224 posts in Linked

Hard to believe that the Domino’s Pizza tracker debuted in 2008. The moment was ripe for them—about a year after the debut of the iPhone. Mobile e-commerce was in its early days.

Alex Mayyasi for The Hustle:

…the tracker’s creation was spurred by the insight that online orders were more profitable – and made customers more satisfied – than phone or in-person orders. The company’s push to increase digital sales from 20% to 50% of its business led to new ways to order (via a tweet, for example) and then a new way for customers to track their order.

Mayyasi weaves together a tale of business transparency, UI, and content design, tracing—or tracking?—the tracker’s impact on business since then. “The pizza tracker is essentially a progress bar.” But progress bars do so much for the user experience, most of which is setting proper expectations.

Here’s a fun project from Étienne Fortier-Dubois. It is both a timeline of tech innovations throughout history and a family tree. For example, the invention of the wheel led to chariots, or the ancestors of the bulletin board system were the home computer and the modem. From the about page:

The historical tech tree is a project by Étienne Fortier-Dubois to visualize the entire history of technologies, inventions, and (some) discoveries, from prehistory to today. Unlike other visualizations of the sort, the tree emphasizes the connections between technologies: prerequisites, improvements, inspirations, and so on.

These connections allow viewers to understand how technologies came about, at least to some degree, thus revealing the entire history in more detail than a simple timeline, and with more breadth than most historical narratives. The goal is not to predict future technology, except in the weak sense that knowing history can help form a better model of the world. Rather, the point of the tree is to create an easy way to explore the history of technology, discover unexpected patterns and connections, and generally make the complexity of modern tech feel less daunting.

I have always wanted to read 6,200 words about color! Sorry, that’s a lie. But I did skim it and really admired the very pretty illustrations. Dan Hollick is a saint for writing and illustrating this chapter in his living book called Making Software, a reference manual for designers and programmers that make digital products. From his newsletter:

I started writing this chapter just trying to explain what a color space is. But it turns out, you can't really do that without explaining a lot of other stuff at the same time.

Part of the issue is color is really complicated and full of confusing terms that need a maths degree to understand. Gamuts, color models, perceptual uniformity, gamma etc. I don't have a maths degree but I do have something better: I'm really stubborn.

And here are the opening sentences of the chapter on color:

Color is an unreasonably complex topic. Just when you think you've got it figured out, it reveals a whole new layer of complexity that you didn't know existed.

This is partly because it doesn't really exist. Sure, there are different wavelengths of light that our eyes perceive as color, but that doesn't mean that color is actually a property of that light - it's a phenomenon of our perception.

Digital color is about trying to map this complex interplay of light and perception into a format that computers can understand and screens can display. And it's a miracle that any of it works at all.

I’m just waiting for him to put up a Stripe link so I can throw money at him.

Interesting piece from Vaughn Tan about a critical thinking framework that is disguised as a piece about building better AI UIs for critical thinking. Sorry, that sentence is kind of a tongue-twister. Tan calls out—correctly—that LLMs don’t think, or in his words, can’t make meaning:

Meaningmaking is making inherently subjective decisions about what’s valuable: what’s desirable or undesirable, what’s right or wrong. The machines behind the prompt box are remarkable tools, but they’re not meaningmaking entities.

Therefore when users ask LLMs for their opinions on matters, e.g., as in the therapy use case, the AIs won’t come back with actual thinking. IMHO, it’s semantics, but that’s another post.

Anyhow, Tan shares a pen and paper prototype he’s been testing, which breaks down a major decision into guided steps, or put another way, a framework.

This user experience was designed to simulate a multi-stage process of structured elicitation of various aspects of strongly reasoned arguments. This design explicitly addresses both requirements for good tool use. The structured prompts helped students think critically about what they were actually trying to accomplish with their custom major proposals — the meaningmaking work of determining value, worth, and personal fit. Simultaneously, the framework made clear what kinds of thinking work the students needed to do themselves versus what kinds of information gathering and analysis could potentially be supported by tools like LLMs.

This guided or framework-driven approach was something I attempted wtih Griffin AI. Via a series of AI-guided prompts to the user—or a glorified form, honestly—my tool helped users build brand strategies. To be sure, a lot of the “thinking” was done by the model, but the idea that an AI can guide you to critically think about your business or your client’s business was there.

Designer Tey Bannerman writes that when he hears “human in the loop,” he’s reminded of a story about Lieutenant Colonel Stanislav Petrov, a Soviet Union duty watch officer who monitored for incoming missile strikes from the US.

12:15 AM… the unthinkable. Every alarm in the facility started screaming. The screens showed five US ballistic missiles, 28 minutes from impact. Confidence level: 100%. Petrov had minutes to decide whether to trigger a chain reaction that would start nuclear war and could very well end civilisation as we knew it.

He was the “human in the loop” in the most literal, terrifying sense.

Everything told him to follow protocol. His training. His commanders. The computers.

But something felt wrong. His intuition, built from years of intelligence work, whispered that this didn’t match what he knew about US strategic thinking.

Against every protocol, against the screaming certainty of technology, he pressed the button marked “false alarm”.

Twenty-three minutes of gripping fear passed before ground radar confirmed: no missiles. The system had mistaken a rare alignment of sunlight on high-altitude clouds for incoming warheads.

His decision to break the loop prevented nuclear war.

Then Bannerman shares an awesome framework he developed that allows humans in the loop in AI systems “genuine authority, time to think, and understanding the bigger picture well enough to question” the system’s decision. Click on to get the PDF from his site.

Simon Sherwood, writing in The Register:

Amazon Web Services CEO Matt Garman has suggested firing junior workers because AI can do their jobs is "the dumbest thing I've ever heard."

Garman made that remark in conversation with AI investor Matthew Berman, during which he talked up AWS’s Kiro AI-assisted coding tool and said he's encountered business leaders who think AI tools "can replace all of our junior people in our company."

That notion led to the “dumbest thing I've ever heard” quote, followed by a justification that junior staff are “probably the least expensive employees you have” and also the most engaged with AI tools.

“How's that going to work when ten years in the future you have no one that has learned anything,” he asked. “My view is you absolutely want to keep hiring kids out of college and teaching them the right ways to go build software and decompose problems and think about it, just as much as you ever have.”

Yup. I agree.

This post from Carly Ayres breaks down a beef between Michael Roberson (developer of an AI-enabled moodboard tool) and Elizabeth Goodspeed (writer and designer, oft-linked on this blog) and explores ragebait, putting in the reps as a junior, and designers as influencers.

The tweet earned 30,000 views, but only about 20 likes. “That ratio was pretty jarring,” [Roberson] said. Still, the strategy felt legible. “When I post things like, ‘if you don’t do X, you’re not going to make it,’ obviously, I don’t think that. These tools aren’t really capable of replacing designers just yet. It’s really easy to get views baiting and fear-mongering.”

Much like the provocative Artisan campaign, I think this is a net negative for the brand. Pretty sure I won’t be trying out Moodboard AI anytime soon, ngl.

But stepping back from the internet beef, Ayres argues that it’s a philosophical difference about the role friction in the creative process.

Michael’s experience mirrors that of many young designers: brand audits felt like busywork during his Landor internship. “That process was super boring,” he told me. “I wasn’t learning much by copy-pasting things into a deck.” His tool promises to cut through that inefficiency, letting teams reach visual consensus faster and spend more time on execution.

Young Michael, the process is the point! Without doing this boring stuff, by automating it with AI, how are you going to learn? This is but one facet of the whole discussion around expertise, wisdom, and the design talent crisis.

Goodspeed agrees with me:

Elizabeth sees it differently. “What’s interesting to me,” Elizabeth noted, “is how many people are now entering this space without a personal understanding of how the process of designing something actually works.” For her, that grunt work was formative. “The friction is the process,” she explained. “That’s how you form your point of view. You can’t just slap seven images on a board. You’re forced to think: What’s relevant? How do I organize this and communicate it clearly?”

Ultimately, the saddest point that Ayres makes—and noted by my friend Eric Heiman—is this:

When you’re young, online, and trying to get a project off the ground, caring about distribution is the difference between a hobby and a company. But there’s a cost. The more you perform expertise, the less you develop it. The more you optimize for engagement, the more you risk flattening what gave the work meaning in the first place. In a world where being known matters more than knowing, the incentives point toward performance over practice. And we all become performers in someone else’s growth strategy.

…Because when distribution matters more than craft, you don’t become a designer by designing. You become a designer by being known as one. That’s the game now.

As a follow-up to yesterday’s item on how Google’s AI overviews are curtailing traffic to websites by as much as 25%, here is a link to Nielsen Norman Group’s just-published study showing that generative AI is reshaping search.

Kate Moran, Maria Rosala and Josh Brown:

While AI offers compelling shortcuts around tedious research tasks, it isn’t close to completely replacing traditional search. But, even when people are using traditional search, the AI-generated overview that now tops almost all search-results pages steals a significant amount of attention and often shortcuts the need to visit the actual pages.

They write that users have developed a way to search over the years, skipping sponsored results and heading straight for the organic links. Users also haven’t completely broken free of traditional Google Search, now adding chatbots to the mix:

While generative AI does offer enough value to change user behaviors, it has not replaced traditional search entirely. Traditional search and AI chats were often used in tandem to explore the same topic and were sometimes used to fact-check each other.

All our participants engaged in traditional search (using keywords, evaluating results pages, visiting content pages, etc.) multiple times in the study. Nobody relied entirely on genAI’s responses (in chat or in an AI overview) for all their information-seeking needs.

In many ways, I think this is smart. Unless “web search” is happening, I tend double-check ChatGPT and Claude, especially for anything historical and mission-critical. I also like Perplexity for that fact—because it shows me its receipts by giving me sources.

The designer of the iconic “007” logo from the James Bond movies has died. Joe Caroff was 103. Jeré Longman, writing for The New York Times:

For the first Bond movie, “Dr. No” (1962), Mr. Caroff was hired to create a logo for the letterhead of a publicity release. He began working with the idea that as a secret agent, James Bond had a license to kill (as designated by the numerals “00”), but Mr. Caroff did not find Bond’s compact Walther PPK pistol to be visually appealing.

As he sketched the numerals 007, he drew penciled lines above and below to guide him and noticed that the upper guideline resembled an elongated barrel of a pistol extending from the seven.

He refined his drawing and added a trigger, fashioning a mood of intrigue and espionage and crafting one of the most globally recognized symbols in cinematic history. With some modifications, the logo has been used for 25 official Bond films and endless merchandising.

John Gruber of Daring Fireball also wrote a piece about Caroff:

Caroff had a remarkably accomplished career. He created iconic posters for dozens of terrific films across a slew of genres. The fact that he created the 007 logo but only earned $300 from it is more like a curious footnote than anything.

Jessica Davies reports that new publisher data suggests that some sites are getting 25% less traffic from Google than the previous year.

Writing in Digiday:

Organic search referral traffic from Google is declining broadly, with the majority of DCN member sites — spanning both news and entertainment — experiencing traffic losses from Google search between 1% and 25%. Twelve of the respondent companies were news brands, and seven were non-news.

Jason Kint, CEO of DCN, says that this is a “direct consequence of Google AI Overviews.”

I wrote previously about the changing economics of the web here, here, and here.

And related, Eric Mersch writes in a LinkedIn post that Monday.com’s stock fell 23% because co-CEO Roy Mann said, “We are seeing some softness in the market due to Google algorithm,” during their Q2 earnings call and the analysts just kept hammering him and the CFO about how the algo changes might affect customer acquisition.

Analysts continued to press the issue, which caught company management completely off guard. Matthew Bullock from Bank of America Merrill Lynch asked frankly, “And then help us understand, why call this out now? How did the influence of Google SEO disruption change this quarter versus 1Q, for example?” The CEO could only respond, “So look, I think like we said, we optimize in real-time. We just budget daily,” implying that they were not aware of the problem until they saw Q2 results.

This is the first public sign that the shift from Google to AI-powered searches is having an impact.

I grew up on MTV and I’m surprised that my Gen Z kids don’t watch music videos. ¯\_(ツ)_/¯

Rob Schwartz, writing in PRINT Magazine:

…the network launched the iconic “I Want My MTV” ad campaign. Created by ad legend George Lois, the campaign featured the world’s biggest rock stars literally demanding MTV. At the time, this was unheard of. Unlike today, rock stars would never sell out to do ads. But here you had the biggest stars: Mick Jagger, David Bowie, Pete Townshend, the Police…and rising star Madonna, all shouting the same line in different executions: ‘I want my MTV!” The campaign was a stroke of genius. It mobilized viewers to call up their cable providers and shout over the phone: “I want my MTV!” In due time, MTV was on damn-near every cable box and damn-near every young person’s TV.

In a fascinating thread about designing a typeface in Illustrator versus a font editor, renowned typographer Jonathan Hoefler lets us peek behind the curtains.

But moreover, the reason not to design typefaces in a drawing program is that there, you’re drawing letters in isolation, without regard to their neighbors. Here’s the lowercase G from first corner of the HTF Didot family, its 96pt Light Roman master, which I drew toward the end of 1991. (Be gentle; I was 21.) I remember being delighted by the results, no doubt focussing on that delicate ear, etc. But really, this is only half the picture, because it’s impossible to know if this letter works, unless you give it context. Here it is between lowercase Ns, which establish a typographic ‘control’ for an alphabet’s weight, width, proportions, contrast, fit, and rhythm. Is this still a good G? Should the upper bowl maybe move left a little? How do we feel about its weight, compared to its neighbors? Is the ear too dainty?

Cap Watkins, Head of Product Design at Lattice, was catching up with a former top-performing designer who was afraid other designers were mad at her for getting all the “cool” projects.

What made those projects glamorous and desirable was her and how she approached the work. There’s that old nugget about making your own luck and that is something she excelled at. She had a unique ability to take really hard or nebulous problems (both design and team-related) and morph them into something amazing that got people excited. Instead of getting discouraged, she’d respond to friction with more energy, more enthusiasm. In so many ways, she was a transformative presence on any team and project.

In other words, this designer cared and made the best of all her assignments.

I enjoyed this interview with Notion’s CEO, Ivan Zhao over at the Decoder podcast, with substitute host, Casey Newton. What I didn’t quite get when I first used Notion was the “LEGO” aspect of it. Their vision is to build business software that is highly malleable and configurable to do all sorts of things. Here’s Zhao:

Well, because it didn’t quite exist with software. If you think about the last 15 years of [software-as-a-service], it’s largely people building vertical point solutions. For each buyer, for each point, that solution sort of makes sense. The way we describe it is that it’s like a hard plastic solution for your problem, but once you have 20 different hard plastic solutions, they sort of don’t fit well together. You cannot tinker with them. As an end user, you have to jump between half a dozen of them each day.

That’s not quite right, and we’re also inspired by the early computing pioneers who in the ‘60s and ‘70s thought that computing should be more LEGO-like rather than like hard plastic. That’s what got me started working on Notion a long time ago, when I was reading a computer science paper back in college.

From a user experience POV, Notion is both simple and exceedingly complicated. Taking notes is easy. Building the system for a workflow, not so much.

In the second half, Newton (gently) presses Zhao on the impact of AI on the workforce and how productivity software like Notion could replace headcount.

Newton: Do you think that AI and Notion will get to a point where executives will hire fewer people, because Notion will do it for them? Or are you more focused on just helping people do their existing jobs?

Zhao: We’re actually putting out a campaign about this, in the coming weeks or months. We want to push out a more amplifying, positive message about what Notion can do for you. So, imagine the billboard we’re putting out. It’s you in the center. Then, with a tool like Notion or other AI tools, you can have AI teammates. Imagine that you and I start a company. We’re two co-founders, we sign up for Notion, and all of a sudden, we’re supplemented by other AI teammates, some taking notes for us, some triaging, some doing research while we’re sleeping.

Zhao dodges the “hire fewer people” part of the question and instead, answers with “amplifying” people or making them more productive.

As a child of immigrant parents, I grew up learning English from watching PBS, Sesame Street, specifically. But there were other favorites like 3-2-1 Contact, The Electric Company, and of course, Mr. Roger’s Neighborhood. The logo, with its head looking like a P was seared into my developing brain.

So I’m incredibly saddened to hear that the Corporation for Public Broadcasting, the government-funded entity behind PBS and NPR, will cease operations on September 30, 2025, because of a recent bill passed by the Republican-controlled Congress and signed into law by President Trump.

While PBS and NPR won’t disappear, it will be harder for those networks to stay afloat, now solely dependent on donations.

Lilly Smith, writing for Fast Company:

More than 70% of CPB’s annual federal appropriation goes directly to more than 1,500 local public media stations, according to a web page of its financials. This loss in funding could force local stations, especially in rural areas, to shut down, according to the CPB. Local member stations are independent and locally owned and operated, according to NPR. As a public-private partnership, local PBS stations get about 15% of their revenue from federal funding.

She reached out to Tom Geismar, who redesigned the PBS logo in 1984—the original was by Herb Lubalin and Ernie Smith in 1971. He had this perspective:

There is an ironic tie-in between the government decision to cut off all funding to public television and public radio, and what prompted the redesign of the PBS logo back in the early 1980s.

That was also a difficult time, financially, for the Public Broadcasting Service, and especially the stations in more remote regions of the country. Much of the public equated PBS with the major television networks CBS, NBC and ABC, and presumed that, like those major institutions, PBS was the parent of and significant funder for all the local public television stations throughout the country. But, in fact, the reality is somewhat the opposite. Although PBS local affiliates received a portion of funding from the federal government, it is the individual stations that have the responsibility to do public fund raising, and PBS, in a sense, works for them.

Because of this confusion, the PBS leadership felt that their existing logo (a famous design by by Herb Lubalin) needed to be more than just the classic 3-initials mark, something more evocative of a public-benefit system serving all people. Thus the “everyone” mark was born.

Geismar ends with, “And now, once again, with federal government funding stopped, it is the stations in the less populous regions who will suffer the most.”

Ben Davies-Romano argues that the AI chat box is our new design interface:

Every interaction with a large language model starts the same way: a blinking cursor in a blank text field. That unassuming box is more than an input — it’s the interface between our human intent and the model’s vast, probabilistic brain.

This is where the translation happens. We pour in the nuance, constraints, and context of our ideas; the model converts them into an output. Whether it’s generating words, an image, a video sequence, or an interactive prototype, every request passes through this narrow bridge.

It’s the highest-stakes, lowest-fidelity design surface I’ve ever worked with: a single field that stands between human creativity and an engine capable of reshaping it into almost any form, albeit with all the necessary guidance and expertise applied.

In other words, don't just say "Make it better," but guide the AI instead.

That’s why a vague, lazy prompt, like “make it better”, is the design equivalent of telling a junior designer “make it intuitive” and walking away. You’ll get something generic, safe, and soulless, not because the AI “missed the brief,” but because there was no brief.

Without clear stakes, a defined brand voice, and rich context, the system will fill in the blanks with its default, most average response. And “average” is rarely what design is aiming for.

And he makes a point that designers should be leading the charge on showing others what generative AI can do:

In the age of AI, it shouldn’t be everyone designing, per say. It should be designers using AI as an extension of our craft. Bringing our empathy, our user focus, our discipline of iteration, and our instinct for when to stop generating and start refining. AI is not a replacement for that process; it’s a multiplier when guided by skilled hands.

So, let’s lead. Let’s show that the real power of AI isn’t in what it can generate, but in how we guide it — making it safer, sharper, and more human. Let’s replace the fear and the gimmicks with clarity, empathy, and intentionality.

The blank prompt is our new canvas. And friends, we need to be all over it.

There are over 1,800 font families in Google Fonts. While as designers, I’m sure were grateful for the trove of free fonts, good typefaces in the library are hard to spot.

Brand identity darlings Smith & Diction dropped a catalog of “Usable Google Fonts.” In a LinkedIn post, they wrote, “Screw it, here's all of the google fonts that are actually good categorized by ‘vibe’.”

Huzzah! It’s in the form of a public Figma file. Enjoy.

Christopher K. Wong argues that desirability is a key part of design that helps decide which features users really want:

To give a basic definition, desirability is a strategic part of UX that revolves around a single user question: Have you defined (and solved) the right problem for users?

In other words, before drawing a single box or arrow, have you done your research and discovery to know you’re solving a pain point?

The way the post is written makes it hard to get at a succinct definition, but here’s my take. Desirability is about ensuring a product or feature is truly wanted, needed, and chosen by users—not just visual appeal—making it a core pillar for impactful design decisions and prioritization. And designers should own this.

Yesterday, OpenAI launched GPT-5, their latest and greatest model that replaces the confusing assortment of GPT-4o, o3, o4-mini, etc. with just two options: GPT-5 and GPT-5 pro. The reasoning is built in and the new model is smart enough to know what to think harder, or when a quick answer suffices.

Simon Willison deep dives into GPT-5, exploring its mix of speed and deep reasoning, massive context limits, and competitive pricing. He sees it as a steady, reliable default for everyday work rather than a radical leap forward:

I’ve mainly explored full GPT-5. My verdict: it’s just good at stuff. It doesn’t feel like a dramatic leap ahead from other LLMs but it exudes competence—it rarely messes up, and frequently impresses me. I’ve found it to be a very sensible default for everything that I want to do. At no point have I found myself wanting to re-run a prompt against a different model to try and get a better result.

It's a long technical read but interesting nonetheless.

Jay Hoffman, writing in his excellent The History of the Web website, reflects on Kevin Kelly's 2005 Wired piece that celebrated the explosive growth of blogging—50 million blogs, one created every two seconds—and predicted a future powered by open participation and user-created content. Kelly was right about the power of audiences becoming creators, but he missed the crucial detail: 2005 would mark the peak of that open web participation before everyone moved into centralized platforms.

There are still a lot of blogs, 600 million by some accounts. But they have been supplanted over the years by social media networks. Commerce on the web has consolidated among fewer and fewer sites. Open source continues to be a major backbone to web technologies, but it is underfunded and powered almost entirely by the generosity of its contributors. Open API’s barely exist. Forums and comment sections are finding it harder and harder to beat back the spam. Users still participate in the web each and every day, but it increasingly feels like they do so in spite of the largest web platforms and sites, not because of them.

My blog—this website—is a direct response to the consolidation. This site and its content are owned and operated by me and not stuck behind a login or paywall to be monetized by Meta, Medium, Substack, or Elon Musk. That is the open web.

Hoffman goes on to say, “The web was created for participation, by its nature and by its design. It can’t be bottled up long.” He concludes with:

Independent journalists who create unique and authentic connections with their readers are now possible. Open social protocols that experts truly struggle to understand, is being powered by a community that talks to each other.

The web is just people. Lots of people, connected across global networks. In 2005, it was the audience that made the web. In 2025, it will be the audience again.

Figma is adding to its keyboard shortcuts to improve navigation and selection for power users and for keyboard-only users. It’s a win-win that improves accessibility and efficiency. Sarah Kelley, product marketer at Figma writes:

For millions, navigating digital tools with a keyboard isn’t just about preference for speed and ergonomics—it’s a fundamental need. …

We’re introducing a series of new features that remove barriers for keyboard-only designers across most Figma products. Users can now pan the canvas, insert objects, and make precise selections quickly and easily. And, with improved screen reader support, these actions are read aloud as users work, making it easier to stay oriented.

Nice work!

My former colleague from Organic, Christian Haas—now ECD at YouTube—has been experimenting with AI video generation recently. He’s made a trilogy of short films called AI Jobs.

You can watch part one above 👆, but don’t sleep on parts two and three.

Haas put together a “behind the scenes” article explaining his process. It’s fascinating and I’ll want to play with video generation myself at some point.

I started with a rough script, but that was just the beginning of a conversation. As I started generating images, I was casting my characters and scouting locations in real time. What the model produced would inspire new ideas, and I would rewrite the script on the fly. This iterative loop continued through every stage. Decisions weren't locked in; they were fluid. A discovery made during the edit could send me right back to "production" to scout a new location, cast a new character and generate a new shot. This flexibility is one of the most powerful aspects of creating with Gen AI.

It’s a wonderful observation Haas has made—the workflow enabled by gen AI allows for more creative freedom. In any creative endeavor where the production of the final thing is really involved and utilizes a significant amount of labor and materials, be it a film, commercial photography, or software, planning is a huge part. We work hard to spec out everything before a crew of a hundred shows up on set or a team of highly-paid engineers start coding. With gen AI, as shown here with Google’s Veo 3, you have more room for exploration and expression.

UPDATE: I came across this post from Rory Flynn after I published this. He uses diagrams to direct Veo 3.

For the past year, CPG behemoth Unilever has been “working with marketing services group Brandtech to build up its Beauty AI Studio: a bespoke, in-house system inside its beauty and wellbeing business. Now in place across 18 different markets (the U.S. and U.K. among them), the studio is being used to make assets for paid social, programmatic display inventory and e-commerce usage across brands including Dove Intensive Repair, TRESemme Lamellar Shine and Vaseline Gluta Hya.”

Sam Bradley, writing in Digiday:

The system relies on Pencil Pro, a generative AI application developed by Brandtech Group. The tool draws on several large language models (LLMs), as well as API access to Meta and TikTok for effectiveness measurement. It’s already used by hearing-care brand Amplifon to rapidly produce text and image assets for digital ad channels.

In Unilever’s process, marketers use prompts and their own insights about target audiences to generate images and video based on 3D renders of each product, a practice sometimes referred to as “digital twinning.” Each brand in a given market is assigned a “BrandDNAi” — an AI tool that can retrieve information about brand guidelines and relevant regulations and that provides further limitations to the generative process.

So far, they haven’t used this system to generate AI humans. Yet.

Coincidentally, I was considering adding a service designer to my headcount plan when this article came across my feeds. Perfect timing. It’s hard to imagine that service design as a discipline is so young—only since 2012 according to the author.

Joe Foley, writing in Creative Bloq:

As a discipline, service design is still relatively new. A course at the Royal College of Art in London (RCA) only began in 2012 and many people haven't even heard of the term. But that's starting to change.

He interviews designer Clive Grinyer, whose new book on service design has just come out. He was co-founder of the design consultancy Tangerine, Director of Design and Innovation for the UK Design Council, and Head of Service Design at the Royal College of Art.

Griner:

Great service design is often invisible as it solves problems and removes barriers, which isn’t necessarily noticed as much as a shiny new product. The example of GDS (Government Digital Service) redesigning every government department from a service design perspective and removing many frustrating and laborious aspects of public life from taxing a car to getting a passport, is one of the best.

The key difference between service design and UX is that it’s end product is not something on a screen:

But service design is not just the experience we have through the glass of a screen or a device: it’s designed from the starting point of the broader objective and may include many other channels and touchpoints. I think it was Colin Burns who said a product is just a portal to a service.

In other words, if you open the aperture of what user experience means, and take on the challenge of designing real-world processes, flows, and interaction—that is service design.

Luke Wroblewski, writing in his blog:

Across several of our companies, software development teams are now "out ahead" of design. To be more specific, collaborating with AI agents (like Augment Code) allows software developers to move from concept to working code 10x faster. This means new features become code at a fast and furious pace.

When software is coded this way, however, it (currently at least) lacks UX refinement and thoughtful integration into the structure and purpose of a product. This is the work that designers used to do upfront but now need to "clean up" afterward. It's like the development process got flipped around. Designers used to draw up features with mockups and prototypes, then engineers would have to clean them up to ship them. Now engineers can code features so fast that designers are ones going back and cleaning them up.

This is what I’ve been secretly afraid of. That we would go back to the times when designers were called in to do cleanup. Wroblewski says:

Instead of waiting for months, you can start playing with working features and ideas within hours. This allows everyone, whether designer or engineer, an opportunity to learn what works and what doesn’t. At its core rapid iteration improves software and the build, use/test, learn, repeat loop just flipped, it didn't go away.

Yeah, or the feature will get shipped this way and be stuck this way because startups move fast and move on.

My take is that as designers, we need to meet the moment and figure out how to build design systems and best practices into the agentic workflows our developer counterparts are using.