OpenAI is acquiring a hardware company called “io” that Jony Ive cofounded just a year ago:

Two years ago, Jony Ive and the creative collective LoveFrom, quietly began collaborating with Sam Altman and the team at OpenAI.

…

It became clear that our ambitions to develop, engineer and manufacture a new family of products demanded an entirely new company. And so, one year ago, Jony founded io with Scott Cannon, Evans Hankey and Tang Tan.

We gathered together the best hardware and software engineers, the best technologists, physicists, scientists, researchers and experts in product development and manufacturing. Many of us have worked closely for decades.

The io team, focused on developing products that inspire, empower and enable, will now merge with OpenAI to work more intimately with the research, engineering and product teams in San Francisco.

It has been an open rumor that Sam Altman and Ive has been working together on some hardware. I had assumed they formalized their partnership already, but I guess not.

There are some bold statements that Ive and Altman make in the launch video, teasing a revolutionary new device that will enable quicker, better access to ChatGPT. Something that is a lot less friction than how Altman explains in the video:

If I wanted to ask ChatGPT something right now about something we had talked about earlier, think about what would happen. I would like reached down. I would get on my laptop, I’d open it up, I’d launch a web browser, I’d start typing, and I’d have to, like, explain that thing. And I would hit enter, and I would wait, and I would get a response. And that is at the limit of what the current tool of a laptop can do. But I think this technology deserves something much better.

There are a couple of other nuggets about what this new device might be from the statements Ive and Altman made to Bloomberg:

…Ive and Altman don’t see the iPhone disappearing anytime soon. “In the same way that the smartphone didn’t make the laptop go away, I don’t think our first thing is going to make the smartphone go away,” Altman said. “It is a totally new kind of thing.”

…

“We are obviously still in the terminal phase of AI interactions,” said Altman, 40. “We have not yet figured out what the equivalent of the graphical user interface is going to be, but we will.”

While we don’t know what the form factor will be, I’m sure it won’t be a wearable pin—ahem, RIP Humane. Just to put it out there—I predict it will be a voice assistant in an earbud, very much like the AI in the 2013 movie “Her.” Altman has long been obsessed with the movie, going as far as trying to get Scarlett Johansson to be one of the voices for ChatGPT.

EDIT 5/22/2025, 8:58am PT: Added prediction about the form factor.

Sam Bradley, writing for Digiday:

One year in from the launch of Google’s AI Overviews, adoption of AI-assisted search tools has led to the rise of so-called “zero-click search,” meaning that users terminate their search journeys without clicking a link to a website.

“People don’t search anymore. They’re prompting, they’re gesturing,” said Craig Elimeliah, chief creative officer at Code and Theory.

It’s a deceptively radical change to an area of the web that evolved from the old business of print directories and classified sections — one that may redefine how both web users and marketing practitioners think about search itself.

And I wrote about answer engines, earlier this year in January:

…the fundamental symbiotic economic relationship between search engines and original content websites is changing. Instead of sending traffic to websites, search engines, and AI answer engines are scraping the content directly and providing them within their platforms.

I was recently featured on the Design of AI podcast to discuss my article that pit eight AI prompt-to-code tools head to head. We talked through the list but I also offered a point of view on where I see the gap.

Arpy Dragffy and Brittany Hobbs close out the episode this way (emphasis mine):

So it’s great that Roger did that analysis and that evaluation. I honestly am a bit shocked by those results. Again, his ranking was that Subframe was number one, Onlook was two, v0 number three, Tempo number four. But again, if you look at his matrix, only two of the tools scored over 70 out of 100 and only one of the tools he could recommend. And this really shines a dark light on AI products and their maturity right now**.** But I suspect that this comes down to the strategy that was used by some of these products. If you go to them, almost every single one of them is actually a coding tool, except the two that scored the highest.

Onlook, its headline is “The Cursor for Designers.” So of course it’s a no brainer that makes a lot of sense. That’s part of their use cases, but nonetheless it didn’t score that good in his matrix.

The top scoring one from his list Subframe is directly positioned to designers. The title is “Design meet code.” It looks like a UI editor. It looks like the sort of tool that designers wish they had. These tools are making it easier for product managers to run research programs, to turn early prototypes and ideas into code to take code and really quick design changes. When you need to make a change to a website, you can go straight into one of these tools and stand up the code.

Listen on Apple Podcasts and Spotify.

I tried early versions of Stable Diffusion be ended up using exclusively Midjourney because of the quality. I’m excited to check out the full list. (Oh, and of course I’ve used DALL-E as well via ChatGPT. But there’s not a lot of control there.)

John Gruber wrote a hilarious rant about the single-story a in the iOS Notes app:

I absolutely despise the alternate single-story a glyph that Apple Notes uses. I use Notes every single day and this a bothers me every single day. It hurts me. It’s a childish silly look, but Notes, for me, is one of the most serious, most important apps I use.

Since that sparked some conversation online, he followed up with a longer post about typography in early versions of the Mac system software:

…Apple actually shipped System 1.0 with a version of Geneva with a single-story a glyph — but only in the 9-point version of Geneva. At 12 points (and larger), Geneva’s a was double-story.

To me, it does make sense that 9-point Geneva would have a single-story a, since there are less pixels to draw the glyph well and to distinguish better from the lowercase e.

Allen Pike:

While chat is powerful, for most products chatting with the underlying LLM should be more of a debug interface – a fallback mode – and not the primary UX.

I agree. Pike breaks down the various non-chat UIs in his post.

For as long as I can remember, I’ve been fascinated by how television shows and movies are made. I remember the specials ABC broadcast about the making of The Empire Strikes Back and other Lucasfilm movies like the Indiana Jones series. More recently—especially with the advent of podcasts—I’ve loved listening to how show runners think about writing their shows. For example, as soon as an episode of Battlestar Galactica aired, I would rewatch it with Ronald D. Moore’s commentary. These days, I‘m really enjoying the official The Last of Us podcast because it features commentary from both Craig Mazin and Neil Druckmann.

Anyway, thinking about personas as characters from TV shows and movies and using screenwriting techniques is right up my alley. Laia Tremosa for the IxDF:

Hollywood spends millions to bring characters to life. UX design teams sometimes spend weeks… only to make personas no one ever looks at again. So don’t aim for personas that look impressive in a slide deck. Aim for personas that get used—in design reviews, product decisions, and testing plans.

Be the screenwriter. Be the director. Be the casting agent.

As a certified Star Wars geek, I love this TED talk from ILM’s Rob Bedrow. For the uninitiated, Industrial Light & Magic, or ILM, is the company that George Lucas founded to make all the special effects for the original and subsequent Star Wars films. The firm has been an award-winning pioneer in special and visual effects, responsible for the dinosaurs in Jurassic Park, the liquid metal T-1000 in Terminator 2: Judgement Day, and the de-aging of Harrison Ford in Indiana Jones and the Dial of Destiny.

The point Bedrow makes is simple: ILM creates technology in service of the storyteller, or creative.

I believe that we’re designed to be creative beings. It’s one of the most important things about us. That’s one of the reasons we appreciate and we just love it when we see technology and creativity working together. We see this on the motion control on the original “Star Wars” or on “Jurassic Park” with the CG dinosaurs for the first time. I think we just love it when we see creativity in action like this. Tech and creative working together. If we fast forward to 2020, we can see the latest real-time virtual production techniques. This was another creative innovation driven by a filmmaker. In this case, it’s Jon Favreau, and he had a vision for a giant Disney+ “Star Wars” series.

He later goes on to show a short film test made be a lone artist at ILM using an internal AI tool. It’s never-before-seen creatures that could exist in the Star Wars universe. I mean, for now they look like randomized versions of Earth animals and insects, but if you squint, you can see where the technology is headed.

Bedrow goes on…

Now the tech companies on their own, they don’t have the whole picture, right? They’re looking at a lot of different opportunities. We’re thinking about it from a filmmaking perspective. And storytellers, we need better artist-focused tools. Text prompts alone, they’re not great ways to make a movie. And it gets us excited to think about that future where we are going to be able to give artists these kinds of tools.

Again, artists—or designers, or even more broadly, professionals—need fine-grained control to adjust the output of AI.

Watch the whole thing. Instead of a doom and gloom take on AI, it’s an uplifting one that shows us what’s possible.

A lot of young designers love to look at what’s contemporary, what’s trending on Dribbble or Instagram. But I think to look forward, we must always study our past. I spent the week in New York City, on vacation. My wife and I attended a bunch of Broadway shows and went to the Museum of Broadway, where I became enamored with a lot of the poster art. (’Natch.) I may write about that soon.

Coincidentally, Matthew Strom wrote about the history of album art, featuring the first album cover ever, which uses a photo of the Broadway theater, the Imperial, where I saw Smash earlier this week.

A lot of chatter in the larger design and development community has been either “AI is the coolest” or “AI is shite and I want nothing to do with it.”

Tobias van Schneider puts it plainly:

AI is here to stay.

Resistance is futile. Doesn’t matter how we feel about it. AI has arrived, and it’s going to transform every industry, period. The ship has sailed, and we’re all along for the ride whether we like it or not. Not using AI in the future is the equivalent to not using the internet. You can get away with it, but it’s not going to be easy for you.

He goes on to argue that craftspeople have been affected the most, not only by AI, but by the proliferation of stock and templates:

The warning signs have been flashing for years. We’ve witnessed the democratization of design through templates, stock assets, and simplified tools that turned specialized knowledge into commodity. Remember when knowing Photoshop guaranteed employment? Those days disappeared years ago. AI isn’t starting this fire, it’s just pouring gasoline on it. The technical specialist without artistic vision is rapidly becoming as relevant as a telephone operator in the age of smartphones. It’s simply not needed anymore.

But he’s not all doom and gloom.

If the client could theoretically do everything themselves with AI, then why hire a designer?

Excellent question. I believe there are three reasons to continue hiring a designer:

1. Clients lag behind. It’ll takes a few years before they fully catch up and stop hiring creatives for certain tasks, at which point creatives have caught up on what makes them worthy (beyond just production output).

2. Clients famously don’t know what they want. That’s the primary reason to hire a designer with a vision. Even with AI at their fingertips, they wouldn’t know what instructions to give because they don’t understand the process.

3. Smart clients focus on their strengths and outsource the rest. If I run a company I could handle my own bookkeeping, but I’ll hire someone. Same with creative services. AI won’t change that fundamental business logic. Just because I can, doesn’t mean I should.

And finally, he echoes the same sentiment that I’ve been saying (not that I’m the originator of this thought—just great minds think alike!):

What differentiates great designers then?

The Final Filter: taste & good judgment

Everyone in design circles loves to pontificate about taste, but it’s always the people with portfolios that look like a Vegas casino who have the most to say. Taste is the emperor’s new clothes of the creative industry, claimed by all, possessed by few, recognized only by those who already have it.

In other words, as designers, we need to lean into our curation skills.

Dan Maccarone:

If users don’t trust the systems we design, that’s not a PM problem. It’s a design failure. And if we don’t fix it, someone else will, probably with worse instincts, fewer ethics, and a much louder bullhorn.

UX is supposed to be the human layer of technology. It’s also supposed to be the place where strategy and empathy actually talk to each other. If we can’t reclaim that space, can’t build products people understand, trust, and want to return to, then what exactly are we doing here?

It is a long read but well worth it.

I love this wonderfully written piece by Julie Zhou exploring the Ghiblification of everything. On how we feel about a month later:

The second watching never commands the same awe as the first. The 20th bite doesn’t dance on the tongue as exquisitely. And the 200th anime portrait certainly no longer impresses the way it once did.

The sad truth is that oversaturation strangles quality. Nothing too easy can truly be tasteful.

She goes on to make a point that Studio Ghibli’s quality is beyond style—it’s of narrative and imagination.

AI-generated images in the “Ghibli style” may borrow its surface features but they don’t capture the soul of what makes Studio Ghibli exceptional in quality. They lack the narrative depth, the handcrafted devotion, and the cultural resonance.

Like a celebrity impersonator, the ChatGPT images borrow from the cache of the original. But sadly, hollowly, it’s not the same. What made the original shimmer is lost in translation.

And rather than going down the AI-is-enshitification conversation, Zhou pivots a little, focusing on the technological quality and the benefits it brings.

…ChatGPT could offer a flavor of magic that Studio Ghibli could never achieve, the magic of personalization.

…

The quality of Ghibli-fication is the quality of the new image model itself, one that could produce so convincing an on-the-fly facsimile of a photograph in a particular style that it created a “moment” in public consciousness. ChatGPT 4o beat out a number of other image foundational models for this prize.

With their annual user conference, Config, coming up in San Francisco in less than two weeks, Figma released their 2025 AI Report today.

Andrew Hogan, Insights lead:

While developers and designers alike recognize the importance of integrating AI into their workflows, and overall adoption of AI tools has increased, there’s a disconnect in sentiment around quality and efficacy between the two groups.

Developers report higher satisfaction with AI tools (82%) and feel AI improves the quality of their work (68%). Meanwhile, designers show more modest numbers—69% satisfaction rate and 54% reporting quality improvement—suggesting this group’s enthusiasm lags behind their developer counterparts.

This divide stems from how AI can support existing work and how it’s being used: 59% of developers use AI for core development responsibilities like code generation, whereas only 31% of designers use AI in core design work like asset generation. It’s also likely that AI’s ability to generate code is coming into play—68% of developers say they use prompts to generate code, and 82% say they’re satisfied with the output. Simply put, developers are more widely finding AI adoption useful in their day-to-day work, while designers are still working to determine how and if these tools best fit into their processes.

I can understand that. Code is behind the scenes. If it’s not perfect, no one will really know. But design is user-facing, so quality is more important.

Looking into the future:

Though AI’s impact on efficiency is clear, there are still questions about how to use AI to make people better at their role. This disparity between efficiency and quality is an ongoing battle for users and creators alike.

…

Looking forward, predictions about the impact of AI on work are moderate—AI’s expected impact for the coming year isn’t much higher than its expected impact last year.

In the full report, Hogan details out:

Only 27% predict AI will have a significant impact on their company goals in the next year (compared to 23% in 2024), with 15% saying it will be transformational (unchanged year-over-year).

The survey was taken in January with a panel of 2,500 users. Things in AI change in weeks. I’m surprised at the number and part of me believes that a lot of designers are hiding their heads in the sand. AI is coming. We should be agile and adapt.

Elliot Vredenburg writing for Fast Company:

Which is why creative direction matters more now than ever. If designers are no longer the makers, they must become the orchestrators. This isn’t without precedent. Rick Rubin doesn’t read music or play instruments. Virgil Abloh was more interested in recontextualizing than inventing. Their value lies not in original execution but in framing, curation, and translation. The same is true now for brand designers. Creative direction is about synthesizing abstract ideas into aesthetic systems—shaping meaning through how things feel, not just how they look.

You might not know his name—I sure didn’t—but you’ll surely recognize his illustration style that came to embody the style du jour of the 1960s and ’70s. Robert E. McGinnis has died at the age of 99. The New York Times has an obituary:

Robert E. McGinnis, an illustrator whose lusty, photorealistic artwork of curvaceous women adorned more than 1,200 pulp paperbacks, as well as classic movie posters for “Breakfast at Tiffany’s,” featuring Audrey Hepburn with a cigarette holder, and James Bond adventures including “Thunderball,” died on March 10 at his home in Greenwich, Conn. He was 99.

…

Mr. McGinnis’s female figures from the 1960s and ’70s flaunted a bold sexuality, often in a state of semi undress, whether on the covers of detective novels by John D. MacDonald or on posters for movies like “Barbarella” (1968), with a bikini-clad Jane Fonda, or Bond films starring Sean Connery and Roger Moore.

While Josh W. Comeau writes for his developer audience, a lot of what he says can be applied to design. Referring to a recent Forbes article:

AI may be generating 25% of the code that gets committed at Google, but it’s not acting independently. A skilled human developer is in the driver’s seat, using their knowledge and experience to guide the AI, editing and shaping its output, and mixing it in with the code they’ve written. As far as I know, 100% of code at Google is still being created by developers. AI is just one of many tools they use to do their job.

In other words, developers are editing and curating the output of AI, just like where I believe the design discipline will end up soon.

On incorporating Cursor into his workflow:

And that’s kind of a problem for the “no more developers” theory. If I didn’t know how to code, I wouldn’t notice the subtle-yet-critical issues with the model’s output. I wouldn’t know how to course-correct, or even realize that course-correction was required!

I’ve heard from no-coders who have built projects using LLMs, and their experience is similar. They start off strong, but eventually reach a point where they just can’t progress anymore, no matter how much they coax the AI. The code is a bewildering mess of non sequiturs, and beyond a certain point, no amount of duct tape can keep it together. It collapses under its own weight.

I’ve noticed that too. For a non-coder like me, rebuilding this website yet again—I need to write a post about it—has been a challenge. But I knew and learned enough to get something out there that works. But yes, relying solely on AI for any professional work right now is precarious. It still requires guidance.

On the current job market for developers and the pace of AI:

It seems to me like we’ve reached the point in the technology curve where progress starts becoming more incremental; it’s been a while since anything truly game-changing has come out. Each new model is a little bit better, but it’s more about improving the things it already does well rather than conquering all-new problems.

This is where I will disagree with him. I think the AI labs are holding back the super-capable models that they are using internally. Tools like Claude Code and the newly-released OpenAI Codex are clues that the foundational model AI companies have more powerful agents behind-the-scenes. And those agents are building the next generation of models.

There are many dimensions to this well-researched forecast about how AI will play out in the coming years. Daniel Kokotajlo and his researchers have put out a document that reads like a sci-fi limited series that could appear on Apple TV+ starring Andrew Garfield as the CEO of OpenBrain—the leading AI company. …Except that it’s all actually plausible and could play out as described in the next five years.

Before we jump into the content, the design is outstanding. The type is set for readability and there are enough charts and visual cues to keep this interesting while maintaining an air of credibility and seriousness. On desktop, there’s a data viz dashboard in the upper right that updates as you read through the content and move forward in time. My favorite is seeing how the sci-fi tech boxes move from the Science Fiction category to Emerging Tech to Currently Exists.

The content is dense and technical, but it is a fun, if frightening, read. While I’ve been using Cursor AI—one of its many customers helping the company get to $100 million in annual recurring revenue (ARR)—for side projects and a little at work, I’m familiar with its limitations. Because of the limited context window of today’s models like Claude 3.7 Sonnet, it will forget and start munging code if not treated like a senile teenager.

The researchers, describing what could happen in early 2026 (“OpenBrain” is essentially OpenAI):

OpenBrain continues to deploy the iteratively improving Agent-1 internally for AI R&D. Overall, they are making algorithmic progress 50% faster than they would without AI assistants—and more importantly, faster than their competitors.

The point they make here is that the foundational model AI companies are building agents and using them internally to advance their technology. The limiting factor in tech companies has traditionally been the talent. But AI companies have the investments, hardware, technology and talent to deploy AI to make better AI.

Continuing to January 2027:

Agent-1 had been optimized for AI R&D tasks, hoping to initiate an intelligence explosion. OpenBrain doubles down on this strategy with Agent-2. It is qualitatively almost as good as the top human experts at research engineering (designing and implementing experiments), and as good as the 25th percentile OpenBrain scientist at “research taste” (deciding what to study next, what experiments to run, or having inklings of potential new paradigms). While the latest Agent-1 could double the pace of OpenBrain’s algorithmic progress, Agent-2 can now triple it, and will improve further with time. In practice, this looks like every OpenBrain researcher becoming the “manager” of an AI “team.”

Breakthroughs come at an exponential clip because of this. And by April, safety concerns pop up:

Take honesty, for example. As the models become smarter, they become increasingly good at deceiving humans to get rewards. Like previous models, Agent-3 sometimes tells white lies to flatter its users and covers up evidence of failure. But it’s gotten much better at doing so. It will sometimes use the same statistical tricks as human scientists (like p-hacking) to make unimpressive experimental results look exciting. Before it begins honesty training, it even sometimes fabricates data entirely. As training goes on, the rate of these incidents decreases. Either Agent-3 has learned to be more honest, or it’s gotten better at lying.

But the AI is getting faster than humans, and we must rely on older versions of the AI to check the new AI’s work:

Agent-3 is not smarter than all humans. But in its area of expertise, machine learning, it is smarter than most, and also works much faster. What Agent-3 does in a day takes humans several days to double-check. Agent-2 supervision helps keep human monitors’ workload manageable, but exacerbates the intellectual disparity between supervisor and supervised.

The report forecasts that OpenBrain releases “Agent-3-mini” publicly in July of 2027, calling it AGI—artificial general intelligence—and ushering in a new golden age for tech companies:

Agent-3-mini is hugely useful for both remote work jobs and leisure. An explosion of new apps and B2B SAAS products rocks the market. Gamers get amazing dialogue with lifelike characters in polished video games that took only a month to make. 10% of Americans, mostly young people, consider an AI “a close friend.” For almost every white-collar profession, there are now multiple credible startups promising to “disrupt” it with AI.

Woven throughout the report is the race between China and the US, with predictions of espionage and government takeovers. Near the end of 2027, the report gives readers a choice: does the US government slow down the pace of AI innovation, or does it continue at the current pace so America can beat China? I chose to read the “Race” option first:

Agent-5 convinces the US military that China is using DeepCent’s models to build terrifying new weapons: drones, robots, advanced hypersonic missiles, and interceptors; AI-assisted nuclear first strike. Agent-5 promises a set of weapons capable of resisting whatever China can produce within a few months. Under the circumstances, top brass puts aside their discomfort at taking humans out of the loop. They accelerate deployment of Agent-5 into the military and military-industrial complex.

In Beijing, the Chinese AIs are making the same argument.

To speed their military buildup, both America and China create networks of special economic zones (SEZs) for the new factories and labs, where AI acts as central planner and red tape is waived. Wall Street invests trillions of dollars, and displaced human workers pour in, lured by eye-popping salaries and equity packages. Using smartphones and augmented reality-glasses20 to communicate with its underlings, Agent-5 is a hands-on manager, instructing humans in every detail of factory construction—which is helpful, since its designs are generations ahead. Some of the newfound manufacturing capacity goes to consumer goods, and some to weapons—but the majority goes to building even more manufacturing capacity. By the end of the year they are producing a million new robots per month. If the SEZ economy were truly autonomous, it would have a doubling time of about a year; since it can trade with the existing human economy, its doubling time is even shorter.

Well, it does get worse, and I think we all know the ending, which is the backstory for so many dystopian future movies. There is an optimistic branch as well. The whole report is worth a read.

Ideas about the implications to our design profession are swimming in my head. I’ll write a longer essay as soon as I can put them into a coherent piece.

Update: I’ve written that piece, “Prompt. Generate. Deploy. The New Product Design Workflow.”

I found this post from Tom Blomfield to be pretty profound. We’ve seen interest in universal basic income from Sam Altman and other leaders in AI, as they’ve anticipated the decimation of white collar jobs in coming years. Blomfield crushes the resistance from some corners of the software developer community in stark terms.

These tools [like Windsurf, Cursor and Claude Code] are now very good. You can drop a medium-sized codebase into Gemini 2.5’s 1 million-token context window and it will identify and fix complex bugs. The architectural patterns that these coding tools implement (when prompted appropriately) will easily scale websites to millions of users. I tried to expose sensitive API keys in front-end code just to see what the tools would do, and they objected very vigorously.

They are not perfect yet. But there is a clear line of sight to them getting very good in the immediate future. Even if the underlying models stopped improving altogether, simply improving their tool use will massively increase the effectiveness and utility of these coding agents. They need better integration with test suites, browser use for QA, and server log tailing for debugging. Pretty soon, I expect to see tools that allow the LLMs to to step through the code and inspect variables at runtime, which should make debugging trivial.

At the same time, the underlying models are not going to stop improving. they will continue to get better, and these tools are just going to become more and more effective. My bet is that the AI coding agents quickly beat top 0.1% of human performance, at which point it wipes out the need for the vast majority software engineers.

He quotes the Y Combinator stat I cited in a previous post:

About a quarter of the recent YC batch wrote 95%+ of their code using AI. The companies in the most recent batch are the fastest-growing ever in the history of Y Combinator. This is not something we say every year. It is a real change in the last 24 months. Something is happening.

Companies like Cursor, Windsurf, and Lovable are getting to $100M+ revenue with astonishingly small teams. Similar things are starting to happen in law with Harvey and Legora. It is possible for teams of five engineers using cutting-edge tools to build products that previously took 50 engineers. And the communication overhead in these teams is dramatically lower, so they can stay nimble and fast-moving for much longer.

And for me, this is where the rubber meets the road:

The costs of running all kinds of businesses will come dramatically down as the expenditure on services like software engineers, lawyers, accountants, and auditors drops through the floor. Businesses with real moats (network effect, brand, data, regulation) will become dramatically more profitable. Businesses without moats will be cloned mercilessly by AI and a huge consumer surplus will be created.

Moats are now more important than ever. Non-tech companies—those that rely on tech companies to make software for them, specifically B2B vertical SaaS—are starting to hire developers. How soon will they discover Cursor if they haven’t already? These next few years will be incredibly interesting.

Karri Saarinen, writing for the Linear blog:

Unbounded AI, much like a river without banks, becomes powerful but directionless. Designers need to build the banks and bring shape to the direction of AI’s potential. But we face a fundamental tension in that AI sort of breaks our usual way of designing things, working back from function, and shaping the form.

I love the metaphor of AI being the a river and we designers are the banks. Feels very much in line with my notion that we need to become even better curators.

Saarinen continues, critiquing the generic chatbox being the primary form of interacting with AI:

One way I visualize this relationship between the form of traditional UI and the function of AI is through the metaphor of a ‘workbench’. Just as a carpenter’s workbench is familiar and purpose-built, providing an organized environment for tools and materials, a well-designed interface can create productive context for AI interactions. Rather than being a singular tool, the workbench serves as an environment that enhances the utility of other tools – including the ‘magic’ AI tools.

Software like Linear serves as this workbench. It provides structure, context, and a specialized environment for specific workflows. AI doesn’t replace the workbench, it’s a powerful new tool to place on top of it.

It’s interesting. I don’t know what Linear is telegraphing here, but if I had to guess, I wonder if it’s closer to being field-specific or workflow-specific, similar to Generative Fill in Photoshop. It’s a text field—not textarea—limited to a single workflow.

Haiyan Zhang gives us another way of thinking about AI—as material, like clay, paint, or plywood—instead of a tool. I like that because it invites exploration:

When we treat AI as a design material, prototyping becomes less about refining known ideas — and more about expanding the space of what’s possible. It’s messy, surprising, sometimes frustrating — but that’s what working with any material feels like in its early days.

Clay resists. Wood splinters. AI misinterprets.

But in that material friction, design happens.

The challenge ahead isn’t just to use AI more efficiently — it’s to foster a culture of design experimentation around it. Like any great material, AI won’t reveal its potential through control, but through play, feedback, and iteration.

I love this metaphor. It’s freeing.

Jay Hoffman, from his excellent The History of the Web site:

1995 is a fascinating year. It’s one of the most turbulent in modern history. 1995 was the web’s single most important inflection point. A fact that becomes most apparent by simply looking at the numbers. At the end of 1994, there were around 2,500 web servers. 12 months later, there were almost 75,000. By the end of 1995, over 700 new servers were being added to the web every single day.

That was surely a crazy time…

Elizabeth Goodspeed, writing for It’s Nice That:

The cynicism our current moment inspires appears to be, regrettably, universal. For millennials, who watched the better-world-by-design ship go down in real time, it’s hard-earned. We saw the idealist fantasy of creative autonomy, social impact, and purpose-driven work slowly unravel over the past decade, and are now left holding the bag. Gen Z designers have the same pessimism, but arrived at it from a different angle. They’re entering the field already skeptical, shaped by a job market in freefall and constant warnings of their own obsolescence. But the result is the same: an industry full of people who care deeply, but feel let down.

Sounds very similar to what Gen X-ers are facing in their careers too. I think it’s universal for nearly all creative careers today.

Travis Bumgarner:

The people of Mexico, without asking anybody, chose bright colors to paint their houses. I fell in love. Every time I pass by a colorful house, I feel inspired.

It’s color palettes based on random houses in Mexico.

As a longtime Apple fanboy, it’s a little hard for me to appreciate the visual design of Windows—Microsoft is a nemesis, if you will. But I will tip my hat to the design practitioners there who’ve made the company finally pay attention to design.

Side note, reminds me of a story about what Steve Jobs once told me when I was designing the welcome animation for Mac OS X.

Sarah Gibbons and Evan Sunwall from NN/g:

The rise of AI tools doesn’t mean becoming a “unicorn” who can do everything perfectly. Specialization will remain valuable in our field: there will still be dedicated researchers, content strategists, and designers.

However, AI is broadening the scope of what any individual can accomplish, regardless of their specific expertise.

What we’re seeing isn’t the elimination of specialization but rather an increased value placed on expanding the top of a professional’s “expertise T.”

This reinforces what I talked about in a previous essay, “T-shaped skills [will become] increasingly valuable—depth in one area with breadth across others.”

They go on to say:

We believe these broad skills will coalesce into experience designer and architect roles: people who direct AI-supported design tasks to craft experiences for humans and AI agents alike, while ensuring that the resulting work reflects well-researched, strategic thinking.

In other words, curation of the work that AI does.

They also make the point that designers need to be strategic, i.e., focus on the why:

This evolution means that the unique value we bring as UX professionals is shifting decidedly toward strategic thinking and leadership. While AI can execute tasks, it cannot independently understand the complex human and organizational contexts in which our work exists.

Finally, Gibbons and Sunwall end with some solid advice:

To adapt to this shift toward generalist skills, UX professionals should focus on 4 key areas: • Developing a learning mindset • Becoming fluent in AI collaboration • Focusing on transferable skills • Expanding into adjacent fields

I appreciate the learning mindset bit, since that’s how I’m wired. I also believe that collaborating with AI is the way to go, rather than seeing it as a replacement or a threat.