This little brain puzzle is par for the course on Ok Go’s new word game, Say the Same Thing. The app digitizes a game that anyone who’s taken an intro to improv course will be familiar with: Two or more participants say a word at the same time, and then try to get on the same wavelength to identify the sum of those two words. The process continues until everyone says the same word. 

The game, now available on iOS and Android, is actually a relaunch that’s been a long time in the making. Ross built a first version of the app back in 2013 that got over a million downloads in a week. The app ultimately fizzled out as Ross and Ok Go focused on other projects, but its loyal fans never forgot it. To this day, he says, he still gets messages asking when the game will come back, including from one memorable pair who met on the app and eventually got married. 

In recent months, as The New York Times’s Wordle has become a daily habit for people worldwide to connect and compete with friends and family, Ross realized that it was time to revive Say the Same Thing.

“The success of some of the New York Times-style word games reminded us, ‘Hey, we have this really great game that people like, and maybe we should bring it back,’” he says. 

How Andy Ross built Say the Same Thing . . . again

The original Say the Same Thing app started on tour.

“When you spend that much time with the same people, you sort of have to pass the time in increasingly more complicated ways,” he says. “This was just a game that we played on the bus.”

In those pre- and early-2010s years, Ross recalls, Ok Go wasn’t selling as many tickets or doing as well as it is today. To earn a secondary income, Ross was using his computer science degree to run an app tech startup, Space Inch, on the side. While playing the word game with his bandmates, he realized that it could make a great app, which led him to coding the interface and server component almost entirely by himself. 

When the app launched, it was such a huge success that the server got “totally hosed.” “I would be freaking out about the server going down and then have to go on stage,” Ross says. This time around, Ross says, he and another developer from the original team paid special attention to making sure that the game’s servers can handle a surge of users. 

While the core premise of the new app is the same as its predecessor, it has a few new capabilities to tap into the growing culture of social word game playing. There’s now a daily challenge that lets every user try their hand at an AI-generated word pairing and find out how their answer compares to the masses, as well as a leaderboard to track top-performing users. A streak function allows users to keep tabs on how many consecutive days they’ve played the game. And, to open the game up to family and friend group chats, it now has a mode for more than two players—the first two in the game to say the same word win.

Ross sees the game as a lighthearted way to share inside jokes, get to know someone new, and take a brain break. In the future, he says, Ok Go fans might even be able to challenge him and his bandmates on the app. 

“I can’t promise how much we’ll play, but anyone can give it a shot,” Ross says. 

Pursuing her vision of art over a lifetime

Kusama insisted she drew the way she saw the world: covered with spots, part of the hallucinations she said she had from childhood. She was in and out of psychiatric institutions for decades, although her sickness did not hold her back as she was one of the first Japanese women to go to New York to pursue her art, in 1957.

Although soft-spoken and shy, Kusama was unabashedly bold in the assertion of her dotty vision, sticking to the style for decades and making it accessible to the masses.

In her later years, as the times caught up with her art, she enjoyed fame. She signed on with fashion brands like Louis Vuitton as well as other licensed products like teacups, T-shirts and key chains with her illustrations, including replicas of her likeness that were used in show windows and other marketing.

In 2008, Christie’s auctioned one of her works for $5.8 million. In 2016, she won the most prestigious award Japan gives its artists, the Order of Culture.

Upon receiving the award, she told reporters she was more determined than ever to pursue her art, stressing that the sincerity and devotion she had demonstrated in her personal life were crucial parts of her artistic legacy.

“I feel there is little left in my life, but I am now still fighting to the death for my art,” she said. “I am giving all I have so that many people will continue to be interested in my art, even after I am dead.”

Categories were not enough to represent her art

Kusama was called pop, avant-garde, feminist, to name some of the categories thrown at her, most of which Kusama brushed off as not quite representative of her art.

She was born in a middle-class family but felt misunderstood, as her parents wanted her to simply get married. They wanted to buy her kimono, not paints and brushes. She knew she had to get away. She chose America.

When she arrived in New York, the fad was “action painting,” characterized by dribbles, swooshes and smears, not dots. She suffered years of poverty and obscurity. But she kept painting dots.

She put circles of paper on people’s bodies, and once a horse, in “happening” antiwar performances in the late 1960s, which got some people arrested for obscenity but helped get media attention for her art. While in New York, she befriended artists like Andy Warhol, Georgia O’Keeffe and Joseph Cornell, who praised her innovative style.

Over the years, Kusama made quirky but stunningly celebratory works like Macaroni Girl, a female figure plastered with macaroni, which expresses the fear of food; The Visionary Flowers, giant sculptures of twisting tulips; and Mirrored Corridor, a room with mirrors that delivers an illusion of a field of phallic protrusions speckled with dots.

Kusama, who has also made films and published several novels, said she wasn’t sure where she got her ideas. She just picked up her brush and started drawing. The process was so mysterious sometimes she surprised herself, she told AP.

“I think, ‘Oh, I drew that? I was thinking that,'” she said.

But she appeared happy to have her art be the focus of media attention and praise, reading one of her poems to a reporter or tackling her artworks with gusto for photos.

But she was also a charmingly disarming mix of vulnerability and defiance, at one moment declaring herself “an artistic revolutionary,” and then the next, mumbling: “I am so afraid, all the time, of everything.”

Fans around the world share messages of loss

The news of her death was met with an outpouring of sadness from around the world, including museums that had exhibited her works.

Fans and artists alike praised her persistence in vowing to continue creating art that, she said, will be recognized thousands of years after her death.

Messages recognizing her death were sent from people throughout Japan including the Tokyo governor, Yuriko Koike, and Yoshitomo Nara, a younger Japanese artist who has exhibited widely abroad.

“RIP Kusama-sensei,” Nara wrote on his X account, respectfully calling her a teacher in Japanese.

Associated Press writer Mari Yamaguchi contributed to this report.

Yuri Kageyama is on Threads: https://www.threads.com/@yurikageyama

—Yuri Kageyama, Associated Press

Sparked by a request to give a lecture on Native American posters by the New York City gallery Poster House, Johnson carried out his project of “cultural sovereignty” for three years. His Native American heritage and career as a designer, curator, and partner at the design studio Polymode earned the trust from the Native and Indigenous makers and guiding Aunties who contributed to the book.

In a two-hour Zoom interview, which has been edited for brevity and clarity, I sat down with Johnson to discuss the significance of this project for Native American and Indigenous makers and why non-Native people still need to be reminded to “Put Native First.”   

Fast Company: Would you please explain the title of your book, Designed to Be Red: Native & Indigenous Graphic Works?

Brian Johnson: The title stems from this idea that everything is by design. And that Native Americans were designed [by colonization] to be “red.” This idea where we have been othered, classified, and made a taxonomy. We are in the past. We are imagined. I think dad jokes are the best thing ever because wordplay is really important to me. I think it is funny. I think it lets your guard down. That’s also a very Native trait. And so, to be red also means to be R-E-A-D, to be read like a book, to be read like a story, to be read to filth. I loved the play on words. To take a word, red, that is used as a weapon and then to flip it and show it was also designed to be read.

Tell me what is your authorial (i.e. both author and authority) relationship to the book?

First, I am Native American. I come from the Monacan Indian Nation, whose ancestral lands are in central Virginia: Lynchburg, Richmond, Charlottesville, and the Shenandoah Valley. Being proud of where I come from has always been one of the deepest parts of me. The Monacan Indian Nation finally got federal recognition in 2018 by the United States government. To some people, unless you have a certain level of recognition, whether it be state or federal, you are not allowed to be that thing.

I went to RISD [Rhode Island School of Design], where I got a great education. I have been in the design world now for 25-plus years. I am a partner at Polymode. I work really hard to uplift marginalized voices. I started BIPOC Design History with Silas Munro. I am here to really show that people of color have been making, doing, striving, and surviving in beautiful waves for eons.

But it’s not just me, I invited other Native American scholars and designers to be in conversation with me: Dr. Jessica Moore Harjo (Otoe-Missouria, Osage, Pawnee, and Sac & Fox Nations), Monique Ortman (Cherokee Nation), Jackson Polys (Tlingit), Sadie Red Wing (Spirit Lake Dakota/Cheyenne River Lakota), Kathleen Sleboda (Nlaka’pamux/Coldwater Indian Band), and Leo Vicenti (Jicarilla Apache Nation).

Would you talk about your process of collecting and curating the over 100 graphic works for the book?

First, it was finding the works. In the Western design thinking, this follows the modernist timeline. From Native viewpoints, this is not the way we conceptualize the world. We think about everything as a web. It is all enmeshed together.

And so, I started by asking, “Where are the pieces that I know?” I knew about Akwesasne Notes, the newspaper that was key for the American Indian Movement, and those posters. As a child of the early ‘80s, I grew up in schools that had the “Hang in There” or “Soar with the Eagles” posters. I grew up remembering the Crying Native ad, with old Iron Eyes Cody, the pretendian. I also knew the extractive negative works that were in the common understanding of the things called Native.

Then once I found hundreds of works, I asked, “What are the stories behind them?” And I had to cold-call people, get on Facebook and Instagram, literally call up the Aunties and email people that I had never met and tell my story 5,000 times.

Then there is all the stuff you’re taught from a higher education standpoint. Go to an archive, open up the flat file, look through it, find what you need. But nine times out of 10, they weren’t documented or attributed correctly. It said “unknown.” That is not true. These are “maker once known.” These individuals were erased.

What are images that you knew you wanted to include in the book from the beginning?

The first image was The Shrine to Hypocrisy by Akwesasne Notes. It is a poster of Mount Rushmore and Chief Sitting Bull’s head rising behind the American presidents that were bastardly carved into those sacred mountains. Everyone knows what Mount Rushmore is because of the American myth behind it. But that poster was saying, “Remember, your ancestors never gave away or sold that land.” It’s this dichotomy between how everyone sees and knows it.

The other one is something closer to me, and that is the Powhatan’s Mantle. It is this large deer skin that has all of these shells so delicately sewn onto it. It has a human, two deer, and all of these circles that represent the different tribes or people that are all bound together in what is now called the Powhatan Confederacy. That object has deep meaning to me, even though I am not from that Native lineage, those people were from nearby lands. I knew of that object and how it was “given” as a gift to the King of England, where it now still sits.

You end the book with a call to action. What are those things that people should be doing to support the cultural sovereignty of Native American graphic works?

Yes, in the book there is an explicit call to action where people can understand “What can I do next?” for young Natives, for older Natives, for students, for non-Natives, for educators. Some of those things are: Try to decolonize what you’re teaching. Try to expand and find more things. Question the authority of what you’ve been shown. Ask the same questions from a different terminology. Sometimes sit down and let other Native people speak. The main point is that you have to slow down the idea of capitalism and the machine to do the real work to reestablish what has been destroyed—community connection and the value between human beings.

Eric Harshbarger has a solution for that.

More than a decade ago, Harshbarger, a senior lecturer at Auburn University’s College of Sciences and Mathematics, and a group of mathematicians and computer scientists embarked on a journey to create a set of five dice that would be unimpeachably fair. Their parameters? Rolling the dice should never result in a tie, and every player should have a mathematically equal chance of rolling the highest number.

After many iterations, they finally came up with a design that works. They created the Go First Dice, a set of five 60-sided dice, with the numbers 1 through 300 spread across their faces.

Their dice are now represented in a sculpture at Auburn University’s new STEM + Agricultural Sciences Complex. The pieces of the sculpture are fashioned from different types of wood—pine, poplar, red oak, walnut, and mahogany. Each weighs more than 100 pounds and is about three-and-a-half feet in diameter.

The idea for the dice came from a conversation Harshbarger had with James Ernest, founder of Cheapass Games, at a gaming convention in 2010. Ernest wondered if it was possible to create no-tie dice. Harshbarger found the question to be an intriguing math problem to solve.

“I don’t think he [Ernest] had any idea so much research would be thrown at the question,” Harshbarger says. “There are plenty of much easier ways to fairly decide who goes first in a board game, but the mathematics behind the question fascinated me, and the quest for sets of ‘Go First Dice’ began.”

Harshbarger began working on the problem with Robert Ford, a mathematician, and they soon found a solution with four 12-sided dice that used the numbers 1 through 48. Then came the bigger challenge: Could they do it with five dice?

Creating five no-tie dice with an equal chance of rolling the highest number requires lots of math to determine the right numbers to include on each die. “We’re talking numbers way greater than the number of atoms in the universe—that kind of thing,” Harshbarger tells Fast Company.

Harder yet was ensuring the dice could roll. An early attempt with a 120-sided die they designed was about the size of a baseball.

Harshbarger’s project to design the dice took more than a decade, work from a core group of about six researchers, and contributions from even more. The Canadian researcher and software engineer Paul Meyer finally found the solution for the 60-sided dice in 2023 that arranged the numbers in just the right way so each piece had an equal chance of rolling the highest number, regardless of where two, three, four, or all five of dice in the set are used. The group’s work, including 60- and 12-sided dice sets, is available at the website Math Art Fun.

Having to roll dice only once saves players seconds. Figuring out how to do it fairly took researchers years, and the new sculpture memorializes the work it took to figure it all out.

Samsara sells software and solutions for fleet management and physical operations. The company describes its customers as asset-heavy and labor-intensive, like the shipping and logistics company DHL, the car rental service Hertz, and Sears Home Services, which provides in-home appliance repair. Samsara does things like improving their routing and reducing accidents and safety incidents, which adds up, especially for its biggest customers that have operating costs totaling about 80% of revenue.

Instead of a vague tech-style visual identity, the rebrand from the creative studio Base Design looks more like a construction company, with a bolder logo and a yellow color scheme that looks like the equipment used by some of its customers. It’s designed to convey the idea of building.

The rebrand comes as Samsara grows due to factors like AI data center construction, government investment in aging infrastructure, waste intelligence for garbage and recycling collection, and ridership management for transit systems. Samsara announced 30% year-over-year growth in June, with nearly $2 billion in annual recurring revenue; the rebrand lands as the company looks to build on that momentum.

After speaking with Samsara customers, including people in senior leadership, frontline operators, and truck drivers, designers from Base Design landed on a central theme for the brand’s visual identity of “build with,” positioning Samsara as a partner for building together.

“It’s an ongoing thing, building with the customers, building with the engineers, like doing things hand in hand, and then iterating and repeating,” Base Design’s creative director, Daniel Peterson, tells Fast Company.

The idea that Samsara is always building “became sort of an organizing idea,” he says. The aim was to blend two different aesthetics to represent Samsara in the lab and in the field, or its work with engineers versus on the ground with customers, drivers, workers, trucks, and equipment.

For the look of the “lab,” designers used technical typography, grids, and structured information. For “field,” it was a signal yellow color, handwriting elements, and a dramatic photography style that give a sense of being at a work site. The handwriting font was designed based on handwriting samples from actual Samsara engineers, to replicate their rhythm and idiosyncrasies. The Samsara wordmark is now written in a wide, all-caps, sans-serif typeface that looks sturdy. Base Design worked with the German type foundry NaN on the fonts, Samsara Sans and Samsara Script.

The logo for the company’s mascot, Sam the owl, also got a significant redesign. The mark was designed to move, like an animation on the company’s homepage that shows it looking to the side and blinking. It’s designed to look active, and emerged after numerous small adjustments around elements like brow, head tilts, speed, and pace. Its personality is meant to be a supporting partner that’s curious, watchful, and protective.

“A big part of this was not just talking about all the wonderful things Samsara does in terms of its products and services, but also to elicit that emotional response from the end users in order for them to almost understand how much they know they love the brand,” says Geoff Cook, a Base Design partner, likening the B2B Samsara to B2C brands like Carhartt, John Deere, DeWalt, or Milwaukee Tool.

Visual identities for companies that are business-facing often focus on products, features, and benefits, which don’t usually inspire the sort of passion, loyalty, and fandom that more expressive consumer-facing brands are capable of building. As Samsara looks ahead to expand on its growth, the hope is the new brand will invite some B2C-style love from the company’s B2B customers by looking more like a company that builds things than just a tech company that makes software.

Some 90 million people around the world watched King defeat Riggs in the match at Houston’s Astrodome. “I underestimated you,” Riggs told King after she beat him. While Riggs wore a “Sugar Daddy” jacket to the match, King wore a mint dress designed by the designer Ted Tinling, with a blue wristband and blue suede Adidas shoes because she wanted some color.

With the 2026 U.S. Open in full swing, Adidas has reimagined King’s famous shoe as a lifestyle shoe for the first time. Called the Billie Jean King Shoe, it’s selling for $110 a pair. The shoes come with an image of King and her signature on the tongue and the match’s set scores on the back. They come in either blue or off-white.

“The goal was to preserve the look and spirit of Billie Jean King’s original Blue Suede Shoe while adapting it into a silhouette that could live naturally in today’s lifestyle footwear landscape,” an Adidas spokesperson tells Fast Company.

King said in a statement that the shoe will always represent more than one match.

“They remind me of the progress we’ve made, but also of the work that is still ahead of us,” she said. “I hope bringing them back inspires the next generation to keep pushing for equality and to use their voices to create positive change.”

King donated her pair to the Center for Women’s History in 2016.

She said she had to push for it at the time instead of wearing a white shoe. “They didn’t want want me to since we all wore white with blue stripes but I said ‘No, I want color,'” she told Hypebae in 2018.

Adidas released the Blue Suede Shoe as a performance tennis shoe in the 1970s, and it was the German sportswear brand’s first colored velour tennis shoe on the market. More recently, it has brought back the colorway to promote King’s Here to Create Change campaign to encourage young women in sports. In 2018, Adidas released a limited-edition blue-and-white AM4BJK shoe, and held a BJK Your Shoes activation at the U.S. Open to spray paint people’s shoes blue no matter the brand.

By bringing back the shoe and colorway in a way new for a wider audience, Adidas is paying homage to the famous shoes and the message they represent for a new generation.

The settlement agreement, which was filed on August 26 in the U.S. District Court for the Northern District of California in Oakland, comes days into a landmark social media trial that put Meta head-to-head with a coalition of state attorneys general who alleged that the tech giant was purposefully designing its social media sites to result in compulsive use among young users. 

Meta’s capitulation means that the company will have to pay significant damages to the parties who brought the suit and, even more consequentially, it will be responsible for implementing a flurry of design changes to Instagram and Facebook. 

It’s a bellwether moment for social media regulation that, ultimately, could use new UX design parameters to put some power back into users’ hands. 

How we got here

The Meta settlement represents the first major regulatory outcome of a series of trials starring the world’s biggest social media companies that have played out over the course of this summer. 

Earlier this year, a court in California found that both Meta and YouTube were negligent for using “addictive” features like autoplay, infinite scroll, and ephemeral content, leading a young woman to develop compulsive social media habits. In a New Mexico trial, Meta was found culpable for failing to protect young users from child predators on Instagram and Facebook. 

In both of these cases, Meta was only ordered to pay damages to the plaintiffs—but those decisions helped set the scene for this most recent case. 

Meta’s trial this August dates back to October 2023, when multiple states sued the company for allegedly designing Facebook and Instagram to be addictive. The suit was spearheaded by the attorneys general of California, Colorado, Kentucky, and New Jersey.  

In a statement leading into the trial, Colorado Attorney General Phil Weiser laid out the states’ key complaints: first, that Meta “deliberately designed its platforms to exploit young users’ vulnerabilities, foster compulsive use, and maximize the time young people spend on Facebook and Instagram”; second, that it “knew about harms to young users but deceived users, parents, and the public about the risks”; and, third, that it “illegally collected personal information from children under 13 without their parents’ consent, in violation of the federal Children’s Online Privacy Protection Act.”

For the past several months, states and lawmakers have been testing whether it might be possible to use the courts to institute broader social media design reforms—and it looks like they have their answer. Now, Meta will need to significantly overhaul its platforms’ user experience to meet the plaintiffs’ requirements. 

The 5 major design changes to expect from Meta

At its core, this Meta trial—and the similar suits that faced companies including Snap, TikTok, and YouTube earlier this year—is all about the role that design has played in causing compulsive, potentially harmful use among young users. 

In the settlement, Meta has agreed to implement several core design changes to Facebook and Instagram. These include:

“Productive pauses” and “non-personalized” feeds

Teen users will now see much more oversight in their doomscrolling. Per the settlement, Meta will need to implement “productive pauses” at 60 minutes and 90 minutes of cumulative daily usage across their platforms, and give teen users a notice if they’ve reached 15 minutes of continuous usage on either Facebook or Instagram. 

By default, teen users will also be barred from seeing likes or comments on their posts and from using cosmetic filters. They will be given the option to turn off their feed’s algorithmic recommendations (one of the “addictive” features identified in the trials) and instead opt for a “non-personalized” feed. 

“Night Mode” for teen users

The settlement breaks down new time management requirements for teen users into two phases. To start, Meta will implement a “Night Access Mode” on teen user’s devices that will disable push notifications and limit messaging from 12 a.m. to 6 a.m. It will also give teen users a two-hour daily limit of cumulative use across platforms to help deter mindless scrolling.

A later phase proposes to expand Night Mode from 10 p.m. to 7 a.m. and impose app-specific time limits of 60 minutes each. 

The introduction of “School Mode”

During school hours, Meta will disable push notifications for teen users (excluding notifications about messaging, account security, or platform integrity) unless their parent modifies that feature. Teen users will not be able to lift their restrictions without their parent, but both the user and the parent will be able to make the settings more restrictive as they see fit. 

New age-authentication techniques

Within a year, Meta will need to implement new, more stringent methods of determining whether someone is a “teen user” (between the ages of 13 and 17) or younger than 13. These categories will then be used to curtail the amount of data that Meta can collect and retain from young users, and help to determine future guardrails around how young users engage with Facebook and Instagram. 

Stronger parental controls

Parents will now have much greater access to their children’s activity on Meta platforms, including information about the amount of time their teen user is spending on Instagram and Facebook, the usernames of people messaging the teen user, and notifications should the teen user search repeatedly for terms related to suicide, self-harm, or eating disorders.

Today, the fashion industry feels like a graveyard to these idealistic brands. Allbirds, once valued at more than $4 billion, was forced into a fire-sale acquisition. Amour Vert filed for bankruptcy. Everlane was acquired by an ultrafast fashion brand.

It sometimes feels like sustainable clothing is a lost cause. But behind the scenes a cohort of heritage brands are still pouring time and capital into curbing the environmental footprint of their clothes and supply chains.

Arc’teryx is among them. The Vancouver-based brand that is grounded in mountaineering has spent the past three years developing an ambitious circularity program called System 0. Today, it announces this new program with the launch of a fully recyclable hard shell jacket called Sperro SV. It’s designed to be a proof of concept for the brand’s broader transformation, coupling technical performance with materials that can be fully recycled at the end of their lives. In the years to come, the company will bring the same approach to all of its products.

“Every product we make starts with solving a problem for the mountain athlete,” says Stuart Haselden, Arc’teryx’s CEO. “If we can solve problems for people who don’t even care about circularity, and just want a great jacket, then we can disrupt the industry.”

The frontlines of climate change

The decision to embark on this ambitious journey stems from the brand’s relationship with mountain climbers. The company’s mission statement is “Leave It Better,” which refers to the idea that climbers should carry their trash down with them, to leave the mountain as pristine as they found it. But in an age of man-made climate change, the motto has taken on a darker, more profound meaning.

“In the past, we designed products that were highly durable and had built-in repair solutions,” says Alexandra Plante, VP of advanced concepts at Arc’teryx. “We knew that wasn’t enough.”

The company’s commitment was reinforced by interactions with the mountaineering community and elite athletes who witness environmental degradation firsthand. Plante recalls that on a recent trip to the French Alpine town of Chamonix for the Arc’teryx Academy, the company’s educational wing, she heard a glaciologist warn that permanent snow in the region could vanish within decades. A few days later, a nearby ice tunnel collapsed.

Many Arc’teryx-sponsored athletes are also demanding accountability from the fashion industry. Ashima Shiraishi, a climber, refused new gear upon joining the team, requesting only preowned ReGear garments. Greg Hill, an extreme ski mountaineer, pledged to eliminate air travel to curb his personal carbon footprint, reshaping how the brand coordinates expedition logistics. “These impacts are real, and our responsibility to stay on this problem and think of a business model transformation is more important than ever,” says Plante.

The most complex jacket to engineer

To prove that System 0 could work, Plante’s team deliberately picked a very difficult product to reengineer: the technical hard shell. To mountaineers, this is an essential piece of safety equipment in addition to a jacket. It is used by search and rescue teams, mountain guides, and alpine athletes, so it must be weatherproof and durable, and have functional features like storm hoods and harness-compatible pockets.

When the team began to create a recyclable version of the hard shell, they focused on making it as basic as possible so it would be easier to disassemble. But simple designs failed to meet mountain safety standards. So Arc’teryx took the harder route, spending years reengineering the materials within the garment with the help of their technical supply partners, like Gore-Tex and Aquafil.

The resulting jacket—the Sperro SV—uses a specialized nylon fabric built for longevity as well as repeated repairs. When the jacket finally reaches the end of its life, removable trims (like zippers, cuff adjusters, and reflectors) are stripped away. Then the main body fabric can undergo Aquafil’s chemical regeneration process to be transformed back into virgin-quality nylon.

“We knew it was the hardest problem, but starting at a pinnacle place seems to make sense,” Plante says. “Solving all the problems that we needed to create a system for a hard shell would create an easier path for other products in the future. Now we have a road map for how we do this.”

Arc’teryx’s economic advantage

Arc’teryx is better positioned to embark on a multiyear, capital-intensive circular strategy where smaller startups have failed. Unlike venture-backed direct to consumer (DTC) brands, Arc’teryx already has formidable scale, and because its products are expensive, it has high margins. This means that the company has a sizable budget for innovation. It is also the world’s largest purchaser of Gore-Tex fabric, which gives it leverage to nudge supply chain partners to tweak their materials so they are able to be processed by recyclers.

Arc’teryx also has a robust DTC business model, accounting for roughly 75% of sales. With a global fleet of more than 200 retail stores—including roughly 50 equipped with dedicated care and repair centers called ReBird—it is easier for the brand to communicate with its customers and get them to trade in their own products. “That [DTC] business model enables us to do this in a way where we’re not dependent on a third-party retail company,” explains Haselden, the CEO.

Haselden wants customers to see their used gear as products with value, much like a used iPhone. Through ReBird, customers can return used garments for a 20% to 30% credit toward a new purchase. The returned gear is then either refurbished for resale or routed directly into the chemical recycling stream.

But ultimately, Haselden doesn’t believe that Arc’teryx can bank on customers buying the Sperro SV out of environmental goodwill. “We don’t expect people to buy it because it’s made in a circular manner,” he says. “We expect them to buy it because of its design and performance.”

While many apparel brands have scaled back on green initiatives because of cost and complexity, Arc’teryx hopes System 0 will be a beacon for the broader outdoor and fashion industries. By proving that high-performance technical outerwear can be designed for full circularity without sacrificing quality—and backed by a profitable, scalable business model—the company aims to inspire other brands to join them in this mission.

“This is not an Arc’teryx problem; this is an industry-wide problem,” Plante says. “It takes hard work and grit, and we’re just getting started.”

In a new campaign called “Playlist List,” the streaming giant worked with Uncommon Creative Studio to create a series of billboards that showcase the unique place the playlist has in culture.

Up in Los Angeles, Miami, and New York City, the billboards feature the names of delightfully obscure user-generated public playlists that tap into local culture. (See a billboard that reads “I refuse to take accountability so I blame it on the 405” that went up in L.A.)

Uncommon’s first playlist campaign for Spotify came in June when the New York Knicks won the NBA championship. The agency took a public playlist called “Waiting for this since ’99” (the year the Knicks last won the championship) and turned it into a billboard. It was one of several they had preapproved depending on the outcome of Game 4.

To build on the playlist concept, the Uncommon Creative team looked at the calendar to find events like Father’s Day or a social feeling that spoke to one of the specific cities the campaign is running in, like tourist season in New York.

There’s a billboard featuring a playlist called “welcome to the big city” in New York, which got neighborhood-specific playlists, too, like one for interns having their first job experiences in Midtown. The billboards all include the text “Public Playlist” and the creators’ Spotify usernames; the agency worked with Spotify Customer Service to get approval from users.

Successful playlists have become an important part of Spotify’s user experience, and updates to how playlists can be created and shared have become a differentiator to draw in listeners. Last year Spotify introduced Prompted Playlists, which allows users to build customized playlists with prompts they can fine-tune themselves, and earlier this month it launched Playlist Notes, a feature that lets playlist creators add personal notes to individual tracks.

Uncommon Creative Studio, which has worked previously for brands like SiriusXM and Quaker Oats, says it noticed an uptick in saves for the featured playlists, but that wasn’t a core objective. (The campaign doesn’t include any QR codes or direct calls to action as a deliberate creative choice.) Instead, the billboards aim to celebrate the act of making and listening to a playlist on Spotify.

The three tiny houses range from about 20 square feet to 48 square feet (1.9 square meters to 4.5 square meters), offering little more than four walls, a roof, a toilet, and a place to sit. There’s not even enough room to lie flat on a bed.

These houses are not actual products, but part of a housing reform campaign led by Riksbyggen, a Swedish housing developer, and the creative agency BBDO Nordics. Rather than explain the challenge of housing affordability with numbers alone, Riksbyggen opted to show, at full scale, what affordable housing would actually look like. The three homes will be taken on a tour throughout Sweden, offering people a chance to step inside the nation’s housing crisis.  

The house sizes are based on Riksbyggen’s Square Meter Index, which combines income, property prices, and mortgage conditions to show what is affordable to young people making the average income in different Swedish cities. Young people in the city of Umeå, for instance, can afford the 48-square-foot model. In Gothenburg, the average affordable house would be 33 square feet. In Stockholm, the house would be just 20 square feet.

By building these three houses, Riksbyggen is hoping to push for three housing affordability reforms: boosting housing production, making lower-cost loans available to first-time buyers, and reintroducing a program that builds social housing for the elderly, which would make more homes available to younger buyers.

The homes may be spurring action. Sweden’s housing minister, Andreas Carlsson, has visited the homes, squeezing his tall frame into one of the house’s half-beds.

“We see real political momentum behind all three proposals, although to varying degrees,” Riksbyggen’s CEO, Johanna Frelin, tells Fast Company. “The strongest political support currently seems to be for a state-backed start loan for first-time buyers, while investment support for housing for older people is also gaining renewed attention.

“Our proposal for the state to share more of the risk in new housing construction is more politically challenging,” Frelin adds, “but the need for action is increasingly recognized. Ultimately, we hope the campaign can help turn that recognition into concrete policy.”

The nine-item collection, named Yxstaby, is a collaboration between Ikea and Xbox focused on furniture and objects meant to be used by gamers. Several of the main design moves come directly from the Xbox controller itself.

There’s a mint green pillow that’s X-shaped like the directional arrows of the controller’s D pad. There’s a faceted metal dish that mimics the form of the rounded D pad buttons on special Elite Series controllers. And there’s a compact stool that looks like a giant version of the controller’s thumbstick, complete with a slight rounded bottom that lets a user pivot in all directions, just like the thumbstick.

But the Yxstaby items are not meant to merely ape the controller. Ikea’s designers worked directly with Xbox’s designers to come up with pieces that address the evolving way gaming happens inside people’s homes.

“Gaming has advanced from the basement. People play games in all the rooms of the home,” says Carl Ledbetter, head of design for Xbox devices. “The biggest change is designing products that meet people where they want to play, how they want to play, and any place they want to play.”

That means playing on computers, consoles, and handhelds, sitting on couches, chairs, or even the floor. Ikea and Xbox designers worked together to develop the furniture and furnishings that could accommodate these varied modes.

For the console player, the designers came up with a chalkboard-style TV stand that can hold a large flat screen while also storing the console and its peripheral cords and devices. Set on wheels, the TV stand can be moved closer to players during game time.

“For people who want to play games, particularly at home, they want to be immersed,” Ledbetter says.

Ikea product developer Philip Dilé, who previously led Ikea’s first gaming-focused collection, says the Ikea Xbox collaboration was designed with gaming top of mind while recognizing that the game-centric storage and furnishings would also need to fit into settings that are used for much more than gaming alone.

Dilé points to the lounge chair, which is designed for edge-sitting during focused gaming, and includes a controller storage pouch beneath the seat. The piece works just as well as a chair for leaning back, deep into a book or conversation.

“I think that’s what people expect from most furniture, and especially gaming furniture, that it should really serve multiple functions but also aesthetically contribute to the home and not be something that you accept just to fulfill a function or to enhance your hobby,” Dilé says.

The designers also injected a bit of playfulness into the collection. Ikea designer David Wahl and Xbox designer Elliott Hsu worked together on the thumbstick stool, which scales up one of the most recognizable elements of the Xbox controller.

Dilé says the pair started their process using the actual design files of the controller thumbstick, pulling its exact geometries and details into their design. “They just had a lot of fun designing that one,” he says.

The Xystaby collection will be available in Ikea stores and online in the fall.

The crackdown comes as a variation of Madonna’s pop hit “Like a Prayer” created by an Australian producer using AI-generated vocals and drums has spent 16 weeks in the Australian Top 20, stoking debate about generative AI’s place in music. It peaked at No. 2 on the May singles chart of the Australian Record Industry Association (ARIA) and remained in fourth place on Tuesday.

Wholly AI-generated tracks will be banned from the ARIA charts from Monday next week, but some AI-assisted music will remain eligible, an ARIA statement said.

Tracks can be eligible only if “humans wrote the song and performed the lead vocal and the primary instruments,” among other requirements, the statement said.

AI-generated music would also be ineligible for the Australian industry’s prestigious ARIA awards.

ARIA’s chief executive, Annabelle Herd, said: “These changes reflect our intent to remain dynamic and promote the human nature of artistry in what is—to say the least—a rapidly developing space.

“The ARIA charts will always remain a transparent measurement of the music Australia consumes, but a chart that rewards unlicensed AI output would undercut the very basis of the recorded music we exist to represent,” Herd added.

Eligible music must also be produced using legal AI services. Music was appearing on streaming services that had apparently been developed with AI tools trained on artists’ music without authorization.

The Australian rules are in step with new principles released in July by the London-based International Federation of the Phonographic Industry. The federation represents the recording industry worldwide.

The international guidelines state that to qualify for official music charts, a track must be “substantially human made.”

Alexis Weaver, a composer and lecturer at the Sydney Conservatorium of Music, told Australian Broadcasting Corp. the debate around AI in music was “exceptionally fraught and very emotional for a lot of musicians and so everyone will have a different opinion.”

Weaver applauded ARIA’s move as a “wonderful step forward” that sent a message ARIA wanted to “prioritize and value human creativity.”

“It can be quite hard to avoid AI tools in the current landscape,” Weaver said. “There is a way to use it while you’re still steering the ship and making the main creative choices.”

—Rod McGuirk, Associated Press

The project started on a trip to Bangkok in 2022, when cofounder Matt Dye was struck by the high levels of air pollution. He learned that the problem wasn’t coming only from traffic exhaust or factories, but from farms outside the city that were burning millions of tons of straw left after harvesting rice. “All the smoke from the fields was just drifting over to Bangkok,” he says.

The city is sometimes so polluted by smoke from farms that schools shut down and people have to work from home. The problem isn’t limited to Thailand: globally, 450 million metric tons of rice straw are burned each year, creating both air pollution and climate emissions.

Farmers burn the straw because it doesn’t have economic value. But Dye, who was working at a biomaterials startup at the time, started to wonder if something could be made from it.

The case for rice straw

The biggest opportunity he saw: turning it into toilet paper, and tackling two problems at once. Right now, most toilet paper is made from wood pulp. “We grow trees now for decades and then we use them for seconds,” Dye says. Mainstream manufacturers like Procter & Gamble source fiber from Canada’s boreal forests, where more than a million acres of trees are clear-cut each year, according to the nonprofit NRDC. Paper towels are made the same way, and offered another opportunity.

Other alternatives are quickly growing. Brands like Who Gives a Crap, Caboo, and Reel Paper all have found success using bamboo. Still, there’s a risk that as bamboo production scales up, it could also displace forests or farmland. “Is it really our best use of resources and land to use good land to grow more bamboo?” says Dye. “We really think that environmentally, using what’s left behind is better than growing more.”

There also might be some advantages compared to recycled paper. Right now, because receipts are mixed into the recycled paper stream—and receipts are made with the hormone-disrupting chemicals BPA and BPS—those chemicals can end up in recycled toilet paper. Rice farming uses pesticides, but Dye says that the pulping process breaks down any pesticide residue.

Turning waste into something useful

The founders discovered a company in Thailand that was producing takeout boxes from rice straw. It hadn’t made toilet paper or paper towels in the past—and no one was sure that it was possible. But it partnered with the startup on the R&D.

Rice straw fibers are stiffer than wood pulp, making them harder to work with. “We had to develop a pulp and tissue-making process that made those fibers flexible enough to form a soft sheet and bond strongly enough to hold together,” says Dye. After the pulp producer reached certain technical specs, the startup went through long trials with a tissue manufacturer to tweak other parts of the process, from refining and enzyme treatments to how the sheets were formed. “That was really where the breakthrough was,” he says.

The first product is undyed, and the natural color may be a hard sell for some consumers. (The company doesn’t want to use bleach, but says it’s exploring other ways to lighten the color in future product runs). The sample I saw was also a little rougher than I expected. In its current state, its paper towels might find a wider market than the toilet paper.

It’s worth noting that it can be cheaper than bamboo. In a limited launch now with an introductory discount, the company is selling a box of 12 rolls of 3-ply toilet paper for $14.99. (A similar box of bamboo toilet paper can cost around twice as much.) The rice-based paper towels start at $11.99 for a pack of six. The startup says that it will use feedback from the initial launch to continue to improve the products.

If the company can get the products right, there’s also plenty of material to use as it scales up—enough, Dye says, that it would be possible to fully replace trees in toilet paper. “Rice is the third most produced crop in the world, and for every one kilogram of rice harvested, roughly one kilogram of the rice straw remains in the field,” says Dye. “The world has 1.6 to 2 trillion pounds of rice straw available [every year]…This could be a complete replacement just because of the availability of the rice straw to replace tree pulp fiber in the future.”

After fashion retailers embraced brand-owned resale, it’s starting to show up in more categories, from toys to bikes. Sur La Table is the first in cookware with its new platform, called Second Servings, which lets people buy and sell secondhand versions of everything that it carries, from sauté pans to espresso machines.

Affordability is one of the motivations, says president Dan Goldman. “Resale is all about helping bridge the existing gap between consumers who love to cook and haven’t necessarily had access to the premium cooking products,” he says.

Goldman says he doesn’t expect the resale site to cannibalize the retailer’s new sales; instead, he believes it will bring in younger, budget-conscious customers the company wants to introduce to its products for the first time.

“We want to build lifelong relationships with those consumers,” he says. Goldman, who joined Sur La Table last year, previously helped launch resale programs at the North Face and Gap, where he says he saw the model successfully recruit new customers.

Of course, secondhand cookware is already ubiquitous on sites like eBay and Facebook Marketplace. But brands are realizing they can be part of the growing market.

“If there is already a larger resale market happening on other platforms, then the brands and retailers are going to want to own that. And it makes the customer experience so much better to have all of the assets and information that a brand has,” says Emily Gittins, CEO of Archive, the technology platform that customized Sur La Table’s new resale site. (Archive previously worked with Goldman on Renewed for the North Face.)

Sur La Table’s platform is launching today to let sellers list products before it opens up for buying. Someone listing an item can look up the SKU, answer a few questions, add a photo, and then automatically pull up original photos and descriptions, including materials, care instructions, and the original price.

Second Servings includes both Sur La Table’s own branded products and any of the brands it carries, from All-Clad to Le Creuset. (The platform suggests the prices, ranging from 20% to 70% off the original price based on condition, with most selling at a 50% to 70% discount.)

After an item sells, the seller will use a prepaid shipping label to ship it to the buyer directly, and then get either cash or store credit. Sellers get a 70% payout in cash, or 100% of the value if they take a store credit; buyers pay for shipping.

Of course, there’s a sustainability advantage to buying secondhand. Buying used clothing, for example, can cut the carbon emissions of an item by 80%. Gittins says brand-owned resale sites can also bring in customers who weren’t previously considering buying secondhand.

“We actually see a high proportion of buyers would have otherwise bought a new product but at a lower price point,” she says.

Cookware is unique in that some of it is designed to last decades, and in some cases to improve over time. “A seasoned cast iron is actually in many ways a desired asset,” Goldman says.

As Sur La Table gets data over time about which secondhand products are most popular and most retain their value, those insights could be valuable.

“What Second Serving gives us is really great insight in terms of what consumers care about—what actually holds up, what has desirability,” Goldman says. “When a Dutch oven changes hands three times over 10 years, it still holds value. That’s a win, right? It’s telling us that the product is durable.”

By the same token, he says, if a product doesn’t last as long, the company could discuss the lifecycle with the supplier to determine “How do you design something that does stand the test of time?”

The meeting software company just revealed a new logo, wordmark, color system, and brand positioning, created by the design studio Smith & Diction. At the same time, Calendly announced in a press release that it will be expanding its productivity offerings to include an AI email assistant and an AI-powered meeting notetaker, “forming a suite of AI tools designed to remove the friction—and frustration—surrounding meeting scheduling and busywork.”

Calendly is facing a conundrum that’s become almost ubiquitous for the broad category of productivity software as a service (SaaS) brands: Its ambitions and capabilities in the AI era are outpacing how consumers perceive it. Most people know Calendly for its original use-case of sending one simple blue link to set up a meeting without the hassle of discussing time zones and availability; now, though, the brand wants to be much more than that. 

As AI fundamentally transforms the productivity SaaS space, its key players are using design to help smooth the transition. In Calendly’s case, that means shifting from a loud, bright brand identity to a softer look that communicates clarity. 

Calendly’s brand problem

Calendly’s brand problem has two key facets. 

First, over the past several years, the brand has moved from powering straightforward meeting scheduling to offering a larger toolbox of AI-powered meeting features. Largely, though, it’s still known as a calendar. 

Second, recent advances in AI coding tools means that, today, almost anyone has the ability to build and deploy their own productivity tools. In this environment, Calendly is fighting an uphill battle to convince users that it’s worth using their software instead. 

“AI is changing the expectation for software,” says Jamal Miller, Calendly’s VP of brand and product marketing. “People no longer want a collection of isolated features; they want tools that understand context, work across the systems they already use, and can take action when they ask. At the same time, people can increasingly assemble their own workflows with general-purpose AI. That means Calendly has to be precise about the value only we can deliver.”

Calendly is far from the only SaaS brand tackling this challenge. Notion, for example, has spent the last several years transforming from a website builder to a broad AI-powered workspace, and the grammar-fixing tool Grammarly has ballooned into a productivity tool that can send AI agents into your Google Docs, email, Notes app, and more. Both of these platforms have used design to help users acclimate to the shift. Notion introduced an amorphous, Clippy-esque mascot to help introduce users to its new AI agents in 2024; and Grammarly underwent an entire rebrand—including changing its name to Superhuman—in 2025. 

According to Mike Smith, the cofounder of Smith & Diction who also spearheaded Grammarly’s rebrand, the key to these projects is identifying a clear pitch to help users understand, at the most fundamental level, why they should use the product—then building an entire design infrastructure around that tentpole. 

With Calendly, that pitch was the idea of “making space for what matters”: designing tools that are so simple to use (whether that be a meeting link or clean AI-powered meeting notes) that they help clear room for everything else in the day.

“When we talked to Calendly, they were like, ‘Our dedication to the simplicity of the thing just working is huge.’” With their original meeting scheduling link, for example, the Calendly team told Smith that most users never even open the app—they simply save the link in a Word document, send it every time they need to schedule something, and never think about it again. That’s the throughline that Calendly wants to hold onto as their actual product offerings expand. 

“They were like, ‘Okay, what if we could do that kind of seamlessness for everything?’” Smith recalls. “That’s what really sparked our interest.”

A gloopy, organic rebrand

Everything in Calendly’s new identity is engineered to look effortless, organic, and simple—a clear departure from the brand’s previous identity, which was designed by Pentagram in 2021. Whereas the brand’s former identity emphasized neon colors, bold shapes, and layers of illustration, the new look is soft and purposefully understated. 

Miller says the old identity worked to make Calendly feel friendly and approachable, but it “could too easily signal ‘booking link’ when we needed to communicate a more connected platform and a bigger role in our customers’ work.” There were also several practical challenges with the former branding that Smith’s team needed to address.

“I love neons and prints, but Calendly had these neon colors from the previous rebrand and they were like, ‘It’s so hard to work with them,’” Smith says. “‘Our pink isn’t printable. Our blue is barely printable. We can’t do any big graphics because the flood is just too intense and it feels like the pink felt like an error or something like that.’ They really wanted this brand to feel like a breath of fresh air.”

Smith & Diction’s new color palette emphasizes Calendly’s core blue but adds in a range of prismatic pastels that can be blended into an unlimited number of background gradients for the brand. The palette, Smith explains, is meant to feel like the little moments of stillness throughout the day: morning sunlight pouring over your desk, the edges of clouds at sunset, or light dancing off water.

Across the brand identity, Smith’s team worked to incorporate elements that would nod to Calendly’s role of “making space” in the workday by serving as the invisible connective tissue powering everyday tasks. One key asset is something that Smith has dubbed “gloop”—a recurring motif of organic, liquid shapes that help visually bridge the custom icons for features like the calendar, email assistant, and notetaker.

Meanwhile, the brand’s new wordmark, made in partnership with designer Gavin Potenza, has been edited so that the tail of the “C” flows seamlessly into the “a,” and the “e” likewise flows into the “n.” 

“Almost like how Calendly is the invisible connector for people, the wordmark can pay off that invisibility as well,” Smith wrote in a case study on Medium.

How Calendly got its new logo

By a long shot, Smith says, the hardest part of the rebrand was giving Calendly a logo that wasn’t a calendar.

Calendly felt strongly that its name needed to remain the same, given the 13 years of brand affinity backing it, but its accompanying icon (formerly a very techy blue “C”) needed to help signal the brand’s new positioning. Smith tasked Tully Ryan, his team’s “symbol guy,” with figuring what to do. 

“He was like, ‘This is so hard, because you just want to draw calendars. That’s all you want to do,’” Smith says. “We even had something that looked kind of like a Slack logo, but then it almost felt like a calendar without the edge. We’re like, ‘Oh, no.’”

From there, the team tried everything: sunrises to represent the start of the day; hourglasses for time; and even a modular system of different symbols that could expand over time. Ultimately, though, they ended up right back where they started. Ryan experimentally mocked up a “C,” this time with a less techy feel, surrounded by a series of ripples. That design was ultimately simplified down to a single ripple, represented by a shape that Smith calls a “squircle.”

“We presented this idea of a ‘C’ with a ripple effect,” Smith wrote in the case study. “To be over conceptually designer-y about it, this symbol represents the idea of how just sending one meeting link can change the trajectory of your business. Calendly is that first ripple.”

Smith might poke a bit of fun at the intense deliberation that went into Calendly’s new look, but that sentiment is a window into how SaaS companies are thinking about design right now. In a landscape where AI is making productivity platforms increasingly broad—and, frankly, confusing—design becomes the wayfinder to help users understand what they’re really signing up for.

“You can just call us HUT for the next 25 weeks,” the pizza chain wrote in a social media post with an image of a temporarily rebranded Pizza Hut storefront.

On the restaurant’s facade, the “Pizza” part of the chain’s wordmark is covered up by a red banner showing a football; another image shows the word “Pizza” similarly covered by a football banner on a store marquee.

Don’t expect your local Pizza Hut to cover up its logo, however. The “rebrand” is actually just a stunt, the chain says, playing off the dual meaning of the word hut in its brand name, which quarterbacks shout in a football snap count. There won’t be a widespread change made to the signage at the shrinking number of Pizza Hut restaurants.

“This is a playful, limited activation deployed at select Pizza Hut locations, designed as a fun nod to our longtime role in football traditions,” Pizza Hut CMO Melissa Friebe tells Fast Company. “This creative take on our signage is simply a reminder to football fans that game day starts with ‘Hut.’”

Pizza Hut’s gimmick rebrand is part of a marketing trend among restaurant chains in the name of creating content. Rather than a work of graphic design, it’s meant to be a fun take on a logo, like IHOP briefly calling itself IHOb by flipping up the last letter in its logo in 2018 to sell more burgers, or Long John Silver’s stunt rebrand swapping out the fish in its logo for a chicken to sell more of the latter last year.

Pizza Hut’s not trying to sell a new product here. What it is trying to do is be top of mind for football fans who want to order pizza. Visually, the chain did it by removing some details of its logo to draw attention to a core brand element.

This is a familiar marketing playbook, and it works. Just look to Levi’s and Heinz covering up their logos for the World Cup, or McDonald’s advertising through just color and scent. Competitor Domino’s released a domino-shaped personal pizza that comes in a box designed like a domino to own the rectangle as its distinctive shape in a category known for circles.

The “rebrand” comes at a challenging time for Pizza Hut. The chain was sold by Yum Brands in June to buyers that include private equity and Yum China Holdings, its China division, which spun off as a separate company in 2016. It said earlier this year it would close nearly 250 U.S. stores.

It’s the last brick in the towering castle that China is building around embodied AI. Beijing has elevated robotics into a matter of strategic importance for its future economy amid its broader technology contest with the United States. The research firm Omdia puts China’s share of worldwide humanoid robot sales in 2025 at nearly 90%, while U.S.-based competitors Tesla, Figure AI, and Agility Robotics each managed only about 150 units delivered.

McKinsey research expects embodied AI—synthetic brains with a body to act in the physical world—to expand faster than nearly any other industry over the next several years. It is a race to build one of the major drivers of the 21st century economy. Unitree’s IPO shows, quite literally, how much slower the U.S. is running.

Unveiled by Unitree’s founder, Wang Xingxing, Superman is by far the most athletic robot we’ve seen to date. It’s capable of jumping 6.6 feet standing and reaching a top speed of 28.3 mph. For comparison: In April, a modified Unitree H1 set a robot running record of about 22.4 mph. And at the 2025 World Humanoid Robot Games, the winning high jump entry cleared about 3.14 feet.

The machine has no head. Estimates put Superman’s height at least 6 foot 3. Its legs are 2.8 feet tall, longer than those on Unitree’s standard H1, a proportion aimed at stretching stride length to push speed higher. It doesn’t have either hands or grippers. And doesn’t have the head-mounted sensor hardware built into the commercial H1 and G1 lines. We know that its joints use Unitree’s typical design, with high torque density built around rare-earth magnets, the pieces that are fundamental for robotics controlled globally by China. There are no other specs at this point.

According to Unitree, Superman is “an experimental platform for studying highly dynamic movements.” The company claims that this is an entirely new unit that “was developed in a little over three months, and there is still substantial room for improvement in the months ahead.” No test venue, measurement methodology, or third-party verification accompanied the figures. There’s also no total weight, degrees of freedom, battery capacity, sensor details, or mass-production timeline.

‘The embodied ChatGPT moment is coming’

It was a spectacular demonstration that was strategically timed, as it was unveiled two days before the August 19 listing. Unitree became the first general-purpose robotics company—not merely the first humanoid maker—to list on a mainland Chinese exchange. Demand for retail shares ran to more than 8,000 times the available supply, a figure Reuters flagged as unmatched on the Star Market—the Chinese equivalent to the NASDAQ.

Unitree sales are skyrocketing now. In the first quarter of 2026 they rose 68.5% year over year, and the company had cumulatively produced a record 18,000 bipedal humanoids across multiple models as of July. Its robots had already gone viral running, dancing, and performing martial arts, while taking over the real world too. CATL, the big lithium-ion battery maker, is deploying humanoids at scale in Luoyang; Japan Airlines is trialing the Unitree G1 for baggage at Haneda, Tokyo’s city airport, through 2028; State Grid, China’s national electrical power network, launched a $1 billion robot-maintenance plan; and Unitree’s R1 sells internationally from under $5,000, against Elon Musk’s projected $20,000 to $30,000 Optimus.

In May, taking a page out of Musk’s epic announcement brand, Wang climbed into the torso of the GD01, a 1,102-pound, roughly $574,000 transformable mecha, walked it around, and punched through a concrete wall in one strike. The company is calling this one Superman, another Musk-line move. The South African billionaire’s Optimus, however, is hardly moving the needle in the real world at this point.

Given this showmanship and his company’s success, it’s no wonder the Unitree CEO got a coveted front row seat at the summit for China’s leading tech entrepreneurs held by President Xi Jinping back in February 2025. But Unitree and UBTech are only the beginning of what’s to come.

Companies we hardly know about in the West—like AgiBot, Deep Robotics, Leju, X Square Robot, and LimX Dynamics—will likely join the public trading fray, alongside big names like Xpeng and BYD, which are investing heavily in the embodied AI industry.

“We are marching towards a ‘ChatGPT moment’ in embodied intelligence,” said Wang at the 2026 World Robot Conference in Beijing, a day earlier. That moment is being built, sold, and now publicly traded in Shanghai while the U.S. seems to still be watching from the stands.

One of Designer Fund’s investments is Framer, a no-code website design platform. [Image: Designer Fund]

The AI industry has been working hard to improve creative output from models through crisp evaluation metrics, rubrics, and automated tests and tools. A few years ago, the challenge was to reduce the volume of slop in the world by ensuring that AI-generated human hands had the correct number of fingers, or AI-generated essays didn’t mix metaphors. But now that “correct” has mostly become table stakes, the path to creative work that convinces, or even impresses, an expert like Blumenrose is less clear-cut. 

Taste, or at least an approximation of it, has become one of the next frontiers in model development. A host of startups are promising that they can encode taste into AI. Meanwhile, the major frontier labs are thinking about how to edge their models into subjective domains like writing and design. Anthropic’s Claude Design, which launched in April, has already encoded a particular aesthetic that’s replicating across the internet.

But as every human knows, even within a narrow profession, opinions on what’s considered “good” can vary based on each person’s alchemical mix of social, cultural, and work experiences. Developing taste in any creative field is both a technical challenge and a personal project: You need to learn enough information about what works from experts and from history, but then you need to develop an understanding of what you like and why. The same is true when teaching a model taste.

The first step in that process, information gathering and evaluating, is similar to how tech companies have operated for decades, and how frontier labs have been developing model intelligence in the last few years. The critical next step—deciding what feels good and unique, and then making a choice based on discernment—is more foreign to the processes and systems for building technology. 

Still, technologists across the industry are attempting to tackle that qualitative quandary. At large and small scales, they’re teaching models what “good” looks like for specific creative fields, aiming to hone AI taste to a fine-enough point. But as it turns out, those methods rely on the taste of a whole lot of human professionals.

Expert data in, expert data out

The first hurdle toward developing AI taste is getting the right data sets. Most general-purpose model providers like OpenAI and Anthropic have built their model knowledge bases by scraping the internet and gathering publicly available information (or proprietary information, for which some AI companies have been sued and, in some cases, held accountable).

For a long time, the big labs have worked with “post-training,” or the process of refining an existing model to perform well at certain tasks. With the goal of developing a generalized model intelligence, that training has been directed toward a best average. In the visual domain, this has resulted in reliable, if not terribly creative, output. But domain-specific expert-level taste requires domain-specific expert-level data. And now, a crop of established companies and new startups are looking to move past a generalist knowledge base to solve this problem in the creative space.

Take Figma, which uses foundation models in-house for productivity and production tools, but notes that they often have shortcomings. “Off-the-shelf models often lack the ability to judge design quality in a reliable way, and the amount of reasoning and resulting latency doesn’t always correlate with the complexity of the task,” says Figma’s AI Research Lead, Sumithra Bhakthavatsalam.

In other words, frontier models can work too hard and for too long to produce less-than-tasteful results. That’s why the company post-trains for faster outputs and less overthinking during iterative design work. It also tunes the model for overall quality, variety, and a deeper understanding of Figma and Figma’s customers’ design systems. To power that post-training, Bhakthavatsalam’s team collects curated “best-in-class” examples of “great” and “good” from Figma’s designers.

At Krea, co-founder Diego Rodriguez knows that general inputs won’t yield results that his designer, filmmaker, and architect customers can use for their professional work. Krea offers a suite of tools for creative professionals including image and video generators, and a model Krea users can help train, which means the output quality must be high. For Krea’s visual generators, Rodriguez’s team has gathered millions of references of film angles and styles, printing techniques and materials, architectural materials and elements for post-training to ensure that the models they build with have the visual vocabulary for professional creative work. 

A recent partnership with architectural firm Henning Larsen has helped Krea improve their architectural-related data, and as a result has refined Krea’s offerings to the architectural industry. It’s a mutually beneficial arrangement: Henning Larsen gives Krea expert feedback on things like the quality and accuracy of their generated visuals, the usefulness of the platform structure, and Krea’s offerings for professional architects; and Krea provides the firm with domain-specific tooling, like an interface tailored to their workflow and an image generator trained on their field.

Rodriguez says now Henning Larsen can spin up far more accurate and expressive client proposals in less time.This is Krea’s first partnership like this with a specific company, but Rodriguez plans for more. “I want to partner with the best across [creative] industries—the best in film, the best in architecture,” says Rodriguez. “They can tell me things I hadn’t even thought about.”  

AI taste is made of human decisions

After teaching AI models what domain-specific information even looks like, the challenge becomes guiding them toward discernment. Edwin Chen, founder and CEO of Surge AI, which supports frontier AI labs with post-training, thinks many in the industry are wrong in the belief that if they make a model smart enough through rubrics and popular benchmarks, that model will be able to reason its way to tasteful outputs. 

“I think people underestimate that this isn’t purely a technical problem,” says Chen. “It’s actually a humanistic and values-based problem, too. You’re not just teaching [the models] a lot of more facts, you’re teaching them what they care about.” 

According to Nick Heiner, Surge AI’s head of RL environments, the key post-training method for taste is reinforcement learning with human feedback (RLHF), a process where a panel of experienced humans provide feedback on AI’s outputs that are then used to push the model in a better (or more tasteful) direction.

To understand how it works, imagine 10 poets reading two side-by-side examples of AI-generated poetry on a screen. They rate and comment on each one based on their professional opinion, submit their answers, and move on to the next set of examples. (Surge AI does in fact work with poets and other published writers.) Now picture hundreds of poets looking at hundreds of poems. Now, thousands—or even hundreds of thousands. Now imagine there is also a collection of designers looking at side-by-side layouts for a homepage. And architects clicking on the material that would be the best match for an east-facing exterior wall. And filmmakers selecting the right lens for a key scene. All of those people are reinforcing what “good” looks like so the model can fine-tune its output. The trick to replicating human taste is getting lots of humans to tell computers what they like and why. 

Surge AI taps into human expertise to help models develop a better sense of taste. [Image: Surge AI]

But even within that process, there are many decisions that Surge AI and other companies running post-training can make that further mold which definition—or definitions—of taste gets synthesized into a model. “Some of them involve how you’re arranging the humans,” says Heiner. “Maybe you have one set of experts doing the original work, and then you have another set of experts reviewing it… Maybe you have some other mechanism where you’re reviewing the reviewers, and then if someone’s not doing good reviews, they get demoted back down.” 

You can adjust the way you write the instructions for the humans, how you set the priorities for their feedback, how you direct the experts’ attention, whose choices are given more or less weight in the results. You can change the criteria for the experts you select in the first place. You can change the number of tasks that go into a data set, or their type—maybe the layouts are in a carousel instead of side-by-side on a screen. All of these choices affect the training outcome. All of these choices are subjective. 

“At the end of the day,” says Heiner, “what it comes down to is the taste of the humans—not just the taste of the experts, but also the taste of the data production lead, and the taste of the people leading your post-training experts.”

And once Surge AI has completed their work, there’s a whole new team of humans on the client side making decisions about what “good” means. The process turns many individual human aesthetic choices into data sets that teach individual models to make “good” choices—or maybe more accurately, human ones.

Surge AI is not the only company directly collaborating with the frontier AI labs and other model developers to hammer on this problem. Taste Labs recently came out of stealth mode with $18.5 million in funding to train models on taste, building infrastructure and tools to collect expert annotations and evaluations—starting with the field of design (the company did not respond to a request for an interview).

This year, the creative hiring platform Contra launched Contra Labs, “a independent human data & creative evaluation lab” that connects its network of more than 1.7 million creative professionals with labs and developers looking for expert feedback on model output. Contra co-founder and CEO Ben Huffman says that designers, writers, video editors, and other creatives at the high-end of their fields can make from around $50 to $250 an hour working on RLHF and other types of post-training. Huffman says he believes this kind of creative expert-level evaluation work is going to become a huge job category.

Post-training toward taste is happening at smaller scales too. Pickford is a startup creating films that respond in real time to audience sentiment through AI. Bernie Su, an Emmy award–winning writer and director, writes the original screenplays and character profiles for the films; AI redirects the story based on audience response within prescribed guardrails. To make sure that the AI has the right level of taste to match the audience’s expectation, Pickford does extensive post-training on a matrix of foundation models, and has a team that reviews video of each screening afterwards to give feedback for the model. (Their film Whispers has been selected as a finalist for the Outstanding Innovation in Emerging Media Emmy.) At this stage in the industry, humans are guiding AI taste from start to finish.

A human’s work is never done

It takes a lot of people to build an automated system today, and many experts believe that will be the case for a while—if not forever. In fact, Contra’s Huffman says he doesn’t believe it’s possible to give a model taste. What Contra Labs offers is feedback from working creatives about which output they think is better from their professional experience and personal taste. 

“LLMs can improve their outputs based on that preference data,” says Huffman, “but they’re never going to be able to create something truly tasteful and competitive that’s net new and resonant, because they just don’t have a pulse.” According to Huffman, for any creative work with multiple definitions of good, humans need to continue to be in the loop.

Krea’s Rodriguez compares his company’s offerings to the sword, not the samurai. He wants to create tools, images, and platforms at a high-enough fidelity for creatives to do their jobs better—not to encode a closed definition of taste. Krea has even trained their own foundation model, and recently made it available as open weights, or free to use with a custom license, for creatives to tweak to their own personal and professional taste. 

And Figma’s Bhakthavatsalam says the company doesn’t see AI as a stand-in for taste: “Designers can use AI to expand creative possibilities, but the discernment and empathy behind each prompt and action remain uniquely human.” 

On the other hand, Surge AI’s Chen is bullish on the industry’s ability to encode taste into AI. “It is inevitable that we will get models with taste very soon,” Chen says. “We teach the models all these aspects of taste, all these things that we thought were very intrinsically human, like creativity and serendipity. Then every few months, we see the models get better at all of them. One by one, the things that we were sure were uniquely human just turn out to be patterns the models can learn.” 

Whether this proves to be true across the board, it’s true that models are getting better at complex work, or at least humans think so. A recent beloved best-selling novel Daggermouth was allegedly written with AI; nearly one in five young Americans uses chatbots for mental health advice, up from one in eight just late last year.

But even if Chen’s prediction is right, Heiner says Surge AI plans to keep experts in the process. “I think you will continue to need humans to inject what [taste] looks like, because if you think that AI is built to serve humans, we do need to speak up and say what the thing is that we’re looking for it to be,” he says.

Models may be able to learn and reproduce existing patterns well enough for most professional purposes, but taste isn’t static—and tastemakers will likely keep pushing past any encoded definition. 

Blumenrose predicts that we’ll start to see more obsessive, elaborate websites that are personal expressions of craft and creativity. At Designer Fund, they often toss around the phrase “AI would never,” to describe the kind of choice that only a human designer could make—out-there, counterintuitive, or even “wrong.”

“Because we’re humans, I think we’ll start doing things that the computers can’t do or won’t do,” he says. And then maybe someone—or thousands of someones—will teach that to an AI, too.

For the last two years, contractors working for Walmart have used 3D-printed construction at a handful of sites across the U.S. The walls for five additions to existing stores in Tennessee, Missouri, Alabama, and Arkansas have been completed in this fashion so far. It’s the country’s largest deployment of 3D-printed architecture in the commercial sector. The retailer’s scale is catalyzing the growth and adoption of the technology, which promises to construct buildings faster and more affordably. 

Digital fabrication meets big-box stores

The use of 3D printing for construction has mostly focused on residential structures, owing to their smaller size and the nascent state of the technology. Expanding 3D printing to the commercial sector could help scale up the industry. Meanwhile, completing larger structures with bigger budgets could help prove the utility of the process—and even help bend the cost curve.  

To complete its 3D-printed builds, Walmart partnered with Alquist, the maker of robotic A1X 3D printers, and FMGI, a longtime contractor of the Arkansas-based retail giant. (Walmart declined the opportunity to comment on this story.) 

“This is what keeps me excited,” says Darin Ross, the president and CEO of FMGI, which is based in Woodstock, Georgia. He founded his company in his basement and as it grew into a nationwide firm, he eagerly awaited more innovation. “For 30 years, I’ve watched the construction industry just do it the same way over and over,” he says. “We as an industry are way behind in adopting new technology and new ways of doing things.”

FMGI is trained to operate Alquist’s 3D printers—which pipe layers of a concrete composite, like toothpaste being squeezed from a tube—and recently agreed to expand an existing arrangement with Walmart to 3D print buildings. This includes full-scale buildings and 30-plus projects in the pipeline through 2027. 

The partnership started with a pilot build at Walmart’s Athens, Tennessee, location in September 2024. Alquist printers laid out the walls for an 8,000-square-foot, 20-foot-high expansion of the superstore. The additional area helped the store to better accommodate increased pickup and delivery traffic. At that time, it was the largest 3D-printed commercial structure in the United States. 

From traditional methods to 3D printing

FMGI has a long history working in the retail and big-box world. It has completed remodels and new buildings for Home Depot, Trader Joe’s, Aldi, and Publix. As a general contractor for Walmart, it has worked on everything from neighborhood stores to building drone nests for new delivery methods.

The company had initially secured the Athens contract with Walmart as a standard masonry build, but the retailer switched gears and informed FMGI that it wanted to try to 3D print the expansion. As Ross says, “our job is to do what the customer wants.”

The process had a steep learning curve. It involved figuring out how to use the rail-mounted robot printer, which extrudes inch-thick layers of special concrete mixed with fibers to avoid cracks while curing. It took 40 days to figure out how to print the expansion’s 20-foot-tall walls, including mastering the new system. FMGI also had to resubmit plans to the city to get approval to use this new method. But the hurdles were all teachable moments. The buildout for FMGI’s next Walmart 3D print job—at a store in Huntsville, Alabama, completed in late February in 2025—took just a week. This was substantial time savings.

“I saw the robot print the walls, and the speed and accuracy in which they were doing it,” says Ross. “The light bulb went on.” FMGI now owns four of Alquist’s robotic rigs, which cost an average of just under $300,000 each.

Time and cost savings

FMGI says these projects cut production time by a third—allowing the retailer to open and start selling that much quicker—and lower construction costs by 15%. 

The technique still faces challenges. As in conventional construction, weather is an issue: The water-and-concrete mix sent through the robotic arm and dispenser needs to be kept below 91 degrees and above freezing to avoid jamming or melting. 

On the Athens job, where they worked with an earlier iteration of the printer, the heat was a sweltering 110 degrees. Ross recalls blowing the hoses that carry the concrete mix to the nozzle due to poor temperature regulation; workers later added ice to the machine to cool it down and ran prints at night. Additionally, severe winds can cause the machine to be unsteady and unable to lay precise lines of the concrete composite. But, as Ross argues, the tradeoffs are much better than having a dozen masons on a scaffold freezing or overheating.

Advanced technology also helps recruit workers, says Ross. It’s a valuable asset at a time when the construction industry suffers from a long-term labor shortage. Ross says convincing young people to take a position at FMGI becomes much easier when they see a future with robotics as opposed to just manual labor.

More buildings coming online soon

The next round of FMGI/Alquist projects for Walmart will mostly be standalone fuel stations, as well as more additions to support online pickup and delivery. Ross says they’re also in final negotiations to build a 50,000-square-foot grocery store from Walmart with this technology, which would be the largest 3D-printed building in the world. 

Other construction firms and retailers are watching what Walmart is doing, and are poised to follow. “With Walmart being the leader of the retail industry, a lot of people will adopt after Walmart figures it out,” he says. 

None of the big AI labs are currently attempting to tackle this challenge. That’s leaving the job up to the companies that actually hold all the data: the architecture firms themselves.

Architecture firms, both small and large, are actively building out their AI capabilities. They’re hiring data scientists and machine learning specialists. They’re running Shark Tank-style AI ideas competitions, vibe-coding bespoke plugins and apps to automate highly specific tasks, and even developing their own hyper-niche large language models that can help them create building forms and floor plans that reflect their signature style.

All this effort amounts to a broad recognition from across the industry that it’s a sink-or-swim moment for architecture. As the practitioners change the way they work—and as client expectations shift—some architecture firms are starting to realize that their own portfolios contain exactly the kind of information that can help them stand out in a business that’s only getting more competitive.

“Everyone has their little gold mine they’re sitting on, with all this data of past projects they’ve done,” says Faizan Zaidi, director of design technology at the architecture firm Spectorgroup. “But the question comes down to which firms are willing to build the tools on top of it.”

Architecture’s AI awakening

“Four years ago I got introduced to Dall-E, which I think was, for a lot of us, an awakening,” says Matthias Hollwich, cofounder of the 15-person New York-based architecture firm HWKN. “I decided to throw the whole office into an AI exploration for three months.”

Since that watershed moment in 2022, the number and variety of AI tools has exploded, and Hollwich has built on his early adoption. His firm is now regularly using many AI tools for its office, residential, and hospitality projects. Here are a few:

·  NYC Zoning AI for zoning analysis and building massing that gives projects an overall size and shape

·  Midjourney, MNML, XFigura, and Nano Banana for conceptual designs and renderings

·  TestFit and Forma for site plans and analysis

·  Maket for floor plans

·  PermitProof and UpCodes for building code compliance

·  Swapp for construction documents

·  Architectures for building information modeling (BIM) to digitally link 3D models and construction documents

Hollwich contends that the tools have fundamentally changed his firm’s approach to design, as well as what his firm actually produces. “Currently, most architects are trying to use AI to optimize the process, or maybe intensify some of their designs,” he says. “What we have done after these experiments is almost take a step back and say, no, this is actually bigger than just using these tools to come up with a better way to design the buildings. . . . There’s a new ideology that is emerging.”

The tools speed up the process of developing design concepts and ease the complexities of translating initial designs into wood and steel on a permitted construction site. But Hollwich says they also make room for deeper analysis that can make those projects better serve their intended users. AI tools have allowed his firm to do more deep research during the proposal and conceptual design phase, tuning designs to be more specific and contextual.

For one recent proposal, an office building in Amsterdam, HWKN used AI to analyze who the future tenants of the building would be, based on other companies and industries in the region. The tools helped the team quickly understand where those potential tenant companies are located and what types of buildings they occupy. AI also provided detailed information about their spaces, including floor plans, ceiling heights, and building amenities.

“We created this whole matrix, and we basically said, ‘If you design this building according to this information, you’re going to be ahead of the game because every one of these companies you would like to attract will see themselves in it,’” Hollwich says. HWKN won the project, and now uses AI for other project lead searches and analyses. It’s also building a custom RFP (request for proposal) response tool using Claude.

“For us, there’s nothing the same from how we worked four years ago,” Hollwich says.

Architects are building custom AI tools

Spectorgroup, the New York firm that Zaidi is part of, has been dabbling with its own AI plugins and tools to work alongside existing design programs. Zaidi says the firm has been using AI-assisted coding tools to build custom software to tackle small but time-intensive parts of the design process. One example is a tallying tool used during the creation of test fits, the example floor plan designs architects mock up to give clients an idea of how, say, an office may be laid out.

“It looks at your plan, does the math, which it’s really good at, and tells you you have five conference rooms, you have two pantries, you have 300 desks for people, and it spits out this legend,” Zaidi says. “That was a manual, tedious task that’s now being replaced by AI.”

Zaidi says the firm has explored whether AI might be able to do the actual test fit itself, deciding where to place those conference rooms, pantries, and desks. But so far the technology hasn’t been able to match the expertise of human designers, who know not to place a noisy pantry right next to a conference room, for example.

“When I’m evaluating AI tools in their current state, what I’m seeing is, yes, they are good, they’re on the right trajectory, but they’re not there yet,” he says.

He and many other experts in the field believe, however, that it’s only a matter of time before these tools move beyond the tedious tasks and early-stage concepts. And this evolution is likely to start happening inside some of the biggest, oldest, and best-known firms in the world.

Developing AI tools and hiring AI specialists

Gensler, the world’s largest architecture and design firm, has built what co-CEO Jordan Goldstein calls an AI sandbox. Less a space than a way of working, the sandbox launched in the early days of generative AI going mainstream, and has functioned as a kind of mirror design realm to experiment with AI tools.

Designers across the company’s 58 offices and 33 practice areas have been encouraged to take real client projects and firewall them into the sandbox, where they can try out AI tools to see where, if at all, they can be useful to the actual project at hand. At the same time, Gensler has been expanding its AI talent pool, hiring specialists in machine learning and data science. 

“We sensed that there was a wave coming, and we started to really invest in experimentation,” Goldstein says.

In the early days, this was about learning which off-the-shelf tools could augment the work of Gensler’s designers, shaving time off conceptual renderings or accelerating site analyses. But as the technology evolved, so did the sandbox, eventually turning into a kind of AI breeding ground.

Designers began turning their experiments into bespoke tools, vibe-coding systems and interfaces that solve specific problems or speed up portions of the design process. That prompted the firm to launch its Innovation Challenge, an annual juried pitch showcase, now in its fourth year, that takes the best of those ideas and turns them into tools for the firm to use.

This focus on experimentation and creation led to a suite of internal tools Gensler has rolled out across the firm. The first, named gBlox, is a design and digital massing AI plug-in that uses text and image prompting to develop strategies for project sites. Another, gFloorz, applies predictive analytics to early-stage floor plans to give clients a window into how spaces will be used and what that means in terms of their sustainability efforts, operations, maintenance, and even return on investment. 

Additionally, the firm is using AI-driven filmmaking tools to add custom short films to project proposals, offering clients a predictive vision of a day in the life of someone using a building that, at the proposal stage, is still just an idea. “That’s an incredibly engaging way to connect with a client in a marketing pitch,” Goldstein says.

For Gensler, which made $1.87 billion in revenue in the 2025 fiscal year and regularly ranks as one of the top-earning architecture firms, deep pockets make these kinds of efforts possible. But Goldstein argues that spending alone won’t be what gives architecture firms the edge in putting AI tools to use.

“We believe that the organizations that will have the lasting advantage, certainly over the next decade, will likely not be those with access to the best models, because those capabilities are becoming increasingly available to everybody,” he says. “The differentiation will come from how effectively a firm like ours continues to use a deep domain of data with the additional creativity that we’re bringing to the table to create lasting value for clients.”

Analyzing a 200,000-image database

KPF, a 50-year-old firm of 650 people, has been using AI to operationalize its vast portfolio, turning old projects into sources of data that can inform or even shape the projects of today.

Luc Wilson, director of global design technology at KPF, says AI tools have been used to analyze and describe every image in the firm’s database, an estimated 200,000 in all. The past four years’ worth of project proposals can be queried or prompted to create new proposals. Complex design specialties, like the core designs used in tall buildings, are cordoned into their own specific databases to provide more detailed responses to queries.

These vast pools of data are now accessible through an AI system KPF built, called KAI. “It’s very much in the vein of Claude or ChatGPT,” Wilson says.

But unlike those tools, which run on large and complex models, KAI runs on several focused databases, as well as KPF’s entire project portfolio and intranet. A designer can use KAI to find images of every office lobby with hardwood furnishings that KPF has designed, or precedents for the placement of elevator shafts in 60-story buildings, or details on which people in the firm have worked with a specific client before. “It’s saving a lot of digging,” Wilson says.

KPF also added a vibe-coding subagent to the system, allowing people to use simple text prompts to build interfaces and web apps, all of which get deployed to KPF’s controlled servers, not the broader internet. Within the first month of launching that subagent, more than 400 apps were created. And it wasn’t just the youngest, most tech savvy employees coming up with new ideas.

“A principal vibe-coded her own interactive web map to help track leads in a new region, which was awesome,” Wilson says. “One of the things that’s exciting for us is now the person closest to the problem can solve it.”

The firm is also using these tools to power image creation, tapping what KAI digs up to then image- or text-prompt various image diffusion models, all while aiming toward outcomes that look like the kind of work KPF has built its business on.

“We’ve spent a lot of time on the system prompts . . . the thing that the model reads before it executes any task,” Wilson says. “That’s where you can really embed organizational standards, best practices, even aspirational goals in terms of how it directs the model to respond. It has made it much more KPF.”

120-plus AI models

Foster+Partners, the U.K.’s largest architecture firm, has been exploring AI and machine learning since 2018 and currently has more than 120 AI models at its disposal.

“We are using large language models, diffusion models, statistical methods, and optimization techniques across the entire design process, from early design explorations and performance feedback to applications that boost knowledge dissemination, productivity, and operational insights,” explains Martha Tsigkari, the firm’s head of applied R&D.

Through the firm’s Ask Foster+Partners tool, staff can query 60 years of design guides and projects in natural language. They can use various AI-augmented configurators for urban plans, stadiums, and adaptive reuse designs. AI-assisted regulation comparison helps with document ingestion and report generation for the firm’s work around the globe. AI agents can generate parametric models from simple images.

“The most visible shift is AI moving from tool to actor, with AI agents ultimately orchestrating entire design pipelines,” Tsigkari says. “We are excited about what is coming: an API-first approach combined with Model Context Protocols will enable agentic workflows, with AI moving fluidly between tools and data sources without human intervention at every step.”

But with the entire toolbox the agent will struggle, Tsigkari says. “Too many capabilities, too little clarity.” Instead of building a monolithic, top-down system, Foster+Partners has developed a modular set of components that designers can plug into when needed, automating small pieces of the design process. Tsigkari says this is all about optimizing the process without losing control of the design.

“We have made a conscious choice to not just automate but completely reinvent the production aspect of our design process, rather than the creative one,” she says. “We have been process-mapping our workflows to identify which parts of the process can potentially be automated.”

So much of this is built on the firm’s long history, but Tsigkari says the unending flow of new project data is essential to keeping the tools trained and productive. Foster+Partners has been developing a strategy for automating data collection, organization, and processing across disciplines, helping its AI tools’ ability to use new sources of data while cutting down repetitive work for staff and designers.

“The same source feeds websites, reports, AI models, and design workflows. It is cross-referenced, agent-traversable, and live,” Tsigkari says. “We want to author knowledge once and use it across workflows while we continuously train the system through everyday work.”

Tapping the portfolio of a 35,000-person firm

For the 35,000-person design, engineering, and consulting firm Arcadis, tapping institutional knowledge can be daunting. It’s taking on thousands of projects a year, ranging from designing ports and building semiconductor factories to planning water treatment facilities. Name any type of building project, and the company has almost certainly done at least a few. But the knowledge behind those projects is often buried somewhere inside a database or archive, out of sight and out of mind.

“When we hire someone new, they don’t really have any direct way to access or learn from what happened five years before they joined,” says Jason King, a principal of architecture and urbanism at Arcadis. “So many problems that they’re going to face have been solved over and over and over and over and over.”

As a specialist in computational design, King has been using AI to try to make the firm’s deep institutional knowledge more readily accessible. Focusing on specific practice areas, like residential architecture, he’s helped build AI systems that can take lessons from the projects of the past and apply them to new ones.

For example, King led the creation of a database of about 7,000 of the firm’s residential projects from the past 20 years—which itself is only a portion of the total—populated with key details, thumbnail images, and CAD drawings. That data became the training material for machine learning models that can now be used to accelerate initial designs.

“When we have a new project, we can upload the specs of that project, and we can have a generative tool make a floor plate for that project site, down to unit layouts,” King says.

Designers need only upload a sketch or basic CAD drawing of a new project, and the system will pull up work from the portfolio that most closely matches it. “It’s a way for people who don’t have any programming skills to interface with thousands and thousands of floor plates that have been done over the past 20 years,” King says.

Arcadis is deploying these kinds of tools across its practice areas, as well as using more standard AI tools for optimizing business tasks like creating expense reports and setting up construction drawing sheets. King says the utility of these tools is obvious, but the firm is proceeding cautiously with design-focused tools like the floor plate generator, rolling them out only to small teams for evaluation. “It’s still something I would say is an R&D tool,” he says.

It likely won’t be long before these generative design tools become essential to the way the firm works, tapping into a gigantic portfolio of experience that few other firms can match. The result, King says, will be projects that build on the strengths of past projects. “I’m interested solely in how do we design better buildings and better cities, not how do we print faster,” King says.

Autodesk’s AI model for architects

For all the power firms are finding in their often massive project databases, the biggest moves in AI for architecture may be coming from the company behind the industry’s design infrastructure: Autodesk, the $53 billion software company that builds industry-standard digital design tools like AutoCAD and Revit.

The company has built AI models from scratch, using its own CAD files as well as many offered up by its client base, which is basically every architecture firm in the world. (In exchange for accessing Autodesk’s advanced AI tools, clients agree to allow some or all of their project data to be used to train the models.) Unlike the LLMs behind ChatGPT and Claude that rely primarily on text, Autodesk has developed AI models trained solely on the geometrical data of the physical world.

These models, dubbed neural CAD, are designed to understand the three-dimensional nature of buildings and the interconnected systems within them. Mike Haley, SVP of research at Autodesk, has been leading the development of neural CAD for nearly a decade, mostly in stealth. It’s work that started years before generative AI went mainstream and captured the imagination of architects.

Haley says Autodesk opted to take a more deliberate approach by building an AI model that could actually understand space rather than just describe it or try to translate a description into an image. “It’s not a language model. It doesn’t look like a language model. It solves a different problem,” he says.

By training on actual CAD models showing millions of buildings and rooms, neural CAD was developed to understand where architects place things like walls, columns, stairwells, windows, and doors, and how they relate to each other, as well as how they relate to the structure of the building, from slabs to trusses to beams.

“It’s not trying to reproduce a specific person’s building, but it’s looked at all of those repeated fundamentals that are inside every single building, and it’s learned those patterns,” Haley says.

Autodesk has created specific neural CAD models for the three industries it builds most of its software for: architecture, structural engineering, and manufacturing. Its architecture-focused model can essentially be prompted to design a building at a high degree of fidelity, which could represent a dramatic evolution for the architecture industry and the people who work in it.

AI is not replacing architects, and neural CAD is hardly the new Claude or ChatGPT, but it does have a very focused depth of knowledge that is unlikely to be matched by the more well-known AI models.

“If you look at the size of some of the frontier models, our models are several hundred times smaller,” Haley says. “But they’re actually better at solving these very specific problems. And not only are they better, but the actual cost of them is far, far less.”

In the architecture industry, Autodesk is filling a hole most firms didn’t even realize existed until generative AI broke out in 2022. “I remember a whole bunch of companies coming to me [in 2022] going like, ‘What do we do? This is a new thing. Should we use ChatGPT? We don’t know,’” Haley says. “And then the next year, everybody came along and said, we’re all going to build our own models.”

Haley was quick to burst that bubble. “Really none of them, even if you’re an architecture firm that’s been around for 100 years, have enough data. I’m sorry,” he says. “It requires not just a volume of data, but a variance. You want that breadth of different designs and different approaches. So you’re just not going to have that, even in a very large company.”

Autodesk, as the main digital medium for how the architecture industry operates, had that power. And while big firms like Gensler or KPF can fine-tune their tools to produce concepts and designs that match their own styles and project types, neural CAD seems to be able to answer more general questions about buildings while also being more specific about how they actually get designed and built. 

Haley says Autodesk’s AI models are trying to do something more transformative for the industry. The AI tools that run on neural CAD have only just started rolling out to AutoCAD users over the past few months. Haley says new developments are imminent. But at the same time, he doesn’t see AI taking out large swaths of the architecture industry. Nor does he see AI eliminating the need for the design software Autodesk sells.

“The ability of these AI models to solve every problem is not evenly distributed today,” Haley says. “There are some of these things we can totally nail and some of them we’re not going to have solutions for for years to come.”