Using What We Don't Trust

We are adopting tools we do not believe in.

"2025 is clearly the year that AI ate common sense."

– Director of Insights Operations

"AI has the potential to be the researcher's best friend, by doing all the heavy lifting associated with analysis, but it also has the potential to cause unimaginable damage."

– UX Researcher

"I don't know if these insights are real or if I'm just being told what patterns look like."

– Research Participant, User Interviews Survey

"We're all using it. Nobody's saying they trust it."

– UX Designer, Reddit

If 91% don't trust the outputs but 80% are using them anyway, what does 'adoption' actually mean?

Are we building expertise, or building dependency on systems we don't believe in?

And what does compelled usage, without conviction, do to a profession built on rigour and integrity?

Here's what I keep sitting with: the distrust you feel using these tools? That's exactly what your users feel when they encounter your AI product.

So this paradox isn't just an industry observation. It's a mirror.

There are specific signals, microinteractions, microcopy, onboarding mechanics, that build trust. And specific ways to break it. When trust breaks with AI, people churn at twice the rate of non-AI products.

The tension between using something and not trusting it, that's not unique to designers. It's the lived experience of everyone encountering AI right now. Including your users.

We are automating the runway that taught people to fly.

"There is an 'empty void' of opportunities for junior designers."

– Design Educator

"AI can now do tasks that juniors used to cut their teeth on... in 5-10 years, we'll face a massive gap."

– Design Leader

"Entry level hiring is cooked. The ladder doesn't exist anymore."

– UX Designer, Reddit

"Learning design through tools instead of people can shortcut the slow, formative work that builds creative muscle. Without exposure to the subtle cues of live collaboration, how a senior designer navigates client pushback, how a project pivots midstream or when to break the rules, new designers risk missing the soft skills required for real-world effectiveness."

– It's Nice That

"We're not just losing jobs. We're losing the mechanism that creates expertise."

– Research Leader

If the tasks that built expertise are now automated, how do emerging practitioners develop the judgment AI cannot provide?

Are we creating a generation of designers who can prompt but cannot evaluate?

And who bears responsibility for rebuilding the apprenticeship model, individuals, organisations, or the industry itself?

Yes, the pipeline is breaking. That's real, and it's painful.

But here's the conversation I think we're missing.

Those young people, the ones struggling to find entry-level roles, they're digital natives. They've grown up with digital tools as an extension of themselves. And they're coming out of education without all the 'this is how we've always done it' baggage that we carry. They don't have to unlearn anything.

That's not a weakness. That's an advantage.

What if we stopped seeing junior hires as charity, or as overhead to be optimised away, and started seeing them as strategic capability? A senior with deep expertise paired with a junior who thinks AI-first? That's not mentorship as a burden. That's a powerhouse.

I've just returned from six years in the EU, where internship culture is strong. We don't have that tradition in Australian design. And right now, with junior roles scarce and senior teams stretched, it's an untapped opportunity.

Fresh minds plus seasoned expertise. That's a strategy that could build AI-augmented capability from the ground up, sustainably, not by automating the apprenticeship away, but by redesigning it.

We're producing more. We're not reflecting more.

"AI tools were meant to automate the boring bits... Instead, creatives speak of endless iterations, escalating client demands and entirely new categories of digital drudgery."

– It's Nice That

"Whoever thought AI would free creatives to be more creative wasn't paying attention; it pushes us to produce more, not reflect more."

– Design Director

"I'm outputting more than ever. I've never felt less creative."

– UX Designer, Reddit

"The time I save with AI gets immediately filled with more requests. There's no net gain, just more volume at the same exhaustion level."

– Product Designer, LinkedIn

"We used to have time to think. Now we have time to produce. Those aren't the same thing."

– Research Lead

If efficiency gains are absorbed by escalating demands, what was actually gained?

Is AI amplifying our capacity or merely accelerating the treadmill?

And how do we design workflows, and AI products, that protect space for the thinking that quality requires?

I want to be honest: this isn't all doom and gloom.

I feel more creatively empowered as a solo operator right now with 3 teams of AI agents that help extend my capabilities in areas I have never had skills or expertise in. The opportunity is real. And from my conversations with design leaders and practitioners, there's an enormous gap between those who've figured out how to leverage AI well and those just coming to it now.

But efficiency without quality is just faster mediocrity.

The only way to get quality is through that 20-30% of human value-add, the expertise, the wisdom, the creative judgment. Without it, you get homogenised slop. Work that sounds like it's saying something until you realise it isn't.

Here's my provocation: the pause is where the power is.

Not everything should be faster. Strategic friction, I know that phrase breaks the brain of every designer who's spent a decade removing friction, but hear me out.

Creative thinking happens in pauses. Critical thinking. Ethical judgment. The moment where you look at AI output and say, 'No, that's not quite right, here's why.'

That's not inefficient. That's where the value lives. Because if it were truly one-and-done, if the button push was all that mattered, you'd already be out of work.

When everyone draws from the same well, everything starts to taste the same.

"When everybody uses the same few 'smart' tools to craft their digital products, it becomes hard to differentiate one from another. Although AI-built user interfaces are usually clean and frictionless, they often lack personality and soul."

– Miyagami Design Agency

"AI-generated images are too homogenous in style. This visual 'sameness' carries over into industrial design, fashion, and game design as well."

– BMW Designworks

"It's all starting to look the same. Clean, competent, completely forgettable."

– Creative Director, LinkedIn

"I can spot AI-assisted work now. Not because it's bad, because it's indistinct. It has no fingerprint."

– Senior Designer

"The first draft is faster. But I spend longer trying to make it not sound like everything else."

– Content Designer

If AI trends toward the average, and we increasingly rely on AI for first drafts and base layers, are we slowly eroding the conditions for distinctiveness?

Can tools trained on convention ever support genuine originality?

And what happens to creative culture when the path of least resistance leads everyone to the same place?

This is the thing I keep coming back to: AI trends toward the middle.

If you're not great at something, AI can lift you up. It can make a weak first draft sound competent. That's real, and it's valuable. But it's also lowering the ceiling.

When you run your best thinking through a system trained on averages, it sands off the edges. The idiosyncratic phrasing. The unexpected choice. The thing that made it yours. What comes back is smoother, and flatter.

Without that 20-30% of human value-add, the expertise, the taste, the willingness to say 'no, that's not quite right', you get homogenised slop. Work that looks professional but isn't actually connecting.

And here's what worries me, this compounds. As more AI-assisted work enters the world, it becomes the new training data. The middle learns from the middle. The edges get harder to find.

Your job now is to protect the edges. To be the one who says, 'This is too smooth, where's the texture?'

Designers are questioning not just their jobs, but their purpose.

"AI makes me feel like a fool. When I look at AI-generated art, I'm reminded of the countless hours I've dedicated to freelance work as a single parent just to make ends meet."

– Graphic Designer

"I've spent 15 years building expertise that a tool can now approximate in 30 seconds. What does that make me?"

– Senior UX Designer, Reddit

"I sense a shift, like I'm on a path to stagnation, potentially fading into obscurity."

– Visual Designer

"It's not that I can't use the tools. It's that I don't know who I am if the tools can do what I do."

– Product Designer, LinkedIn

"Everyone's posting about how AI makes them more productive. I just feel more replaceable."

– Junior Designer

If design identity is threatened by AI capability, how do practitioners rebuild a sense of purpose?

Is this a temporary crisis of adjustment, or a fundamental renegotiation of what design means?

And how do we create space for honest conversation about professional fear in cultures that reward performance of confidence?

I feel this one in my gut.

Most designers I know have a lot of identity wrapped up in what we do. If I had a drug of choice, it would be creative problem-solving. It's part of who I am, part of who I want to be.

So I want to tell you something honestly: I've been doing this for a bloody long time. And I've never felt more creatively empowered than in the last three years.

AI isn't killing your craft. It's offering you a new medium for it.

I build my own tools now, not because I suddenly became a coder, but because I have ideas about problems I want to solve, and I know how to think through a problem, find the best information and how to get AI to build them with me. And not to an average level, but based on best-in-class examples. That's not replacing my creativity. It's extending it.

But I had to go through the identity crisis to get here. I had my own months of 'what does this mean for me?' I grappled with it. And I came out the other side loving what I do more than I have in years.

Here's something I want you to do.

Think of a piece of work you're genuinely proud of. Something that got meaningful feedback, or something you were just quietly, intrinsically proud of. Now unpack it. What context did you bring that no brief contained? What micro-decisions did you make along the way? What did you notice that someone else might have missed? What creative leap came from your particular way of seeing?

All of those things, every single one, are things only YOU could bring.

That's not abstract value. That's the living, breathing embodiment of what AI cannot replicate.

Don't let that be sanded off. Don't let efficiency flatten it. The uniqueness you bring to a specific context, that's how we distinguish ourselves in an AI-driven world. Don't lose yourself.

What AI cannot do is precisely where human value lives.

"What AI cannot replace is the human ability to think strategically, to empathise with consumers, and to create something truly original."

– David Droga, Creative Chairman, Accenture Song

"AI can read the data. But it can't read the room."

– Head of Design

"The value isn't in producing outputs. It's in knowing which outputs matter."

– Jeff Gothelf, Author/Product Coach

"AI gives me answers. It can't tell me if I'm asking the right questions."

– Senior Researcher

"We're not being replaced by AI. We're being challenged to prove what only we can do."

– Design Director

If human value lives in context, judgment, and lived experience, how do we cultivate these in a world accelerating toward automation?

How do we protect the pause for reflection when everything demands speed?

And how do we help organisations understand that the irreplaceable human contribution isn't overhead to be optimised, it's the source of quality itself?

This is where I want to speak directly to my fellow human-centred designers.

We have built our careers on bringing empathy to understanding, the lived experience, the needs of customers, and responding with designs that invent the future. That capacity isn't diminished by AI. It's more vital than ever.

Because here's what we need to understand: we're not designing AI systems for the systems themselves. We're designing for humans to interact with AI. We're designing a new form of intelligence to collaborate with our intelligence.

That makes this fundamentally a relationship-building challenge.

AI operates on one layer: logic, reasoning, pattern matching. But when humans make decisions, we're processing on multiple layers simultaneously. There's the logical layer, where AI participates. But there's also emotional processing: how will this affect others, how will I be perceived? And there's fear, the layer that enables or shuts down our ability to think creatively, to experiment, to see strategically.

You've heard the fear throughout this entire piece. It's pervasive. And fear functionally impairs creative thinking. It closes us down.

Here's something else that's changed. Every time you use a generative AI system, even with the same prompt, you get a different result. We used to design and control user flows. We mapped them out. But now, the flow happens during the experience.

So the goal now needs to be: designing the best conditions for healthy collaboration. The mechanics that help humans and AI partner well together. The trust signals, the friction points, the moments of human agency.

I want to activate human-centred designers more than any other professionals to not lose that soul we bring to our work. Because it is more important than ever.

Don't give up your empathy. Don't give up your contextual understanding. Don't give up your ethical judgment. Those aren't soft skills to be optimised away, they're the entire point. We're building humanity's future relationship with intelligence that may soon surpass our own. And you, the person trained to centre humans in everything you design, are exactly who should be shaping that relationship.

This article synthesises findings from over 80 sources, including industry reports from User Interviews, Figma, Maze, Nielsen Norman Group, UXPA, Roy Morgan, and Deutsche Bank Research; academic research; and community discussions from Reddit, LinkedIn, and design forums. Nearly 100 direct quotes were gathered and categorised by theme, sentiment, and source type.

Community voices have been de-identified to protect contributors who share vulnerable perspectives. Named attributions are reserved for public figures making statements on their own platforms or in public interviews.

This research was conducted in late 2025. The landscape is evolving rapidly; these findings represent a snapshot of sentiment at a particular moment in a fast-moving transformation.