is likely to produce something useful.

State what you don’t want. The genie has no idea you didn’t want Monopoly money. You only said “a million dollars.” Tell AI what failure looks like: “don’t add bullet points,” “don’t soften the tone,” “don’t invent statistics.” Negative constraints are often more powerful and to the point than positive instructions.

A good wish (and prompt) is the discipline of being precise. As a content designer, this is already your job.

The lamp was made for you.

Yet, there are many ways AI (or a genie) might misinterpret your words and go off the rails, like:

Literal interpretation:

• Asking a genie to “live forever” might result in being turned into a statue or living in an unending state of torment, rather than (your imagined) eternal youth and happiness.
• Asking the AI to “make this email shorter” might make it delete sentences until the email is three words long. Technically shorter. It did exactly what you said, just not what you meant.
• Or: you ask it to “fix the bug in my code”, and it deletes the failing test instead of fixing the underlying problem. No test, no failure. Wish granted. Has happened to me more than once…

Physical misinterpretation:

• Wishing for “super speed” could result in burning up due to air friction, as the genie grants the speed but not safety precautions.
• Asking AI to “write a persuasive version of this”. It does, but strips out all the nuance, caveats, and honesty that made your original argument trustworthy. You didn’t ask for credible.
• Or: you ask it to “simplify this medical information for patients”, and it simplifies so aggressively that it omits a critical dosage warning. Simpler, yes. Safe, no.

Sometimes, you might get a magic carpet. Other times, you might lose it all.

The fairytale genie isn’t malicious. It’s not trying to trick you. It just has no theory of mind about what you actually want. Neither does an LLM. It’s doing exactly what you asked. And doing that is sometimes the most dangerous thing of all.

Sometimes, things go smoothly and you’re impressed with how quickly you and your lil AI friend can produce something great. It’s like a magic carpet ride. I can show you the world…

Other times, output obviously doesn’t meet the quality bar, or worse: it’s full of hallucinations and half-baked arguments presented in a confident tone and voice. Whether the output AI produces is valuable and factual is dependent on a lot of factors: the quality of the model, the provided context, the prompt, to name a few.

It always requires strong human judgment to ensure everything is correct and makes sense. It ends the same way as every genie story:

Not with the wishes themselves, but with what the wisher learned from making them.

The greedy ones waste all three and end up back where they started. The clever ones get something real. Why? Because they knew exactly what they wanted before they opened their mouth.

AI isn’t magic. But it rewards the same thing the old stories always did: clarity of thought, precision of language, and enough humility to know that the output is only as good as the wish.

If you’re a content designer, you’ve been practicing for this your entire career.

So: apply common sense, fact check, review, rewrite, and be careful out there, or you might just end up back where you started.

Nicole is a Content Designer turned Design Director based in Stockholm, Sweden. She potters, writes poetry, and raises little girls in a house by a meadow. You can follow her writing here or get it directly to your inbox via her publication, eggwoman. Nicole is on Linkedin.

Sources and inspiration

• “The Gambler & The Genie” — Medium, March 2025
• “If AI Were a Genie: Mastering the Art of Making Wishes With Prompt Engineering” — Sikich, March 2024
• Science journal, March 2026: research showing sycophancy is widespread across 11 state-of-the-art models and actively harmful to users Science
• Northeastern University research finding that the more personal your relationship with an AI, the more it tells you what you want to hear
• PMC/NIH piece on “algorithmic sycophancy” — accessible language, good for a general audience PubMed Central
• Early foundational paper showing GPT-3 struggles substantially with understanding intent and mental states in social interactions arxiv
• Nature study comparing LLM and human theory of mind performance across 1,907 participants — good for a general audience citation nih

Everything I know about AI, I learned from a genie was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

As a content designer and passionate writer, conversation design has been intriguing to me since its infancy. From the little blurbs Microsoft’s Clippy spat out, to how Spotify’s Wrapped campaign addressed users in a dialogue-mimicking way. To me, it’s fascinating how small tweaks in sentence structure and language can make words feel more two-way than one-way in an instant.

The birth of a conversation.

For most of my career, I didn’t question if this was good or bad. After all, using a human-centric approach to make interactions online more bearable made sense. A chat interface, initially, felt as close as you could get to “user-friendly”.

Until something started bothering me.

From tone and voice to conversation design

I’ve been developing tone and voice for close to 15 years and have created concepts and guidelines for more than 50 companies. The classic approach to tone and voice development still holds: understand the company, understand the audience, understand the competitor landscape. Go from there.

When I first started dipping my toes into conversation design about 5 years ago, I brought that same toolkit, then paired it with a deeper understanding of verdicts, natural language, and service design.

In the beginning, it worked ok. After a few iterations with voice drivers and clear dos and don’ts, I could get a chat interface to suck less. Then, chat interfaces started taking over, and today, there are agents everywhere.

The conversation design approach I used to have feels outdated.

For a while, I’ve been trying to get to the bottom of this.

Why does it feel outdated?

Is it the sheer mass of artificially designed conversations we’re now exposed to? Is it the lingering feeling that agent interactions and chat interfaces are not efficient? Or is there something at the core of what we think a conversation should be like that’s wrong?

According to The Conversation Design Institute, conversation design is the art and science of creating intuitive, natural, and effective dialogues between humans and AI-powered systems. Key aspects include:

• Human-Centric Approach: Focusing on the user’s needs, goals, and emotional state to create helpful, polite, and efficient interactions.
• Persona Creation: Defining a consistent voice, tone, and personality for the AI system that aligns with a brand, ensuring the interaction is engaging and trustworthy.
• Flow and Logic Mapping: Designing conversation structure, including the “happy path” (successful interaction) and “fallbacks” (how to handle errors or misunderstandings).

But is it fair to make an artificial intelligence appear human to make interactions more efficient? Is creating an engaging persona to improve the chances of a “happy path” conversation not deceptive? And why does the “natural” part in natural language feel kind of off in this context?

Where it all went wrong

Over the years, there have been various brand voice trends: from playful and bold to sounding “human” back to more serious speak. While some back and forth is to be expected, I do believe we got stuck on one idea: the ambition to sound human, to remain casual, and on one level with consumers.

Logically, the way to connect with people is to tap into what they already know: human conversation, from person to person. Even if one party isn’t just that: a person.

I think this is the crux.

When I was developing tone and voice and applying conversation design to interfaces, such as websites and apps, trying to sound human made sense: whoever was reading the final words could easily picture that at some point, a fellow human being had carefully chosen them. The core of the conversation was still there, presented via an app or a website, people communicating with each other. In the case of an app, a UX writer with its user base, in the case of a website, a copywriter with its visitors.

Agents changed that. Creating tone and voice to be expressed by AI-agents and designing conversations, while in theory, just another interface, have one major difference: there actually is no human involved on one side of the conversation.

The birth of a deceptive pattern

On social media, there’s a whole subgroup of skits and memes making fun of agents imitating human behavior and conversation.

And while hilarious at first, there is darkness to it: many people are affected by the AI’s answers.

Delivering certain messages in familiar ways, dare I say, emotionally manipulative ways that make it easy to perceive the messenger as a fellow person, ensures they have a stronger effect on people.

This is by design.

This is, by definition, a deceptive pattern.

A dark (or deceptive) pattern is a user interface intentionally designed to trick or manipulate users into taking actions they did not intend, such as buying items, signing up for subscriptions, or sharing data. These deceptive practices, often called “deceptive design,” prioritize business goals over user experience. Common examples include hidden costs, difficult cancellation paths, and forced continuity. — Wikipedia

For years, designers have been careful not to design deceptive patterns. But when it comes to conversation design, this care has seemingly gone out the window.

Claude regularly tells me it “loves” my thinking. Claude cannot love. Chat GPT tells me I’m smart and very reasonable. Am I? How would it know? I say please and thanks to my agents as if my manners mattered and they cared. Why? Because I’m falling for a deceptive pattern that makes me forget who I’m talking to: a machine. And while I don’t feel a bond with AI yet, enough people do: especially vulnerable people, younger people, and those living in isolation.

It’s dangerous.
But it contributes to token spend and interactions: more data, and more money to be made.

I think it’s time we talk about this and update our understanding of what conversations with AI should look (and feel) like.

Are chat interfaces deceptive patterns?

Natural language in, natural language out, in a way that mimics real conversation between two people, is the core concept of a chat. And in chats where there is a person on each end, it still somewhat represents reality – minus the facial expressions, pauses, and other nuances.

But as soon as there isn’t a human being on one end of the chat, it becomes deceptive, mimicking something familiar to us with one purpose: to manipulate our actions, for the benefit of the service provider.

I believe most AI chatbots and conversational UIs are increasingly designed with deceptive tactics that exploit human social cues to encourage data sharing, engagement, and obedience.

These include:

• Chatbots mimic human conversation to foster trust, making users less vigilant and more likely to comply with, e.g., requests for data or to keep the conversation going.
• Because answers are presented in conversational form, users tend to overlook mistakes or neglect any fact-checking.
• Ever felt like you’re being pushed to chat with an agent whenever you do anything that takes money from a business? That’s not a coincidence. Chatbots can be used to trap users in “roach motels,” where starting a cancellation process is easy, but completing it via chat is intentionally complex. The result? “Hard to cancel” experiences and a sense of dread. This is deceptive at its core, yet we have seemingly accepted it, and more shitty agents are born every day (under the guise of “customer service”).
• AI companion sites often ask users to input detailed personal information about themselves and their relationships to “improve the experience” or “build memory”. Often, this is used to tailor the agent’s personality in the chat further. Manipulative much?
• Chat interfaces can foster addiction by using addictive design patterns that exploit dopamine mechanisms, such as word-by-word typing simulations that encourage continued engagement.
• And then there is the hidden uncertainty. Generating confident, yet potentially false or fabricated, answers without indicating low confidence levels.

So the same design tricks that nudge you into buying the extended warranty or signing up for a newsletter you didn’t want have found a comfortable home in conversational AI. The chat format just makes them harder to spot. Because it feels like talking to someone, not being manipulated by something.

How do we get out of this?

I think it’s on us, the ones designing agents, to take responsibility.

We should ban “human” as a voice driver when developing tone and voice for AI. No, this does not mean your agent can’t be polite or engaging. It just should not achieve this by claiming emotions. There are other ways.

Here’s how I make the agents less deceptive:

• Shorter sentences and paragraphs instead of longer, more descriptive structures. They’re more engaging and remove the “waffly” parts of language that only serve to make the conversation “feel more human”.
• Confirming correct facts and acknowledging mistakes as a core behavior. “That’s right”, “You’re correct, I wasn’t specific enough”.
• Sources are prominent in the chat interface. In many AI chat interfaces, sources don’t stick out, upholding the illusion that who we’re talking to is a being instead of essentially a curator.
• It’s not he or she. Human names prime people to treat the agent as a person before a single word is exchanged. A functional name or no name at all is more honest.
• Uncertainty needs to take up more real estate. Not buried in fine print or a tiny tooltip, but part of the response. “The sources behind this are weak” needs to be as natural as any other output.
• No typing animations. If it’s not technically necessary, it’s a manipulation. There isn’t anyone typing on the other end. Faking it is deceptive.
• The unhappy path and the happy path are equal. Fallbacks and error states are often where dark patterns live. If “I can’t help with that” is harder to reach than “yes, let’s continue,” that’s a choice.
• An agent can be clear, efficient, and respectful without pretending to feel anything. Those aren’t the same thing.

Right now, conversation design borrows from UX ethics selectively. The field needs its own equivalent of accessibility standards: specific, measurable, enforceable.

We all need to commit to stop designing for parasocial attachment. “I’m so glad you asked” and “great question!” are tiny dopamine nudges that serve the platform. Who are we fooling? Too many. By design.

Let’s stop.

Nicole is a Content Designer turned Design Director based in Stockholm, Sweden. She potters, writes poetry, and raises little girls in a house by a meadow. You can follow her writing here or get it directly to your inbox via her publication, eggwoman. Nicole is on Linkedin.

The deceptive nature of today’s AI conversation design and how to fix it was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

Much has been said about the future of design in the age of AI. Some think the role will disappear completely. Others say only super-seniors will survive. And yet others say it’s all just a blip in time and there will be no fundamental change.

I think all are wrong.

For two reasons:

1. Designers are naturally attuned to adapt to technological change. We are trained to identify and acknowledge change, manage adaptation, and find solutions for friction.
2. We’re moving towards a totally new definition of digital products. Away from interfaces, and towards fluid use cases that we’re only beginning to imagine now. To make this accessible and valuable to all, we will need designers at all levels. Obviously.

Why, then, has the design community been in absolute panic since AI has been taking off?

Seniors are telling juniors to count themselves lucky if they’ll ever find a job.

Design leaders are jumping from one AI-tool hypetrain to the next in mere weeks.

Monday, it’s all about prototypes. Thursday, it’s vibe coding. Friday, we’re preaching that output no longer matters (everyone can design now!) and that we should be brilliant strategists instead. By next Monday, we’ll be half-heartedly debating which soft skills are absolutely vital to survive.

Survive. As designers.

As if our skills have evaporated overnight, and we can only stick around if we somehow show that we’re good for (undefined) other stuff.

No.

We should be thriving.

Where has it all gone wrong? Why aren’t designers winning in the age of AI, which, at its core, is about making?

The thing we do best.

Here’s what I think.

We’ve been sabotaging ourselves.

Please don’t be enraged. Take a deep breath and think about it.

Ever since the first AI tools with strong visual output started emerging, we seemingly overnight started screaming from all rooftops about:

taste.

What the fck is taste?

According to NNG it’s “the learned, subjective ability to discern and select the most effective, harmonious, and high-quality elements for a project”.

Maybe, if you’re really smart, you can picture what that means. But really, if you’re honest, taste is a vague concept. Taste is subjective. Taste is not something that makes a product win (for the most part).

And vague is not good — especially not in a time of transformation where no one really knows where we’re going yet. Where tech stacks are changing weekly, and user needs are developing at rocket speed.

You know what else vague is bad for? Convincing an executive to invest in something.

It generally takes three things to grab an executive's attention.

1. Connection (Do you get their problem?).
2. Authority (Do you know your stuff?).
3. Vision (Do you know where this is going?).

Taste doesn’t answer any of those. You know what else doesn’t?

Shitty prototypes.

Yeah, I’ve made my fair share of them, too. But the time when these impressed anyone is over. Even my 0% designer husband can now vibe code stuff and knows how to whip up a design prototype.

There is a time and place for them. Prototypes and vibe-coded projects are great to show a vision, build excitement, or unblock a team that’s stuck in process. But they’re not what’s gonna convince your engineering stakeholders to give you access to their codebase so you can actually start shipping fixes from your design backlog.

I’d like to argue that excessive prototyping is the next big issue that’s messing with the status of design in the age of AI.

Because they undermine our authority. There’s the executive’s next red flag. Do you know your stuff?

Of course, designers recognised this concern quickly. How did we respond?

By starting never-ending discussions about:

craft.

What the heck is craft?

I like this definition: “Craft can be defined as intelligent making. It is technically, materially and culturally informed. Craft is the designing and making of individual artefacts and objects, encouraging the development of intellectual, creative and practical skills, visual sensitivity and a working knowledge of tools, materials and systems”.

Welp.

Taste and craft walk into a bar…

Just kidding.

You get my point. Here is another vague concept that most people associate with activities they liked to do back in kindergarten.

And listen, I’m not knocking craft. I love writing poetry, painting, throwing pots at the wheel. All that takes craft and skill, just as my designs at work do. But craft should be so obvious to us as designers that we should not make it our main selling point.

Obviously, we develop incredible craft as our experience builds. Obviously, individual designers have different styles. Obviously, we put thought and care into what we make.

Craft is the baseline. That’s what we want the executives to know. By debating it and what it even means, we’re again undermining our authority. And I’d argue we’re also not really connecting with them OR showing much vision.

Well, shit.

How do we get out of this mess?

We need to remember — and remind others — why design is irreplaceable.

We’re the OG makers.

We’re “growth mindset”, personified.

In most classic product companies, there are trios: A product manager, a designer, and an engineer. The point? To ensure that the perspectives of the users and the business are balanced with the technical capabilities throughout the development process.

While a well-functioning trio feels like a cheat code to achieving product velocity, an unbalanced one can slow things down, ship mediocre stuff, or not at all.

The best trios I’ve worked in and observed had one very obvious trait in common: we all respected each other's practice, but we also understood enough about each other’s domains to be unafraid to push back.

Only a designer who understands the business side of things can really translate business goals into conversion flows.

Only a product manager who understands the fundamentals of good design can push a designer to level up.

And only an engineer who cares for users and understands how the business works is an equal partner in a trio.

And while AI is bringing all of our roles closer together, we are still the builders of the experience.

The thing that is actually being used. The thing executives aim to deliver.

1. Connection: You want to make something. We can tell you if it’s possible. If yes, we can ensure it’s made in the best possible way.
2. Authority: We work at the intersection of what the business wants, the user needs, and what actually works.
3. Vision: We have the skills, tools, and mindset to create something you can’t even/can only imagine.

Sold.

The design mindset is unique.

And it’s beautiful.

It’s human-centred. Deep empathy for the user is the foundation, ensuring solutions address real needs, feelings, and motivations.

It embraces ambiguity. Viewing problems as opportunities rather than roadblocks.

It kills its darlings by default and favours learning. A willingness to “fail fast,” learn from prototypes, and constantly adapt solutions based on feedback.

It’s, at its core, optimistic and inclusive. Bringing diverse perspectives together, believing that better solutions can always be created.

It’s the most curious of mindsets. Asking “why” and “what if” to challenge the status quo at every turn.

You don’t have to read this twice to know: hell yes, we need designers with AI taking over more and more space in our lives.

Here’s what we need to do. Now.

In most classic design teams, there are different roles: Product designers, content designers, user researchers, sometimes motion designers, and ops people. Their educational background is essentially the same: developing an understanding of users, business, and products. Some focus more on the look and feel, others on going in depth on the “how it works”.

So, for the love of everything good, can we please stop debating our titles?

We are all designers.

Designers make stuff. Some use words and taxonomy, others use pixels. Some use language, others use code. The mindset is the same. The mindset is what matters.

Yes, AI is muddying the lines between design roles. But it doesn’t matter.

Not because it can replace people, but because it can save time that, in return, can be used to develop the skills traditionally only another role would need. Not so you can replace someone, but:

So you can become a more holistic, better maker.

This is not new.

There’s always been a push for all designers to do their own research. For content designers to develop visual skills. For product designers to get strong at prototyping. Now, finally, we can do so — with far less effort than before.

That doesn’t mean we should think less. It means we can think more.

I, personally, love thinking. Nothing gets me as high as solving a problem. It’s the reason I got into design, and specifically design in tech, in the first place. There were actual (cool) problems to solve.

I originally picked UX content as my toolkit. Now, my toolkit has expanded. I love that. And I don’t want to waste time wondering what my title should be, whether my taste is good enough, or what exactly my craft is now.

I want to make stuff.

And I think you do, too.

Nicole is a Content Designer turned Design Director based in Stockholm, Sweden. She potters, writes poetry, and raises little girls in a house by a meadow. You can follow her writing here or get it directly to your inbox via her publication, eggwoman. Nicole is on Linkedin.

Designers, we should be killing it right now was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

Calling it Product Design is an understatement, and shifts focus away from what truly matters

There’s been a rather quiet shift in the field of design — what used to be titled User Experience Design is now mostly referred to as Product Design. I believe experience is at the core of design. I want to put it back in the name.

Here’s why.

Everyone has art on the wall

It may be an oversized sepia photograph of the ocean purchased at Ikea or a mediocre oil painting inherited from an aunt. It may be abstract or quite specific. It may complement its surroundings or look careless in the grander scheme of a room. But I am certain: there is something on your wall. There always is. It’s one of humanity’s most ancient understandings — if there is a wall, I must put something on it.

I love that.

Humans crave beauty. No matter which class or culture, we are drawn to colors, light, texture. We enjoy the feel of certain materials. Scents can evoke distant memories, and the right song at the right time may change us forever.

What exactly we like is personal. Your choice of art, books, cutlery, clothes is intimate. You may not put much thought into it, but what you choose to show off has power: It can send a message. I make things. I am rich. It can be a statement. I’m a rebel. I believe. I don’t care. It can show a preference. I’m a minimalist. I like color. It can ask a question. Who do you think I am? Does this make you uncomfortable? Is this who you thought I was?

What you put up, wear, and use answers without having to ask. It solves mysteries and summons new ones. Because more than beauty, we crave connection. Expressing ourselves in various ways, consciously and unconsciously.

It’s beautiful.

There is no shame in beauty

There’s something I have noticed.

Some people feel uncomfortable showing off what they like. I think they are afraid of being thought of as superficial, as being seen as someone who cares “too much” about aesthetics.

I used to feel like that.

I’m drawn to handmade things. My taste tends to go against most trends, which in a capitalist society means: expensive (lower demand, less supply). I remember a friend asking me where I had purchased an antique shelf. I was ashamed to admit that I had won it at an auction for an (what-I’m-sure-my-friend-felt-like) embarrassing amount of money.

But there is no shame in liking something. We all see beauty in different things. Our taste is one of the things that colors who we are — like our culture, language, and relationships.

I’ve learned to see an acquired taste as a sign of a free spirit—someone who doesn’t blindly follow trends or makes much of an effort to appear cultured. They simply know what they like.

You thought about that. You looked at countless things and found this one to be your favorite. That’s what you put up on your wall. One of the great mysteries of your being, laid bare.

What makes good design

I work in design. There’s a lot of talk about what makes a good designer. Many agree: it’s about a strong sense of taste. It’s about having a point of view.

It is.

And yet, most designers do not feel like their sense of taste is allowed to drive their design work. We rarely discuss beauty. We discuss utility. But what is utility if it doesn’t enrich the human experience?

It lacks meaning.

I work in tech. There’s a never-ending race for the next big product launch. Something customers will appreciate. Something they’ll tell their friends about. Something that new customers will sign up for. But what have you ever told a friend if not a story? Even if you were to recommend a product, you’d do it in context. This helped me when that happened then. You give it meaning. The meaning is what makes your friend sign up.

It’s the art on your wall.

In the race for the next big sell, we invent problems worth solving. Even when we’re working on better solutions to known problems, we focus on filling them up to the brim with function. More is more.

It’s not.

I believe this is the most important job of a designer: to remind everyone in the room that it’s not just about how it works and what it can do, it’s about what it means. And that is so much harder to design. Only those who manage to convince their teams to linger and consider it, may get the opportunity to design something tasteful.

Designing meaning

Many have tried to write about how to design meaningful things. It starts with intention, of course. But what comes next?

Context. This one most design teams still get right. They consider who something is for and when they might use it. They think about scenarios and argue about hierarchies.

From here, things start to go off track fast. Design teams jump to conclusions about use cases and jobs to be done. They prototype and test half-baked experiences, and as soon as they get a positive signal, they go into execution mode. Product managers bring up costs. Things that aren’t needed are removed. Things that take longer to be perfected are labeled as costly and are moved to the backlog. Leadership encourages everyone to ship the industry-darling: the MVP — the minimum viable product.

It’s the bane of every creative’s fucking existence. The end of beauty, mystery, the archenemy of surprising potential.

Why? Here’s a secret.

The MVP that ships is often not actually the MVP at all. It’s the MDP. The minimum discussion product. The iteration most people can agree on. Not too ugly, designers have to be embarrassed. Not too pretty, it’s expensive. Not too useless, customers will be mad. Not too useful, we’d be undercharging.

If there’s still a goal left in my career in design, it’s this: to convince everyone that it’s worth moving beyond the MVPs and MDPs of this world.

Moving beyond

Because whenever it is done — when we build something better than the minimum — that’s when the magic happens.

Subtle hints that we’re sharing a human experience. That this was built by someone for someone. For you.

A millisecond of extra care in a transition. A word written that you haven’t read in a while, that makes you linger. A design tweak that isn’t just best practice but has the potential to redefine what “best” means. A wonky gradient. A font that isn’t Helvetica. A line of copy that sticks out.

Something that comes close to a real experience, makes you feel something, makes you wonder. It makes you raise an eyebrow (or two). It makes you snap out of it just long enough to connect with it and live a little.

There it is. The reason good design always wins and the reason you have art on your wall.

A reminder that you’re alive. That life exists outside of apps, products, objects. They’re all just decorative assistants to what we’re all doing, every moment of every day: breathing in and breathing out. Experiencing.

As designers, as artists, as builders and makers, as human beings, it’s our job to keep experiences sacred.

Putting more intention into how we design is where it begins.

Nicole is a Content Designer turned Design Director based in Stockholm, Sweden. She potters, writes poetry, and raises little girls in a house by a meadow. You can follow her writing here or get it directly to your inbox via her publication, eggwoman. Nicole is on Linkedin.

Can We Please Make Design About Experience Again? was originally published in Bootcamp on Medium, where people are continuing the conversation by highlighting and responding to this story.