Globally, the European Commission reports, a truckload of textiles heads to landfills or is incinerated every single second.

The fashion industry accounts for some 10 percent of greenhouse gas emissions worldwide.

Less than 1 percent of the materials used to make clothing is recycled, according to a report by the Ellen MacArthur Foundation.

The best way to keep clothes out of a landfill? Don’t make them.

Picture this: Not long ago, it wasn’t unusual for a customer to walk into an H&M store and not be able to find a basic white t-shirt in their size. Why? Like most long-established retail operations, H&M was operating with a push model—placing seasonal bets on what the demand for a given item of clothing might be, locking orders far in advance, and hoping that they were able to sell out. Make a mistake in that kind of model, though, and you can end up with customers unable to find the basics they rely on, or excess inventory you can’t sell. That’s a problem for the business, but for the environment as well—and one that doesn’t fit H&M’s aggressive sustainability goals.

Though H&M had tried to tackle the problem before, the launch of the H&M Group Design Studio, co-created with IDEO, gave the retailer the confidence to go after it again. The team started out by mapping out the complex network of people that make up its supply chain across the globe, meeting with everyone from designers to garment suppliers, logistics managers, and even folks selling the final product in H&M stores. 

They then created an algorithm for more precise ordering, as well as a tool that provides a shared view of information. Instead of placing orders, the new automated flow—dubbed “Cruise Control"—allows staff to work with a demand forecast that gives them more time to focus on customer-centric operations, like providing guidance and inspiration. It also makes it easier for H&M to follow the demands of the market, and place much more accurate bets on what consumers will want, when. Already, H&M has significantly increased sales while cutting excess inventory—a result that improves its bottom line, as well as its environmental impact.

Estimates project that by 2030, global apparel consumption will hit 102 million tons.

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22%

reduction in stock during a pilot program

34%

increase in sales during a pilot program

To foster creative projects that spoke to the needs of local communities and environments, the BSA began to engage its members in collaborative, user-led design. The organization launched its first-ever innovation challenge, putting out a call for all members to pitch their ideas for projects in the built environment.

The BSA expected those pitches to have dollar signs attached. To the organizer’s surprise, only one asked for funding. The rest requested less tangible, but equally meaningful support: advice, connections, specific expertise. Those are resources that the BSA has aplenty, but hadn’t learned how to leverage well.

From that first go at an innovation challenge, the BSA learned how to better support its members. It also learned that to lead with purpose and make strategic choices, one must develop the confidence to say no. Instead of spreading itself thin across many projects, the organization decided to take small steps, focusing on one initiative at a time, so that it can deliver focused impact and stay true to its evolving mission.

With 36% of global energy going to buildings and 8% of global emissions caused by cement alone, the architectural community is inextricably intertwined with both the causes and solutions related to climate change.

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According to a survey conducted by the AIA, the architecture profession in the US is less diverse than the population as a whole. This lack of diversity can contribute to a disconnect between architects and the communities they serve.

31

submissions received from innovators in the Boston region working on equity and sustainability challenges

5

innovation teams invited to pitch ideas

The resources that NYU students needed were organized by administrative department, and buried on disparate websites and calendars. But students don’t think in terms of departments—they just need to know where their classes are.

All of this became patently clear as IDEO shadowed students. Team members visited classrooms, walked with them through their days, and hosted pop-ups in highly trafficked areas to get ideas from passersby. That helped the team develop a common understanding of the overall student journey and grounded employees in the key moments that can either make or break a student’s experience. Visualizing their journey helped departments gain a shared understanding of the fractured communications challenge and develop possible solutions.

To synthesize those insights, we formed cross-functional action learning groups, challenging existing practices at the university and coming up with new ways of working. Participants came from a diverse set of departments, including enrollment management, student affairs, marketing and communications, IT, global inclusion, student services, the provost’s office, digital communications, and more. The cohorts sketched early hypotheses and got feedback from faculty, administrators, and students from all of NYU's undergraduate schools to refine initial concepts.

Collaborating with students gave staff a first-hand look at what students were struggling with and a way to engage students directly in addressing those challenges. Just as importantly, there was an overall culture shift at NYU: Administrators started with questions instead of agendas, came together around a table, and reached beyond their job descriptions to meet the needs of students.

First-year students reported receiving hundreds of paper forms, and email from more than 70 unique senders.

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NYU needed to offer students the right resources at the right time—and collaborate to serve them as a unified administration.

New services

in-production include centralized calendars, dynamic campus mapping, and customized notifications

Design-thinking cohorts

now support the student experience

Diversity alone isn’t enough—in marketing or any other facet of business. Today’s brands must authentically engage with and create the conditions for co-design with existing communities.

That sort of deep, meaningful engagement with customers doesn’t happen over Instagram. It requires a playbook, a prototype, and a healthy amount of trust. So, IDEO worked with Athleta and Gap to tap into the wisdom of a council of women who represent intersectional identities and lived experiences. The council surfaced powerful insights for the brand, like the fact that active women don’t need a brand to empower them—they already feel powerful.

After only a few weeks, Athleta experienced the difference between research and a truly reciprocal relationship. The way their brand engages with communities is adding depth, understanding, and authenticity to Athleta’s content, products, and experiences. And women can feel the difference.

76% of US adults say that the media promotes an unattainable body image for women.

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Fewer than 1 in 10 businesses review for inclusion as part of product design and marketing campaigns.

>2X

user growth in 2022

13,000+

Athleta WellPro well-being providers active on the platform

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Direct feedback that teaches rather than chides requires trust, and trust takes time.

Unlike humans, AI craves to know when it’s wrong. The entire premise behind machine learning is that it learns from failure or success. For example, we may feel that weather algorithms always get it wrong, but they can only improve by comparing their forecast to what actually happened. It’s therefore our job to tell the system when it’s wrong and acknowledge when it gets it right—but that requires a level of trust.

We can often tell if our friends or partners are unhappy with our actions, even if they don’t directly tell us. We can read their facial expressions, subtle changes in their tone of voice, and body language. Machines, however, don’t have that kind of intuition. Until we’re all walking around with some kind of lightweight EEG machine strapped to our head, machines will need to find proxies for how to sense human emotions, desires, and preferences. The obvious way to do this is to just to ask us, but that is manufacturing a transactional relationship, not a trusting one.

All I’m asking AI is for a little respect

A good example of an AI asking for trust rather than earning it can be found in the personal styling services that are popping up. They are doing some amazing things in integrating data science into every component of their business to drive growth. But my experience put me in the awkward position of feeling like I needed to explain myself and share sensitive personal details with someone I’d just met.

Signing up for the subscription service begins with a long and detailed survey about body type and style preferences. I spent a good 30 minutes thoughtfully answering myriad questions to create my style profile, and yet I returned everything that they sent me in the first box. None of it jived with my personal style, and only one thing fit.

I had put the time into creating a relationship with the AI—training it with my intimate information, such as how my body was shaped—and that process had given me a false sense of mutual understanding. If it had just taken a look at my Instagram profile and sent me a box, I would have given it permission to get it all wrong. But its surprisingly clunky and unnatural way of “sensing” my preferences for clothes set an unrealistically high expectation of what was to follow. Questions about slightly nuanced styles of plaid let me to believe that it could understand I like simple patterns—and then it sent me a shirt better fit for my grandfather.

By asking me to train a system before we had established a relationship of trust, the AI had breached it before it had even formed. When I’m in a trusting relationship, I can forgive someone for an error. But this felt different—like I’d been duped into a false sense of intimacy. I’m going to try one more box, but I have a strong hunch I’ll be cancelling my subscription soon.

AI is a relationship

While rare, there are a few AI with whom I’m in a trusting relationship of mutual respect. I relish my moments of training them because they respect my time and provide me with value as I train them—even when the AI is mostly wrong. I’ve come to truly value those moments when they tell me I’m wrong, because they do so in a way that deeply respects my position as a human and their position as a machine.

The format of the playlist is likewise brilliant: I don’t need to rate the songs but simply listen to them or skip them after I’ve heard a bit, and songs that I love get added to my own playlists or saved to my phone. Over time, it learns my preferences and can dish me up even better tunes. There’s no artificiality to training Spotify, and each and every time I train it, there’s value back to me as the user. Most importantly, Spotify has earned my trust through helping me discover a slew of new artists and, in doing so, earned its right to be wrong. (I don’t really love St. Vincent, but I understand why Spotify thinks I would.)

But we’re not the only one doing the teaching. When it comes to “training” humans, Waze and Google Maps are setting the standard. Their features are clearly based on a deep understanding of the common human emotional conditions that we endure during traffic: stress, anxiety, impatience, and a deep temptation to bail on our algorithmically recommended route in favor of our own “shortcuts” (which are almost never shorter).

Maybe the most brilliant bit of human-centered design inside of these navigation apps is how they highlight alternative routes and how much longer they’d take. Instead of having to blindly trust the route the AI has chosen, it considers its own possible fallibility and assures us by showing us the other routes we might be considering. Thinking of bypassing the highway? That’ll take another four minutes. Want to cut through that residential area? Here’s some construction of which you might not be aware.

In this way, Waze and Google Maps serve as a sort of angel on our shoulder, counseling us away from our inner traffic-hating demons. They’re not “AI-splaining” or chiding us for considering alternate routes, but rather giving us the necessary information to make the right decision. They’re rooted in a deep respect of human agency.

At IDEO, we prefer to think of AI as Augmented Intelligence rather than Artificial Intelligence. Taking a human-centered approach to building relationships between AI and humans compels us to meet humans on their terms, building relationships of trust and respect, and always remembering that intelligent systems must exist in service of humanity, not the other way around.

Beautiful, human-centered, human-AI relationships are about understanding human beings and our wonderful, weird intricacies and inconsistencies. They’re about designing the experience of AI around humanity, rather than the other way around. AI can only function well if it learns from its mistakes, and that means establishing trusting relationships with humans so they’ll feel comfortable saying when it’s wrong.

This article was originally published in Quartz Ideas.

Illustrations by Cassandra Fountaine.

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It’s awkward to correct a stranger when they’re wrong. How did you feel when Miguel from IT, whom you’ve only met once, told you that “learnings” wasn’t a real word? Or when Robin lectured you about the correct pronunciation of “macaron” at the office holiday party?

But, for the most part, that relationship is passive. And that begs the question, what if we could teach our machines more actively? Could we school them on very specific things that are important to us? That thought became the perfect setup to build a little experiment involving selfies.

A self-portrait or a selfie is actually more than just one picture. It’s one picture out of endless tries which all look pretty much the same ... except that they don’t. At least not to the person taking the self-portrait. That person’s individual aesthetic, packed with subtle and subjective nuances, must be captured at exactly the right moment. Adding to the challenge, it's hard to explain to someone else the way you squint your eyes when you nail your smile or how you purse your lips to look roguish.

Enter Brainchild: a machine learning–based camera that can be taught individual preferences and make them accessible as a function. You can train the camera to understand how you like to see yourself without going through 100 iterations. And most importantly, it allows you to share your sensibility with somebody else.

Brainchild is just a prototype for now, but I'm hoping others will be inspired to build on the idea.

Beautiful, baby, beautiful

Machines are a billion times faster in quantitative tasks than we are. For a long time, the problem was one of not understanding quality. But that has changed with the improvement of so-called deep learning techniques—a subset of AI or machine learning.

By teaching Brainchild how you would like to be portrayed, it can assess the quality of what it sees in real time, and give you haptic feedback when it perceives you looking your best, so you can simply take that one picture.

It also allows others taking a snapshot of you to see you as if through your own eyes. Taking portraits is a very intimate art. Rather than taking the human out of the loop, Brainchild augments this human-to-human interaction by making it more collaborative.

A feel-good feedback loop

The intervals between interacting with machine-learning products and experiencing their learnings are often very long. There are many technical challenges, but more accessibility and immediate feedback would help us teach more actively and develop a better intuition for them. In return, machines could learn better and become more personal.

Brainchild is designed to provide immediate feedback. You teach it how you like to be portrayed, instead of letting it guess, and every picture you take based on its haptic recommendation serves as feedback on how well it learned and how well you taught it. (Plus, it's private. Brainchild works offline and stores all of your information locally, so there's no danger of it being shared.)

Smile!

How does the Brainchild prototype work? There are countless technical parameters we could talk about, but in the most simple terms, it makes use of a so-called transfer learning process based on a fine-tuned convolutional neural network—a technique frequently used in computer vision.

Brainchild needs to be taught five fundamentals:

1. What a human face looks like
2. How to learn about new faces
3. How to isolate the face in a picture
4. The way the portrait subject looks normally
5. The way the portrait subject looks when they deem themselves looking their best

The first two points require huge amounts of coding, data, number crunching, and time. That’s what consumed most of my time (next to working on the wrong file for a day!).

The third point addresses the fact that when you take a portrait there is always something behind you. To focus only on you without distraction, Brainchild extrudes your face from the background. That is something it learns before you use it.

Then you become the teacher. In order to learn how you look your best, it needs to know how you look normally. So, you feed it a few examples of both, either by taking new snapshots, or by using pictures stored on your phone.

To help Brainchild differentiate between the two picture sets you simply twist the front plate, switching between two learning modes. The serious smiley means every picture you take will teach Brainchild how you look “normally”. Switching to the happy smiley means every picture you take will teach it what it looks like when you look your best. This is how you get to know each other.

As soon as the camera spots you in a way that matches what you taught it about looking good, it will gently vibrate and light up the camera’s trigger button. The vibration and brightness increase the closer your match with your own ideal.

Brainchild doesn’t snap the photo for you, it just signals you at the optimal moment to do so. You can also hand the camera to someone else to capture you in the way you like to be captured. And the more you teach it, the more accurate it becomes.

It might seem mundane to teach a camera how to take a better selfie, but the technology has already inspired one of our clients to rethink parts of a high precision medical device that could dramatically reduce treatment costs for patients, and another software engineer to work on a playful version of FaceID.

I myself am now working on a body pose add-on for fashion photography, and am curious to see all of the other ways we might actively teach our machines and help them augment us in more personal ways.

Instagram: @brain_children
Concept and Technology: Jochen Maria Weber
Visual Design: Tiffany Yuan
Industrial Design: Leo Marzolf
Special thanks: Tobias Toft

We teach our machines every day. Voice assistants learn how to talk to us based on what we say to them. Navigation apps guide us based on the routes we take. Without millions of teachers like you, our machines would be much dumber.

What do you do when a panel is truly the star of the show? Can you still design meaningful participation from your guests beyond the Q&A?

This was our challenge when IDEO CEO Tim Brown, pioneering social entrepreneur Jacqueline Novogratz (Founder of Acumen), and visionary CEO Carlos Rodriguez-Pastor (CEO of Intercorp and Founder of Innova Schools) sat down for a conversation about design and bravery in our New York office. We knew these three would inspire guests with heroic stories of using design to create change at scale and challenging orthodoxies. But could we keep the crowd inspired after the conversation ended?

Our best bet, we decided, was to design a series of activities that would send guests on a heroic journey of their own—from hearing the call to adventure to facing fears to celebrating triumph over adversity. The result? A happy swirl of exploration and discovery, with guests staying late to gather around the hands-on exhibits like family in the kitchen at the holidays.

Here are seven ways to leverage a panel discussion and design an event they’ll never forget.

1. A moment to unwind

Create a ritual to help guests wash away the baggage of the day and be present for all that follows. We invited guests to partake in a sonic shower, an immersion in music that stretches the ears and awakens the senses.

Try this: An aromatherapy bar for deep breaths of invigorating or relaxing scents.

2. Active imagining

Even the most thought-provoking panels amount to a type of passive contemplation for listeners. Follow a panel with a prompt to actively imagine. We asked guests to hear the call of service, by imagining they were a person they serve and writing in that person’s voice in response to questions like, “What would it take for you to feel confident about the future?”

Try this: A finish-the-doodle mural wall or a listening booth with a guided meditation.

3. Space to be vulnerable

Offer your guests the surprising jolt of inspiration that comes from being vulnerable and naming your fears. We designed an anonymous confessional, tricked out with blacklight and digital wizardry, where guests wrote their fears on a private screen, then watched their words magically appear on a monitor in type for party guests to see.

Try this: Pair strangers up using matching name tags and asking them to find each other and swap secrets.

We designed an anonymous confessional to help guests embrace vulnerability.

4. Fast-tracked inspiration

Inspiration can also be fast and light. We sent guests on a tour of themed rooms devoted to New York’s legendary cultural movements—Punk, BeBop, Hip Hop, the Beats, and the Harlem Renaissance. Each room was chock-a-block with images, sound, and strips of paper bearing anthemic words from the movement. It was a blast of culture felt through the senses.

Try this: Give guests a deck of cards created from your favorite inspirational Instagram memes.

Guests were invited to tour the most iconic cultural movements in New York history.

5. Skin in the game

Putting one’s body on the line—through movement, physical sensation, or embodied action—is the ultimate form of engagement. For our biggest test of bravery, we dared guests to stick their arm through a hole in a black screen to blindly receive a semi-permanent Jagua-ink tattoo from an artist on the other side. No requests or restrictions permitted.

Try this: Ask guests to enter a space through one of two thresholds marked by declarations of identity, like “I’m an artist” or “I’m an activist.”

The ultimate trust exercise—blindly allowing a stranger to draw on your skin with semi-permanent ink.

6. Making

Who doesn’t like to roll up their sleeves and craft something fun? Making gives your guests an outlet for any new, creative ideas your event has inspired. We handed guests shortbread cookies, pipette bags full of icing, and examples of exquisite cookie decoration to emulate.

Try this: A collage-making table with old magazines, construction paper, scissors, and glue sticks.

7. Silly celebration

Even the most buttoned-up among us appreciate the chance to let their hair down. We gave permission to get silly with an IDEO designer’s GIF Dance Party, accompanied by another designing spinning live music. Guests grooved in front of a camera for a few seconds, then watched their avatar populate a digital dance floor on monitors throughout the space.

Try this: A box of masks and capes and the promise of a free drink for anyone who orders as a superhero.

We asked guests to get jiggy with it, and they obliged.

To summarize: Get creative. Get physical. Get weird. Your guests will thank you for it.

Got an idea for an out-of-the-box event activity? Share it with us on Twitter using the hashtag #DisruptThePanel.

Ever attend a conference? Then it won’t surprise you to know that every event planner out there wants to disrupt the panel conversation. (We’ve been experimenting with the format for years and sometimes a panel is still the best choice.)

There was a lot to celebrate. We’d launched an innovation lab and piloted new products as part of a larger, multi-year collaboration to expand the organization’s human-centered design capabilities and enable culture change.

And our collaboration had revealed something crucial: In order for the product to be successful we also needed to support the underlying organization, which meant looking more holistically at strategy, process, tools, and incentives.

That became an opportunity for me, an interaction and product designer by training, to apply my trade through an organizational lens. It turned out that the prototyping methods that I had learned could also be applied to people and processes. By experimenting, responding to feedback, and iterating, we could help nudge behavior change within the organization.

Here are the five big lessons I took away:

1. Speak their language

We were working with product managers, engineers, and designers, so instead of explaining what we planned to change with academic research and abstract frameworks, we used language they already understood. We were going to run scrappy experiments, learn quickly, and iterate. If the ideas didn’t work, we’d pivot and try something new. But instead of prototyping products, we were experimenting with people and processes.

Big insight: Meet people where they are. Ground organizational design in a familiar context, and make it tangible in order to build trust.

2. When facing big change, start small

Behavior change is hard. So, we started with small experiments. What was the least that we needed to design in order to learn—the minimum viable product (or MVP)—and to help nudge new behaviors? In one instance, to test our hypothesis on the need for a company-wide training program, we started by creating a workshop for one multi-disciplinary team of 10. Its success soon paved the way for a broader rollout.

Big insight: Organizational change doesn’t have to begin with big initiatives. Starting small with prototypes can lead to big learnings and start to shift behavior.

3. Measure impact in creative ways

Email surveys are an obvious way to take the pulse and determine whether experiments are resonating with individuals, but they can easily fall to the bottom of a priority list. We tested more lightweight, in-the-moment feedback methods. After prototyping a beer and knowledge-sharing gathering, we asked attendees to place a magnet on a board to indicate their responses to a few questions, like: “Would you come again?” and “Did you learn something?” It was an easy and immediate way to figure out if the idea was worth evolving.

Big insight: Simple and in-the-moment measurement tools can be enough proof of concept to move forward quickly.

4. Find the right ambassadors

Early on, I was determined to get all of the senior executives involved in our prototypes. What was I thinking? Their understandably divided attention and packed calendars made that next to impossible. In the end, some young, energetic community managers were the ones who embraced our ideas and helped push them forward. Not only did they have the time and passion for the projects, but they were also trusted leaders within the company. Community managers helped us organize an informal gathering of product people to share methods over beers. (Yes, beer again. When in Germany!) We asked a VP to simply show up. When he saw the overwhelming attendance and impact on the community, he became one of our biggest advocates. Teams that had been working in silos started to meet regularly. The more we demonstrated quick wins, the more senior leaders got on board to encourage new behaviors throughout the organization.

Big insight: The best agents of change may not be the most senior folks. Look for individuals with passion and social capital.

5. Make it stick

There are two things that we did that helped ensure that our ideas lived on after we were gone. First, we found owners within the organization who would commit to moving our ideas forward. Second, we helped create an internal group dedicated to maintaining momentum. This group provides leadership training and coaches teams to ensure both a top-down and bottom-up approach to behavior change.

Big insight: Prototypes are just the beginning. Organizational transformation requires dedicated owners and a multi-layered strategy.

It all adds up to a set of behaviors: Get scrappy, get tangible, find your people, and learn by doing. It never hurts to do some reflection from a rooftop, too.

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Not long ago, a coworker and I found ourselves contemplating the eclectic Berlin skyline. We were on the rooftop of our client’s office—a leading European brand in online retail—and thinking back on what we’d learned from embedding with them for a year.

I help organizations cut through complexity by shaping how they show up, through brand strategy, identity, and storytelling.

Working at the intersection of brand and business, I bring an editorial eye and a bias toward making—turning ideas into tangible experiences that people can understand, trust, and believe in.

My work spans government, healthcare, financial services, media, and consumer goods, from launching Gen Z-focused ventures to building innovation labs and reimagining legacy brands. I relish moments of ambiguity and enjoy translating across teams, perspectives, and priorities to move ideas forward.

I started my career in editorial and digital design, shaping cross-platform experiences and identity systems in publishing and agency environments. That foundation still shapes how I work today: I’m detail-oriented, collaborative, and overreliant on the Oxford comma. I hold a BFA in Visual Communication from the School of the Art Institute of Chicago.

For 25 years, I've asked the same question: Who does this design exclude—and what are we going to do about it?

My work spans human-centered strategy, inclusive design, and qualitative research, with clients ranging from Microsoft, Google, and Verizon to the National Science Foundation, and San Francisco Unified School District.

Before IDEO, I taught elementary school in East Palo Alto and spent a decade doing design work with social service organizations—where I learned firsthand what it costs people when systems are built without them in mind.

I am currently writing User Error, a nonfiction book about digital access and the design decisions behind it. I live in Oakland, California.

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My expertise spans brand, communication, and product design across tech, education, the arts, and social impact.

I believe great work demands both high-level vision and obsessive attention to detail, and only happens through collaboration.

I bring an inclusive, hands-on approach and a deep understanding of business, which enables me to consistently deliver excellence while always asking why.

Before joining IDEO, I worked at the brand firms Pentagram and VSA Partners. I began my career as a designer, leading teams at the School of the Art Institute of Chicago and the Milwaukee Art Museum. I hold an MFA in graphic design from Maryland Institute College of Art and a BFA in communication design from the Milwaukee Institute of Art & Design, and have taught at some of the best design schools in the US.

I am responsible for IDEO’s long-term success in China and working with clients to use design as a tool to enable growth. Specifically, I have helped Chinese companies design holistic brand solutions through the development of their products, communication, services, and innovation teams, and I have helped multinationals expand their presence and influence in China.

I advise global leaders on developing China-led innovation and capabilities.

In over 15 years in IDEO Shanghai, I have worked on projects that use design to elevate the quality of the experience of healthcare products and services, streamline processes that increase productivity, create spaces and programs that promote and enable inclusive communities, and build next generation mobility solutions that are planet-positive.

Before joining IDEO, I worked at Philips Electronics and the Electrolux Group in Italy, the Netherlands, and Singapore on a number of breakthrough commercial products. I am a member of the Young President Organization in Shanghai.

Most AI products today are built for work—a space with clear problems and established systems. One founder noticed a gap: personal life is messier, harder to systematize, and mostly left behind by the AI boom. So she built Cue, a personal AI that lives inside iMessage and group chats, to go where the other tools haven't.

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In this episode, Becca Carroll, IDEO's Chief Strategy Officer, talks with Sida Li, co-founder and CEO of Shared Context Lab, about staying anchored to a human need while the technology around her keeps changing shape, why she treats her business model with the same rigor she'd bring to a product, and what it feels like to build a company at this particular, disorienting moment in AI.

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The conversation also gets into how Sida makes design decisions: the language Cue uses to describe itself, the tradeoffs behind building inside iMessage instead of a new app, and a real story about a business idea that didn’t pan out.

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This is the second in a two-part series profiling founders from IDEO's Startups-in-Residence program. The first conversation is with Johannes Seemann, founder of Sooner, on designing GenAI for the emotional side of money.

Most personal financial tools are built to run the numbers and optimize towards a budget. While that works for some, most people experience money as a lived relationship that does not neatly fit into a spreadsheet. Johannes Seemann and Becca Carroll discuss why money is emotional before it is mathematical, and what a human-centered approach to building a generative AI financial product looks like in practice.

Mina Seetharaman talks with Scott Shigeoka, author of Seek and Head of Curiosity Cultivation at the Eames Institute, about what distinguishes genuinely curious leadership from performative curiosity, how power dynamics shape curiosity, and why practicing curiosity can restore energy rather than drain it.

Mina Seetharaman talks with David Epstein, author of Inside the Box and Range, about why total freedom often produces average work, how leaders can design useful limits on purpose, and what organizations such as General Magic and Pixar reveal about the relationship between boundaries and creativity.