Nielsen Norman Group weighs in on iOS 26 Liquid Glass. Predictably, they don’t like it. Raluca Budiu:

With iOS 26, Apple seems to be leaning harder into visual design and decorative UI effects — but at what cost to usability? At first glance, the system looks fluid and modern. But try to use it, and soon those shimmering surfaces and animated controls start to get in the way.

I get it. Flat—or mostly flat—and static UI conforms to the heuristics. But honestly, it can get boring and homogenous quickly. Put the NNg microscope on any video game UI and it’ll be torn to shreds, despite gamers learning to adapt quickly.

I’ve had iOS 26 on my phone for just a couple of weeks. I continue to be delighted by the animations and effects. So far, nothing has hindered the usability for me. We’ll see what happens as more and more apps get translated.

Ian Dean, writing for Creative Bloq, revisits the impact the original TRON movie had on visual effects and the design industry. The film was not nominated for an Oscar for visual effects as the Academy’s members claimed that “using computers was ‘cheating.’” Little did they know it was only the beginning of a revolution.

More than four decades later, TRON still feels like a moment the film industry stopped and changed direction, just as it had done years earlier when Oz was colourised and Mary Poppins danced with animated animals.

Dean asks, now what about AI-powered visual effects? Runway and Sora are only the beginning.

It’s always interesting to hear how others think about the design process from the outside. Eli Woolery and Aaron Walter interview creativity researcher and author Keith Sawyer to learn about what he’s found to be true after interviewing hundreds of art and design professors and students over a decade for his new book:

The creativity doesn’t come at the beginning. You don’t start by having a brilliant insight. You just dive into the process. And then as you’re engaging in the process, the ideas emerge.

Sawyer emphasizes that art and design schools are not just teaching students how to create, but how to “see.” He found that many professors believe students already possess creativity, but the role of art and design school is to help them realize and develop that potential by teaching them to observe, critique, and reflect more deeply on their own work.

When I interviewed these artists and designers, I would say, how are you teaching students how to create? And everyone was quite uncomfortable with that question. A lot of them would say, we’re not teaching students how to create. Or they’ll say something like, the students are already creative. We’re teaching them how to realize the potential they have as creatives.

Sawyer notes that the hardest thing for students to learn is how to see their own work—that is, to understand what they have actually made rather than sticking rigidly to their original idea.

When we talk about learning to see, you’re talking about learning to see yourself. The hardest thing to teach a student is how to see their own work, to see something that they’ve just generated. Because these studio classes, students have opportunities to share their work in interim stages along the way. You don’t go off and work for two weeks or four weeks and then bring back in the finished product. You bring in your interim and you get a lot of feedback and comments on it.

And what the professors tell me is these 18, 19, and 20-year-olds, they don’t realize what they put on the canvas. Or if they’re a graphic designer, they don’t realize what it is that they’ve generated. A lot of times, they’ll think they’ve done a certain thing. So they have this kind of linear approach—model of the creative process where I’m going to have an idea and I’m going to execute it so they’ll start with their idea and they’ll execute it. They’ll think that what they put on the canvas is their original idea, but in a lot of cases, it’s not. They can’t see what they’ve done themselves, so that’s kind of powerful how do you teach someone that what you put on the canvas isn’t what you say you’re doing.

You can’t just tell them, “Hey, you’re wrong. Let me tell you what you’ve done.” You have to lead someone through that. You have to walk them through it.

One way you do it is you put students in the classroom together and then have them comment on other students’ work so they will be on the other side. And they’ll see another student. talking about what they’ve done and not really describing what’s really on the canvas.

So I think that’s the hardest thing about learning to see is learning to see yourself, learning to see your own work.

I think that’s the power of art and design school, this studio learning environment. I’m biased, of course, because that’s how I learned. Those who are self-taught or have gone through bootcamps miss out on a lot of this experience. The other thing the design school environment teaches is how to give and take critiques. It’s about the work, not you.

In the scenario “AI 2027,” the authors argue that by October 2027—exactly two years from now—we will be at an inflection point. Race to build the superintelligence, or slow down the pace to fix misalignment issues first.

In a piece by Derek Thompson in The Argument, he takes a different predicted AI doomsday date—18 months—and argues:

The problem of the next 18 months isn’t AI disemploying all workers, or students losing competition after competition to nonhuman agents. The problem is whether we will degrade our own capabilities in the presence of new machines. We are so fixated on how technology will outskill us that we miss the many ways that we can deskill ourselves.

Degrading our own capabilities includes writing:

The demise of writing matters because writing is not a second thing that happens after thinking. The act of writing is an act of thinking. This is as true for professionals as it is for students. In “Writing is thinking,” an editorial in Nature, the authors argued that “outsourcing the entire writing process to LLMs” deprives scientists of the important work of understanding what they’ve discovered and why it matters.

…

The decline of writing and reading matters because writing and reading are the twin pillars of deep thinking, according to Cal Newport, a computer science professor and the author of several bestselling books, including Deep Work. The modern economy prizes the sort of symbolic logic and systems thinking for which deep reading and writing are the best practice.

More depressing trends to add to the list.

Our profession is changing rapidly. I’ve been covering that here for nearly a year now. Lots of posts come across my desk that say similar things. Tom Scott repeats a lot of what’s been said, but I’ll pull out a couple nuggets that caught my eye.

He declares that “Hands-on is the new default.” Quoting Vitor Amaral, a designer at Intercom:

Being craft-focused means staying hands-on, regardless of specialty or seniority. This won’t be a niche role, it will be an expectation for everyone, from individual contributors to VPs. The value lies in deeply understanding how things actually work, and that comes from direct involvement in the work.

As AI speeds up execution, the craft itself will become easier, but what will matter most is the critical judgment to craft the right thing, move fast, and push the boundaries of quality.

For those looking for work, Scott says, “You NEED to change how you find a job.” Quoting Felix Haas, investor and designer at Lovable:

Start building a real product and get a feeling for it what it means pushing something out in the market

Learn to use AI to prototype interactively → even at a basic level

Get comfortable with AI tools early → they’ll be your co-designer / sparring partner

Focus on solving real problems, not just making things look good (Which was a problem for very long in the design space)

Scott also says that “Design roles are merging,” and Ridd from Dive Club illustrates the point:

We are seeing a collapse of design’s monopoly on ideation where designers no longer “own” the early idea stage. PMs, engineers, and others are now prototyping directly with new tools.

If designers move too slow, others will fill the gap. The line between PM, engineer, and designer is thinner than ever. Anyone tool-savvy can spin up prototypes — which raises the bar for designers.

Impact comes from working prototypes, not just facilitation. Leading brainstorms or “owning process” isn’t enough. Real influence comes from putting tangible prototypes in front of the team and aligning everyone around them.

Design is still best positioned — but not guaranteed

Designers could lead this shift, but only if they step up. Ownership of ideation is earned, not assumed.

Is the AI bubble about to burst? Apparently, AI prompt-to-code tools like Lovable and v0 have peaked and are on their way down.

Alistair Barr writing for Business Insider:

The drop-off raises tough questions for startups that flaunted exponential annual recurring revenue growth just months ago. Analysts wrote that much of that revenue comes from month-to-month subscribers who may churn as quickly as they signed up, putting the durability of those flashy numbers in doubt.

Barr interviewed Eric Simons, CEO of Bolt who said:

“This is the problem across all these companies right now. The churn rate for everyone is really high,” Simons said. “You have to build a retentive business.”

If you’ve ever wondered why every version of Hokusai’s “The Great Wave off Kanagawa” feels just a little bit different, this video from the British Museum is a gem. It dives into the subtle variations across 111 known prints and shows how art, time, and technique all leave their mark.

Capucine Korenberg from the British Museum spent over 50 hours just staring at different versions of the print, joking “This is about the same amount of time you would spend brushing your teeth over two years. So, next time you brush your teeth just think of me looking at The Great Wave.”

I love this framing by Patrizia Bertini:

Let me offer a different provocation: AI is not coming for your job. It is coming for your tasks. And if you cannot distinguish between the two, then yes — you should be worried. Going further, she distinguishes between output and outcome: Output is what a process produces. Code. Copy. Designs. Legal briefs. Medical recommendations. Outputs are the tangible results of a system executing its programmed or prescribed function — the direct product of following steps, rules, or algorithms. The term emerged in the industrial era, literally describing the quantity of coal or iron a mine could extract in a given period. Output depends entirely on the efficiency and capability of the process that generates it.

Outcome is what happens when that output meets reality. An outcome requires context, interpretation, application, and crucially — intentionality. Outcomes demand understanding not just what was produced, but why it matters, who it affects, and what consequences ripple from it. Where outputs measure productivity, outcomes measure impact. They are the ultimate change or consequence that results from applying an output with purpose and judgment.

She argues that, “AI can generate outputs. It cannot, however, create outcomes.”

This reminds me of a recent thread by engineer Marc Love:

It’s insane just how much how I work has changed in the last 18 months.

I almost never hand write code anymore except when giving examples during planning conversations with LLMs.

I build multiple full features per day , each of which would’ve taken me a week or more to hand write. Building full drafts and discarding them is basically free.

Well over half of my day is spent ideating, doing systems design, and deciding what and what not to build.

It’s still conceptually the same job, but if i list out the specific things i do in a day versus 18 months ago, it’s almost completely different.

Care about the outcome, not the output.

In an announcement to users this morning, Visual Electric said they were being acquired by Perplexity—or more accurately, the team that makes Visual Electric will be hired by Perplexity. The service will shut down in the next 90 days.

Today we’re sharing the next step in Visual Electric’s journey: we’ve been acquired by Perplexity. This is a milestone that marks both an exciting opportunity for our team and some big changes for our product.

Over the next 90 days we’ll be sunsetting Visual Electric, and our team will be forming a new Agent Experiences group at Perplexity.

While we’ve seen acquihires and shutdowns in either the AI infrastructure space (e.g., Scale AI) or coding space (e.g., Windsurf), I don’t believe we’ve seen one in the image or video gen AI space have an exit event like this yet. Obviously, The Browser Company announced their acquisition by Atlassian last month.

I believe building gen AI tools at this moment is incredibly competitive. I think it takes an even stronger stomached entrepreneur than in the pre-ChatGPT moment. So kudos for the folks at Visual Electric for having a good outcome and getting to continue to do their work at Perplexity. But I do think this is not the last that we’ll see consolidation in this space.

Tim Berners-Lee, the father of the web who gave away the technology for free, says that we are at an inflection point with data privacy and AI. But before he makes that point, he reminds us that we are the product:

Today, I look at my invention and I am forced to ask: is the web still free today? No, not all of it. We see a handful of large platforms harvesting users’ private data to share with commercial brokers or even repressive governments. We see ubiquitous algorithms that are addictive by design and damaging to our teenagers’ mental health. Trading personal data for use certainly does not fit with my vision for a free web.

On many platforms, we are no longer the customers, but instead have become the product. Our data, even if anonymised, is sold on to actors we never intended it to reach, who can then target us with content and advertising. This includes deliberately harmful content that leads to real-world violence, spreads misinformation, wreaks havoc on our psychological wellbeing and seeks to undermine social cohesion.

And about that fork in the road with AI:

In 2017, I wrote a thought experiment about an AI that works for you. I called it Charlie. Charlie works for you like your doctor or your lawyer, bound by law, regulation and codes of conduct. Why can’t the same frameworks be adopted for AI? We have learned from social media that power rests with the monopolies who control and harvest personal data. We can’t let the same thing happen with AI.

In my most recent post, I called out our design profession, for our part in developing these addictive products. Jeffrey Inscho, brings it back up to the tech industry at large and observes they’re actually publishers:

The executives at these companies will tell you they’re neutral platforms, that they don’t choose what content gets seen. This is a lie. Every algorithmic recommendation is an editorial decision. When YouTube’s algorithm suggests increasingly extreme political content to keep someone watching, that’s editorial. When Facebook’s algorithm amplifies posts that generate angry reactions, that’s editorial. When Twitter’s trending algorithms surface conspiracy theories, that’s editorial.

They are publishers. They have always been publishers. They just don’t want the responsibility that comes with being publishers.

His point is that if these social media platforms are sorting and promoting posts, it’s an editorial approach and they should be treated like newspapers. “It’s like a newspaper publisher claiming they’re not responsible for what appears on their front page because they didn’t write the articles themselves.”

The answer, Inscho argues, is regulation of the algorithms.

When I read this, I thought to myself, “Geez, this is what a designer does.” I think there is a lot of overlap between what we do as product designers and what product managers do. One critical one—in my opinion, and why we’re calling ourselves product designers—is product sense. Product sense is the skill of finding real user needs and creating solutions that have impact.

So I think people can read this with two lenses:

• If you’re a designer who executes the assignments you’re given, jumping into Figma right away, read this to be more well-rounded and understand the why of what you’re making.
• If you’re a designer who spends 80% of your time questioning everything and defining the problem, and only 20% of your time in Figma, read this to see how much overlap you actually have with a PM.

BTW, if you’re in the first bucket, I highly encourage you to gain the skills necessary to migrate to the second bucket.

While designers often stay on top of visual design trends or the latest best practices from NNG, Jules Walter suggests an even wider aperture. Writing in Lenny’s Newsletter:

Another practice for developing creativity is to spend time learning about emerging trends in technology, society, and regulations. Changes in the industry create opportunities for launching new products that can address user needs in new ways. As a PM, you want to understand what’s possible in your domain in order to come up with creative solutions.

The headline rings true to me because that’s what I look for in designers and how I run my team. The software that we build is too complex and too mission-critical for designers to vibe-code—at least given today’s tooling. But each one of the designers on my team can fill in for a PM when they’re on vacation.

Kai Wong, writing in UX Collective:

One thing I’ve learned, talking with 15 design leaders (and one CEO), is that a ‘designer who codes’ may look appealing, but a ‘designer who understands business’ is far more valuable and more challenging to replace.

You already possess the core skill that makes this transition possible: the ability to understand users with systematic observation and thoughtful questioning.

The only difference, now, is learning to apply that same methodology to understand your business.

Strategic thinking doesn’t require fancy degrees (although it may sometimes help).

Ask strategic questions about business goals. Understand how to balance user and business needs. Frame your design decisions in terms of measurable business impact.

I’m happy that the conversation around the design talent crisis continues. Carly Ayres, writing for It’s Nice That picks up the torch and speaks to designers and educators about this topic. What struck me—and I think what adds to the dialogue—is the notion of the belief gap. Ayres spoke with Naheel Jawaid, founder of Silicon Valley School of Design, about it:

“A big part of what I do is just being a coach, helping someone see their potential when they don’t see it yet,” Naheel says. “I’ve had people tell me later that a single conversation changed how they saw themselves.”

In the past, belief capital came from senior designers taking juniors under their wing. Today, those same seniors are managing instability of their own. “It’s a bit of a ‘dog eat dog world’-type vibe,” Naheel says. “It’s really hard to get mentorship right now.”

The whole piece is great. Tighter than my sprawling three-parter. I do think there’s a piece missing though. While Ayres highlights the issue and offers suggestions from designer leaders, businesses need to step up and do something about the issue—i.e., hire more juniors. Us recognizing it is the first step.

As much as I defended the preview, and as much as Apple wants to make Liquid Glass a thing, the new UI is continuing to draw criticism. Dan Moren for Six Colors:

“Glass” is the overall look of these updates, and it’s everywhere. Transparent, frosted, distorting. In some places it looks quite cool, such as in the edge distortion when you’re swiping up on the lock screen. But elsewhere, it seems to me that glass may not be quite the right material for the job. The Glass House might be architecturally impressive, but it’s not particularly practical.

It’s also a definite philosophical choice, and one that’s going to engender some criticism—much of it well-deserved. Apple has argued that it’s about getting controls out of the way, but is that really what’s happening here? It’s hard to argue that having a transparent button sitting right on top of your email is helping that email be more prominent. To take this argument to its logical conclusion, why is the keyboard not fully transparent glass over our content?

I’ve yet to upgrade myself. I will say that everyone dislikes change. Lest we forget that the now-ubiquitous flat design introduced by iOS 7 was also criticized.

Jason Spielman put up a case study on his site for his work on Google’s NotebookLM:

The mental model of NotebookLM was built around the creation journey: starting with inputs, moving through conversation, and ending with outputs. Users bring in their sources (documents, notes, references), then interact with them through chat by asking questions, clarifying, and synthesizing before transforming those insights into structured outputs like notes, study guides, and Audio Overviews.

And yes, he includes a sketch he did on the back of a napkin.

I’ve always wondered about the UX of NotebookLM. It’s not typical and, if I’m being honest, not exactly super intuitive. But after a while, it does make sense. Maybe I’m the outlier though, because Spielman’s grandmother found it easy. In an interview last year on Sequoia Capital’s Training Data, he recalls:

I actually do think part of the explosion of audio overviews was the fact it was a simple one click experience. I was on the phone with my grandma trying to explain her how to use it and it actually didn’t take any explanation. I’m like, “Drop in a source.” And she’s like, “Oh! I see. I click this button to generate it.” And I think that the ease of creation is really actually what catalyzed so much explosion. So I think when we think about adding these knobs [for customization] I think we want to do it in a way that’s very intentional.

Chatboxes have become the uber box for all things AI. The criticism of this blank box has been the cold start issue. New users don’t know what to type. Designers shipping these product mostly got around this problem by offering suggested prompts to teach users about the possibilities.

The issue on the other end is that expert users end up creating their own library of prompts to copy and paste into the chatbox for repetitive tasks.

Sharang Sharma writing in UX Collective illustrates how these UIs can be smarter by being predictive of intent:

Contrary, Predictive UX points to an alternate approach. Instead of waiting for users to articulate every step, systems can anticipate intent based on behavior or common patterns as the user types. Apple Reminders suggests likely tasks as you type. Grammarly predicts errors and offers corrections inline. Gmail’s Smart Compose even predicts full phrases, reducing the friction of drafting entirely.

Sharma says that the goal of predictive UX is to “reduce time-to-value and reframe AI as an adaptive partner that anticipates user’s intent as you type.”

Imagine a little widget that appears within the chatbox as you type. Kind of a cool idea.

Ah, this brings back memories! I spent so much time in MacPaint working with these patterns when I was young. Paul Smith faithfully recreates them:

I was working on something and thought it would be fun to use one of the classic Mac black-and-white patterns in the project. I’m talking about the original 8×8-pixel ones that were in the original Control Panel for setting the desktop background and in MacPaint as fill patterns.

I figured there’d must be clean, pixel-perfect GIFs or PNGs of them somewhere on the web. And perhaps there are, but after poking around a bit, I ran out of energy for that, but by then had a head of steam for extracting the patterns en masse from the original source, somehow. Then I could produce whatever format I needed for them.

Thinking about this morning’s link about web forms, if you abstract why it’s so powerful, you get to the point of human-computer interaction: the computer should do what the user intends, not the buttons they push.

Matt Webb reminds us about the DWIM, or Do What I Mean philosophy in computing that was coined by Warren Teitelman in 1966. Webb quotes computer scientist Larry Masinter:

DWIM is an embodiment of the idea that the user is interacting with an agent who attempts to interpret the user’s request from contextual information. Since we want the user to feel that he is conversing with the system, he should not be stopped and forced to correct himself or give additional information in situations where the correction or information is obvious.

Webb goes on to say:

Squint and you can see ChatGPT as a DWIM UI: it never, never, never says “syntax error.”

Now, arguably it should come back and ask for clarifications more often, and in particular DWIM (and AI) interfaces are more successful the more they have access to the user’s context (current situation, history, environment, etc).

But it’s a starting point. The algo is: design for capturing intent and then DWIM; iterate until that works. AI unlocks that.

Forms is one of the fundamental things we make users do in software. Whether it’s the login screen, billing address form, or a mortgage application, forms are the main method for getting data from users and into computer-accessible databases. The human is deciding what piece of information to put into which column in the database. With AI, form filling should be much simpler.

Luke Wroblewski makes the argument:

With Web forms, the burden is on people to adapt to databases. Today’s AI models, however, can flip this requirement. That is, they allow people to provide information in whatever form they like and use AI do the work necessary to put that information into the right structure for a database.

How can it work?

With AgentDB connected to an AI model (via an MCP server), a person can simply say “add this” and provide an image, PDF, audio, video, you name it. The model will use AgentDB’s template to decide what information to extract from this unstructured input and how to format it for the database. In the case where something is missing or incomplete, the model can ask for clarification or use tools (like search) to find possible answers.

I believe purity tests of any sort are problematic. And it’s much too easy to throw around the “This is AI slop!” claim. AI was used in the main title sequence for the Marvel TV show Secret Invasion. But it was on purpose and aligned with the show’s themes of shapeshifters.

Anyway, Daniel John, writing in the Creative Bloq:

[Lady] Gaga just dropped the music video for The Dead Dance, a song debuted in Season 2 of Netflix’s Wednesday. Directed by Tim Burton, it’s a suitably nightmarish black-and-white cacophony of monsters and dolls. But some are already claiming that parts of it were made using AI.

John shows a tweet from @graveyardquy as an example:

i didn’t think we’d ever be in a timeline where a tim burton x lady gaga collab would turn out to be AI slop… but here we are

We need to separate quality critiques from tool usage. If it looks good and is appropriate, I’m fine with CG, AI, and whatever comes next that helps tell the story. Same goes for what we do as designers, ’natch.

Gaga’s song is great. It’s a bop, as the kids say, with a neat music video to boot.