I’ve been on the receiving end of Layer 1226 before and it’s not fun. While I’m pretty good with my layer naming hygiene, I’m not perfect. So I welcome anything that can help rename my layers. Apparently, when Adobe showed off this new AI feature at their Adobe MAX user conference last week, it drew a big round of applause. (Figma’s had this feature since June 2024.)

There’s more than just renaming layers though. Adobe is leaning into conversational UI for editing too. For new users coming to editing tools, this makes a lot of sense because the learning curve for Photoshop is very steep. But as I’ve always said, professionals will also need fine-grained controls.

Writing for CNET, Katelyn Chedraoui:

Renaming layers is just one of many things Adobe’s new AI assistants will be able to do. These chatbot-like tools will be added to Photoshop and Express. They have an emphasis on “conversational, agentic” experiences — meaning you can ask the chatbot to make edits, and it can independently handle them.

…

Express’s AI assistant is similar to using a chatbot. Once you toggle on the tool in the upper left corner, a conversation window pops up. You can ask the AI to change the color of an object or remove an obtrusive element. While pro users might be comfortable making those edits manually, the AI assistant might be more appealing to its less experienced users and folks working under a time crunch.

A peek into Adobe’s future reveals more agentic experiences:

Also announced on Tuesday is Project Moonlight, a new platform in beta on Adobe’s AI hub, Firefly. It’s a new tool that hopes to act as a creative partner. With your permission, it uses your data from Adobe platforms and social media accounts to help you create content. For example, you can ask it to come up with 20 ideas for what to do with your newest Lightroom photos based on your most successful Instagram posts in the past. 

These AI efforts represent a range of what conversational editing can look like, Mike Polner, Adobe Firefly’s vice president of product marketing for creators said in an interview. 

“One end of the spectrum is [to] type in a prompt and say, ‘Make my hat blue.’ That’s very simplistic,” said Polner. “With Project Moonlight, it can understand your context, explore and help you come up with new ideas and then help you analyze the content that you already have,” Polner said.

In thinking about the three current AI-native web browsers, Fanny on Medium sees what lessons product designers can take from their different approaches.

On Perplexity Comet:

Design Insight: Comet succeeds by making AI feel like a natural extension of browsing, not an interruption. The sidecar model is brilliant because it respects the user’s primary task (reading, researching, shopping) while offering help exactly when context is fresh. But there’s a trade-off — Comet’s background assistant, which can handle multiple tasks simultaneously while you work, requires extensive permissions and introduces real security concerns.

On ChatGPT Atlas:

Design Insight: Atlas is making a larger philosophical statement — that the future of computing isn’t about better search, it’s about conversation as an interface. The key product decision here is making ChatGPT’s memory and context awareness central. Atlas remembers what sites you’ve visited, what you were working on, and uses that history to personalize responses. Ask “What was that doc I had my presentation plan in?” and it finds it.

On The Browser Company Dia:

Design Insight: Dia is asking the most interesting question — what happens when AI isn’t a sidebar or a search replacement, but a fundamental rethinking of input methods? The insertion cursor, the mouse, the address bar — these are the primitives of computing. Dia is making them intelligent.

She concludes that they “can’t all be right. But they’re probably all pointing at pieces of what comes next.”

I do think it’s a combo and Atlas is likely headed in the right direction. For AI to be truly assistive, it has to have relevant context. Since a lot of our lives are increasingly on the internet via web apps—and nearly everything is a web app these days—ChatGPT’s profile of you will have the most context, including your chats with the chatbot.

I began using Perplexity because I appreciated its accuracy compared with ChatGPT; this was pre-web search. But even with web search built into ChatGPT 5, I still find Perplexity’s (and therefore Comet’s) approach to be more trustworthy.

My conclusion stands though: I’m still waiting on the Arc-Dia-Comet browser smoothie.

Celine Nguyen wrote a piece that connects directly to what Ethan Mollick calls “working with wizards” and what SAP’s Ellie Kemery describes as the “calibration of trust” problem. It’s about how the interfaces we design shape the relationships we have with technology.

The through-line is metaphor. For LLMs, that metaphor is conversation. And it’s working—maybe too well:

Our intense longing to be understood can make even a rudimentary program seem human. This desire predates today’s technologies—and it’s also what makes conversational AI so promising and problematic.

When the metaphor is this good, we forget it’s a metaphor at all:

When we interact with an LLM, we instinctively apply the same expectations that we have for humans: If an LLM offers us incorrect information, or makes something up because it the correct information is unavailable, it is lying to us. …The problem, of course, is that it’s a little incoherent to accuse an LLM of lying. It’s not a person.

We’re so trapped inside the conversational metaphor that we accuse statistical models of having intent, of choosing to deceive. The interface has completely obscured the underlying technology.

Nguyen points to research showing frequent chatbot users “showed consistently worse outcomes” around loneliness and emotional dependence:

Participants who are more likely to feel hurt when accommodating others…showed more problematic AI use, suggesting a potential pathway where individuals turn to AI interactions to avoid the emotional labor required in human relationships.

…

However, replacing human interaction with AI may only exacerbate their anxiety and vulnerability when facing people.

This isn’t just about individual users making bad choices. It’s about an interface design that encourages those choices by making AI feel like a relationship rather than a tool.

The kicker is that we’ve been here before. In 1964, Joseph Weizenbaum created ELIZA, a simple chatbot that parodied a therapist:

I was startled to see how quickly and how very deeply people conversing with [ELIZA] became emotionally involved with the computer and how unequivocally they anthropomorphized it…What I had not realized is that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people.

Sixty years later, we’ve built vastly more sophisticated systems. But the fundamental problem remains unchanged.

The reality is we’re designing interfaces that make powerful tools feel like people. Susan Kare’s icons for the Macintosh helped millions understand computers. But they didn’t trick people into thinking their computers cared about them.

That’s the difference. And it matters.

Speaking of trusting AI, in a recent episode of Design Observer’s Design As, Lee Moreau speaks with four industry leaders about trust and doubt in the age of AI.

We’ve linked to a story about Waymo before, so here’s Ryan Powell, head of UX at Waymo:

Safety is at the heart of everything that we do. We’ve been at this for a long time, over a decade, and we’ve taken a very cautious approach to how we scale up our technology. As designers, what we have really focused on is that idea that more people will use us as a serious transportation option if they trust us. We peel that back a little bit. Okay, well, How do we design for trust? What does it actually mean?

Ellie Kemery, principal research lead, advancing responsible AI at SAP, on maintaining critical thinking and transparency in AI-driven products:

We need to think about ethics as a part of this because the unintended consequences, especially at the scale that we operate, are just too big, right?

…

So we focus a lot of our energy on value, delivering the right value, but we also focus a lot of our energy on making sure that people are aware of how the technology came to that output,…making sure that people are in control of what’s happening at all times, because at the end of the day, they need to be the ones making the call.

…

Everybody’s aware that without trust, there is no adoption. But there is something that people aren’t talking about as much, which is that people should also not blindly trust a system, right? And there’s a huge risk there because, humans we tend to, you know, we’ll try something a couple of times and if it works it works. And then we lose that critical thinking. We stop checking those things and we simply aren’t in a space where we can do that yet. And so making sure that we’re focusing on the calibration of trust, like what is the right amount of trust that people should have to be able to benefit from the technology while at the same time making sure that they’re aware of the limitations.

Ethan Mollick, a professor of entrepreneurship at the Wharton School says that AI has gotten so good that our relationship with them is changing. “We’re moving from partners to audience, from collaboration to conjuring,” he says.

He fed NotebookLM his book and 140 Substack posts and asked for a video overview. AI famously hallucinates. But Mollick found no factual errors in the six-minute video.

We’re shifting from being collaborators who shape the process to being supplicants who receive the output. It is a transition from working with a co-intelligence to working with a wizard. Magic gets done, but we don’t always know what to do with the results. This pattern — impressive output, opaque process — becomes even more pronounced with research tasks.

Mollick believes that the most wizard-like model today is GPT-5 Pro. He uploaded an academic paper that took him a year to write, which was peer-reviewed, and was then published in a major journal…

Nine minutes and forty seconds later, I had a very detailed critique. This wasn’t just editorial criticism, GPT-5 Pro apparently ran its own experiments using code to verify my results, including doing Monte Carlo analysis and re-interpreting the fixed effects in my statistical models. It had many suggestions as a result (though it fortunately concluded that “the headline claim [of my paper] survives scrutiny”), but one stood out. It found a small error, previously unnoticed. The error involved two different sets of numbers in two tables that were linked in ways I did not explicitly spell out in my paper. The AI found the minor error, no one ever had before.

Later in his post, Mollick says that there’s a problem with this wizardry—it’s too opaque. So what can we do?

First, learn when to summon the wizard versus when to work with AI as a co-intelligence or to not use AI at all. AI is far from perfect, and in areas where it still falls short, humans often succeed. But for the increasing number of tasks where AI is useful, co-intelligence, and the back-and-forth it requires, is often superior to a machine alone. Yet, there are, increasingly, times when summoning a wizard is best, and just trusting what it conjures.

Second, we need to become connoisseurs of output rather than process. We need to curate and select among the outputs the AI provides, but more than that, we need to work with AI enough to develop instincts for when it succeeds and when it fails.

And lastly, trust it. Trust the technology, he suggests. “The question isn’t ‘Is this completely correct?’ but ‘Is this useful enough for this purpose?’”

I think we’re in that transition period. AI is indeed dastardly great at some things and constantly getting better at the tasks it’s not. But we all know where this is headed.

In this era of AI, we’ve been taught that LLMs are probabilistic, not deterministic, and that they will sometimes hallucinate. There’s a saying in AI circles that humans are right about 80% of the time, and so are AIs. Except when less than 100% accuracy is unacceptable. Accountants need to be 100% accurate, lest they lose track of money for their clients or businesses.

And that’s the problem Intuit had to solve to roll out their AI agent. Sean Michael Kerner, writing in VentureBeat:

Even when its accounting agent improved transaction categorization accuracy by 20 percentage points on average, they still received complaints about errors.

“The use cases that we’re trying to solve for customers include tax and finance; if you make a mistake in this world, you lose trust with customers in buckets and we only get it back in spoonfuls,” Joe Preston, Intuit’s VP of product and design, told VentureBeat.

So they built an agent that queries data from a multitude of sources and returns those exact results. But do users trust those results? It comes down to a design decision on being transparent:

Intuit has made explainability a core user experience across its AI agents. This goes beyond simply providing correct answers: It means showing users the reasoning behind automated decisions.

When Intuit’s accounting agent categorizes a transaction, it doesn’t just display the result; it shows the reasoning. This isn’t marketing copy about explainable AI, it’s actual UI displaying data points and logic.

“It’s about closing that trust loop and making sure customers understand the why,” Alastair Simpson, Intuit’s VP of design, told VentureBeat.

We’ve been hearing a lot about AI agents and now enough time has passed that we’re starting to see some learnings in industry. Writing in Harvard Business Review, Linda Mantia, Surojit Chatterjee and Vivian S. Lee showcase three case studies of enterprises that have deployed AI agents.

They write about Hitachi Digital and how they deployed an AI agent as the first responder to the 90,000 questions employees send to their HR team annually.

Every year, employees put over 90,000 questions about everything from travel policies and remote work to training and IT support to the company’s HR team of 120 human responders. Answering these queries can be difficult, in part because of Hitachi’s complex infrastructure of over 20 systems of record, including multiple disparate HR systems, various payroll providers, and different IT environments.

Their system, called “Skye,” is actually a system of agents, coordinating with one another and firing off queries depending on the intent and task.

For example, the intent classifier agent sends a simple policy question like “What are allowed expenses for traveling overseas?” or “Does this holiday count in paid time off?” to a file search and respond agent, which provides immediate answers by examining the right knowledge base given the employee’s position and organization. A document generation agent can create employee verification letters (which verify individuals’ employment status) in seconds, with an option for human approval. When an employee files a request for vacation, the leave management agent uses the appropriate HR management system based on its understanding of the user’s identity, completes the necessary forms, waits for the approval of the employee’s manager, and reports back to the employee.

The authors see three essential imperatives when designing and deploying AI agents into companies.

1. Design around outcomes and appoint accountable mission owners. Companies need to stop organizing around internal functions and start building teams around actual customer outcomes—which means putting someone in charge of the whole journey, not just pieces of it.
2. Unlock data silos and clarify the business logic. Your data doesn’t need to be perfect or centralized, but you do need to map out how work actually gets done so AI agents know where to find things and what decisions to make.
3. Develop the leaders and guardrails that intelligent systems require. You can’t just drop AI agents into your org and hope for the best—leaders need to understand how these systems work, build trust with their teams, and put real governance in place to keep things on track.

It’s interesting to me that Figma had to have a separate conference and set of announcements focused on design systems. In some sense it’s an indicator of how big and mature this part of design has become.

A few highlights from my point-of-view…

Slots seems to solve one of those small UX paper cuts—those niggly inconveniences that we just lived with. But this is a big deal. You’ll be able to add layers within component instances without breaking the connection to your design system. No more pre-building hidden list items or forcing designers to detach components. Pretty advanced stuff.

On the code front, they’re making Code Connect actually approachable with a new UI that connects directly to GitHub and uses AI to map components. The Figma MCP server is out of beta and now supports design system guidelines—meaning your agentic coding tools can actually respect your design standards. Can’t wait to try these.

For teams like mine that are using Make, you’ll be able to pull in design systems through two routes: Make kits (generate React and CSS from Figma libraries) or npm package imports (bring in your existing code components). This is the part where AI-assisted design doesn’t have to mean throwing pixelcraft out the window.

Design systems have always been about maintaining quality at scale. These updates are very welcomed.

As a follow-up to our previous item on Claude Code, here’s an article by Nick Babich who gives us three ways product designers can use Claude to code.

Remember that Anthropic’s Claude has been the leading LLM for coding for a while now.

With Cursor and Lovable as the darlings of AI coding tools, don’t sleep on Claude Code. Personally, I’ve been splitting my time between Claude Code and Cursor. While Claude Code’s primary persona is coders and tinkerers, it can be used for so much more.

Lenny Rachitsky calls it “the most underrated AI tool for non-technical people.”

The key is to forget that it’s called Claude Code and instead think of it as Claude Local or Claude Agent. It’s essentially a super-intelligent AI running locally, able to do stuff directly on your computer—from organizing your files and folders to enhancing image quality, brainstorming domain names, summarizing customer calls, creating Linear tickets, and, as you’ll see below, so much more.

Since it’s running locally, it can handle huge files, run much longer than the cloud-based Claude/ChatGPT/Gemini chatbots, and it’s fast and versatile. Claude Code is basically Claude with even more powers.

Rachitsky shares 50 of his “favorite and most creative ways non-technical people are using Claude Code in their work and life.”

Slow and steady wins the race, so they say. And in Waymo’s case, that’s true. Unlike the stereotypical Silicon Valley of “Move fast and break things,” Waymo has been very deliberate and intentional in developing its self-driving tech. In other words, they’re really trying to account for the unintended consequences.

Writing for The Atlantic, Saahil Desai:

Compared with its robotaxi competitors, “Waymo has moved the slowest and the most deliberately,” [Bryant Walker Smith] said—which may be a lesson for the world’s AI developers. The company was founded in 2009 as a secretive project inside of Google; a year later, it had logged 1,000 miles of autonomous rides in a tricked-out Prius. Close to a decade later, in 2018, Waymo officially launched its robotaxi service. Even now, when Waymos are inching their way into the mainstream, the company has been hypercautious. The company is limited to specific zones within the five cities it operates in (San Francisco, Phoenix, Los Angeles, Austin, and Atlanta). And only Waymo employees and “a growing number of guests” can ride them on the highway, Chris Bonelli, a Waymo spokesperson, told me. Although the company successfully completed rides on the highway years ago, higher speeds bring more risk for people and self-driving cars alike. What might look like a few grainy pixels to Waymo’s cameras one moment could be roadkill to swerve around the very next.

OK, so there’s workslop, but there’s also general AI slop. With OpenAI’s recent launch of the Sora app, there going to be more and more AI-generated image and video content making the rounds. I do believe that there’s a place for using AI to generate imagery. It can be done well (see Christian Haas’s “AI Jobs”). Or not.

Casey Newton, writing in his Platformer newsletter:

In Sora we find the entire debate over AI-generated media in miniature. On one hand, the content now widely derided as “slop” continually receives brickbats on social media, in blog posts and in YouTube comments. And on the other, some AI-generated material is generating millions of views — presumably not all from people who are hate-watching it.

As the content on the internet is increasingly AI-generated, platforms will need to balance how much of it they let in, lest the overall quality drops.

As Sarah Perez noted at TechCrunch, Pinterest has come under fire from its user base all year for a perceived decline in quality of the service as the percentage of slop there increases. Many people use the service to find real objects they can buy and use; the more that those objects are replaced with AI fantasies, the worse Pinterest becomes for them.

Like most platforms, Pinterest sees little value in banning slop altogether. After all, some people enjoy looking at fantastical AI creations. At the same time, its success depends in some part on creators believing that there is value in populating the site with authentic photos and videos. The more that Pinterest’s various surfaces are dominated by slop, the less motivated traditional creators may be to post there.

Sticking with the workslop or outsourcing our main work to AI, Douglas Rushkoff writes in Fast Company:

By using the AI to do the big stuff—by outsourcing our primary competencies to the machines instead of giving them the boring busywork—we deskill ourselves and deprive everyone of the opportunity for AI-enhanced outputs. Too many of us are using AI as the primary architect for a project, rather than the general contractor who supports the architect’s human vision.

People forget that it’s the process of doing something that helps us synthesize and form the connections necessary for innovation.

As the researcher behind MIT’s study “This is Your Brain on ChatGPT” explained at a recent ANDUS event, when people turn to an AI for a solution before working on a problem themselves, the number of connections formed in their brains decreases. But when they turn to the AI after working on the problem for a while, they end up with more neural connections than if they worked entirely alone.

That’s because the value of the AI is not its ability to create product for us, but to engage with us in our process. Working and iterating with an AI—doing what we could call generative thinking—is actually a break from Industrial Age thinking. We focus less on outputs than on cycles. Less on the volume of short-term results (assembly line), and more on the quality and complexity of thought and innovation.

Speaking of workslop, here’s an article from NN/g on how to avoid falling into over-reliance on AI in our design field. They call it the “7 Deadly AI Sins for UX Professionals.”

1. Outsourced Thinking
2. Wasted Time
3. Lost Details
4. Isolated Ideation
5. Naïve Trust
6. Bland Taste
7. Defensive Outlook

As Tanner Kohler writes:

It’s not about avoiding AI. It’s about maintaining your own growth and the quality of your work as you use AI. AI will constantly be changing. Never let yourself slip into repeatedly committing the sins that weaken you and your UX skills.

Definitely use AI at work if you can. You’d be guilty of professional negligence if you don’t. But, you must not blindly take output from ChatGPT, Claude, or Gemini and use it as-is. You have to check it, verify that it’s free from hallucinations, and applicable to the task at hand. Otherwise, you’ll generate “workslop.”

Kate Niederhoffer, Gabriella Rosen Kellerman, et. al., in Harvard Business Review, report on a study by Stanford Social Media Lab and BetterUp Labs. They write, “Employees are using AI tools to create low-effort, passable looking work that ends up creating more work for their coworkers.”

Here’s how this happens. As AI tools become more accessible, workers are increasingly able to quickly produce polished output: well-formatted slides, long, structured reports, seemingly articulate summaries of academic papers by non-experts, and usable code. But while some employees are using this ability to polish good work, others use it to create content that is actually unhelpful, incomplete, or missing crucial context about the project at hand. The insidious effect of workslop is that it shifts the burden of the work downstream, requiring the receiver to interpret, correct, or redo the work. In other words, it transfers the effort from creator to receiver.

Don’t be like this. Use it to do better work, not to turn in mediocre work.

Workslop may feel effortless to create but exacts a toll on the organization. What a sender perceives as a loophole becomes a hole the recipient needs to dig out of. Leaders will do best to model thoughtful AI use that has purpose and intention. Set clear guardrails for your teams around norms and acceptable use. Frame AI as a collaborative tool, not a shortcut. Embody a pilot mindset, with high agency and optimism, using AI to accelerate specific outcomes with specific usage. And uphold the same standards of excellence for work done by bionic human-AI duos as by humans alone.

The web is a magical place. It started out as a way to link documents like research papers across the internet, but has evolved into the representation of the internet and the place where we get information and get things done. Writer Will Leitch on Medium:

It is difficult to describe, to a younger person or, really, anyone who wasn’t there, what the emergence of the Internet — this thing that had not been there your entire life, that you had no idea existed, that was suddenly just everywhere — meant to someone who wanted to write. When I graduated college in 1997, the expectation for me, and most wanna-be writers, was that we had two options: Start on the bottom rung of a print publication and toil away for years, hoping that enough people with jobs above you would retire or die in time for you to get a real byline by the time you were 40, or write a brilliant novel or memoir that turned you into Dave Eggers or Elizabeth Wurtzel. That was pretty much it! Then, suddenly, from the sky, there was this place where you could:

• Write whatever you wanted.
• Write as long as you wanted.
• Have your work available to read by anyone, anywhere on the entire freaking planet.

This was — and still is — magical.

The core argument of what Leitch write is that while the business and traffic models that fueled web publishing are collapsing—due to changing priorities of platforms like Google and the dominance of video on social media (i.e., TikTok and Reels), the essential, original magic of publishing on the web isn’t dead.

But that does not mean that Web publishing — that writing on the Internet, the pure pleasure of putting something out in the world and having it be yours, of discovering other people who are doing the same thing — itself is somehow dead, or any less magical than it was in the first place. Because it is magical. It still is. It always was.

Noah Davis writing in Web Designer Depot, says aloud what I’d thought—but never wrote down—before AI, templates started to kill creativity in web design.

If you’re wondering why the web feels dead, lifeless, or like you’re stuck in a scrolling Groundhog Day of “hero image, tagline, three icons, CTA,” it’s not because AI hallucinated its way into the design department.

It’s because we templatified creativity into submission!

We used to design websites like we were crafting digital homes—custom woodwork, strange hallways, surprise color choices, even weird sound effects if you dared. Each one had quirks. A personality. A soul.

When I was coming up as a designer in the late 1990s and early 2000s, one of my favorite projects was designing Pixar.com. The animation studio’s soul—and by extension the soul I’d imbue into the website—was story. The way this manifest was a linear approach to the site, similar to a slideshow, to tell the story of each of their films.

And as the web design industry grew, and everyone needed and wanted a website, from Fortune 500s to the local barber shop, access to well-designed websites was made possible via templates.

Let’s be real: clients aren’t asking for design anymore. They’re asking for “a site like this.” You know the one. It looks clean. It has animations. It scrolls smoothly. It’s “modern.” Which, in 2025, is just a euphemism for “I want what everyone else has so I don’t have to think.”

Templates didn’t just streamline web development. They rewired what people expect a website to be.

Why hire a designer when you can drop your brand colors into a no-code template, plug in some Lottie files, and call it a day? The end result isn’t bad. It’s worse than bad. It’s forgettable.

Davis ends his rant with a call to action: “If you want design to live, stop feeding the template machine. Build weird stuff. Ugly stuff. Confusing stuff. Human stuff.”

Designer Ben Holliday writes a wonderful deep dive into how caring is good design. In it, he references the conversation that Jony Ive had with Patrick Collison a few months ago. (It’s worth watching in its entirety if you haven’t already.)

Watching the interview back, I was struck by how he spoke about applying care to design, describing how:

“…everyone has the ability to sense the care in designed things because we can all recognise carelessness.”

Talking about the history of industrial design at Apple, Ive speaks about the care that went into the design of every product. That included the care that went into packaging – specifically things that might seem as inconsequential as how a cable was wrapped and then unpackaged. In reality, the type of small interactions that millions of people experienced when unboxing the latest iPhone. These are details that people wouldn’t see as such, but Ive and team believed that they would sense care when they had been carefully considered and designed.

This approach has always been a part of Jony Ive’s design philosophy, or the principles applied by his creative teams at Apple. I looked back and found an earlier 2015 interview and notes I’d made where he says how he believes that the majority of our manufactured environment is characterised by carelessness. But then, how, at Apple, they wanted people to sense care in their products.

The attention to detail and the focus and attention we can all bring to design is care. It’s important.

Holliday’s career has been focused in government, public sector, and non-profit environments. In other words, he thinks a lot about how design can impact people’s lives at massive scale.

In the past few months, I’ve been drawn to the word ‘careless’ when thinking about the challenges faced by our public services and society. This is especially the case with the framing around the impact of technology in our lives, and increasingly the big bets being made around AI to drive efficiency and productivity.

The word careless can be defined as the failure to give sufficient attention to avoiding harm or errors. Put simply, carelessness can be described as ‘negligence’.

Later, he cites Facebook/Meta’s carelessness when they “used data to target young people when at their most vulnerable,” specifically, body confidence.

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.”